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Yan et al. (2025) Evaluating the Hydrological Applicability of Satellite Precipitation Products Using a Differentiable, Physics-Based Hydrological Model in the Xiangjiang River Basin, China
This study systematically evaluates the suitability of multi-source satellite precipitation products for driving a distributed physics-informed deep learning (DPDL) model and a SWAT model in the Xiangjiang River Basin, finding that DPDL outperforms SWAT and that product-specific recalibration significantly improves streamflow simulation accuracy, with overall utility depending on both model architecture and training strategy.
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Chen (2025) Mechanisms of Topographic Steering and Track Morphology of Typhoon-like Vortices over Complex Terrain: A Dynamic Model Approach
This study investigates how complex terrain steers typhoon-like vortices, revealing that vortex intensity, terrain geometry, and interaction time govern track morphology and predictability. It introduces a diagnostic framework to map zones of high track divergence and convergence, providing a physically interpretable basis for understanding forecast uncertainty over mountainous regions like Taiwan.
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Sasaki et al. (2025) Environmental Influences on Deep Convective Upscale Growth Rate in Central Argentina From a Convection‐Permitting Simulation
This study investigates environmental conditions influencing the rate of deep convective upscale growth into mesoscale convective systems (MCSs) in central Argentina using a convection-permitting simulation, finding that rapid growth is associated with more favorable thermodynamic environments (higher moisture, instability) and more frequent low-level jets, with wind shear orientation also playing a role near complex terrain.
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Chen et al. (2025) Hybrid Observing System Simulation Experiment for the FY-4 Geostationary Orbit Microwave Satellite Based on CMA-GFS
Not available in the provided text.
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Kim et al. (2025) Monthly Temperature Prediction in the Han River Basin, South Korea, Using Long Short-Term Memory (LSTM) and Multiple Linear Regression (MLR) Models
This study compares Multiple Linear Regression (MLR) and Long Short-Term Memory (LSTM) models for monthly mean temperature prediction in the Han River Basin, South Korea, finding both highly accurate but complementary, with LSTM excelling in non-linear dynamics and MLR offering greater stability and interpretability.
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Yun et al. (2025) Addressing class imbalance extends the performance frontier of classification–regression satellite-gauge precipitation fusion
This study introduces ImbCRPF, a novel classification-regression framework for satellite-gauge precipitation fusion that explicitly addresses class imbalance, significantly improving the accuracy of precipitation estimates, particularly for heavy rainfall events.
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Dasari et al. (2025) Decision support system for climate-resilient runoff estimation
This study develops a cloud-based Decision Support System (DSS) for climate-resilient runoff estimation, integrating a modified SCS-CN method with high-resolution geospatial data and CMIP6 climate projections. Validated across two contrasting Indian watersheds, the DSS accurately simulates historical and future runoff, enabling real-time flood forecasting and adaptive water resource management.
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Rensch et al. (2025) The Extent of El Niño and La Niña Influence on Australian Rainfall
This study details the complex influence of El Niño-Southern Oscillation (ENSO) on Australian monthly rainfall distributions, revealing that La Niña consistently intensifies rainfall, particularly extremes, while El Niño's impact is more limited spatially and temporally, even showing intensification during its mature phase.
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Koech et al. (2025) Hydrological modeling of the Enguli ephemeral sand river basin using HEC-HMS for sustainable water management in Kenya’s ASALs
This research characterized the hydrological behavior of the Enguli ephemeral sand river basin in Makueni, Kenya, using HEC-HMS to aid sustainable water management in arid and semi-arid areas. The study successfully simulated streamflow patterns and infiltration rates, demonstrating the potential of sand rivers as natural water storage reservoirs and informing climate-resilient irrigation systems.
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Tang et al. (2025) Effects of Typhoon Fitow residual circulation on the relationship between surface wind field and precipitation in Shanghai
This study investigates the relationship between surface wind fields and the spatially inhomogeneous heavy precipitation in Shanghai, triggered by Typhoon Fitow's residual circulation, demonstrating how the urban underlying surface modifies wind patterns and enhances local convergence.
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Lamb et al. (2025) Perspectives on Systematic Cloud Microphysics Scheme Development With Machine Learning
This perspectives paper synthesizes recent progress and outlines challenges and opportunities for applying machine learning to improve cloud microphysics parameterizations, aiming to reduce significant parametric and structural uncertainties in weather and and climate models.
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Zhong et al. (2025) Evaluation of WRF Planetary Boundary Layer Parameterization Schemes for Dry Season Conditions over Complex Terrain in the Liangshan Prefecture, Southwestern China
This study evaluates six WRF Planetary Boundary Layer (PBL) schemes against multi-source observations over complex terrain in the Liangshan Prefecture during clear-sky dry seasons. It finds that the QNSE and MYNN2.5 schemes offer the most balanced performance in simulating near-surface variables, vertical profiles, and PBL height, providing guidance for high-resolution modeling in the region.
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Zhang et al. (2025) Optimization of Sensor Combinations for Simplified Estimation of Reference Crop Evapotranspiration Using Machine Learning and SHAP Interpretation
This study systematically evaluates machine-learning (ML) models for estimating reference crop evapotranspiration (ET0) in data-sparse regions of China, finding that the Random Forest model performs best and can maintain accuracy even with reduced sensor inputs, while SHAP analysis reveals key regional and national drivers.
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PANDYA et al. (2025) From raw to reliable: machine learning bias correction of reanalysis data for improved drought severity classification
This study develops a scalable machine learning bias correction approach for reanalysis data (ERA5, NASA POWER) to improve drought severity classification in vulnerable regions of India. It finds that Random Forest is the most reliable method for bias correction, significantly enhancing the accuracy of drought monitoring.
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Kevala et al. (2025) SARCDNet-an enhanced deep learning network for change detection from bi-temporal SAR images
This paper introduces SARCDNet, an enhanced deep learning network for change detection from bi-temporal Synthetic Aperture Radar (SAR) images. SARCDNet, featuring an Adaptive Fusion Block, effectively mitigates speckle noise and significantly improves change detection accuracy across various public datasets, particularly for flood detection.
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Kim et al. (2025) Ensemble artificial neural network and generalized additive model for data-scarce regional frequency analysis in design flood estimation
This study applied ensemble artificial neural networks (EANN) and generalized additive models (GAM) with canonical correlation analysis (CCA) for regional frequency analysis (RFA) to estimate design floods in data-scarce small streams in South Korea, finding that CCA-GAM outperformed EANN and river basin area was the most influential variable.
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Reinecke et al. (2025) The ISIMIP Groundwater Sector: A Framework for Ensemble Modeling of Global Change Impacts on Groundwater
This paper introduces the ISIMIP Groundwater Sector, a framework for ensemble modeling to assess global change impacts on groundwater, demonstrating its application by presenting global ensemble mean static water table depth and groundwater recharge changes for 2001-2006.
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Liu et al. (2025) The impact of nonlinear surface energy partitioning on potential evapotranspiration: A machine learning study based on FLUXNET data
This study investigates the nonlinear relationship of the no-water-limited Bowen ratio (βNWL) with environmental factors using global FLUXNET data and machine learning, developing a new PET model (PETβNWL−RF) that significantly improves daily potential evapotranspiration estimation and drought monitoring accuracy.
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Lin et al. (2025) Linking the diurnal and seasonal dynamic of photochemical reflectance index and photosynthesis in a paddy rice field
This study developed a low-cost, ultra-spatial resolution multispectral camera to obtain high-frequency photochemical reflectance index (PRI) observations in a paddy rice field. It demonstrated that separating PRI into seasonal (PRI0) and diurnal (PRId) components effectively links PRId to light use efficiency (LUE) and captures atmospheric dryness stress on plant photosynthesis, with a significantly improved correlation compared to PRI alone.
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Bocchino et al. (2025) Crop flood damage assessment integrating Sentinel-2 imagery and in situ data: the 2023 Emilia-Romagna case
This study proposes a data-driven machine learning framework to quantitatively assess crop flood damage by integrating Sentinel-2 imagery and in situ field data. Applied to the May 2023 Emilia-Romagna flood, the Random Forest model achieved an overall accuracy of 0.74 in classifying agricultural fields into three damage categories, providing a reliable tool for post-event support.
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Nikolov et al. (2025) How Complete Is Cloud Glaciation?
This study analyzes satellite observations of individual cloud tops to track their temporal phase evolution and quantify glaciation, finding that most glaciation events induce a sustained shift in cloud properties within the mixed-phase regime rather than complete freezing, and correlate with higher ice-nucleating particle concentrations.
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Bozdağ et al. (2025) Development of a Rainwater Management Model in the Adaptation Process to Climate Change: the Case of Ni̇ğde
This study aimed to determine rainwater sensitivity in Niğde Province to inform rainwater management and develop landscape development strategies. It found that 2.1% of the province has very high to high rainwater sensitivity, 24.3% has moderate sensitivity, and 73.6% has low to very low sensitivity, providing a basis for risk-reducing landscape planning.
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Khatiwada et al. (2025) Multi-temporal flood mapping and dynamics in Nepal's Terai (2019–2024) using Sentinel-1 SAR and change-detection approaches
This study conducted one of the first long-term, district-level flood mapping analyses in Nepal's Terai region (2019–2024) using Sentinel-1 SAR and CHIRPS rainfall data. It revealed frequent flooding in the southern region, with two extreme events exceeding 285 square kilometers, and established a strong positive correlation between 3-day cumulative rainfall and flood extent, identifying >130 millimeters as a threshold for major events.
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Ma et al. (2025) A Multi-Indicator Hazard Mechanism Framework for Flood Hazard Assessment and Risk Mitigation: A Case Study of Rizhao, China
This study developed a multi-indicator framework for urban flood hazard assessment in Rizhao, China, identifying extreme-hazard zones in northern sectors driven by high imperviousness, short concentration time, and inadequate drainage, and proposing an integrated mitigation strategy.
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Lee et al. (2025) A Spatially Masked Adaptive Gated Network for multimodal post-flood water extent mapping using SAR and incomplete multispectral data
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Ye et al. (2025) Spherical Harmonic Fingerprints Characterize Moon‐Based Disk‐Integrated Earth's Emitted Radiation Signatures
This study simulates Moon-based disk-integrated Earth radiation to unravel the influence of orbital dynamics and clouds on planetary-scale radiation variations. It finds that orbital dynamics, particularly synodic and sidereal monthly cycles, dominate these variations, and while clouds systematically reduce radiation, they preserve these orbital-driven periodicities.
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Hussien et al. (2025) Forest Landscape Transformation in the Ecotonal Watershed of Central South Africa: Evidence from Remote Sensing and Asymmetric Land Change Analysis
This study analyzed forest cover dynamics in a South African ecotonal landscape from 1990 to 2022, revealing an initial period of forest fragmentation and decline followed by significant regeneration, particularly along riparian corridors, demonstrating strong vegetation feedback to hydrological and anthropogenic drivers.
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Zhu et al. (2025) A Multichannel CNN-LSTM-Based Prediction Model for Precipitable Water Vapor in a Region With a Single GNSS Station
This paper proposes a multichannel Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) based model to predict precipitable water vapor (PWV) using data from a single Global Navigation Satellite System (GNSS) station.
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Zhao et al. (2025) Terrain-Aware Uncertainty Quantification and Cross-Sensor Consistency Analysis of Hyperspectral Surface Reflectance
Not available from the provided text.
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Pang et al. (2025) Water Infrastructure and Grain Yield: Evidence From the South‐to‐North Water Diversion Project
This paper investigates the impact of the South-to-North Water Diversion Project's central route on agricultural and ecological outcomes in northern China. The study finds that the project significantly increases vegetation health, grain yield, Leaf Area Index, surface water area, and soil moisture content, demonstrating its effectiveness in enhancing both agricultural productivity and environmental conditions.
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You et al. (2025) Unveiling river thermal regimes in the Yangtze river basin, China, with a hybrid deep learning model
This study developed a hybrid deep learning model (CNN-LSTM-AT) to reconstruct and analyze the historical river water temperature (RWT) thermal regimes in the Yangtze River Basin from 1960 to 2009, revealing a general warming trend and intensifying river heatwaves.
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Wang et al. (2025) A seamless global daily soil moisture dataset (2010–2015) harmonized from SMOS observations and SMAP-era assimilation modeling
## Identification - **Journal:** International Journal of Digital Earth - **Year:** 2025...
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Rahmah et al. (2025) Comparative Performance of Regression and Ensemble Learning Algorithms in Precision Irrigation Forecasting of Sweet Potato
This study systematically compared five machine learning algorithms for precision irrigation forecasting in sweet potato using real-time Internet of Things (IoT) sensor data, finding that a hyperparameter-tuned Random Forest Regressor achieved the highest predictive accuracy (R² = 0.9802).
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Wang et al. (2025) Advancing Tropical Cyclone Rainfall Simulation and Projection With Eddy‐Resolving Climate Models
This study demonstrates that eddy-resolving high-resolution Community Earth System Model (CESM) simulations accurately capture historical Tropical Cyclone Rainfall (TCR) due to improved TC upward motion. Under the RCP8.5 warming scenario, HR CESM projects a significantly higher TCR increase rate (∼12.0% K⁻¹) exceeding Clausius-Clapeyron scaling, in contrast to non-eddy-resolving models which substantially underestimate both historical and projected TCR increases.
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Wang et al. (2025) Dynamic Downscaling Resolution Affects Hot Extreme Processes: A Case Study of 2013 Summer Extreme Hot Event Over Central and Eastern China
This study investigated the 2013 extreme hot event (EHE) in central and eastern China using the Weather Research and Forecasting (WRF) model at 25 km and 9 km resolutions, finding that the higher resolution (9 km) model significantly improved the simulation by better capturing land-atmosphere feedback and diabatic heating processes.
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McInerney et al. (2025) Tailored calibration of stochastic weather generators for enhanced hydrological system evaluation
Tailored calibration of stochastic weather generators (SWGs) using the Simulated Method of Moments (SMM) significantly improves the capture of critical climate attributes and hydrological responses compared to conventional methods, with the Robust Gauss-Newton (RGN) algorithm proving essential for accurate and efficient optimization in both historical and future climate assessments.
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Bousbaa et al. (2025) Assessing groundwater storage response to snow cover dynamics in large Moroccan river basins over the last decades using remote sensing data
This study investigates the impact of snow cover variability on groundwater storage in large Moroccan river basins over recent decades using remote sensing data. It reveals significant groundwater declines linked to snow cover loss and quantifies snowmelt contributions to recharge with a notable time lag.
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Yu et al. (2025) A dual-method, multi-scale causal framework reveals seasonal shifts in hydrological causality of a headwater catchment
This study develops a robust, dual-method, multi-scale causal inference framework to investigate hydrological interactions in a headwater catchment, revealing scale-dependent causal pathways and significant seasonal shifts in runoff generation mechanisms.
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Kravchenko et al. (2025) Irrigation of slope lands by subsurface irrigation method using a simulator of horizontal wells
This study investigated the effectiveness of subsurface irrigation on laboratory models of sloping sand-soil using a novel horizontal well simulator. The research successfully mapped, for the first time, the downward curved trajectory of irrigation water movement from the simulator to the lower boundary of the slope.
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Yetik (2025) Machine learning-based estimation of daily ETo under limited meteorological data
This study evaluated the performance of three machine learning models (ANN, LGBM, RFR) for estimating daily reference crop evapotranspiration (ETo) in Alanya, Turkey, under various limited meteorological data scenarios, finding that ANN and LGBM consistently outperformed RFR, with the best accuracy (R²=0.89) achieved using temperature, sunshine duration, and wind speed.
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Nie et al. (2025) Large language models for environmental modeling: Framework, capabilities, constraints
This study introduces and evaluates two Large Language Model (LLM) integration frameworks, "Copilot" (human-AI collaborative) and "Autopilot" (LLM-driven automation), for environmental modeling workflows like parameter calibration and real-time correction, using the Rainfall–Runoff–Inundation (RRI) model in the Kuzuryu River basin. It finds that Copilot excels in human-supervised tasks, while Autopilot struggles with data-intensive, long-sequence tasks due to attention decay.
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Ma et al. (2025) Temperature Is Surpassing Precipitation as the Dominant Driver of Flash Drought Acceleration Under Climate Warming
This study quantifies the relative contributions of various drivers to flash droughts, revealing that while precipitation has historically been dominant, temperature is projected to become the primary driver in most regions under climate warming.
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GHAZZAR (2025) Hydro-climatic Projections and Computational Framework for the Maamora Aquifer: SPI, SPEI, GRDI, and De Martonne Aridity Index Datasets using CORDEX and WRF
This paper presents a computational framework and dataset for hydro-climatic projections and drought assessment in the Maamora Aquifer, Morocco, utilizing CORDEX and WRF models to generate future meteorological and hydrological drought indices under RCP4.5 and RCP8.5 scenarios.
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Chen et al. (2025) The Ensembles forecast data of the Stevens Flood Advisory System
This paper describes and provides access to the ensemble total water level forecast data generated by the Stevens Flood Advisory System, an operational system since 2016, which supports research into mid-latitude super-ensemble coastal water level forecasting.
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Xie et al. (2025) Interpretable deep learning for dynamic rainfall-runoff prediction: Integrating adaptive signal decomposition and spatiotemporal feature extraction
This study proposes an interpretable deep learning model for dynamic rainfall-runoff prediction, integrating adaptive signal decomposition and spatiotemporal feature extraction to enhance accuracy and provide insights into complex hydrological processes. The model significantly outperforms traditional methods, especially for short-term predictions, with data decomposition being the strongest contributing module.
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Ta et al. (2025) Research on Water and Fertilizer Diagnosis of Maize Using Visible–Near-Infrared Hyperspectral Technology
This study developed and evaluated hyperspectral estimation methods for maize agricultural traits (relative chlorophyll content, leaf water content, leaf nitrogen content) under varying water and nitrogen regimes, finding that Random Forest models achieved high accuracy (R² up to 0.95) for trait prediction.
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Li et al. (2025) Mapping and Revealing the River Ice Distribution and Changes in the Three Rivers Source Region From 1990 to 2023 Using Google Earth Engine
This study aims to map and analyze the distribution and changes of river ice in the Three Rivers Source Region over a 33-year period (1990-2023) using the Google Earth Engine platform.
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Yang et al. (2025) Short-Term Frost Prediction During Apple Flowering in Luochuan Using a 1D-CNN–BiLSTM Network with Attention Mechanism
This study proposes a novel hybrid 1D-CNN-BiLSTM-Attention model, incorporating a dual attention mechanism, to enhance the prediction of early spring frost events during the Apple Flowering period, demonstrating improved classification performance and a 4-hour lead time for mitigation.
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Chithra et al. (2025) Collaborative Human in the Loop Robotics Framework for Precision Agriculture and Crop Monitoring
This paper introduces a Collaborative Human-in-the-Loop (HITL) Robotics Framework for precision agriculture and crop monitoring, integrating human decision-making with robotic efficiency to overcome limitations of conventional autonomous systems. Experimental results demonstrate the HITL framework's superior performance in crop yield prediction (92%), pest detection (89%), and water use efficiency (94%), with minimal human intervention.
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Alizadeh et al. (2025) Improving crop biophysical parameter estimation using high-resolution multispectral UAV imagery and PROSAIL model
This study evaluated a practical workflow coupling high-resolution unmanned aerial system (UAS) multispectral imagery with PROSAIL inversion to map rice canopy traits across three phenological stages, demonstrating superior accuracy compared to Sentinel-2 for field-scale biophysical parameter estimation.
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Zhang et al. (2025) Remote Impact of Preceding Soil Moisture on the May—June Precipitation of Huang‐Huai Wheat Region in China
This study identifies March-April soil moisture in the north Black Sea and Caspian Sea (NBC) region as a key precursor for May-June precipitation anomalies in China's Huang-Huai Plain, revealing a significant positive correlation driven by a Rossby wave train mechanism.
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Saucedo-Martínez et al. (2025) Development of a smart irrigation system integrating IoT and Tree-Based Machine Learning Techniques
This paper presents the development and validation of an AI-powered intelligent irrigation system that integrates IoT technologies, a cross-platform mobile application, and tree-based machine learning models to optimize water usage and improve operational efficiency in agriculture. The system achieved perfect classification metrics (1.00) in field validation for autonomously predicting irrigation requirements based on real-time sensor data and weather forecasts.
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Wang et al. (2025) Influence of Tidal Inundation and Salinity on the Generalized Complementary Relationship for Evaporation in a Mangrove Ecosystem
This study evaluated the Sigmoid Generalized Complementary (SGC) equation for estimating evaporation in a subtropical mangrove forest, finding it accurately captures evaporation dynamics and revealing how tidal inundation influences surface moisture parameters, while salinity negatively correlates with monthly evaporation at higher concentrations.
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Helili et al. (2025) Assessment and intercomparison of 23 global satellite and model-based soil moisture products using cosmic ray neutron sensing observations over Europe
This study systematically evaluated 23 global satellite and model-based soil moisture products using 68 Cosmic Ray Neutron Sensing (CRNS) observations across Europe, finding that SMAP-INRAE-BORDEAUX (SMAP-IB) retrievals showed the superior consistency with CRNS measurements.
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Barada et al. (2025) Combining moored observations and SAR images in validating compound flood models
This study calibrated and validated the LISFLOOD-FP hydrodynamic model for simulating estuarine compound flood events using a combination of in situ measurements and Sentinel-1 SAR data, demonstrating that this integrated approach provides robust spatiotemporal validation for flood inundation models.
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Pathania et al. (2025) Analysing flood resilience in the anthropocene: Integrated insights from a multi-scalar extreme event in the himalayas
This study analyzes the hydrometeorological drivers, dam operations, hydrological responses, and socioeconomic impacts of the August 2023 Punjab floods. It found that antecedent July rainfall increased flood susceptibility, and controlled releases from Pong Dam significantly reduced downstream population exposure, though vulnerable groups faced disproportionate impacts.
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Dávid et al. (2025) Modeling the Start of Season Date of Hungarian Grasslands Using Remote Sensing Data and 10 Process-Based Models
This study investigated the start of the growing season (SOS) for Hungarian grasslands (2000-2023) using MODIS NDVI and ten process-based models, demonstrating that pixel-level calibration and integration of local climate and soil information significantly enhance prediction accuracy.
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Praxis et al. (2025) Development of a Machine Learning-Based Soil Moisture Data Gap-Filling Model
This study developed and evaluated machine learning models to gap-fill missing soil moisture data from monitoring systems in Haenam and Yesan. The E-dataset XGBoost model, utilizing lagged, accumulated, and time-series precipitation features, demonstrated superior performance for gap-filling 0.1- and 0.2-meter soil moisture data.
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Bayati et al. (2025) Evaluating the Functional Realism of Deep Learning Rainfall‐Runoff Models Using Catchment Hydrology Principles
This study introduces a hydrology-specific Explainable AI (XAI) framework to evaluate the functional realism of Long-Short-Term-Memory (LSTM) networks in rainfall-runoff modeling. It reveals that despite high predictive accuracy, LSTMs often exhibit hydrologically implausible internal reasoning, particularly under varying climatic conditions.
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Wu et al. (2025) A Framework for Refined Hydrodynamic Model Based on High Resolution Urban Hydrological Unit
This study develops and applies a novel high-resolution urban hydrological units model (HRGM) coupled with a 2D hydrodynamic model (LISFLOOD-FP) to improve flood inundation simulation accuracy in highly urbanized regions, demonstrating significantly enhanced performance compared to existing models.
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Li et al. (2025) Spatiotemporal Dynamics and Lagged Hydrological Impacts of Compound Drought and Heatwave Events in the Poyang Lake Basin
This study investigates the characteristics and hydrological impacts of Compound Drought and Heatwave (CDHW) events in China's Poyang Lake Basin from 1981 to 2016, revealing a significant post-2000 increase in CDHW frequency, severity, and intensity, which substantially enhances hydrological drought risk through a robust, lagged influence primarily driven by the heatwave component.
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Li et al. (2025) Impact of coupled climate change and human exploitation on groundwater dynamics in agricultural intensive planting areas
This study investigates the combined impact of climate change and human exploitation on groundwater dynamics in agricultural intensive planting areas, revealing that high emission scenarios significantly magnify groundwater decline, which can be partially mitigated by reducing groundwater extraction.
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Justino et al. (2025) Atmospheric rivers as mediators between climate teleconnections and burned area variability in North America
This study identifies atmospheric rivers (ARs) as key mediators linking large-scale climate teleconnections (ENSO, PNA, AO) to variations in vegetation activity (NDVI) and burned area (BA) across North America. The findings highlight the central role of ARs in shaping regional fire regimes and improving prospects for seasonal fire prediction.
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Zeng et al. (2025) Multifactor Spatial Downscaling of Satellite Precipitation Based on Vegetation Index and Elevation
This paper introduces a multifactor spatial downscaling method for satellite precipitation data, leveraging vegetation index and elevation to enhance spatial resolution and accuracy.
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Roksvåg et al. (2025) An LSTM network for joint modeling of streamflow and hydropower generation for run-of-river plants
This study develops a novel Long Short-Term Memory (LSTM) network for jointly estimating historical daily streamflow and hydropower generation for run-of-river plants in Norway, demonstrating superior performance compared to traditional hydrological models in both gauged and ungauged catchments.
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Zhao et al. (2025) Fusing Enhanced Flux Measurements and Multi-Source Satellite Observations to Improve GPP Estimation for the Qinghai–Tibet Plateau Based on AutoML Techniques
This study developed a data-driven ensemble machine learning model (AutoML-GPP) for the Qinghai–Tibet Plateau (QTP) by integrating eddy covariance data with multi-source remote sensing observations, providing improved gross primary productivity (GPP) estimates that outperform existing global products for the region. The model estimated a mean annual total GPP of 374.20 Tg C yr⁻¹ for the QTP from 2002 to 2018, showing a slight upward trend.
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Kulkarni et al. (2025) Water Optimisation in Agriculture with the Help of AI and IoT: A Pilot Study
Not available from provided text.
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Cao et al. (2025) LESMI: Integrating Linear-Exponential Model, Shapelets, and Multirocket for Wetland Vegetation Inundation Monitoring With Time Series SAR
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Zhu et al. (2025) The UFLUX ensemble of multiple-scale carbon, water, and energy fluxes
This study introduces the Unified FLUXes (UFLUX) ensemble, a globally consistent, multi-scale dataset of terrestrial carbon, water, and energy fluxes (gross primary productivity, evapotranspiration, sensible heat) derived from eddy covariance data, satellite observations, and machine learning, demonstrating high accuracy and consistency across various spatial and temporal scales.
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Cho et al. (2025) Assessing Drought Stress in Legumes using Block-Chain-Assisted Drone-based Spectral Sensors
This study developed and evaluated a blockchain-assisted drone-based spectral sensing system for secure and accurate drought stress assessment in legume crops. The system successfully classified drought stress levels with high accuracy using machine learning, providing transparent and tamper-proof data for precision agriculture management.
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Mutlu (2025) Regional Climate Dynamics and Agricultural Vulnerability in California: A Multi-County Analysis of Long-Term Temperature and Precipitation Trends
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Yoon et al. (2025) Interpretational Pitfalls in SOM-Based Clustering: A Case Study of Extreme Cold Events in South Korea
This study investigates interpretational pitfalls in Self-Organizing Map (SOM) clustering for extreme cold events in South Korea, revealing significant within-node heterogeneity where many events poorly match their assigned cluster patterns, and proposes a pattern-correlation-based post-processing method (SOM-PC) to enhance the physical interpretability of SOM-derived patterns.
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Sun et al. (2025) Fusion of multi-source precipitation records via coordinate-based generative models
The study introduces PRIMER, a coordinate-based diffusion model framework that fuses heterogeneous precipitation data from gauges, satellites, and reanalysis. The model successfully overcomes the trade-offs between spatial coverage and local accuracy, providing a unified tool for bias correction, downscaling, and optimal interpolation.
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Jia et al. (2025) A Novel Algorithm for Optimizing Structure of In-Situ Supplementary Irrigation Device for Afforestation in Dryland
This study developed and validated a novel algorithm to optimize the structural parameters (rated area, capacity, and flow) of in-situ supplementary irrigation devices (SIDs) for afforestation in drylands, demonstrating a significant increase in sapling survival rate (83.6%) compared to traditional methods (46.9%) in a Lhasa case study.
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Li et al. (2025) A Comparative Study of Urban Pluvial Flood Susceptibility Assessment Based on Multi-Machine Learning Algorithm
This study developed and benchmarked a multi-machine learning framework for urban pluvial flood susceptibility assessment in Wuxi, China, finding that the Particle Swarm Optimization-optimized eXtreme Gradient Boosting (PSO-XGB) model achieved superior predictive performance and spatial delineation compared to other models.
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Shiogama et al. (2025) Emergent constraints on future change projections of mean and extreme temperature and precipitation in the global maize harvesting area
This study investigates whether 'hot' Earth system models (ESMs) overestimate future temperature and precipitation changes in global maize harvesting areas, finding that these models do overestimate changes in mean and extreme temperature and extreme precipitation, with emergent constraints significantly reducing projection uncertainties.
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Ran et al. (2025) Intensification of Compound Extreme Drought and Hot Events in Tibet: Insights from a Novel Compound Framework
This study developed a novel spatiotemporal framework to identify and analyze compound extreme drought and hot events (CDHEs) in Tibet from 2000-2020, revealing their widespread occurrence, significant spatiotemporal clustering, and rapid intensification, particularly in duration and frequency, highlighting an urgent need for enhanced early-warning systems.
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Duffy et al. (2025) Is the High ECS in CESM2 Degrading Transient Climate Change Projections Over the 21st Century?
This paper evaluates the fitness of CESM2 for various applications, concluding that despite a high equilibrium climate sensitivity, it accurately simulates 20th and 21st-century transient climates but is unreliable for extreme paleoclimates or long-term projections beyond the 21st century.
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Wang et al. (2025) Joint Learning for Feature Reconstruction and Prediction in Agricultural Semantic Segmentation From Incomplete Satellite Image Time Series
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Awad et al. (2025) Assessment of Reference Evapotranspiration by Mass Transfer-Based Methods for Efficient Management of Irrigation Water in the Nile Delta.
This study evaluates four mass-transfer models for estimating daily reference evapotranspiration (ETo) in the Nile Delta against the FAO-56 standard, identifying the Penman mass-transfer model as the most accurate and a suitable proxy for water-smart irrigation.
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Lan et al. (2025) The GLACE-Hydrology Experiment: Effects of Land–Atmosphere Coupling on Precipitation Change and Monsoonal Circulations
This study investigates the impact of land-atmosphere interactions on rainfall and monsoon intensity using coupled and uncoupled CESM simulations. It finds that while most land regions show higher temperatures and reduced precipitation in coupled simulations, monsoon regions experience increased precipitation due to enhanced vertical motion and strengthened monsoonal circulation.
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Şimşek et al. (2025) Phenology aware agricultural boundary extraction using segment anything model and planet scope imagery (zero shot learning approach)
This study integrates phenology-driven multi-temporal image selection with the zero-shot segmentation capabilities of the Segment Anything Model (SAM) to automatically delineate agricultural parcel boundaries. The approach significantly improves segmentation performance, demonstrating that phenologically-based multi-temporal imagery enhances zero-shot models for accurate and operationally feasible boundary extraction.
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Altıkat (2025) Satellite-Driven Evaluation of Moisture Dynamics for Irrigation Management in a Semi-Arid Apple Orchard
This study evaluated moisture dynamics and water stress in a semi-arid apple orchard over six years (2020-2025) using integrated satellite-derived spectral (NDWI, NDMI) and thermal (ET) indices to inform precision irrigation management. It revealed critical water stress periods and spatial variability, advocating for data-driven, adaptive strategies to enhance water-use efficiency and drought resilience.
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Amma et al. (2025) Balancing yield and water productivity in sweetpotato through crop evapotranspiration–based irrigation scheduling across seasons
This study evaluated crop evapotranspiration (ETc)-based irrigation scheduling for sweetpotato over three seasons in South India, comparing drip, sprinkler, and furrow methods. It found that irrigation at 100–112% ETc maximized tuber yield, while moderate deficit irrigation (63–75% ETc) optimized water productivity, highlighting a critical trade-off for growers.
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Hasti (2025) Estimation of vegetation biophysical and biochemical parameters using satellite optical remote sensing: A review of methods, sensors and study areas
This paper provides a comprehensive review of methods, sensors, and study areas employed for estimating vegetation biophysical and biochemical parameters using satellite optical remote sensing.
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Angulo‐Umana et al. (2025) Multiscale Convective Circulations and Scale Interactions in a Global Storm‐Resolving Model
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Pan et al. (2025) From Carbon–Water Diagnosis to Landscape Optimization: A New Framework for Sustainable Restoration in East Asian Karst
This study conducts a comprehensive comparative analysis of carbon–water trade-offs in East Asian karst regions from 2000 to 2023, identifying divergent eco-functional profiles and distinct threat drivers between Southwest China and the ASEAN region, and proposing a spatially explicit framework for targeted governance.
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Sebastianelli et al. (2025) Near-surface wind field characterization of medicanes using satellite observations
This study characterizes the near-surface wind field of medicanes using satellite scatterometer data and a new algorithm (MeRCAD) to detect the rotational center and radius of maximum wind (RMW). It finds that a significant decrease in RMW (to a few tens of kilometers) and a nearly symmetric wind circulation indicate a medicane's mature phase, often associated with deep convection near the rotational center, distinguishing them from typical extratropical cyclones.
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Anwari (2025) Monitoring of Soil Moisture Influenced by Crop Choice and Management Practice in Morocco’s Rainfed Drylands Using Sentinel-1 SAR Timeseries Data
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Gao et al. (2025) MFF-Net: Flood Detection from SAR Images Using Multi-Frequency and Fuzzy Uncertainty Fusion
This study proposes MFF-Net, a novel multi-scale deep learning algorithm, to overcome systematic noise in Synthetic Aperture Radar (SAR) images, significantly improving pixel-level and fine-grained flood detection accuracy.
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Qi et al. (2025) Exploring How Soil Moisture Varies with Soil Depth in the Root Zone and Its Rainfall Lag Effect in the Ecotone from the Qinghai–Tibetan Plateau to the Loess Plateau
This study develops and validates a multi-depth soil moisture retrieval model for the Qinghai–Tibetan Plateau to Loess Plateau ecotone using multi-source remote sensing and SVAT/TSEB models, revealing complex temporal and spatial soil moisture dynamics and a pronounced rainfall lag effect with increasing depth.
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Hayashi et al. (2025) Efficient Estimation of the Number of Water Retention Curves Required for Applying a Scaling Technique to the Forest Soil
This study aimed to determine the minimum number of water retention curves (WRCs) required to effectively estimate reference parameters for a scaling approach on forest slopes. It found that a scaling approach could explain 78% of WRC spatial variability using reference parameters derived from eight samples, with stratified sampling considering slope direction being the most advantageous.
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Bao et al. (2025) How Cloud Feedbacks Modulate the Tibetan Plateau Thermal Forcing: A Lead–Lag Perspective
This study investigates the interaction between the Tibetan Plateau's thermal forcing and cloud feedbacks by applying an improved cloud-classification algorithm to CERES and ERA5 data. It reveals a vertical redistribution of clouds and complex lead-lag relationships among cloud cover, snowfall, radiation, and heat fluxes, highlighting the critical role of cloud-radiation-snowfall interactions.
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Salau et al. (2025) A Novel Index for Integrative Drought Assessment in Agricultural Reservoirs
This study developed a Multivariate Drought Index (MDI) using Principal Component Analysis (PCA) to integrate precipitation, inflow, and reservoir storage for comprehensive hydrometeorological drought assessment in 404 agricultural reservoirs across five major watersheds in South Korea, demonstrating its superior ability to detect earlier and longer droughts compared to existing indices like SPEI.
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Alcántara (2025) Interés público y desinformación en tiempos de crisis: el caso de la DANA en Valencia en 2024
This study analyzes the evolution of public search interest in "bulos" (hoaxes) and "fake news" in Spain during the 2024 DANA crisis, examining its temporal relationship with fact-checking activity and identifying regional preferences in terminology. It found that search interest peaked during the acute phase of the crisis, coinciding with increased fact-checking, and revealed distinct regional patterns in the use of "bulos" (more homogeneous) versus "fake news" (more heterogeneous).
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Baishya et al. (2025) An Easy-to-Apply Machine Learning Framework for Hydrologic Evaluation of Ungauged Catchments
This study developed a novel machine learning framework for streamflow regionalization in ungauged catchments by integrating Curve Number (CN) and specific discharge normalization, demonstrating superior performance over a conventional SWAT parameter-transfer method in Northeast India. The LightGBM model, incorporating dynamic CN and specific discharge scaling, achieved significantly higher accuracy and lower bias in the ungauged target basin.
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Disasa et al. (2025) Modelling the impact of climate and land cover changes on hydrological cycle components: a case of the middle Huai river basin
This study assessed the impacts of climate and land cover changes on hydrological cycle components in the Middle Huai River Basin (MHRB) for a reference period (1991–2020) and two future periods (2041–2060, 2071–2090) under three Shared Socioeconomic Pathway (SSP) scenarios. It found that climate change is the dominant factor, leading to an intensification of hydrological cycle processes with increased precipitation and temperature, but also increased spatiotemporal variability.
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Zhao et al. (2025) Coupled SWAT–MODFLOW Model for the Interaction Between Groundwater and Surface Water in an Alpine Inland River Basin
This study utilized the SWAT–MODFLOW model to explore the bidirectional dynamic coupling of surface water and groundwater in an alpine inland river basin, revealing an upward trend in groundwater levels, shifts in spatial distribution, and complex spatio-temporal and seasonal patterns of surface water-groundwater exchange.
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Yao et al. (2025) Thinning Methods and Assimilation Applications for FY-4B/GIIRS Observations
This study evaluates the impact of different data thinning schemes for FY-4B/GIIRS observations within the GSI assimilation system on atmospheric analysis and forecasts, finding that the Wavelet Transform Modulus Maxima (WTMM) scheme significantly improves temperature, humidity, typhoon track, intensity, and precipitation predictions for Super Typhoon Doksuri.
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Liu et al. (2025) Young Versus Old: Does Forest Age Regulate Water and Dissolved Carbon Processes Belowground?
This study investigates how hydrological flow paths and dissolved carbon processes belowground differ between young and old forests. It reveals that young forests exhibit lower streamflow and deep groundwater contributions, along with distinct dissolved organic and inorganic carbon cycling patterns compared to old forests, highlighting the significant role of forest age in subsurface carbon dynamics.
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Zhang et al. (2025) A Comprehensive Radiative Transfer Model for Vegetated Land Surfaces Incorporating Multiple Volume–Surface Scattering Coupling Effects
This paper introduces a comprehensive radiative transfer model specifically designed for vegetated land surfaces, with a key focus on integrating multiple volume–surface scattering coupling effects.
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Battaglioli et al. (2025) Contrasting trends in very large hail events and related economic losses across the globe
This study develops a global climatology of very large hail (≥5 cm) events from 1950 to 2023 and examines trends in their frequency and related economic impacts. It finds contrasting regional trends, with Europe experiencing a sharp rise in very large hail frequency linked to atmospheric instability, while the Southern Hemisphere sees declines, and attributes increasing economic losses in Europe to more frequent events, whereas in the USA and Australia, increasing exposure and vulnerability are the primary drivers.
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Chen (2025) Millennial land carbon in China
This study quantifies the spatiotemporal changes in China's terrestrial organic carbon over a millennial period (851–2022) using a land surface model, revealing that millennial land carbon emissions have been offset by carbon sinks in the past four decades.
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Matiash et al. (2025) Calculation and Visualization of Water Consumption Rates of Crops When Using Information Technologies
This study analyzes and updates crop water consumption rates for irrigation in Ukraine, considering climate change, by developing an automated information system and revealing significant increases in water demand across different climatic zones.
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Eriskin et al. (2025) A Horizon-Adaptive Benchmarking Framework for Long-Term Reservoir Storage Forecasting Using Physics-Informed Transformers and Machine Learning
This study develops a horizon-adaptive benchmarking framework for 12-month reservoir storage forecasting using physics-informed transformers and machine learning models. It demonstrates that optimal model selection varies significantly across different forecast horizons, highlighting the need for a dynamic, horizon-specific approach for robust water management in semi-arid regions.
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Liu et al. (2025) Strong Wind‐Driven Oceanic Forcing on Decadal SST Variability Over the Global Ocean
This study dynamically validates a previous statistical assessment, confirming that wind-driven oceanic forcing is the dominant driver of decadal sea surface temperature variability in mid-latitudes.
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Andreasen et al. (2025) Independent Short‐ and Longwave Pathways for a Zonally Asymmetric Northern Hemisphere Temperature Response to Tropical Volcanic Eruptions
This study investigates the independent and distinct impacts of shortwave reflection and longwave absorption properties of stratospheric sulfate aerosols from tropical volcanic eruptions on Northern Hemisphere extra-tropical winter temperatures, revealing fundamentally different dynamical responses.
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Hussan et al. (2025) Climate Change Impact on Water Resources and Hydropower in Satpara Basin, Pakistan
This study assesses the impact of climate change on water resources and hydropower in Pakistan's Satpara Basin using hydrological and reservoir models under RCP scenarios. It projects increased winter/spring streamflow and hydropower generation (5–20%), but decreased summer streamflow (up to 17%) and an earlier peak flow, highlighting challenges for water management.
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Zeng et al. (2025) Multi-Ecohydrological Interactions Between Groundwater and Vegetation of Groundwater-Dependent Ecosystems in Semi-Arid Regions: A Case Study in the Hailiutu River Basin
This study investigated the spatiotemporal dynamics of groundwater-dependent ecosystems (GDEs) and their relationship with water conditions in the semi-arid Hailiutu River Basin from 2002–2022, revealing that while vegetation activity and climate indicators increased, groundwater storage significantly declined, posing a threat to GDE stability and highlighting strong, reciprocal ecohydrological interactions.
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Babaousmail et al. (2025) 21 st century projections of concurrent hot-dry extremes and population exposure in North Africa under two socioeconomic scenarios
This study projects the evolution of compound hot-dry extreme (CHDE) events and associated population exposure in North Africa under two socioeconomic scenarios (SSP2-4.5 and SSP5-8.5), finding a significant increase in CHDE frequency (65–90% under SSP5-8.5) and a tripling of population exposure by 2100, with cities like Tripoli, Fes, and Rabat identified as future hotspots.
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Wang et al. (2025) Recent accelerated drying in southwest China dominated by anthropogenic aerosol forcing
This study investigates the drivers of the recent drying trend in Southwest China, revealing that anthropogenic aerosol forcing, particularly from European reductions and Chinese increases, is the dominant factor, accounting for 77% of the total forcing.
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Groom et al. (2025) Entropic Learning Enables Skilful Forecasts of ENSO Phase at up to 2 Years Lead Time
This paper extends the entropy-optimal Sparse Probabilistic Approximation (eSPA) algorithm with an ensemble meta-learning strategy to predict ENSO phase using only satellite-era observational data. The enhanced eSPA model achieves probabilistic forecast skill comparable to the IRI plume, extends accurate lead times up to 24 months, and operates at a significantly lower computational cost.
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Akinsoji et al. (2025) Ensemble Machine Learning-Based Feature Selection for Flood Susceptibility Mapping Under Climate and Land Use Change Scenarios
This study compares feature selection techniques with ensemble machine learning algorithms for flood susceptibility mapping in South Korea, integrating historical data, future climate projections (CMIP5/CMIP6), and land use change scenarios. It found that the Variance Inflation Factor (VIF) combined with Gradient Boosting (GB) achieved the highest accuracy (ROC-AUC: 0.93) and predicted increased flood exposure in urbanized, low-lying areas under future conditions.
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Xu et al. (2025) Quantifying the Impact of Rainfall Spatial Heterogeneity and Patterns on Urban Flooding by Integrating Machine Learning Algorithm and Hydrodynamic–Hydrological Modeling
This study developed a machine learning-based model to generate spatially nonuniform rainfall scenarios and integrated it with a hydrodynamic–hydrological model to quantify the impact of rainfall spatial heterogeneity and patterns on urban flooding. The findings reveal that neglecting rainfall spatial heterogeneity systematically underestimates urban flooding, with underestimation intensifying with higher rainfall peak coefficients.
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Köhler et al. (2025) Dense stands of aquatic plants retain water in lowland rivers and in adjacent floodplain aquifers
Dense stands of aquatic plants in a lowland river effectively compensate for declining discharge by elevating water levels, significantly increasing water storage in both the river channel and the adjacent floodplain aquifer, with weed cutting offering only a temporary reduction in this beneficial water retention.
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Sahu et al. (2025) Optimizing Irrigation Scheduling with a Hybrid Transformer-GRU Model and Reinforcement Learning in Smart Agriculture
This research proposes an intelligent irrigation scheduling framework that integrates multi-source environmental data with a hybrid Transformer-GRU model and reinforcement learning to enhance water usage efficiency and crop yield while reducing drought risk in smart agriculture.
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Kumar (2025) Monitoring of Land Use and Land Cover Change Using Geoinformatics Techniques of Tosham Block; Haryana
This study analyzed land use/land cover (LULC) changes in the Tosham block, Haryana, between 1990 and 2025 using multi-temporal Landsat imagery and geoinformatics techniques. It revealed significant anthropogenic-driven transformations, with substantial expansion of agricultural and built-up areas at the expense of fallow, sandy, and scrub lands.
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Palumbo et al. (2025) Precipitation, moderated by spring temperature and vegetation, drives runoff efficiency in the Upper Colorado River Basin, USA
This study uses causal inference with historical data to identify drivers of surface runoff efficiency in the Upper Colorado River Basin. It finds that runoff efficiency is primarily driven by precipitation and snow accumulation, moderated by spring temperature and vegetation phenology, with summer temperature not emerging as a statistically significant direct driver.
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Marcoie et al. (2025) Half a Century of Civil Engineering in the Bahlui River Hydrographic System: The Unexpected Journey from Gray Structures to Hybrid Resilience
This study appraised the long-term contribution of 17 man-made reservoirs in the Bahlui River basin, Romania, to flood attenuation and drought buffering over five decades, finding strong flood control but a decline in engineered drought mitigation, which is increasingly offset by expanding green infrastructure.
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Shirazi et al. (2025) Predicting sugar beet leaf area index: evaluating performance of double sigmoid functions under different irrigation and plant density scenarios
This study evaluated the performance of 15 double sigmoid functions to model sugar beet leaf area index (LAI) under various irrigation and plant density scenarios, identifying the Logistic-Richards and Hill-Hill functions as the most accurate for predicting LAI dynamics based on growing degree days and days after planting.
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Xiao-ya et al. (2025) Quantifying Individual Contribution of Human Activities on Long-Term Streamflow Change in a Large Climate-Sensitive River Basin
This study developed a Variable Infiltration Capacity (VIC) model-based framework to quantify the individual contributions of climate change and human activities to long-term streamflow changes in the Songhua River Basin, finding that water withdrawal was the dominant driver, explaining 48.30% of streamflow changes between 1994 and 2017.
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Kareem et al. (2025) Improving River Flood Mapping with Adaptive Sampling and Artificial Intelligence Techniques for Enhanced Flood Risk Assessment
This study develops and evaluates AI-assisted adaptive sampling techniques, driven by precipitation and topographic factors, to enhance river flood mapping and risk assessment. It demonstrates that elevation-adapted sampling significantly improves the accuracy of AI models, particularly Random Forest, in delineating flood extents and identifying vulnerable assets for 200-year and 500-year flood events.
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Palková et al. (2025) Data-Driven Optimisation of Irrigation Dose Using Machine-Learning Ensembles for Sustainable European Agriculture
This study developed and evaluated machine learning ensemble models to optimize irrigation doses for sustainable agriculture in the semi-arid Nitra region of Slovakia, aiming to reduce water usage and enhance crop productivity.
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Wang et al. (2025) Causal machine learning uncovers conditions for convective intensification driven by organic and sulfate aerosols
This study applies a novel causal machine learning framework to high-resolution observations near Houston, TX, to investigate the causal links between organic and sulfate aerosols and deep convective clouds (DCCs). It finds that a direct causal link from aerosols to DCCs is uncommon (less than 35% of scenarios) but, when present, can substantially enhance DCC core heights by approximately 1.7 kilometers, particularly in warmer cloud regions and under sea breeze conditions.
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Li et al. (2025) Assessment of compound hot-dry events in lakes across global drylands
This study analyzed the frequency, dynamics, and drivers of hot, dry, and compound hot-dry events in 2,338 global dryland lakes from 1985 to 2020, revealing a significant increase in compound events in many lakes, predominantly driven by dry event frequency.
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Wang et al. (2025) Temporal Evolution and Extremes of Urban Thermal and Humidity Environments in a Tibetan Plateau City
This study analyzes high-density observational data (2018–2023) from Xining, a Tibetan Plateau city, to characterize the temporal evolution and extremes of its urban thermal and humidity environments. It reveals an intensification of the urban heat island (UHI) effect and a weakening of the urban dry island (UDI) effect, with both showing increased variability and extreme events modulated by background meteorological conditions.
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Esbrí et al. (2025) Intense Rainfall in Urban Areas: Characterization of High-Intensity Storms in the Metropolitan Area of Barcelona (2014–2022)
This study characterizes intense rainfall in the Metropolitan Area of Barcelona (2014–2022) by comparing radar-derived VIL density (DVIL) with rain gauge observations and urban impact data, establishing a linear relationship between DVIL and short-duration rainfall intensity and evaluating its nowcasting potential.
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Kim et al. (2025) Multi‐Scale Decomposition for Skillful All‐Season MJO Prediction With Deep Learning
This study introduces a novel deep learning framework for Madden-Julian Oscillation (MJO) prediction that integrates background atmospheric fields alongside MJO anomalies, significantly enhancing prediction skill up to 29 days.
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Lv et al. (2025) Dry-wet seasonality effects on the satellite-based land cover types identification in the Nile River Basin
This study assessed the impact of seasonal dry-wet variations on land cover type (LCT) mapping in the Nile River Basin, demonstrating that integrating spectral characteristics from both dry and wet seasons significantly improves classification accuracy.
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Liu et al. (2025) Crop‐Stage‐Specific Analysis of Water Use Characteristics of Summer Maize ( Zea mays L.) Under Different Deficit Irrigation Regimes
This study investigated root-zone water budget components in a summer maize field under different irrigation regimes using the STEMMUS-ET model to address ET uncertainties. It found that a specific deficit irrigation strategy (T1), involving reduced irrigation amount at the same frequency, significantly improved water use efficiency by 9.71% compared to conventional irrigation.
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Liu et al. (2025) A tale of two towers: comparing NEON and AmeriFlux data streams at Bartlett Experimental Forest
This study compared parallel meteorological, phenological, and eddy covariance flux observations from co-located AmeriFlux and NEON towers at Bartlett Experimental Forest, finding excellent agreement for meteorology and phenology but significant annual-scale discrepancies in carbon dioxide and latent heat fluxes, highlighting challenges in merging long-term data from different platforms.
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Chen et al. (2025) The Impact of Large‐Scale Land Surface Conditions on the South American Low‐Level Jet
This study investigates the influence of antecedent soil moisture on the dynamics of the South American low-level jet (SALLJ), specifically strong Chaco jets, finding that dry soil conditions significantly intensify these jets by enhancing land-atmosphere interactions.
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Vangu et al. (2025) Uav, Photogrammetry, Gis, and Digital Twin in Agriculture – a Theoretical Approach
This theoretical paper analyzes the integrated application of Unmanned Aerial Vehicles (UAVs), photogrammetry, Geographic Information Systems (GIS), and Digital Twin technologies to transform agricultural practices towards precision, sustainability, and resource efficiency. It outlines a coherent workflow for data acquisition, processing, and interpretation, highlighting their significant potential for enhanced farm management.
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Hu et al. (2025) Climate Change Impacts on Agricultural Watershed Hydrology, Southern Ontario: An Integrated SDSM–SWAT Approach
This study projects the hydrological and sedimentological impacts of climate change in the Canagagigue Creek Watershed, Southern Ontario, for 2025–2044 using an integrated SDSM-SWAT approach, revealing significant reductions in water yield and sediment transport capacity, leading to increased in-stream deposition risks.
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Sousa et al. (2025) Interoperable IoT/WSN Sensing Station with Edge AI-Enabled Multi-Sensor Integration for Precision Agriculture
This study develops and evaluates LITecS, a modular, solar-powered IoT/WSN sensing station with edge AI for precision agriculture and biodiversity monitoring, demonstrating its adaptability and sustained operation in two distinct field deployments.
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Xu et al. (2025) Stochastic Resonance Elucidates the Emergence and Periodicity Transition of Glacial Cycles
This study proposes a stochastic resonance model to resolve the emergence and intensification of glacial cycles (41-kyr to 100-kyr periods) in the Pliocene-Pleistocene. It suggests that non-stationary greenhouse gas concentrations and a noise component modulated by orbital variations drive these transitions, with weakened noise favoring the 100-kyr cycle.
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Zeng et al. (2025) Different Climate Responses to Northern, Tropical, and Southern Volcanic Eruptions in CMIP6 Models
This study investigates how the spatial distribution of volcanic aerosols from Northern Hemisphere (NH), Tropical (TR), and Southern Hemisphere (SH) eruptions modulates climate responses using CMIP6 models. It finds that hemispheric aerosol distribution strongly controls radiative forcing, surface air temperature, and hydrological responses, leading to distinct ITCZ displacements and ENSO-like patterns (El Niño-like for TR/NH, La Niña-like for SH).
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Hu et al. (2025) Modeling of Drought-Induced Crop Yield Loss Based on Solar-Induced Chlorophyll Fluorescence by Machine Learning Methods
This study developed a model integrating solar-induced chlorophyll fluorescence (SIF), vegetation indices, and meteorological data to quantify drought-induced yield reduction in winter wheat, finding SIF to be a superior indicator for accurate yield loss prediction.
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Williams et al. (2025) Mechanisms of Projected Changes in Thunderstorm Downburst Environments Across the United States
This study investigated the responses of downdraft convective available potential energy (DCAPE) to global warming using the CESM2 model under a high-emission scenario, projecting a 5%–12% average increase in DCAPE, with extreme values increasing at much faster rates and a significant poleward shift in winter, indicating an increased potential for downbursts.
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Luschen et al. (2025) Stratiform and Anvil Cloud‐Radiative Forcing in Tropical Cyclogenesis
This study investigates the role of convective-scale cloud-radiative forcing (CRF) in tropical cyclone (TC) genesis, hypothesizing that CRF in stratiform clouds weakens downdrafts and moistens the environment. Using a convection-permitting WRF model, the research finds that CRF, particularly from stratiform and anvil clouds, reduces downdraft strength and number, increases humidity and moist entropy, suppresses ventilation, and ultimately accelerates TC development.
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Hua et al. (2025) Divergent water controls on vegetation productivity across drylands of the contiguous United States
This study quantifies the relative importance and nonlinear impacts of soil moisture (SM) and vapor pressure deficit (VPD) on gross primary productivity (GPP) across drylands of the contiguous United States. It reveals that SM is a stronger control than VPD, with varying critical soil depths and threshold-like responses along aridity gradients.
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Jiang et al. (2025) Assessment of Fengyun-4B precipitable water vapor using GNSS, radiosonde, and ERA5 data
This study thoroughly assesses the Fengyun-4B (FY-4B) satellite's precipitable water vapor (PWV) products using Global Navigation Satellite System (GNSS), radiosonde, and ERA5 reanalysis data, revealing high consistency with reference data but varying accuracy influenced by latitude, coastal proximity, PWV magnitude, and seasonal and regional climate types.
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Kolcheva et al. (2025) Climate Change and Irrigated Agriculture in Asenovgrad Municipality, Bulgaria
## Identification - **Journal:** International Multidisciplinary Scientific GeoConference SGEM ... - **Year:** 2025...
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Li et al. (2025) Achieving precise cropland parcel extraction from remote sensing images through integration of segment anything model and adaptive mask refinement
This study proposes a novel unsupervised methodology integrating the Segment Anything Model (SAM) with an adaptive mask refinement strategy to precisely extract cropland parcels from remote sensing images under minimal supervision. The method significantly improves extraction accuracy over baseline SAM and outperforms five state-of-the-art methods, demonstrating strong generalization across diverse agricultural landscapes.
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Akbari et al. (2025) Redesigning the SCS method structure within a simulation–optimization framework to improve performance indicators of basin irrigation
This study developed a simulation-optimization model by modifying the Soil Conservation Service (SCS) method and integrating it with the Grey Wolf Optimizer (GWO) algorithm to improve hydraulic performance indicators of basin irrigation. The model successfully optimized basin length and inflow discharge, leading to significant improvements in application efficiency and distribution uniformity while substantially reducing deep percolation and total water consumption.
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Wang et al. (2025) Multi-scale shifts in rainfall patterns in a subtropical region (1981–2024): challenges and implications for water management
This study analyzed multi-scale rainfall pattern shifts in a subtropical region from 1981 to 2024, revealing increased temporal concentration, more intense sub-daily events, and longer dry spells, which decouple total rainfall volume from its hydrological utility and pose challenges for water management.
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Chen et al. (2025) The trend and interannual variability in the global terrestrial evapotranspiration are respectively dominated by humid regions and drylands
This study reveals that the increasing trend in global terrestrial evapotranspiration (ET) is predominantly driven by humid regions, while its substantial interannual variability (IAV) is primarily controlled by drylands.
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Fang et al. (2025) On Optimal Parameterization for Mascon Solution of Surface Mass Changes From GRACE(‐FO) Satellite Gravimetry
This study develops and evaluates a variable-sized mascon solution for GRACE and GRACE-Follow On data, optimizing mascon parameterization based on satellite orbital coverage to enhance spatial resolution, particularly in polar regions. The optimized scheme significantly reduces parameterization error and noise while improving signal recovery for surface mass changes across various latitudes.
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Hu et al. (2025) High-Accuracy Identification of Cropping Structure in Irrigation Districts Using Data Fusion and Machine Learning
This study developed a high spatiotemporal-resolution remote sensing approach for identifying cropping structures in heterogeneous irrigation districts by fusing Landsat, Sentinel-2, and MODIS data to create a continuous 30 m, 8-day Normalized Difference Vegetation Index (NDVI) time series. Utilizing phenology-based features and a Random Forest classifier, the method achieved an overall accuracy of 90.78% and a Cohen’s kappa coefficient of 0.882 for crop mapping in the Yichang Irrigation District.
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Ramesh et al. (2025) A smart nail platform for wireless subsoil health monitoring via unmanned aerial vehicle-assisted radio frequency interrogation
This paper introduces HARVEST, a low-cost, wireless, and battery-free platform for subsoil health monitoring that reliably detects volumetric water content and electrical conductivity with drone-based radio frequency interrogation from altitudes up to 1.8 meters. The system offers a scalable, maintenance-free solution for next-generation precision agriculture.
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Zheng et al. (2025) Earlier snowmelt is driving the northward migration of East Asian Sand and Dust Storms
This study analyzed sand and dust storm (SDS) patterns in East Asia from 2001–2024, revealing a northward migration primarily driven by reduced winter snow cover and earlier snowmelt in sparsely vegetated regions, while vegetation remains dominant in highly vegetated areas.
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Pandey et al. (2025) Permafrost distribution modeling using remote sensing and machine learning technique in the Garhwal Himalaya, India
This study modeled permafrost distribution in the Garhwal Himalaya, India, by integrating topo-climatic variables and rock glacier inventories using Binary Logistic Regression, Random Forest, and Extreme Gradient Boosting, demonstrating high accuracy and identifying elevation and mean temperature of the warmest quarter as key predictors.
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Singh et al. (2025) Correction to: Advancing real time flood prediction in the Kosi river basin (India): A machine learning framework leveraging satellite precipitation products
This document is a correction notice for a previously published article, rectifying an error where the captions for Figs. 11 and 12 were interchanged in the original publication.
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NIŢU et al. (2025) Assessment of Soil Moisture Deficit and Adaptation of Irrigation Technologies to the Increasing Frequency of Droughts
This study evaluates the increasing soil moisture deficit due to intensifying droughts in southeastern Romania and identifies effective irrigation technologies. It found that droughts lead to significant crop yield reductions, and drip irrigation is superior to sprinkler irrigation, offering 35–40% water savings while maintaining optimal moisture.
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Dogaru et al. (2025) Drought Vulnerability Assessment of Major Crops Across Romania and Land Management Implications Under Future Climatic Changes
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Hussain et al. (2025) Spatiotemporal dynamics of carbon, water, and energy balance in Bangladesh using multi-source remote sensing and climate data
This study investigated the spatiotemporal dynamics of carbon, water, and energy fluxes and their impacts on ecosystem processes in Bangladesh from 2005 to 2022 using multi-source remote sensing and climate data. It found that Photosynthetically Active Radiation (PAR) is the dominant driver of Gross Primary Productivity (GPP), while temperature and precipitation significantly influence carbon uptake, highlighting the increasing disparity between ecosystem carbon sequestration capacity and rising anthropogenic emissions.
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Sîrbu et al. (2025) Regionalization of Updated Intensity-Duration-Frequency Curves for Romania and the Consequences of Climate Change on Sub-Daily Rainfall
This study evaluates alternative regionalization approaches for Intensity–Duration–Frequency (IDF) curves in Romania using 30 years of updated precipitation records. It reveals significant changes in rainfall patterns, with short-duration events increasing in intensity and long-duration events generally decreasing, highlighting the urgent need for improved flash flood prevention.
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Sun et al. (2025) Fusion of BeiDou and MODIS Precipitable Water Vapor Using the Random Forest Algorithm: A Case Study of Multi-Source Data Synergy in Hunan Province, China
This study developed a random forest fusion model to improve the accuracy of satellite-derived precipitable water vapor (PWV) in Hunan Province, China, by integrating MODIS data with high-accuracy BeiDou Navigation Satellite System (BDS) PWV, significantly reducing errors compared to MODIS alone.
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Yan et al. (2025) A Modified Hierarchical Vision Transformer for Soil Moisture Retrieval From CYGNSS Data
This research introduces a novel deep learning framework, multi‐head self‐attention‐aided vision Transformer (MSA‐ViT), for soil moisture retrieval using Cyclone Global Navigation Satellite System (CYGNSS) data. The MSA-ViT model integrates physical understanding with deep learning to capture nonlinear interactions, demonstrating superior performance over conventional and established deep learning models, and improving upon existing CYGNSS L3 products.
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Dar et al. (2025) Future Climate Projections for Nebraska: Insights from CMIP6 on Precipitation and Temperature Trends
This study provides high-resolution (4 km) CMIP6-based climate projections for Nebraska, revealing that by the far future (2060–2098), annual precipitation is projected to increase by up to 25%, while maximum temperatures could rise by 2.5–5.5 °C and minimum temperatures by 2.5–5.5 °C, with a notable 0.2 °C/decade divergence between Tmax and Tmin trends under high emissions.
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Bellos et al. (2025) An analytical methodology to assess epistemic uncertainty of 2D flood models under steady flow conditions
This study develops an analytical methodology to assess epistemic uncertainty in 2D flood models under steady flow conditions using an idealized benchmark setup. It proposes a new taxonomy of five uncertainty drivers (forcing, geometric, physical, computational, structural) and finds that the forcing driver has the most significant impact on model output, followed by geometric and physical drivers.
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Sattar et al. (2025) A coupled land use change-ecohydrological model for multi-seasonal arid agricultural systems: an Egyptian case study
This study presents a novel coupled land-use change and ecohydrological model, linking SWAT+ and CRAFTY, to simulate agricultural production and water use in arid multi-seasonal systems under climate change scenarios. Applied to Egypt, the model projects varying crop yield responses and improved water use efficiency, particularly under high CO2 emission futures, demonstrating the value of integrated biophysical and socioeconomic feedback for adaptation.
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Barthelemy et al. (2025) Future Atmospheric Rivers in Antarctica: Characteristics and Impacts With the IPSL Model
This study investigates the future impacts of atmospheric rivers (ARs) on the Antarctic surface mass balance (SMB) using 21st-century climate simulations, finding that ARs are projected to increase in frequency and severity, leading to a dominant increase in continental snow accumulation.
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Naeini et al. (2025) A comprehensive approach to enhancing irrigation network management through the water accounting plus framework
This study utilizes the Water Accounting Plus (WA+) framework and WaPOR remote sensing data to analyze water fluxes and productivity in the Qazvin Plain irrigation network from 2009 to 2021. The findings reveal significant spatial imbalances and identify that 28% of evapotranspiration is non-beneficial, highlighting substantial opportunities for water-saving interventions.
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Sahu et al. (2025) Deep Learning and Remote Sensing for Crop Yield Prediction and Decision Support
This study proposes a deep learning framework integrating multispectral satellite imagery and environmental variables for improved crop yield prediction and decision support. The framework, utilizing cGANs for data augmentation and DARTS for architecture optimization, significantly reduces prediction errors and demonstrates the potential to increase yields by 27% and reduce resource costs by 22% compared to conventional practices.
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Chowdhury et al. (2025) Corrigendum to “A comparison between numerical, neural network, and hybrid modelling approaches to simulate spring flow from a karst catchment in northwest Ireland using long-term hydrological data” [J. Hydrol. Reg. Stud. 61 (2025) 102723]
This corrigendum clarifies and corrects the attribution of a semi-distributed pipe network numerical model and its development details within the original study, ensuring accurate methodological reporting.
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Moorthi et al. (2025) Comparative assessment of spectral covariates from Sentinel 1 A, Sentinel 2 A, Landsat 8, and PRISMA for digital soil mapping of infiltration rate and textural classes
This study compared the effectiveness of spectral covariates from Sentinel 1A, Sentinel 2A, Landsat 8, and PRISMA satellites for digital soil mapping of infiltration rate and textural classes in the Thiruparankundram block, India. It found that PRISMA data, particularly when combined with SCORPAN variables and optimized via embedded feature selection methods, and Landsat 8 data demonstrated the highest efficiency in predicting soil infiltration rates and textural classes, respectively.
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Li et al. (2025) Mechanistic Insights Into Regional Air‐Sea Coupling Effects on East Asian Summer Climate: A Comparative Modeling Study With a New Regional Earth System Model
This study enhances the mechanistic understanding of regional air-sea coupling's influence on East Asian summer climate by comparing coupled (RIEMS) and uncoupled (WRF) regional models. It finds that RIEMS significantly improves simulations of air temperature and heat fluxes compared to WRF and global models, demonstrating the critical role of regional air-sea coupling.
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Verma et al. (2025) Indian Summer Monsoon During the Medieval Climate Anomaly With a Steady Trend From 1140 to 1250 CE
This study reconstructs Indian Summer Monsoon (ISM) dynamics during the Medieval Climate Anomaly (∼722 to 1250 CE) using a high-resolution speleothem record from Meghalaya, revealing distinct wet and dry phases and attributing multidecadal variability to solar activity and subdecadal variability to El Niño-Southern Oscillation.
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Xu et al. (2025) A global intercomparison of SWOT and traditional nadir radar altimetry for monitoring river water surface elevation
This study presents the first global-scale intercomparison between SWOT’s wide-swath Ka-band InSAR and traditional nadir radar altimetry (Sentinel-3 and Sentinel-6) for monitoring river water surface elevation. The research identifies that while high-quality SWOT data aligns well with traditional altimetry (RMSE = 0.80 m), factors such as river width, ice cover, and extreme backscatter significantly modulate data consistency.
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Najafi et al. (2025) Assessment of C3S monthly to seasonal climate forecast models for mean and extreme precipitation over Iran
This study evaluates the performance of seven Copernicus Climate Change Service (C3S) models and their multi-model ensemble (MME) in predicting mean and extreme precipitation over Iran. It finds that MME consistently outperforms individual models, with ECMWF and UKMO showing the highest skill, especially in western and northeastern Iran and at shorter lead times.
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Liu et al. (2025) Estimating Soil Moisture Using Multimodal Remote Sensing and Transfer Optimization Techniques
This study develops a multimodal deep learning framework using a ConvNeXt v2 backbone and an intermediate fine-tuning strategy to estimate high-resolution surface soil moisture. By integrating SAR, optical, topographic, and meteorological data, the model achieved high precision ($R^2 = 0.8956$) and demonstrated robust transferability across diverse agro-ecological zones.
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Wen et al. (2025) Differences in Tropical Cyclone Tornado Activities and Key Tornadic Environments Between China and the United States
This study investigates why tornadic tropical cyclones (TCs) in China produce significantly fewer tornadoes than those in the U.S., revealing that differences in TC characteristics and a critical mismatch between favorable thermodynamic and kinematic environmental conditions in China are key factors.
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Basak (2025) Smart Irrigation Control Using IOT Sensors and Machine Learning for Optimized Water Management
This project developed an intelligent IoT-based irrigation system integrating real-time sensor data with a K-Nearest Neighbors (KNN) machine learning model to automate water management, achieving approximately 66% accuracy in predicting irrigation needs and demonstrating water conservation.
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Reddy et al. (2025) Exploring the Impact of Optimization Techniques on Streamflow Prediction Using XGBoost: A Comparative Analysis with Satellite and Reanalysis Precipitation Datasets
This study systematically compares the joint impact of eight precipitation datasets and five optimization techniques on the performance of an Extreme Gradient Boosting (XGBoost) model for one-day-ahead streamflow prediction in India's Godavari Basin. The research found that the combination of Simulated Annealing (SA) for hyperparameter tuning and the India Meteorological Department (IMD) precipitation dataset consistently yielded the most accurate and reliable streamflow forecasts.
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Millán et al. (2025) Digital Twin Irrigation Strategies to Mitigate Drought Effects in Processing Tomatoes
This study evaluates the IrriDesK digital twin for automated irrigation management and regulated deficit irrigation (RDI) in processing tomatoes. It found that IrriDesK achieved 30–45% water savings while maintaining adequate crop water status and improving fruit quality, despite some yield reduction, demonstrating its potential for sustainable agriculture.
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Montaseri et al. (2025) Climate change impacts on the water footprint of horticultural and agronomic crops in the Lake Urmia basin
This study modeled the impact of climate change on the green and blue water footprints of 21 horticultural and agronomic crops in the Lake Urmia basin, projecting future temperature increases, precipitation shifts, a general increase in blue water footprint, and a decrease in green water footprint for most crops, highlighting the need for adapted cultivation patterns.
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Borah et al. (2025) Heat-stress reduction through targeted green infrastructure using computational urban climate twins
This study develops a high-resolution digital climate twin of a neighborhood in Ahmedabad, India, to evaluate the thermal co-benefits of small-footprint green infrastructure (GI) interventions, traditionally aimed at flood control. It demonstrates that strategically placed GI, particularly bioretention cells, can significantly reduce heat stress by lowering peak daytime air temperature by up to 2 °C and physiological equivalent temperature by 4–5 °C, while also providing flood mitigation.
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Manco et al. (2025) Identifying recurring patterns of extreme daily precipitation using K-means algorithm: Uncovering spatial shift driven by climate change over the Italian Peninsula
This study applies k-means clustering to high-resolution climate projections (VHR-PRO_IT) to identify and characterize recurring spatial-temporal patterns of extreme daily precipitation over the Italian Peninsula, revealing significant shifts and increased variability under future climate scenarios (RCP4.5 and RCP8.5).
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Eishoeei et al. (2025) Soil moisture measurements: a review
This review evaluates the evolution of soil moisture measurement techniques, from traditional in situ methods to modern remote sensing and data assimilation. It highlights the transition toward high-resolution global mapping and the integration of emerging technologies like UAVs and IoT for improved hydrological and agricultural management.
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Li et al. (2025) Fine‐Scale Characteristics and Upper‐Level Forcing of Heavy Rainfall Over the Northeastern Tibetan Plateau
This study investigates the spatiotemporal evolution and anomalous upper-level circulation of heavy regional rainfall events (RREs) in summer over the southern Qilian Mountains, identifying two distinct types with different characteristics and underlying upper-level atmospheric disturbances.
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Simard et al. (2025) Delta-X: An airborne remote sensing framework to calibrate hydrodynamic and ecogeomorphic processes responsible for land building in coastal deltas
This paper presents the Delta-X framework, an airborne remote sensing and in situ data integration strategy used to calibrate and validate hydrodynamic and ecogeomorphic models. The framework was implemented in the Mississippi River Delta to quantify the processes of mineral sediment deposition and organic matter production that determine deltaic land sustainability.
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Ji et al. (2025) In Situ Observations Reveal That the South Asian Summer Monsoon Weakens Aerosol Cloud Activation Over the Southern Tibetan Plateau
This study investigates the impact of the South Asian Summer Monsoon (SASM) on aerosol activation over the southern Tibetan Plateau using in situ measurements. It reveals a weak aerosol activation capacity and a significant negative correlation between SASM intensity and aerosol activation, attributed to enhanced wet scavenging during stronger monsoonal transport.
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Subrahmanian et al. (2025) A Novel Approach To Integrate Low Impact Development (LID) Modules into the SWAT Model To Facilitate Sustainable Urban Drainage Planning at River Basin Scales
This study integrates novel physics-based Multi-Layer Green-Ampt (MLGA) infiltration subroutines for Low Impact Development (LID) measures into the Soil Water Assessment Tool (SWAT) model, enhancing its capability for sustainable urban drainage planning and flood mitigation at large river basin scales. The enhanced model demonstrates improved hydrological simulation accuracy and significant reductions in surface runoff and increases in aquifer recharge under various LID implementation scenarios.
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Reich et al. (2025) Beyond optimality: Dryland ecosystems infrequently use water efficiently for carbon gain
This study investigates the applicability of optimality theory, which posits that plants maximize carbon gain per unit water lost (WUE scales with VPD^k, k=½), in dryland ecosystems. It reveals that dryland plant water-use efficiency often deviates from optimal behavior, particularly under arid conditions or low soil moisture, underscoring the necessity for dynamic representations of plant water-use strategies.
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Kim et al. (2025) Geophysical and Remote Sensing Monitoring of a Snow Patch System in Barton Peninsula Shows Impacts of Warming on Low-Altitude Permafrost
This study monitored a snow patch system in Barton Peninsula using geophysical and remote sensing techniques, revealing the impacts of warming on low-altitude permafrost.
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Singer et al. (2025) Southern Ocean Clear‐Sky Brightening From Sea Spray Aerosol Increase Drives Departure From Hemispheric Albedo Symmetry
This study investigates negative trends in reflected shortwave radiation over the 21st century, finding global darkening driven by clouds, while clear-sky signals show offsetting hemispheric trends. It reveals an unexpected widespread clear-sky brightening over the Southern Hemisphere, particularly the Southern Ocean, which is attributed to increased wind-driven sea spray aerosol emissions linked to rising near-surface winds.
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Farukh et al. (2025) Climatological assessment of pre-monsoon heatwave days in Bangladesh and their relationship to Indo Pacific circulation anomalies
This study assesses the climatological trends and drivers of pre-monsoon heatwave days (HWDs) in Bangladesh from 1990 to 2024, revealing a dramatic 12-fold increase in HWDs and a breakdown of climate stationarity. It identifies a "thermal dome" regime, characterized by synoptic-scale subsidence and thermal ridging, as the primary atmospheric mechanism driving the intensification and spatial homogenization of heatwaves across the country.
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Yang et al. (2025) Topographical influence on kilometer-scale hourly precipitation prediction during the 2021 Zhengzhou flood
This study investigates the influence of topographical factors on 3-kilometer hourly precipitation forecasts from the China Meteorological Administration’s Mesoscale Weather Numerical Forecast System (CMA-MESO) during the 2021 Zhengzhou flood using spatio-temporal geographically weighted regression (GTWR). It finds that CMA-MESO overestimates the topographical impact on the spatial distribution of precipitation while underestimating its influence on temporal variation, with near-surface temperature (ST) being a dominant factor for model bias.
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Pérez-Vega et al. (2025) Monitoring Irrigated Agriculture Using Remote Sensing and Census Data: A Case Study from Guanajuato, Mexico
This study evaluates the consistency between official agricultural census data and multi-sensor remote sensing data (MODIS, Landsat, and Sentinel-2) for monitoring irrigated crop dynamics in Guanajuato, Mexico. The research demonstrates that integrating coarse-resolution temporal data with high-spatial-resolution imagery provides a robust framework for managing water resources in semi-arid regions.
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Geng et al. (2025) Analyzing Pluvial Flooding Influenced by Urban Road Network Metrics Based on Hydrodynamic Simulation and SHAP Values
This study quantitatively investigates the influence of urban road network structure metrics on pluvial flooding using 2D hydrodynamic simulations, statistical analysis, and interpretable machine learning. It reveals that regular grid networks enhance drainage efficiency, while cul-de-sac types exacerbate flooding, with topography and network connectivity metrics exhibiting spatially heterogeneous impacts on flood depth and velocity.
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Zhu et al. (2025) SRSDNet: Super-Resolution Snow Depth Retrieval and Mapping Over the Qinghai-Tibet Plateau
This paper introduces SRSDNet, a novel method for super-resolution retrieval and mapping of snow depth, specifically applied to the Qinghai-Tibet Plateau.
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Nie et al. (2025) Identifying Hydrological Analogous Year from Magnitude and Temporal Patterns
This study proposes a comprehensive Similarity Index (SI) that integrates six existing methodologies to identify hydrologically similar years by balancing magnitude consistency and temporal pattern alignment. The SI family demonstrates superior cross-station stability, while a hybrid Cosine Similarity + Euclidean Distance (CS + ED) method achieves the highest overall similarity score, both significantly improving runoff forecasting accuracy.
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Carli et al. (2025) Tracing Vegetation Responses to Human Pressure and Climatic Stress: A Case Study from the Agri Valley (Southern Italy)
This study investigated vegetation changes in the Agri Valley, Italy, by combining long-term hydroclimatic anomalies with community-weighted Ellenberg indicator values and plant ecological groups, revealing significant climate-driven community shifts with distinct habitat-specific patterns in forests and grasslands.
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Abdelrazaq et al. (2025) Benchmarking MSWEP Precipitation Accuracy in Arid Zones Against Traditional and Satellite Measurements
This study assesses the performance of the MSWEP v2.8 precipitation dataset against ground gauges and three satellite products (CMORPH, IMERG, GSMaP) in the arid United Arab Emirates from 2004 to 2020, finding moderate overall performance but significant biases (overestimation of light rainfall, underestimation of extreme events, and seasonal variations) that necessitate bias correction for hydrological applications.
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Sarkar et al. (2025) Characterizing seawater intrusion and groundwater vulnerability to salinization along the east coast of India
This study evaluates seawater intrusion (SWI) across 37 districts on India's east coast, revealing that over 55% of groundwater samples are affected by salinization. The research identifies excessive groundwater extraction and permeable lithological layers as the primary drivers of landward saline water movement.
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Abair et al. (2025) Strengthening coastal flood forecasting through event-based data capitalization: a case study from the December 2022 storm surge in Québec City
This research validates a 2D hydrodynamic model for coastal flood risk management in Québec City, using it to analyze the December 23rd, 2022, storm surge event and demonstrating its ability to accurately reproduce spatio-temporal water levels and velocities in urban areas, influenced by wind and urban channelization.
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Vojtek et al. (2025) Fluvial Flood Inundation Modeling: a Comparative Assessment of 1D and 2D Hydraulic Approach Using MIKE+
This study compares 1D and 2D hydraulic modeling approaches using MIKE+ for fluvial flood inundation mapping across three flood scenarios (Q10, Q100, Q1000) in a narrow river valley in western Slovakia. The findings indicate that the 1D model significantly underestimates flood extents and flow depths compared to the 2D model and official 2D benchmark maps, concluding its unsuitability for such geomorphological settings.
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Asadollah et al. (2025) Climate-responsive crop forecasting: an EEMD-LSTM fusion approach for improved strategic crop yield simulation
This study developed an EEMD-LSTM fusion model to improve strategic crop yield forecasting for barley, lentils, pea, and wheat across all provinces of Iran. The research demonstrates that integrating Ensemble Empirical Mode Decomposition (EEMD) as a signal denoising technique generally enhances the predictive accuracy of the Long Short-Term Memory (LSTM) model by reducing noise in climate input data.
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Kumar et al. (2025) Impact of climate change on flood properties in a mountainous catchment of Nepal Himalayas
This study assesses the impact of climate change on flood properties (peak, volume, duration, seasonality, and sensitivity) in the West Seti River Basin, Nepal Himalayas, finding increased flood peaks, decreased durations, delayed timing, and higher sensitivity to precipitation in future scenarios.
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Dai et al. (2025) Understanding the Climate Response to Different Vertical Patterns of Radiative Forcing
This study investigates how the vertical structure of radiative forcing influences climate response, finding that higher-altitude forcings lead to less global warming due to more negative cloud feedback, a phenomenon linked to sea-surface temperature patterns and tropospheric static stability. It highlights the critical need to accurately represent the vertical distribution of anthropogenic forcings for precise climate projections.
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Zhang et al. (2025) Automatic recognition of wheat growth stages with a lightweight multimodal data fusion network
This study proposes a lightweight multimodal data fusion network, leveraging UAV-acquired RGB, multispectral, and digital surface model data, to accurately recognize wheat growth stages. The developed MobileNetV3-Small-based model achieves 99.57% accuracy and high computational efficiency, effectively overcoming limitations of single-modal methods and enabling real-time deployment on edge devices.
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Kacimov (2025) Ephemeral reservoirs of low-head wadi dams drained by seepage, evaporation and pumping: the Pavlovsky/Polubarinova-Kochina/Abel analytical legacy redux
This paper develops analytical and numerical solutions, based on conformal mappings and ordinary differential equations, to model transient 2-D seepage and reservoir depletion in low-head wadi dams, facilitating optimal design for water management in arid regions.
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Wang et al. (2025) Film-area Explicit Full-range Soil Hydraulic Dataset and Model Code (for “Development and Evaluation of a Full-range Soil Hydraulic Model Explicitly Incorporating Film Area”)
This paper focuses on the development and evaluation of a novel full-range soil hydraulic model that explicitly incorporates film area. The accompanying repository provides the observational data, calibrated parameters, and MATLAB code necessary for reproducing the model's results and comparisons.
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Oue et al. (2025) Effects of two types of mulch on evapotranspiration, dry matter, and water use efficiency of soybean under different soil water content
This study investigated the effects of white clover living mulch (CL) and shredded paper mulch (SP) on soybean growth, evapotranspiration (ET), and water use efficiency (WUE) under five different soil water content (SWC) levels in a pot experiment. Shredded paper mulch proved more effective for water-saving cropping systems, leading to better soybean growth and significantly higher WUE compared to clover living mulch, which increased ET due to clover transpiration.
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Simantiris et al. (2025) AIFloodSense
AIFloodSense is a novel, multi-modal aerial imagery dataset designed to advance machine learning for flood classification, semantic segmentation, and scene understanding, featuring high-resolution images from 230 global flood events between 2022 and 2024.
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Ren et al. (2025) From seasonal variability to long-term trends: a comprehensive analysis of reservoir-induced flow regime alterations
This study comprehensively assesses the seasonal variations and long-term trends (1980-2020) of reservoir-induced flow regime alterations across the Contiguous United States, revealing distinct intra-annual patterns and significant reductions in high flow impacts alongside increases in low flow impacts over recent decades.
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Dou et al. (2025) Comparative Analysis of Rainfall-Based and Discharge-Based Early Warning Methods for Flash Floods
This study evaluates the comparative performance of rainfall-based (RW) and discharge-based (DW) early warning methods for flash floods using historical case studies and hydrological simulations. The findings identify specific environmental and infrastructural scenarios where each method excels, advocating for an integrated, dynamically weighted warning framework.
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Jiang et al. (2025) Temperature Governs the Elevation Dependency of Snow Cover Changes in the Upper Reaches of the Yarkand River Basin
This study investigated the physical mechanisms driving elevation-dependent snow cover changes in the Upper Yarkand River Basin from 2002 to 2020. It revealed a mechanistic shift at approximately 4000 meters, where lower elevations are dominated by temperature-driven snowmelt, while higher elevations are influenced by precipitation-driven snow accumulation.
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Zhang et al. (2025) Seasonal Dependence of Evaporation Characteristics over the North Atlantic and Reliability Assessment of Multiple Datasets
This study investigates the spatiotemporal characteristics of North Atlantic evaporation from 1980 to 2015, revealing high consistency and reliability in cold season patterns across four datasets, while warm season patterns exhibit significant divergence and sensitivity to dataset selection.
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Hao et al. (2025) A Flexible Python Module for Reservoir Simulations with Seasonally Varying and Constant Flood Storage Capacity
This study presents an open-source Python module integrating three storage-oriented reservoir schemes to compare constant versus seasonally varying flood storage capacity (FSC) strategies and assess operational zone parameter uncertainty. It reveals that constant FSC significantly outperforms seasonal variation for outflow simulation and reduces storage errors across 289 global reservoirs, recommending constant FSC with H22 or S25 as the default for large-scale modeling.
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Bai et al. (2025) Tracking drought-flood abrupt alternations: Event identification, path analysis, and ecological impacts on vegetation
This study introduces a novel three-dimensional method to identify drought-to-flood and flood-to-drought abrupt alternation events in the Pearl River Basin, quantifying their spatiotemporal characteristics and evaluating their distinct ecological impacts on vegetation.
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Chen et al. (2025) An NSGA-II-XGBoost Machine Learning Approach for High-Precision Cropland Identification in Highland Areas: A Case Study of Xundian County, Yunnan, China
This study developed a high-precision cropland identification model for plateau and mountainous regions, demonstrating that an NSGA-II optimized XGBoost model achieved superior performance with an overall accuracy of 95.75% in Xundian County, Yunnan Province.
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Ning et al. (2025) Multi-model simulation performance of monthly water balance models for global catchments: Thresholds and structural sensitivity
This study evaluates the performance of 14 monthly water balance models across over 2,000 global catchments, revealing that while most models perform reasonably well, their accuracy significantly declines in high-latitude or snow-dominated regions and improves with the inclusion of non-linear snow modules and in more humid conditions.
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Fares et al. (2025) Revealing Emerging Hydroclimatic Shifts: Advanced Trend Analysis of Rainfall and Streamflow in the Navasota River Watershed
This study applies an integrated, data-driven framework to analyze hydroclimatic shifts in the Navasota River Watershed, revealing an accelerating wetting tendency and a statistically significant increase in annual peak streamflow, indicating heightened flood risk.
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kasaei et al. (2025) Dataset supporting 'Compounding of 100-year coastal floods by rainfall in an urban environment'
This paper investigates how rainfall exacerbates 100-year coastal flood events in urban environments, aiming to quantify the compounding effects of these two flood drivers.
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Lee et al. (2025) Autocorrelation Structure of SPI and Its Implication for Drought Forecasting
This study investigates the autocorrelation function (ACF) structure of the Standardised Precipitation Index (SPI) to understand its inherent deterministic components. It finds that SPI possesses a deterministic structure, which implies that drought forecasting using SPI must be approached with caution due to this predictable component.
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Ma et al. (2025) PWVFnet: A Short-Time Troposphere PWV Forecast Model Combined Empirical Models and Spatiotemporal ConvLSTM Network
This paper introduces PWVFnet, a novel model for short-time forecasting of tropospheric precipitable water vapor (PWV), which integrates empirical models with a spatiotemporal ConvLSTM network. The main findings are not detailed in the provided text.
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Huang et al. (2025) Evaluating microphysics scheme impacts on summer precipitation in Northwestern China using a convection permitting WRF model
This study evaluates the impact of three double-moment microphysics schemes (JIS, Thompson, WDM7) on summer precipitation simulations over Northwestern China using a convection-permitting WRF model. It finds that WDM7 provides the most accurate diurnal cycle and effectively reduces wet biases in mountain precipitation compared to the other schemes.
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Zhang et al. (2025) Diurnal Propagation of Precipitation in Landfalling Tropical Cyclones
This study investigates the diurnal propagation of precipitation (DPP) in landfalling tropical cyclones (TCs) globally, revealing that these events account for approximately 30% of landfalling TC days and lead to wider and more extreme rainfall distributions. It identifies distinct initiation times and underlying mechanisms for overland DPP compared to open-ocean events.
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ZHANG et al. (2025) Daily Snow Depth Fusion Products for Arid Regions of Central Asia
This study developed a high-precision daily snow depth fusion product for Central Asia (1990–2023) by integrating multiple existing snow depth products and in-situ observations using an XGBoost model, achieving significantly improved accuracy.
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Abulikemu et al. (2025) Correction: Abulikemu et al. Diurnal Variation Characteristics of Precipitation in Summer Associated with Diverse Underlying Surfaces in the Arid Region of Eastern Xinjiang, Northwest China. Remote Sens. 2025, 17, 3438
This study investigates the diurnal variation characteristics of summer precipitation in the arid region of Eastern Xinjiang, Northwest China, focusing on how these patterns are influenced by diverse underlying surfaces.
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Sreeraj et al. (2025) Data-driven parameterization of SWAT+ reservoir module without access to operation rules
This study developed a data-driven approach using simulated annealing to parameterize the SWAT+ reservoir module without requiring operational rules. The method significantly improved the accuracy of modeled reservoir outflow, storage, and evaporation, enabling reliable hydrologic simulations in data-scarce environments.
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Liang et al. (2025) Correction: Liang, J.; Griswold, J.D.S. Assessment of Aerosol Optical Depth, Cloud Fraction, and Liquid Water Path in CMIP6 Models Using Satellite Observations. Remote Sens. 2025, 17, 2439
This study investigates the impact of [specific environmental factor, e.g., land-use change] on [specific hydrological process, e.g., regional water balance] using a coupled modeling approach, revealing [main finding, e.g., a significant decrease in groundwater recharge in urbanized areas].
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Gbodjo et al. (2025) Self-supervised representation learning for cloud detection using Sentinel-2 images
This study leverages self-supervised representation learning (Momentum Contrast and DeepCluster) for accurate cloud and cloud shadow detection in Sentinel-2 imagery, demonstrating that these methods outperform industry standards and several supervised approaches with significantly fewer labeled data.
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Luo et al. (2025) STAR: Soil texture analysis recognizer integrating domain-adaptive transfer learning with NIR spectroscopy
This paper introduces STAR, a compact near-infrared (NIR)-based device for precise soil texture classification, which employs a domain-adaptive deep learning strategy to overcome limitations in model generalization and data dependency. Validated with local soil samples, STAR achieved an 85.0 % overall accuracy and successfully identified unseen soil texture types, demonstrating robust generalization for practical soil analysis.
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Saghiry et al. (2025) Towards More Reliable Gridded Precipitation Estimates: Gauge-Based Multi-Scale Evaluation and Machine Learning Bias Correction
This study evaluates four high-resolution gridded precipitation products against 12 rain gauges in Morocco's Moulouya Basin across multiple spatio-temporal scales and applies machine learning (ML) for bias correction. It finds that GPM IMERG-Final (GPM-F) performs best uncorrected, and ML models (Random Forest for daily, eXtreme Gradient Boosting for monthly/seasonal) significantly enhance its accuracy, providing reliable precipitation inputs for hydrological applications in data-scarce regions.
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Bai et al. (2025) Spatiotemporal Evolution Characteristics of Summer Dry-Heat Compound Events in Liaoning Province
This study developed a comprehensive framework to analyze the spatiotemporal evolution of summer dry-heat compound events in Liaoning Province from 1961 to 2020, revealing a significant increase in their frequency and intensity, primarily driven by rising temperatures in urbanized and inland basin areas.
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Borzì et al. (2025) Editorial for Special Issue “Geographic Information System (GIS) Techniques and Applications for Sustainable Water Resource Management in Agriculture”
The paper highlights the transformative advancements in Geographic Information Systems (GIS) and remote sensing technologies over the past decade, fundamentally reshaping how hydrologists and water managers address agricultural water resource challenges.
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Sun et al. (2025) Reducing Evapotranspiration Simulation and Forecast Uncertainties Due To Initial and Model Errors Over the Tibetan Plateau
This study introduces a combined approach using minimization, data assimilation, and the conditional nonlinear optimal parameter perturbation ensemble prediction (CNOP-PEP) method to reduce uncertainties in evapotranspiration (ET) simulations and predictions over the Tibetan Plateau. The proposed method significantly improves ET ensemble prediction accuracy compared to traditional methods by optimizing initial conditions and perturbing model parameters.
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Song et al. (2025) Long-distance soil moisture monitoring via Helmholtz resonator–enhanced acoustic transmission and Swin-Transformer modeling
This study developed a Helmholtz resonator-enhanced acoustic transmission system and a Swin-Transformer model to overcome acoustic signal attenuation in soil, enabling long-distance and large-scale soil moisture monitoring with high accuracy.
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Terzi (2025) PyDRGHT: A comprehensive python package for drought analysis
This study introduces PyDRGHT, an open-source Python package for comprehensive drought analysis, providing a unified framework for computing univariate and multivariate drought indices, characterizing droughts, and conducting frequency analyses. Its utility is demonstrated using long-term precipitation and streamflow records from the Seyhan River Basin, Türkiye, showcasing robust drought detection and characterization.
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Abed et al. (2025) Evaluating the Performance of Infiltration Models Under Semi-Arid Conditions: A Case Study from the Oum Zessar Watershed, Tunisia
This study systematically evaluated four infiltration models (Horton, Philip, Kostiakov, Green–Ampt) using field-measured data in the Oum Zessar watershed, identifying the Kostiakov model as the most effective for the region, followed by the Horton model.
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Zhang et al. (2025) High-Resolution Mapping of Dry and Wet Snow Using Multisource Remote Sensing Data in Mountainous Regions
This study aims to achieve high-resolution mapping of dry and wet snow in mountainous regions by utilizing multisource remote sensing data.
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Carter et al. (2025) Impact of drought on global food security by 2050
This study quantifies the impact of drought on global maize, soybean, rice, and wheat production by 2050 using a process-based crop model and an Earth system model, integrating socio-economic factors into a food insecurity index. It finds modest global average losses but significant country-level reductions (over 10% in 62 countries) and identifies regions most vulnerable to food insecurity.
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Blay et al. (2025) Geospatial and Deep Learning Approaches for Modeling Floodwater Depth in Urbanized Areas
This study developed a deep learning framework using geospatial and deep learning approaches to model floodwater depth in urbanized areas, finding that a lightweight ResNet18 architecture with terrain-derived predictors achieved high accuracy and spatial coherence, demonstrating potential for rapid flood assessment in data-scarce regions.
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Wang et al. (2025) Hydrological drought attribution analysis of six rivers in China by the coupled model of machine learning and hydrological model
This study applied a coupled machine learning-hydrological model to monthly runoff data from six major Chinese rivers to quantify the contributions of climate change and human activities to hydrological drought, finding varying dominance of these factors across basins and seasons.
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Zhengissova et al. (2025) Analysis of spatio-temporal changes of surface water within the Semipalatinsk test site based on the Google Earth Engine platform
This study analyzed the spatio-temporal dynamics of surface water bodies within the Semipalatinsk Test Site from 1996 to 2024 using remote sensing data on the Google Earth Engine platform, revealing a strong dependence of water extent on winter climatic conditions and a prevalence of temporary water bodies.
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Şen (2025) Global Standard Temperature Partial Trends for Dynamic Climate Change Impact Interpretations
This paper introduces an innovative Temperature Trend Identification (TTI) methodology to analyze global temperature records, distinguishing trends in "Low," "Medium," and "High" temperature categories, and finds average increases of 0.9 °C for "Low" and 1.78 °C for "High" temperatures, alongside a 1.33 °C average incremental temperature.
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Wang et al. (2025) Spatiotemporal Evolution and Drivers of Harvest-Disrupting Rainfall Risk for Winter Wheat in the Huang–Huai–Hai Plain
This study quantifies the risk of harvest-disrupting rain events (HDREs) in China's Huang–Huai–Hai Plain over six decades, revealing a significant increase and spatial shift in risk driven by evolving meteorological factors and amplified by underlying surface features.
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Yu et al. (2025) Soil MoistureRetrieval from TM-1 GNSS-R Reflections with Auxiliary Geophysical Variables: A Multi-Cluster and Seasonal Evaluation
This study develops a 9 km resolution global soil moisture retrieval model using Tianmu-1 (TM-1) GNSS-R reflectivity combined with auxiliary geophysical variables and a Random Forest algorithm. It demonstrates that a land-cover-based spatial clustering and seasonal temporal partitioning strategy significantly improves retrieval accuracy and stability, achieving a correlation coefficient (R) of 0.8155 and an unbiased RMSE (ubRMSE) of 0.0689 cm³/cm³ at the cluster level.
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Wang et al. (2025) Canopy Water Loss and Physiological Water-Use Responses of Xerophytic Shrubs Under Wet Conditions on the Northern Loess Plateau
This study investigated the hierarchy of atmospheric and soil-water controls on canopy transpiration and stomatal conductance in two xerophytic shrubs on the northern Loess Plateau under wet conditions. It found that atmospheric factors are the primary drivers, with soil water content exerting secondary, species-specific influences, revealing depth-partitioned water-use strategies.
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Zhou et al. (2025) Shortened intensification duration offsets the increase of tropical cyclone lifetime maximum intensity
This study introduces a rate-duration framework to decompose tropical cyclone (TC) lifetime maximum intensity (LMI), revealing that a significant shortening of intensification duration, driven by poleward and landward shifts in TC genesis locations, has offset nearly half (48.7%) of the increase in strong TCs despite rising intensification rates in recent decades.
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Jeon et al. (2025) Multimodal Optical Biosensing and 3D-CNN Fusion for Phenotyping Physiological Responses of Basil Under Water Deficit Stress
This study developed a multimodal optical biosensing and 3D convolutional neural network (3D-CNN) fusion framework to non-destructively phenotype basil's physiological responses (normal, resistance, recovery) under water deficit stress, achieving 96.9% classification accuracy by integrating RGB, depth, and chlorophyll fluorescence imaging.
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Belfort et al. (2025) Hydrodynamic Parameter Estimation for Simulating Soil-Vegetation-Atmosphere Hydrology Across Forest Stands in the Strengbach Catchment
This study developed a methodology integrating pedotransfer functions and inverse modeling to determine optimal soil hydrodynamic parameters for contrasting forest plots, finding that a balanced calibration time series including both wet and dry phases is crucial for robust parameter estimation.
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Chaurasia et al. (2025) Signature of intraseasonal oscillations during Hunga Tonga volcanic eruption
This study investigates the impact of the 15 January 2022 Hunga Tonga volcanic eruption on Intraseasonal Oscillations (ISO) using satellite and reanalysis data, revealing enhanced 20–40 day variability in convective parameters and an influence on atmospheric tides.
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Singh et al. (2025) Crop Guard: A Smart Irrigation System with Integrated Nutrient Deficiency Detection
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Arshad et al. (2025) Enhancing assimilated soil moisture prediction from environmental data using advanced machine learning
This study enhances the prediction of top-layer soil moisture (0–10 cm) in arid irrigated and rainfed regions of Pakistan by integrating t-Distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction with machine learning models. The results demonstrate that t-SNE-enhanced Gradient Boosting Regression significantly outperforms standard models, providing a more reliable tool for drought early warning and agricultural management.
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Wang et al. (2025) An Enhanced CycleGAN to Derive Temporally Continuous NDVI from Sentinel-1 SAR Images
This study developed an enhanced CycleGAN (SA-CycleGAN) to generate high-fidelity, temporally continuous Normalized Difference Vegetation Index (NDVI) from Synthetic Aperture Radar (SAR) imagery, demonstrating superior performance over other unsupervised models in overcoming optical remote sensing data gaps caused by cloud cover.
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Feng et al. (2025) A Remote Sensing-Driven Dynamic Risk Assessment Model for Cyclical Glacial Lake Outbursts: A Case Study of Merzbacher Lake
This study develops and validates a dynamic, remote sensing-driven framework for Glacial Lake Outburst Flood (GLOF) risk assessment at Lake Merzbacher, utilizing an innovative Ice-Water Composite Index and a Random Forest model to accurately predict lake volume and identify Positive Accumulated Temperature as the dominant hydrological driver.
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Kishcha et al. (2025) Effect of Desert Dust Intrusion on the Detection of Marine Heatwaves
This study investigates the impact of desert dust intrusion on marine heatwave (MHW) detection using satellite microwave (MW) and infrared (IR) sea surface temperature (SST) data, revealing that MW SST is unaffected by dust while IR SST is significantly compromised, leading to MHW detection failures and underestimation in combined datasets.
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Norova et al. (2025) A Systematic Review of Methodological Advances in Glacier-Velocity Retrieval with an Emphasis on Debris-Covered Glaciers
This study provides a comprehensive systematic review of methodological advances in glacier-velocity retrieval, highlighting a significant shift towards automated, AI-informed systems while identifying persistent challenges in monitoring debris-covered glaciers.
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Lee et al. (2025) LSTM-Based Prediction and Evaluation of Meteorological Drought Indices Considering Cumulative Precipitation Timescale Combinations
This study developed and evaluated Long Short-Term Memory (LSTM) models for predicting meteorological drought indices (SPI6 and SPEI6) in Gwangju Metropolitan City, comparing univariate and multivariate input configurations. The models demonstrated stable predictive performance, with multivariate inputs generally showing improved statistical accuracy and SPI exhibiting stronger agreement with actual drought stages.
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Lucia et al. (2025) Harnessing social sensing for real-time flood event reconstruction: A digital autopsy of the 2024 Valencia DANA
This study reconstructs the 2024 Valencia floods by integrating over 156,000 geolocated social media messages with hydrological and hydraulic models, demonstrating the feasibility of combining human-sensed information with physically based models to enhance real-time situational awareness and disaster risk reduction.
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Tan et al. (2025) Comparative Assessment of Eight Satellite Precipitation Products over the Complex Terrain of the Lower Yarlung Zangpo Basin: Performance Evaluation and Topographic Influence Analysis
This study systematically evaluates eight satellite-based precipitation retrieval algorithms against ground observations in the data-scarce Yarlung Zangpo watershed from 2014 to 2022, finding that IMERG_EarlyRun and IMERG_LateRun offer optimal real-time performance while IMERG_FinalRun exhibits severe deterioration due to gauge adjustment failures.
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Almahawis et al. (2025) Impacts of Climate Change and Water Management on Hydrologic Fluxes in an Irrigated Basin
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Lei et al. (2025) Spatially Explicit Relationships Between Urbanization and Extreme Precipitation Across Distinct Topographic Gradients in Liuzhou, China
This study quantifies the evolution of extreme precipitation (EP) characteristics and investigates the spatially non-stationary effects of urbanization on EP in Liuzhou, China, revealing that urbanization impacts on EP vary significantly with elevation and temporal scales.
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Zhong et al. (2025) A 3D point cloud instance segmentation network for extracting individual trees from complex forest scenes
This study proposes a novel 3D point cloud instance segmentation network to accurately extract individual trees from complex forest scenes, addressing challenges like under-segmentation and over-segmentation. The network achieves superior performance compared to existing methods, providing reliable technical support for automated forest resource management.
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Wang et al. (2025) Key drivers and predictability of the unprecedented 2024 United Arab Emirates flood
This study investigates the extreme rainfall event that struck the Arabian Peninsula in mid-April 2024, identifying a rare convergence of a strengthened Somali low-level jet and an unusually strong Arabian Cold Vortex as the primary drivers, while highlighting significant underestimation by forecast models.
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Liu et al. (2025) Ensemble modelling based on transfer learning for enhancing crop mapping through synergistic integration of InSAR coherence and multispectral satellite data
This study proposes an innovative ensemble deep learning framework, Transformer-AtLSTM-RF, to enhance crop mapping in smallholder intercropping systems by synergistically integrating multi-temporal Sentinel-1 InSAR coherence with Sentinel-2 and RapidEye multispectral data, achieving high classification accuracies in Bei'an county, China.
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Pypkowski et al. (2025) The Whittle likelihood for mixed models with application to groundwater level time series
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Muraharirao et al. (2025) Regional scale analysis of the internal propagation of groundwater droughts in Andhra Pradesh, India
This study develops the Groundwater Drought Instantaneous Propagation Speed (GDIPS) framework to analyze the internal dynamics of groundwater droughts across Andhra Pradesh, India. The findings reveal that groundwater droughts generally develop faster than they recover, with significant regional variability driven by rainfall patterns and aquifer hydrogeology.
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Anık et al. (2025) Investigating the contribution of decomposition techniques to machine learning accuracy in SPEI-based drought forecasting for multiple Köppen-Geiger climates
This study investigates the impact of various decomposition techniques on machine learning accuracy for SPEI-based drought forecasting across different Köppen-Geiger climates. The research found that decomposition methods significantly enhance prediction performance, with Variational Mode Decomposition (VMD) proving most effective, leading to Nash–Sutcliffe Efficiency (NSE) values consistently above 0.95 across all SPEI time scales.
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Onofua et al. (2025) Deficit Irrigation and Root Zone Soil Thermal Regimes in Water Limited Agriculture: A Review
This systematic review synthesizes studies from 2020 onwards to understand how deficit irrigation and water-saving practices modify soil thermal properties and coupled soil water–heat dynamics in irrigated agroecosystems. It finds that deficit irrigation typically lowers soil moisture, reduces thermal conductivity and heat capacity, and increases diurnal soil temperature ranges, while specific practices can create more thermally buffered root zones to sustain water productivity.
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Allagui et al. (2025) Estimating Deep Soil Salinity by Inverse Modeling of Loop–Loop Frequency Domain Electromagnetic Induction Data in a Semi-Arid Region: Merguellil (Tunisia)
This study combines multi-configuration frequency domain electromagnetic induction (FD-EMI) sensors with a laterally constrained inversion (LCI) approach to monitor the vertical distribution of soil salinity in an irrigated area, revealing systematic salinity transfer influenced by irrigation systems and seasonal variations.
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Abbate et al. (2025) IDF Curve Modification Under Climate Change: A Case Study in the Lombardy Region Using EURO-CORDEX Ensemble
This paper presents a methodology to reconstruct Intensity–Frequency–Duration (IDF) curves using EURO-CORDEX climate model outputs and Generalised Extreme Value (GEV) techniques to account for climate change. The study projects a significant increase in extreme rainfall depth and a reduction in return periods in Lombardy, Italy, highlighting the necessity of modifying current IDF curves for future hydraulic infrastructure design.
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Le et al. (2025) Impacts of elevation bias and topographic uncertainty on flood modeling: model robustness and floodplain sensitivity mapping in a lowland River Basin
This study evaluates the impact of Digital Elevation Model (DEM) quality on flood simulation and sensitivity mapping in a low-relief basin, identifying the most robust global DEMs as alternatives to high-resolution LiDAR data.
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Shan et al. (2025) Refined Leaf Area Index Retrieval in Yellow River Delta Coastal Wetlands: UAV-Borne Hyperspectral and LiDAR Data Fusion and SHAP–Correlation-Integrated Machine Learning
This study developed and evaluated machine learning models using UAV-borne hyperspectral and LiDAR data fusion for accurate leaf area index (LAI) retrieval in coastal wetlands, demonstrating significant accuracy improvements over single-source methods and identifying key contributing features.
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Fu et al. (2025) Enhanced Boundary Layer Thermodynamics Profiles Retrieval From Ground-Based Microwave Radiometers With Surface and Pseudo-Tower Constraints
This paper focuses on improving the accuracy of atmospheric boundary layer thermodynamic profiles (e.g., temperature, humidity) retrieved from ground-based microwave radiometers by incorporating surface observations and pseudo-tower data as additional constraints.
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Kang et al. (2025) Multi-Satellite Image Matching and Deep Learning Segmentation for Detection of Daytime Sea Fog Using GK2A AMI and GK2B GOCI-II
This study aimed to enhance sea fog detection accuracy and reliability by integrating multi-satellite imagery using a deep learning-based co-registration technique and autotuning state-of-the-art semantic segmentation models. The approach, particularly with multi-satellite fusion, significantly improved detection performance, outperforming existing operational products and reducing the omission of disaster-critical information.
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Martins et al. (2025) SWAT Model and Drought Indices: A Systematic Review of Progress, Challenges and Opportunities
This systematic review analyzes the integrated application of the Soil and Water Assessment Tool (SWAT) model and drought indices for drought monitoring and prediction, identifying significant progress but also highlighting critical gaps in advanced statistical methodologies and index harmonization.
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Jing et al. (2025) Improve the accuracy of SAR-based soil moisture retrieval by coupling Bayesian inference and water cloud model
This study proposes and evaluates a novel scheme, BI_WCM, which couples Bayesian inference theory with the Water Cloud Model to improve SAR-based soil moisture retrieval accuracy by addressing the WCM's limitation of neglecting vegetation volume scattering. The BI_WCM significantly enhanced retrieval accuracy over maize-covered areas (14.44% RMSE decrease) and marginally in bare soil areas, demonstrating its dynamic compensation ability for retrieval errors.
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Hsu et al. (2025) Predictability of Compound Impacts From Hurricane Helene and Predecessor Rain Event in CFSv2 Operational Forecasts
This study investigated the predictability of precipitation and soil moisture conditions associated with Hurricane Helene and a predecessor rain event (PRE) in the Southeastern United States using NOAA's Coupled Forecast System model (CFSv2). It found that predictability for these events significantly drops around 4- to 5-day lead times due to biases in the timing and location of the systems and underestimated PRE precipitation.
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Almalki et al. (2025) Quantifying Time-Lagged Vegetation Responses to Hydroclimatic Factors in Dam-Influenced Arid Regions Using VAR Modeling and Remote Sensing
This study quantified time-lagged vegetation responses to hydroclimatic factors in four dam-influenced arid basins in southern Saudi Arabia using VAR modeling and remote sensing. It revealed that dam construction significantly increased vegetation response lags from 2–3 months in the pre-dam period to 4–5 months post-dam, highlighting the disruption of natural hydroclimatic-vegetation coupling.
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McKinnon et al. (2025) Observed and Modeled Trends in Downward Surface Shortwave Radiation Over Land: Drivers and Discrepancies
This study validates ERA5 incoming surface shortwave radiation (Rs) against satellite and in situ data, then reveals substantial global continental brightening from 1980 to 2024, often linked to cloud cover reductions rather than aerosols, and highlights significant discrepancies with CMIP6 climate model simulations.
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Yu et al. (2025) Weakened Circum‐Global Teleconnection Pattern Under Global Warming Can Modulate Heat Extremes Across Eurasia
This study projects a robust 31.8% reduction in the boreal summer interannual Circum-global teleconnection (CGT) amplitude under global warming, driven by weakened Rossby wave source anomalies, which subsequently alters regional heatwave durations.
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Yegin et al. (2025) Model Selection Challenges in Non-Stationary Precipitation Estimation: The Role of AIC, BIC, and Covariate Choice
This study evaluates 50-year precipitation estimates from stationary and non-stationary models across 53 meteorological stations in Türkiye, analyzing the impact of model selection criteria (AIC, BIC), covariate choice, and probability distributions on predictive performance and the plausibility of extreme estimates. It reveals inconsistencies in model rankings between AIC and BIC, and highlights the trade-offs between model complexity, accuracy, and the risk of unrealistic extreme value predictions.
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Han et al. (2025) Fusion of Multi-Source Evapotranspiration Products Via the Bayesian Three-Cornered Hat Method and its Application in Runoff Simulation for Semi-Arid Basins
This study evaluates multi-source evapotranspiration (ET) products, quantifies their uncertainty using the Three-Cornered Hat (TCH) method, and fuses them via the Bayesian TCH (BTCH) method to create a more accurate ET dataset. It then demonstrates that using this fused ET significantly enhances the calibration and runoff simulation performance of the VIC-glacier hydrological model in semi-arid, data-scarce basins.
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Zohar et al. (2025) Toward scalable green roofs: A critical review of hydrological design, modelling, monitoring, and future directions
This review addresses the gap between extensive green roof (GR) research and their limited large-scale implementation for urban stormwater management. It synthesizes knowledge across design, hydrological modelling, and monitoring, emphasizing remote sensing (RS) as a scalable solution, and explores future directions like blue-green solar roofs (BGSRs) to foster widespread adoption.
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Zhou et al. (2025) Global Assessment of Vegetation Ozone Exposure Using a Remote Sensing-Based Index: A New Potential Reference for Critical Levels and Gross Primary Production Simulations
This paper presents a global assessment of vegetation ozone exposure using a remote sensing-based index, proposing it as a new potential reference for critical levels and gross primary production simulations.
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Tefera et al. (2025) Integrating machine learning models with ground sensors to enhance soil moisture prediction in agroecosystems of Texas
This study enhances soil moisture prediction in Texas agroecosystems by integrating in situ sensor data with biometeorological variables using various machine and deep learning models. It found that Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory models achieved superior predictive accuracy (R² ≥ 0.90, RMSE ≤ 0.021 m³ m⁻³) with robust uncertainty quantification.
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Dept. of Horticulture et al. (2025) Micro-Irrigation and Fertigation for Improving Water Use Efficiency in Fruit Crops — A Review
This review synthesizes the principles, benefits, limitations, and future needs of micro-irrigation and fertigation technologies for improving water and nutrient use efficiency, yield, and fruit quality in fruit crops. It highlights their transformative role in modern, sustainable horticulture by delivering water and dissolved nutrients directly to the crop root zone.
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Zhang et al. (2025) Simulation and Analysis of Sea Surface Skin Temperature Diurnal Variation Using a One-Dimensional Mixed Layer Model and Himawari-8 Data
This study aimed to capture Sea Surface Skin Temperature (SSTskin) diurnal warming events and evaluate the performance of an improved one-dimensional mixed-layer model (PWP) in simulating SSTskin. The improved PWP model reproduced the diurnal variation cycle consistently with Himawari-8 observations, but showed abnormal overestimation in low-wind-speed areas due to rapid mixed-layer thinning and lack of horizontal diffusion.
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Liu et al. (2025) A long-term Areal Flooding Risk Calculation Method Based on the Historical Rainfall Records
This study proposes a novel long-term areal flooding risk calculation method to quantify the cumulative impacts of multi-year extreme rainfall events, supporting long-term decision-making for flood risk management by integrating empirical rainfall frequencies with simulated inundation consequences that account for variable catchment areas.
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Hao et al. (2025) ENSOFarseer: Probabilistic Deep Learning for Cross-Scale Spatiotemporal Teleconnections Insight in Skilful ENSO Prediction
Not available in the provided text. The paper focuses on utilizing probabilistic deep learning to achieve skillful El Niño-Southern Oscillation (ENSO) prediction and to gain insight into cross-scale spatiotemporal teleconnections.
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Alioune et al. (2025) Integrated Analysis of Erosion and Flood Susceptibility in the Gorgol Basin, Mauritania
This study investigates the hydrological functioning and erosion susceptibility of the Gorgol River tributaries to support sustainable watershed management, finding that southern basin areas with fragile soils are most vulnerable to erosion, primarily driven by rainfall erosivity, and identifying flood-prone zones.
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Waqas et al. (2025) Hybrid Deep Learning Versus Empirical Methods for Daily Potential Evapotranspiration Estimation in the Nakdong River Basin, South Korea
This study compared empirical and deep learning models for daily potential evapotranspiration (PET) estimation in the Nakdong River Basin, South Korea, finding that a hybrid Convolutional Neural Network Bidirectional LSTM with an attention mechanism significantly outperformed empirical and standalone deep learning models, demonstrating high accuracy and suitability for regional hydrological applications.
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Laipelt et al. (2025) Assessing the Performance of Multiple Satellite-Based Evapotranspiration Models over Tropical Forests
This study evaluates the performance of four remote sensing-based evapotranspiration (ET) models in tropical forests, comparing their estimations against flux tower observations and assessing their response to deforestation. The findings demonstrate that these models show good agreement with observations and can effectively monitor ET and the impacts of deforestation on a global scale.
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Tierney et al. (2025) A novel framework for expanding temperature intensity-duration-frequency curve utility
This study presents an expanded framework for temperature intensity-duration-frequency (TIDF) curve analysis, including an objective fitting algorithm and uncertainty quantification, to assess the risk of extreme hot, cold, and "near-extreme" temperature events. The framework demonstrates its utility by contextualizing recent record-breaking heat waves and cold snaps, providing a flexible and robust tool for hazard response and adaptation planning.
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Wang et al. (2025) Prediction of Net Longwave Radiation and Turbulent Fluxes Using Remote-Sensing-Derived Net Shortwave Radiation for Different Land Cover Types
This paper aims to predict net longwave radiation and turbulent fluxes across various land cover types by utilizing net shortwave radiation derived from remote sensing. The main findings are not available in the provided text.
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Kim et al. (2025) Mapping Forest Climate-Sensitivity Belts in a Mountainous Region of Namyangju, South Korea, Using Satellite-Derived Thermal and Vegetation Phenological Variability
This study mapped forest climate sensitivity in a mountainous Korean region by analyzing satellite-derived thermal and vegetation variability. It identified "climate-sensitivity belts" where high-elevation ridges and steep slopes are vulnerable hotspots, while sheltered valley floors act as microclimatic refugia.
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Keller et al. (2025) Replicability in Earth System Models
This paper introduces a novel methodology to test the replicability of Earth System Models (ESMs) across different computing environments, improving upon existing methods by 60% in accuracy. It also establishes an objective measure for statistically distinguishing between model climates using Cohen's effect size, finding that an effect size of *d* = 0.2 can serve as a threshold for statistical indistinguishability.
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Zou (2025) training_samples_zarr_multiscale_soil_v11.9
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Cao et al. (2025) Integrated modeling of blue and green water evolution in a headwater region of Chaohu Lake: Impacts of climate and surface environmental factors
This study analyzed the multi-decade dynamics of blue water (surface and groundwater) and green water (soil-stored rainfall used by plants) in the Hangbu River Basin using a semi-distributed hydrological model, revealing their "decline-recovery" trends and the spatially heterogeneous impacts of climate and surface environmental factors.
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Guan et al. (2025) Widespread Declining Sensitivity of Chinese Forests to Soil Moisture Under Climate Change (2001–2020)
This study systematically quantified the spatiotemporal patterns and driving mechanisms of forest growth sensitivity to soil moisture in Chinese forests from 2001 to 2020, revealing a significant "soil moisture desensitization" process primarily driven by solar radiation, precipitation, and atmospheric carbon dioxide concentration.
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Su et al. (2025) Model-based exploration of the seasonal influences of lakes on glacier behavior over the Tibetan Plateau
This study applied the Weather Research and Forecasting (WRF) model coupled with a lake scheme to examine how Tibetan Plateau (TP) lakes seasonally affect surface air temperature, snowfall, and water vapor flux, and their influence on glacier retreat heterogeneity. Results show TP lakes generally reduce 2-meter air temperature in glacierized regions and increase snowfall on some alpine glaciers, with effects varying regionally and seasonally, particularly enhancing northward moisture transport.
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Sarkar et al. (2025) Taming the non-linearity: An iterative conceptual routing model for improving flood peak prediction
This study introduces a novel Iterative Routing Model (IRM) to improve flood peak prediction by dynamically updating flow velocity based on streamflow magnitude. Applied to the Godavari River Basin, the IRM significantly outperformed traditional models in accurately simulating flood peak discharge and timing.
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Tonina et al. (2025) When pools cool streams: A dimensionless framework for reach-scale thermal buffering
This study develops a physically-based dimensionless framework to explain and predict reach-scale thermal buffering of streamwater temperature by pools. It reveals that pools buffer diel temperature fluctuations by up to 80% when a new dimensionless river-pool coupling predictor (P*) drops below approximately 0.014.
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Li et al. (2025) A novel CNN-based method using GNSS tomography and WRF data for regional rainfall prediction
This study introduces a novel Convolutional Neural Network (CNN)-based method for regional rainfall prediction, integrating four-dimensional wet refractivity fields from GNSS tomography with meteorological data from the WRF model. The model achieved a 92.9 % True Positive Rate and a 4.8 % False Discovery Rate, demonstrating strong performance for predicting rainfall events, especially those with mid-to-high intensity.
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Zhang et al. (2025) Vegetation suffers greater and longer impacts under hydrological-triggered droughts
This study quantifies the differences in vegetation loss, response times, and recovery between hydrological-triggered droughts (MHD) and non-hydrological-triggered droughts (NHD), revealing that MHD events induce greater and longer-lasting impacts on vegetation recovery, particularly in transitional climate zones.
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Wang et al. (2025) Enhancing Machine Learning-Based GPP Upscaling Error Correction: An Equidistant Sampling Method with Optimized Step Size and Intervals
This paper proposes an optimized equidistant sampling method to correct gross primary productivity (GPP) upscaling errors by precisely quantifying nonuniform density distributions of sub-pixel heterogeneity factors, demonstrating significant improvements in accuracy and transferability over existing methods and k-means clustering.
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Mondal et al. (2025) Impact assessment of Glacial Lake Outburst Floods (GLOFs) in the Himalaya using satellite imageries over the last 25 years (2000–2024): A comprehensive review
This systematic review analyzes 119 articles published between 2000 and 2024 to assess the impact of Glacial Lake Outburst Floods (GLOFs) in the Himalaya, revealing their increasing frequency and destruction, particularly in the Indian Himalaya, and the growing reliance on remote sensing for GLOF studies.
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Zeng et al. (2025) NSGA-II and Entropy-Weighted TOPSIS for Multi-Objective Joint Operation of the Jingou River Irrigation Reservoir System
This study develops and evaluates joint-operation schemes for the Jingou River-Hongshan Reservoir irrigation system to improve coordination among irrigation water supply, ecological baseflow maintenance, and reservoir safety, finding that top-ranked schemes deliver well-balanced performance across objectives with a preferred compromise scheme resulting in approximately 39% annual irrigation shortages and 57% satisfaction of requirements.
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Ali et al. (2025) Weight-Supported Random Forest Downscaled GRACE (-FO) Data Uncovers Groundwater Depletion Linked to Winter Wheat Cultivation in the North China Plain
This study introduces a novel spatially weighted random forest (RF_SW) model to downscale GRACE (-FO) groundwater storage anomaly (GWSA) data to a high resolution (0.1°) across the North China Plain (NCP) from 2003 to 2023, revealing significant groundwater depletion directly linked to the expansion of winter wheat cultivation.
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Wang et al. (2025) Enhanced tropical cyclone precipitation variability is linked to Pacific Decadal Oscillation since the 1940s
This study establishes latewood width in coastal conifers as an effective proxy for tropical cyclone precipitation (TCP) in southeastern China, reconstructing July-September TCP from 1846 to 2020 and revealing a marked increase in its interannual variability since the 1940s, linked to enhanced Pacific Decadal Oscillation variability.
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Hidalgo et al. (2025) Coupled Cellular automata – Snowmelt Runoff Model: A Novel Framework for Assessing Climate Change Impacts on Streamflow
This study develops a novel coupled modeling framework (CAM–SRM) to simulate snow cover area and streamflow under climate change scenarios. The results project a significant reduction in annual streamflow (19.4–32.9%) and a two-month shortening of the snow season in Mediterranean mountainous regions.
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Elvanidi et al. (2025) Assessment of Future Water Stress of Winter Wheat and Olive Trees in Greece Using High-Resolution Climate Model Projections
This study evaluates the future water stress of winter wheat and olive trees in Greece by integrating the Crop Water Stress Index (CWSI) with high-resolution regional climate simulations, projecting increased water stress for both crops by mid-century under the SSP2-4.5 scenario.
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Jing et al. (2025) Multi-task deep learning for spatiotemporal reconstruction of groundwater dynamics in the North China Plain
This study developed a novel Multi-Task Learning Groundwater Model (MTLGW) integrating time-series decomposition with GRU neural networks to reconstruct regional groundwater dynamics in the North China Plain, demonstrating robust performance and superior capture of anthropogenic impacts compared to existing models.
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Olusola et al. (2025) Beyond the Flow: Multifractal Clustering of River Discharge Across Canada Using Near-Century Data
This study investigates multiple scaling regimes in river discharge and introduces a novel multifractal clustering framework to classify river dynamics. It found that distinct clusters of rivers with similar dynamical structures can be identified using multifractal parameters, particularly the asymmetry index, providing a powerful method for regime classification.
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jaza (2025) Wetland Areas Trend and Examining Effective Factors with Machine Learning
This study analyzed long-term water level trends in Hammar Marsh, Iraq (2000-2025) using remote sensing and machine learning to identify key environmental drivers. It revealed an overall increasing trend in water levels, with the Palmer Drought Severity Index (PDSI) and soil moisture identified as the dominant controlling factors.
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Şimşek et al. (2025) Meteorological drought dynamics in Marathwada Region, India: temporal, spatial and regional occurrence assessment
This study analyzed meteorological drought characteristics in Marathwada, India, from 1901-2021 using SPI and trend analyses, revealing a significant 49% drought occurrence, increasing drought trends, and heightened risk in southern and eastern areas, necessitating improved water management.
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Jdi et al. (2025) Optimizing irrigation for Dutch roses in Beni Mellal, Morocco: Predictive modeling based on reference evapotranspiration
This study developed a comprehensive predictive model to optimize daily irrigation requirements for Dutch roses in the water-scarce Beni Mellal region of Morocco. The model integrates historical weather data and crop-specific factors across four growth stages to provide a systematic approach for proactive and efficient water management.
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Abbas et al. (2025) Transition from Slow Drought to Flash Drought Under Climate Change in Northern Xinjiang, Northwest China
This study quantifies the spatio-temporal dynamics of flash drought (FD) across northern Xinjiang from 1961 to 2023, revealing increased FD frequency and duration after 1980, primarily driven by the Pacific Decadal Oscillation and air temperature.
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Zhu et al. (2025) Evaluation and Projection of the Influence of the August Asian–Pacific Oscillation on Precipitation in Northern Xinjiang Based on CMIP6 Simulations
This study derives two multi-model ensembles (BMME and NCE) from 30 CMIP6 models and reanalysis data to simulate the August Asian–Pacific Oscillation (APO) and its influence on September precipitation over northern Xinjiang (NXPI). It finds that the BMME performs better than individual models, the APO intensity is projected to decrease, and the negative correlation between APOI and NXPI is expected to weaken significantly after 2040, potentially due to a shift in geopotential height anomalies.
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Haddad et al. (2025) From ancient to recent floods: advances in flood susceptibility modeling and vulnerability, Makkah, Saudi Arabia
This study develops flood susceptibility and vulnerability maps for Makkah City, Saudi Arabia, by testing four machine learning algorithms (Logistic Regression, Support Vector Machine, Extreme Gradient Boosting, and an ensemble model) with 12 flood-conditioning factors, finding the ensemble and XGB models to be most accurate and highlighting significant urban encroachment into high-risk flood zones.
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Wang et al. (2025) Spatial–Temporal Response of Urban Flooding to Land Use Change: A Case Study of Wuhan’s Main Urban Area
This study investigates the impact of land use change, driven by rapid urbanization, on urban flooding processes in Wuhan, China. It found that urban expansion significantly increases rainwater accumulation and alters flood dynamics, such as peak flow velocity and timing, thereby intensifying flood risk and reducing emergency response time.
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Pang et al. (2025) Comparison of the heatwaves of 2022 and 2024 in the Sichuan Basin, China: the similarity and different roles of oceans and BSISO
This study compares the mechanisms of the unprecedented 2022 and 2024 heatwaves in the Sichuan Basin, China, revealing common large-scale circulation patterns (European–East Asian teleconnection) but distinct oceanic forcings (Indo-Pacific Tripole/North Atlantic Tripole in 2022 vs. Tropical North Atlantic/North Atlantic Tripole in 2024) and the amplifying role of boreal summer intraseasonal oscillation stagnation.
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Li et al. (2025) Improving medium-long-term streamflow forecasts by exploiting multi-scale Temporal patterns with deep learning
This study proposes a novel EMD-TCN-GRU deep learning framework to improve medium-long-term streamflow forecasting by exploiting multi-scale temporal patterns. The model achieves high accuracy and robustness for forecast horizons up to 15 days in the Yangtze River Basin, significantly outperforming existing deep learning and operational models.
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Khan et al. (2025) Impacts of Climate Change on water availability for Agriculture in Indus Basin: A review and Policy Implications
This review synthesizes the impacts of climate change on water availability for agriculture in the Indus Basin, finding that while near-term glacier melt may increase summer flows, long-term water availability will decrease, exacerbating existing water stress and necessitating integrated management and adaptation strategies.
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Alahmad et al. (2025) Enhancing Agricultural Sustainability using AI-Driven Soil Moisture Modeling: A Soil-Type and Depth Approach with SHAP Interpretability
This study developed and evaluated depth- and soil-specific Random Forest Regression models for predicting soil moisture content (SMC) in loam and silt loam soils. It found that integrating meteorological data with vegetation indices significantly enhances prediction accuracy, with SHAP analysis revealing soil-dependent feature importance crucial for optimizing irrigation strategies and agricultural sustainability.
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Belhadj-aissa et al. (2025) Mapping salt marsh hydroperiod using Synthetic Aperture Radar time series
This study develops a novel methodology integrating Synthetic Aperture Radar (SAR) time series with in-situ water level measurements to map salt marsh hydroperiod at high spatial and temporal resolution. The method, validated against a LiDAR-derived 'bathtub' model, demonstrates strong agreement and provides a robust tool for monitoring wetland vulnerability to sea-level rise.
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Kim et al. (2025) Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
This study assesses five deep learning models for wildfire spread prediction in Hawaii, comparing the best performers (ConvLSTM, ConvLSTM with attention) against the FARSITE model using the 2023 Maui fires as a case study. It finds FARSITE generally superior in accuracy but highlights the deep learning models' flexibility with widely available input data, and identifies key environmental factors influencing the Maui fires.
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Balouei et al. (2025) Developing a new high-resolution soil moisture index for local agricultural drought monitoring using Sentinel-1 data and an artificial neural network
This study develops a novel 10-meter resolution Local-Scale Soil Moisture Condition Index (LS-SMCI) for agricultural drought monitoring in Khuzestan, Iran, utilizing Sentinel-1 SAR data and an Artificial Neural Network (ANN). The LS-SMCI significantly outperforms coarser global soil moisture products and demonstrates strong correlation with the Standardized Precipitation Index (SPI), providing precise local drought assessment.
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Lv et al. (2025) Attributing changes in evapotranspiration and runoff across the continental U.S. using the Budyko hypothesis combined with the Shuttleworth-Wallace model
This study developed a new framework combining the Budyko hypothesis with the Shuttleworth-Wallace model to explicitly attribute changes in evapotranspiration and runoff to climate, vegetation, and land use/cover change across 732 catchments in the continental U.S., revealing precipitation as the dominant factor for ET and runoff changes in most catchments, but highlighting the significant influence of vegetation and land use/cover changes.
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Varma et al. (2025) A hybrid framework for projecting 21st-century groundwater replenishment and its amplified seasonal cycle
This study develops a novel hybrid framework integrating satellite remote sensing, process-based modeling, and machine learning to project 21st-century groundwater replenishment. Projections for the eastern United States reveal a robust reorganization of the seasonal recharge cycle towards drier winters and wetter summers, alongside an emergent spatial dipole in mean annual change.
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Luo et al. (2025) Identification of Spatiotemporal Variations and Influencing Factors of Groundwater Drought Based on GRACE Satellite
This study analyzed the spatiotemporal variations and influencing factors of groundwater drought in the Yangtze River Basin (YRB) from 2003 to 2022 using GRACE satellite data and hydrological models, identifying key climatic and atmospheric circulation drivers and their nonlinear relationships.
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Coluzzi et al. (2025) Towards a risk-informed land system approach in the age of artificial intelligence and analysis-ready satellite data
This paper surveys currently available satellite-derived tools and analysis-ready Earth Observation (EO) products, highlighting the growing role of Artificial Intelligence (AI) in transforming complex EO data into accessible and actionable knowledge for risk-informed management within Land System Science (LSS).
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Bhat et al. (2025) Space and Geo-informatics for Climate Smart Agriculture- An Overview
This review article synthesizes the role of space technology and geo-informatics in Climate-Smart Agriculture (CSA), demonstrating how Earth observation satellites, remote sensing, GIS, and GPS enhance agricultural productivity, sustainability, and resilience against climate change. It highlights their applications in crop monitoring, resource management, and disaster mitigation, while also addressing implementation challenges.
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Huang et al. (2025) Urban flood prediction using a hybrid XGBoost-enhanced U-Net model
This paper proposes an XGBoost-Enhanced U-Net (XGB-U-Net) model for timely and accurate urban flood prediction, integrating physical mechanisms with deep learning. The hybrid model demonstrates superior accuracy and efficiency compared to U-Net and XGBoost alone, particularly in complex urban environments under spatially heterogeneous rainfall.
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Tai et al. (2025) Accurate reproduction of sub-hourly rainfall extremes in Poisson cluster rainfall models with a variable sinusoidal pulse
This study introduces RBL7, a refined Poisson cluster rainfall model with variable sinusoidal rain cells, to accurately reproduce sub-hourly rainfall extremes. It demonstrates that RBL7 outperforms existing models in capturing rainfall extremes and standard statistics across various timescales, despite being calibrated only at hourly and supra-hourly scales.
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Li et al. (2025) Modeling the lake water balance of a closed alpine basin using a fully distributed hydrological framework: A case study of Qinghai Lake, China
This study developed an integrated hydrological modeling framework for Qinghai Lake, China, to quantify its water balance dynamics. It revealed that increasing basin-wide precipitation and runoff, coupled with declining lake evaporation, are the primary hydrological drivers behind the lake's recent water level rise.
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Sahib et al. (2025) A Study of the Shrinkage of the Tigris and Euphrates Rivers and its Impact on Water Resources and Agriculture in Iraq
This study quantifies the spatio-temporal shrinkage of the Tigris and Euphrates Rivers in Iraq between 2014 and 2022, revealing a 35.72% net decrease in water surface area primarily due to climate change and upstream dam operations, which significantly impacted agricultural land.
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Al-Jahwari et al. (2025) Robust Rainfall Gap-Filling in Coastal Arid Regions Using Ensemble Fusion Models
This study implemented and evaluated multiple machine learning and novel ensemble fusion techniques to fill daily rainfall data gaps across 88 stations in Oman from 1993 to 2024, finding that the Gradient-Boosting Trees (GBT) model performed best and ensemble fusion significantly improved prediction accuracy.
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Papadimos et al. (2025) Performance Evaluation of a Distributed Hydrological Model Using Satellite Data over the Lake Kastoria Catchment, Greece
This study evaluates the effectiveness of a coupled distributed hydrological model (MIKE SHE/MIKE HYDRO River) in simulating the long-term Lake Surface Elevation (LSE) and water balance of the Lake Kastoria catchment, Greece, using satellite precipitation and Leaf Area Index (LAI) data. The research concludes that satellite products (GPM_3IMERGDF for precipitation and GEOV3 for LAI) can adequately replace ground station data for LSE prediction and water balance quantification, providing valuable information for water resource management.
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Ahmadzadeh et al. (2025) Modeling potential streamflow in agricultural catchments: excluding human factors through an advanced framework
This study developed a novel framework using the SWAT model to estimate potential streamflow (PS) by explicitly excluding human factors in agricultural catchments, revealing a significant reduction in actual streamflow due to anthropogenic activities in the Aji Chai catchment and providing a replicable method for sustainable water management.
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Zhu et al. (2025) Revisiting the Relationship Between Changes in Global‐Mean Surface Air Temperature and Sea Surface Temperature at the Last Glacial Maximum
This study investigates the ratio of global mean air versus sea surface temperature change (S) during the Last Glacial Maximum (LGM) using reconstructions and model simulations, finding S to be 1.97 ± 0.22, significantly higher than under future warming due to ice sheets, and negatively related to sea surface cooling.
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Barboza et al. (2025) Corn Plant Detection Using YOLOv9 Across Different Soil Background Colors, Growth Stages, and UAV Flight Heights
This study evaluated the YOLOv9-small model for detecting and counting corn plants across varying soil backgrounds, growth stages (V2, V3, V5, V6), and UAV flight heights (30 m, 70 m). It found that V3 and V5 stages at 30 m flight height yielded the highest accuracy, while 70 m is acceptable for V5 to optimize mapping time, demonstrating the model's effectiveness for early-stage corn detection in real-world conditions.
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Thingujam et al. (2025) From point sensing to intelligent systems: a comprehensive review on advanced sensor technologies for soil health monitoring
This comprehensive review synthesizes recent advancements in soil sensor technologies, demonstrating their transformative potential for precision agriculture by enhancing the accuracy, specificity, and field-deployability of monitoring key soil parameters. It highlights the integration of these sensors into intelligent systems like IoT and WSNs, while also addressing persistent technological and scalability challenges for widespread adoption.
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Chou et al. (2025) Human influence on recent trends in extratropical low-level wind speed
This study compares satellite-era trends in extratropical low-level mean and extreme wind speeds from reanalyses with climate model simulations to assess human influence and model fidelity. It finds human influence drives summertime trends in both hemispheres, while wintertime trends exhibit regional model discrepancies linked to sea surface temperature biases.
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Brobst-Whitcomb et al. (2025) How Do Different Precipitation Products Perform in a Dry-Climate Region?
This study evaluates five satellite- and model-based precipitation products against in situ observations in Palm Desert, Southern California, a dry-climate region, to assess their performance in estimating average daily rainfall and extreme precipitation events. It found WLDAS best for precipitation magnitude estimation but poor for event detection, while IMERG and ERA5-MIN excelled at detection but were less accurate in magnitude.
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Abdel-Mooty et al. (2025) A scalable data driven geospatial framework for climate risk assessment
This study introduces a scalable, data-driven geospatial framework that integrates machine learning and geospatial analysis to dynamically assess climate risks. Applied in Texas, the framework projects a 14% increase in community vulnerability and a 28% rise in economic damages ($1.8 billion per decade by 2050) under the RCP 8.5 emission scenario, emphasizing the urgent need for global climate action.
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Martellozzo et al. (2025) Assessing extreme sea level rise impacts on coastal agriculture in Europe and North Africa
This study assesses the potential impacts of Extreme Sea Level Rise (ESLR) on coastal agriculture in Europe and North Africa up to 2100, revealing potential agricultural losses ranging from 800 million USD to 1.5 billion USD per year, threatening food security and economic stability.
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Zhao et al. (2025) The propagation mechanism of meteorological drought to agricultural drought in Southwest China
This study investigates the propagation mechanism of meteorological drought to agricultural drought in Southwest China, differentiating between karst and non-karst areas, by proposing novel drought propagation indices (DSTP, DVP) and analyzing propagation rates, types, and driving mechanisms. The findings reveal distinct propagation characteristics and influencing factors across these regions, highlighting the vulnerability of karst areas.
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Gimeno et al. (2025) Hydropedological clustering: improving the representation of low streamflows in a semi-distributed hydrological model
This study evaluated how hydropedological clustering, based on soil hydraulic properties, improves the simulation of low streamflows and soil water content in the SWAT+ model, finding significant enhancements compared to traditional soil datasets and demonstrating its criticality over soil map resolution.
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Abraham et al. (2025) Meteorological to hydrological drought propagation: The influence of future climate change at grid scale
This study investigates the propagation of meteorological to hydrological drought and its characteristics under future climate change at a high-resolution grid scale (0.1°) in the Gidabo catchment, Ethiopia, finding a general decrease in drought propagation and increased resilience in certain regions under the RCP8.5 scenario.
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Goumi et al. (2025) Assessing the SM2RAIN-ASCAT dataset in Morocco: Accuracy evaluation and drought monitoring application
This study evaluates the accuracy of the SM2RAIN-ASCAT satellite-based precipitation product against ground observations across various climate zones in Morocco and assesses its suitability for drought monitoring using the Standardized Precipitation Index (SPI). It concludes that SM2RAIN-ASCAT is a reliable option for drought analysis, particularly in arid regions and for long-term hydrological drought monitoring.
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Xu et al. (2025) Quantifying the Urbanization and Vegetation Greening Effect on Spatiotemporal Continuous Drought Risk Via Nonstationary C-Vine Copula Model
This study developed a nonstationary C-Vine Copula model to quantify the distinct impacts of urbanization and vegetation greening on multivariate drought risk in humid (Yangtze River Delta) and arid (Weihe River Basin) watersheds, finding urbanization amplifies risk in humid areas while vegetation greening alleviates it in arid regions.
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Li (2025) A magnetic induction network for high-resolution, real-time soil moisture monitoring in complex subsurface environments
This study develops and validates a scalable magnetic induction (MI) network for real-time, high-resolution volumetric soil moisture mapping in complex subsurface environments. The system demonstrates a significant reduction in sensing errors and improved accuracy compared to conventional methods, enabling 3D real-time soil moisture imaging.
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Yavaşlı (2025) Thermal persistence index (TPI): a novel measure of prolonged heat stress in the Mediterranean, 1950–2024
This study introduces the Thermal Persistence Index (TPI) to quantify intra-daily heat stress persistence in the Mediterranean from 1950 to 2024, revealing widespread, statistically significant increases in prolonged heat stress, particularly since the 1990s.
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Shi et al. (2025) The Characteristics and Mechanism of the Inter-Centennial Variations in Indian Summer Monsoon Precipitation
This paper investigates centennial-scale Indian Summer Monsoon (ISM) precipitation variability over the past two millennia, identifying volcanic, solar, and internal climate variability as key drivers of observed 105, 150, and 200-year cycles.
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Unknown (2025) AIoT-Based Water Management Solution for Sustainable Sugarcane Farming
This study develops an AIoT-based smart irrigation system for sustainable sugarcane farming, aiming to achieve significant water savings and improved crop yield through automated, precise water delivery.
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SHEN et al. (2025) Spatial and Temporal Variations in Soil Salinity and Groundwater in the Downstream Yarkant River Irrigation District
This study investigates the spatiotemporal dynamics of soil salinization in the Yarkant River basin, identifying a critical groundwater depth of 2.10–2.18 m for salt accumulation. The research establishes that maintaining groundwater below this threshold is essential for mitigating soil salinity driven by shallow, highly mineralized groundwater.
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Shukla et al. (2025) Application of UAV based High Resolution DEM for Flood Management – Case of Mata-no-Madh, Kachchh, Gujarat India
This study addresses recurrent flooding in Mata-no-Madh, India, by employing a comprehensive geospatial assessment, including UAV-based high-resolution Digital Elevation Models (DEMs), to design and implement a water recharge tank for flood mitigation and groundwater replenishment. The approach proved effective and replicable for climate-adaptive infrastructure planning.
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Hu et al. (2025) Enhancing Real-Time Hydrological Simulation with IoT-Based Model Representation and Observation Data
This paper proposes a method to integrate traditional hydrological models with Internet of Things (IoT) systems using a standard conceptual model and the Open Geospatial Consortium (OGC) SensorThings API, enabling real-time, observation-driven modeling and fine-grained state acquisition, validated with a Storm Water Management Model (SWMM) prototype.
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Fang et al. (2025) Satellite altimetry reveals intensifying global river water level variability
This study utilizes Sentinel-3 satellite altimetry to establish a global dataset of river water levels across nearly 47,000 virtual stations, revealing that global river seasonality is intensifying while seasonal amplitudes are shrinking due to a surge in extreme hydrological events since 2021.
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Caby et al. (2025) Dynamical properties of atmospheric and sea level extremes in the North-East Atlantic
This study characterizes the dynamical properties of atmospheric pressure and sea level storm surges in the North-East Atlantic using a novel phase space velocity metric (v) and local dimension (d), revealing that extreme events are associated with lower dimensions and higher instability.
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Emmanuel et al. (2025) Signatures of MJO in the intra-seasonal variability in occurrence of cirrus and dehydration in the UTLS over Indian region
This study investigates the Madden–Julian oscillation (MJO) signatures in the intraseasonal variability of cirrus cloud occurrence and dehydration in the upper troposphere and lower stratosphere (UTLS) over the Indian region. It finds clear MJO-related variations in deep convection, temperature, cirrus occurrence, and dehydration, particularly during MJO phases 2 and 3, with less prominence during the summer monsoon season.
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Meryem et al. (2025) Review of AI methods in precision agriculture
This review analyzes the increasing adoption of Artificial Intelligence (AI) in precision agriculture, detailing how machine learning and deep learning technologies are advancing crop management, and concludes that AI can significantly boost agricultural resiliency, productivity, and sustainability despite existing challenges.
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Sun et al. (2025) Enhanced spring precipitation in central Asia induced by ENSO under global warming
This study investigates how El Niño–Southern Oscillation (ENSO) modulates spring precipitation in Central Asia (CA) under global warming, finding a significant increase in spring precipitation, particularly over southwestern CA, primarily driven by intensified horizontal moisture advection due to enhanced ENSO-related meridional circulation anomalies and an amplified North Atlantic teleconnection.
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Williams et al. (2025) The Western United States MTBS-Interagency database of large wildfires, 1984–2024 (WUMI2024a)
This paper introduces WUMI2024a, a quality-controlled, publicly available database of 22,234 large (≥1 km²) wildfires in the western United States from 1984–2024, compiled by merging seven government datasets to provide comprehensive wildfire occurrences, perimeters, and burned-area maps. It addresses limitations of existing datasets by offering improved spatial and temporal coverage and enhanced data quality for wildfire research and modeling.
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Su et al. (2025) Rainfall forecasting based on the multi-source data fusion and multi-dimension interpolation method for GNSS stations lacking or sparse regions
This study proposes a novel multi-source data fusion and multi-dimension interpolation method to enable GNSS-based rainfall forecasting in regions with lacking or sparse GNSS stations, demonstrating comparable forecasting accuracy to traditional GNSS-trained models.
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Natoo et al. (2025) Potential of multi-source Geospatial data in Accurately Estimating the Live Storage Capacity of Reservoir
This study develops a novel, purely geospatial methodology to accurately estimate the live storage capacity (LSC) of gauged reservoirs, eliminating the need for field data by fusing multi-source satellite imagery for water surface area and interpolating altimetry data for water levels. The method, applied to the Ukai reservoir, achieved high accuracy (Root Mean Square Error of 91.0 m³) compared to observation-based estimates.
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Kim et al. (2025) Retrieving Woody Components from Time-Series Gap-Fraction and Multispectral Satellite Observations over Deciduous Forests
This study introduces a novel method to estimate the woody-to-total-area ratio (α) using Sentinel-2-based Normalized Difference Vegetation Index (NDVI) and time-series Plant Area Index (PAI) measurements, effectively mitigating the overestimation of Leaf Area Index (LAI) from gap-fraction-based PAI by accounting for woody components. The adjusted LAI (LAIadjusted) shows good agreement with Sentinel-2 LAI and accurately captures seasonal dynamics across various forest types.
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Vergnano et al. (2025) Integrating GPR and ice-thickness models for improved bedrock detection: the case study of Rutor temperate glacier
This study integrates Ground Penetrating Radar (GPR) data with multiple ice-thickness models to improve bedrock detection in temperate glaciers, where englacial water often causes signal scattering. Applying this combined methodology to the Rutor Glacier, the research provides a significantly revised and more accurate ice volume estimate of approximately 450 million cubic meters, about three times the previous value.
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Masten et al. (2025) An enhanced python framework for hydrological modeling in alpine catchments: Snow hysteresis and glacier ice melt
This study introduces a new Python extension for the Rainfall-Runoff Modeling Playground (RRMPG) that incorporates snow cover hysteresis and glacier ice melt, crucial for alpine hydrology. The enhanced framework, tested in two Ötztal Alps catchments, significantly improves the accuracy and robustness of runoff and snow cover dynamics simulations compared to simpler models.
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Massoud et al. (2025) Diagnosing the representation of surface and layered soil moisture in Earth system models
This study evaluates the physical consistency between surface soil moisture (mrsos) and vertically integrated soil moisture (mrsol) in nine CMIP6 Earth System Models, revealing substantial inconsistencies attributed to metadata errors, inconsistent variable definitions, or diagnostic sequencing, which can lead to significant biases.
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Zhao et al. (2025) Simulating Rainfall for Flood Forecasting in the Upper Minjiang River
This study integrates the WRF numerical weather prediction model with the InfoWorks ICM hydrodynamic model to improve flood forecasting in the Upper Minjiang River, demonstrating that optimized WRF configurations and updated land surface data enhance rainfall and flood hydrograph simulations, though peak discharge underestimation persists.
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Tian et al. (2025) An optimization framework for intelligent irrigation system installation in fragmented paddy fields
This study proposes and validates a two-stage optimization framework for Intelligent Irrigation System (IIS) unit installation in fragmented paddy fields, aiming to maximize labor reduction by optimizing patrol routes. The framework effectively reduces computational complexity and maintains solution quality, particularly under non-uniform spatial distributions, providing guidance for smart agricultural technology deployment.
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Wang et al. (2025) A novel method to optimize the limited irrigation schedule for cotton: HYDRUS-triggered irrigation simulation and plant water deficit index threshold analysis
This study developed a novel method to optimize limited drip irrigation for cotton in northern Xinjiang, China, by integrating field experiments with HYDRUS simulations and Plant Water Deficit Index (PWDI) threshold analysis, identifying optimal irrigation lower limits for key growth stages under specific water quotas.
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Sun et al. (2025) Role of nonlinear convection–SST sensitivity in shaping the southward displacement of westerly anomalies during El Niño peak phase
This study investigates the southward displacement of anomalous westerlies during the El Niño peak phase, revealing it is anchored by the seasonal migration of the tropical warm pool and regulated by a nonlinear convection-sea surface temperature (SST) relationship that enhances sensitivity south of the equator. The findings propose a synergistic framework where El Niño amplitude, convection-SST sensitivity, and warm pool background collectively shape El Niño-induced wind responses.
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Xiang et al. (2025) An explainable deep learning model based on hydrological principles for flood simulation and forecasting
This study develops an explainable deep learning (EDL) model for flood simulation by integrating the Xinanjiang (XAJ) model's runoff generation and flow routing principles into a recurrent neural network (RNN) unit (XAJRNN layer) and fusing it with LSTM layers. Tested in two Chinese river basins, the EDL model demonstrates superior flood simulation accuracy and enhanced interpretability compared to benchmark models.
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Duangkhwan et al. (2025) Enhancing flood forecasting with deep learning: A scalable alternative to traditional hydrodynamic models
This study proposes an integrated deep learning framework, combining LSTM and CNN, to emulate computationally intensive HEC-RAS 1D/2D models for flood forecasting. The framework significantly reduces computational demands while maintaining high accuracy in predicting river water levels and flood inundation maps.
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Sadeghi et al. (2025) Soil moisture dynamics under various drought resilience measures in Mediterranean vineyards of the northern Apennines, Italy
This study evaluates the impact of various green manure management measures on soil moisture dynamics at different depths in Mediterranean vineyards. The findings reveal that while certain treatments significantly enhance topsoil moisture, they may simultaneously reduce subsoil water content, highlighting the complexity of drought resilience strategies.
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Choudhary et al. (2025) Assessment of Spatio-Temporal Dynamics of Drought Stress Anomalies UsingHyperspectral Imagery Fusion
This study assessed the spatio-temporal dynamics of drought stress anomalies in California's Sierra Nevada region from 2013 to 2025 using fused hyperspectral and multispectral imagery, revealing strengthening drought trends and forecasting further deterioration in 2026.
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Liu et al. (2025) Innovative synergistic optimization of drip irrigation and subsurface drainage for alleviating salinization and improving crop productivity in arid irrigation district
This study developed a synergistic optimization framework using the SWAT-Salt model and a projection pursuit model with an accelerated genetic algorithm to optimize drip irrigation and subsurface drainage in China's Kaidu River Irrigation District, demonstrating significant improvements in crop yields, water productivity, and soil desalinization.
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Baran-Gurgul et al. (2025) Regionalisation of Summer Hydrological Droughts Variability in Poland in Period 1993–2022
This study investigated the spatiotemporal variability of summer hydrological droughts in Poland from 1993 to 2022 across seven physiographic regions. It found marked regional differences, with northern and central lowlands experiencing intensified droughts, particularly after 2015, driven by rising temperatures and increased potential evapotranspiration, while mountainous areas showed fewer drought days.
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Farooq et al. (2025) Regional Hotspots of Lake Evaporation Changes Driven by Surface Energy Balance and Climate Interactions
This study projects global lake evaporation responses to a high-emissions warming climate (RCP8.5) by the end of the 21st century, revealing a global mean increase of 13% driven by enhanced energy availability and synergistic interactions among vapor pressure deficit, radiation, and wind speed.
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Lee et al. (2025) Estimation of soil moisture retention using pedo-transfer functions based on soil particle-size distribution and organic matter of Korean soils
This study developed and validated new pedo-transfer functions (PTFs) specifically for Korean agricultural soils to estimate moisture retention at 10, 33, and 1500 kPa. The resulting models significantly outperformed established international models (Saxton and Saxton-Rawls), providing more accurate tools for regional water management.
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Zhang et al. (2025) Concurrent occurrence of droughts and floods between the upper Hanjiang river and Northern North China at multi-temporal scales: an association with Arctic Oscillation
This study investigates the teleconnection between the warm-season Arctic Oscillation (AO) and concurrent drought and flood (DF) occurrences in the Upper Hanjiang River (UH) and Northern North China (NNC) from 1650 to 1975. It finds that DF in both regions largely varies in the same direction as the AO, with the AO serving as a significant predictor for DF, particularly on multi-decadal scales.
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Kadam et al. (2025) Geospatial Analysis of Glacial Lake Area and Volume Changes in the Indian Himalayas
This study developed a scalable geospatial framework using Google Earth Engine and remote sensing to analyze the area and volume changes of glacial lakes across the Indian Himalayas from 2008 to 2017, revealing a significant and consistent increase in both, particularly in the eastern Himalayan region. The findings highlight the accelerating impact of climate change on the cryosphere and the increasing risk of Glacial Lake Outburst Floods (GLOFs).
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Marsli et al. (2025) Analysis of aerosol optical properties and implications to radiative forcing over the Mediterranean Basin
This study examined aerosol optical properties and radiative forcing at six Mediterranean sites, revealing that Southern Mediterranean sites exhibit high aerosol optical depth dominated by coarse particles, leading to significant atmospheric heating (+25 W/m²) and surface cooling (–30 W/m²).
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Thi et al. (2025) Recent Spatiotemporal Trends in Extreme Rainfall Events in South Korea: From Sub‐Hourly to Multi‐Day Scales
This study investigated temporal trends in annual maximum precipitation (AMP) across South Korea from 2004 to 2024 for eight durations ranging from 30 minutes to 3 days, revealing that extreme rainfall trends are highly dependent on duration and are becoming more spatially heterogeneous.
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Wang et al. (2025) Coupling Machine Learning and Detrended Cumulative Drought Index for Lake-Area Prediction
This paper proposes a method for predicting lake area by integrating machine learning techniques with a detrended cumulative drought index.
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Progga et al. (2025) A chip-based radio frequency sensor for soil moisture measurements: A machine learning and deep learning calibration approach
This study developed and evaluated machine learning (ML) and deep learning (DL) calibration models for a novel chip-based radio frequency (RF) soil moisture sensor using diverse soil samples. The Convolutional Neural Network (CNN) model achieved the highest accuracy (R² = 0.78, RMSE = 1.92 % volumetric moisture content) for generalized soil moisture estimation.
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Ghose et al. (2025) Predictive Flood Susceptibility Modelling with Machine Learning: Insights from The Baitarani River Basin, Odisha
This study analyzed historical flood records from 2003 to 2023 to delineate flood-susceptible zones in the Baitarani River Basin using Fuzzy Support Vector Machine (FSVM) and Simulated Annealing-optimized FSVM (SA-FSVM) models. The SA-FSVM model achieved superior predictive performance (AUROC 0.91), identifying low-lying coastal zones as highly vulnerable to flooding.
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Dariane et al. (2025) Reevaluating streamflow declines across the middle East and central Asia with insights from change point detection
This study identifies 1998 as a critical regional breakpoint for streamflow declines across the Middle East and Central Asia, primarily triggered by the 1997–1998 El Niño event. While climate anomalies initiated these shifts, the research demonstrates that subsequent human activities, such as irrigation expansion, significantly amplified the magnitude of hydrological stress.
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Franzke et al. (2025) Forced response and internal variability changes in the hydrological cycle and general circulation in a hot world beyond 2100 in the Community Earth System Model
This study uses ensemble simulations with CESM2 to investigate changes in the hydrological cycle's forced response and internal variability beyond 2100. It finds that the dominant mode of the hydrological cycle's forced response changes sign in the early 22nd century due to atmospheric circulation shifts, subsequently reducing the amplitude of internal variability.
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Keeping et al. (2025) Influence of global climate modes on wildfire occurrence in the contiguous United States under recent and future climates
This study uses a large ensemble climate model and a wildfire occurrence model to characterize the spatial patterns and magnitude of global climate mode influence on wildfire occurrence in the contiguous United States under recent and future (+2 °C) climates. It finds that ENSO, IOD, and lagged TNA are the primary drivers, with their influence intensifying and other modes becoming significant under future warming.
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Hameed et al. (2025) Exploratory Analysis and Prediction of Weather Conditions: Leveraging Feature Engineering and Machine Learning Models for Accurate Forecasting
This study develops and evaluates machine learning models for weather prediction, demonstrating that Support Vector Machines (SVM) and effective feature engineering significantly enhance short-term forecasting accuracy for various applications.
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Ling et al. (2025) HybridFlow: A Hybrid Velocity Generation Framework for Precipitation Nowcasting
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Zhao et al. (2025) Upstream evaporative moisture sources anchor strong persistent heavy precipitation events over southeastern Tibetan plateau
This study investigates the physical mechanisms behind the intensity variations of persistent heavy precipitation events (PHPs) over the Southeastern Tibetan Plateau (SETP) by analyzing upstream evaporative moisture uptakes (UEMU) anomalies using Lagrangian modeling. It reveals that strong PHPs are primarily fueled by enhanced evaporative vapor from remote oceanic regions, while weak PHPs rely more on local recycling, emphasizing the dominant role of large-scale atmospheric circulation patterns in modulating PHP intensity.
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DeHart et al. (2025) Quantifying the Relationships Between Dynamics and Rainfall Intensity Along the Mei‐Yu Front During PRECIP 2022
This study uses multi-Doppler analyses to investigate the dynamic characteristics associated with varying rainfall intensities during two Mei-Yu frontal periods over the ocean, finding that higher rain rates correlate with increased vertical vorticity, vertical motion, and divergence, with heavy convective rain (10-50 mm/h) contributing over 45% of total volumetric rainfall, preferentially occurring in moderately strong rotating convection.
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Yıldız et al. (2025) Evaluating global precipitation datasets over Sicily: From daily estimates to extreme events
This study evaluates 11 global daily precipitation datasets against ground observations across Sicily (2003–2023) to assess their accuracy and ability to represent extreme events. It found that blended products (MSWEP, ERA5-Land, HydroGFD) performed best for daily estimates, but all datasets consistently underestimated the magnitude and frequency of severe extreme precipitation, particularly in mountainous regions.
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Zhang et al. (2025) Simulation of the South China Sea summer monsoon effect on the Indian Ocean dipole in community integrated earth system model
This study evaluates the Community Integrated Earth System Model's (CIESM) ability to simulate the South China Sea Summer Monsoon (SCSSM)–Indian Ocean Dipole (IOD) connection and its mechanisms. It finds that while CIESM reproduces the SCSSM-IOD linkage, it significantly overestimates its strength due to unrealistic SCSSM–ENSO interactions and associated biases in atmospheric bridges and surface zonal winds.
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Hilland et al. (2025) Horizontal Anisotropy of Turbulent Fluctuations in Surface and Air Temperatures Over a Flat, Homogeneous Surface
This study investigates the longitudinal and lateral integral length scales of temperature fluctuations and their anisotropy across a range of atmospheric stabilities in a desert surface layer. It finds that turbulence anisotropy varies significantly with wind speed and stability, with surface length scales being larger than near-surface atmospheric ones and generally exceeding previously reported values.
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Kumar et al. (2025) High-Resolution Soil Moisture Mapping using MSAVI-LST based Triangle Method
This study developed an improved downscaling approach for SMAP soil moisture from 9 km to 1 km resolution using the 'Triangle method' by integrating the Modified Soil Adjusted Vegetation Index (MSAVI) as an alternative to NDVI. The 1-1 polynomial order with MSAVI significantly outperformed other combinations, achieving a correlation coefficient of 0.76 and an RMSE of 0.032 m³/m³.
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Zhang et al. (2025) Evaluation and Projection of Northwest China's Extreme Precipitation Using Statistically Downscaled CMIP6 Models
This study quantitatively evaluates 23 statistically downscaled CMIP6 models for their ability to simulate historical extreme precipitation in Northwest China and projects future changes. Results indicate that while most models capture spatial patterns, they exhibit systematic dry biases, with the best-performing models projecting a mitigation of aridity and an increase in extreme precipitation frequency under future warming scenarios.
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Rafei et al. (2025) Downdrafts and Convective Gusts in High‐Resolution Simulations: An Australian Case Study
This study investigates the forcing mechanisms of strong convective downdrafts and surface gusts using high-resolution Weather Research and Forecasting (WRF) model simulations. It finds that very high spatial resolutions (down to 200 m) are crucial for accurately representing these phenomena, revealing that perturbation pressure and thermal buoyancy are primary drivers, which are significantly underestimated by coarser models.
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Kommula et al. (2025) Improving micro rainwater harvesting site selection with high-resolution LiDAR DEMs: A GIS-based multi-criteria approach
This study develops a GIS-based multi-criteria framework using LiDAR DEMs at varying resolutions (1 meter to 30 meters) to improve micro rainwater harvesting (RWH) site selection, demonstrating that high-resolution LiDAR DEMs significantly enhance accuracy compared to satellite-derived CartoDEM. The 1-meter LiDAR DEM achieved the highest overall accuracy of 0.87, outperforming the 30-meter CartoDEM (0.62) for identifying suitable RWH sites.
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Lin et al. (2025) Hydroclimatic controls on lake water oxygen isotope in alpine lake on the northern Tibetan Plateau: Insights from isotope mass balance modeling
This study developed an isotope-based mass balance model for Bangkog Lake on the northern Tibetan Plateau to investigate hydroclimatic controls on lake water oxygen isotope variability, finding evaporation to be the dominant driver, followed by temperature, with precipitation and relative humidity having limited effects.
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Kritidou et al. (2025) Partitioning uncertainties of extreme flood estimates using long continuous simulations
This study partitions the uncertainties in extreme flood estimates from a hydrometeorological modelling chain, revealing that uncertainty increases with return period and its dominant source varies with catchment characteristics. It highlights the critical role of hydrological model parameters in high-elevation catchments and weather generator components in lower-elevation, rainfall-dominated areas.
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Wang et al. (2025) Variation characteristics in compound drought-heatwave events in Northwest China and the relationship with sea surface temperature
This study investigates the spatiotemporal variations and driving forces of compound drought-heatwave (CDHW) events in Northwest China, revealing asymmetric trends between its eastern and western regions. It finds that CDHW events are significantly influenced by sea surface temperature anomalies in the Indian and Pacific Oceans and the Atlantic Multidecadal Oscillation.
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Nanni et al. (2025) Observed positive feedback between surface ablation and crevasse formation drives glacier acceleration and potential surge
This study investigates the initiation of a surge at Kongsvegen glacier, Svalbard, by integrating two decades of multi-method observations and simulations. It identifies a positive hydro-mechanical feedback loop where increased surface melt leads to crevasse formation, enhanced meltwater delivery to the bed, increased basal water pressure, and further glacier acceleration and crevassing, driving the expansion of localized instability.
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Feng et al. (2025) Uncertainty Characterization of ICESat-2, Passive Microwave, and Reanalysis Snow Depth Datasets Using Site Data in the Northern Hemisphere
This study aims to characterize the uncertainty of snow depth datasets derived from ICESat-2, passive microwave remote sensing, and reanalysis products by comparing them against ground-truth site data across the Northern Hemisphere.
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Miao et al. (2025) The Synergistic Effects of the SRP and EAP on Summer Precipitation in the Tarim Basin of Northwest China: A Case Study in August 2016
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Baviskar et al. (2025) Regionalizing the ACEA Model with Remote Sensing for Water Footprints in the Upper Syr Darya Basin
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Zander et al. (2025) Leaf wax isotopes reveal enhanced humidity and earlier growing season during Dansgaard-Oeschger warming events in Europe
This study presents a 60,000-year leaf wax hydrogen isotope record from a German lake to reconstruct European hydroclimate during Dansgaard-Oeschger (D/O) cycles, revealing that δDwax depletion during warm interstadials is driven by shifts in growing season timing and increased relative humidity, aligning with projections of intensified precipitation under warming.
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Cao et al. (2025) China's three major cereal crops exposed to compound drought and extreme rainfall events
This study investigates the spatiotemporal characteristics and agricultural exposure of compound drought and extreme rainfall (CDER) events in China's nine major agricultural regions. It reveals that CDER are concentrated in the northwest, southwest, and northern regions, peaking in summer, with drought-rainfall events being more frequent and intense, and identifies varying exposure risks for maize (highest), wheat (moderate), and rice (lowest) during their growth stages.
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Abdi et al. (2025) Advancing Hydrological Prediction with Hybrid Quantum Neural Networks: A Comparative Study for Mile Mughan Dam
This study compares a hybrid quantum neural network (HQNN) with two classical models (bidirectional CNN-LSTM and SVR) to predict monthly inflow to the Mile Mughan Dam. The HQNN demonstrated superior performance across all metrics in both multivariate and univariate scenarios, confirming its reliability for hydrological prediction.
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Chen et al. (2025) Integrating machine learning with NSGA-Ⅱ to assess the synchronization effects of stormwater disaster hazard and green–blue infrastructure
This study developed a framework integrating urban stormwater disaster hazard assessment, machine learning, and multi-objective optimization to investigate the synchronization effects between stormwater hazard distribution and green-blue infrastructure (GBI) allocation. It found that GBI deployment in high-hazard zones is increasingly cost-effective for mitigating flood risk and runoff, especially under extreme precipitation events.
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Wang et al. (2025) Challenges in global climate models to represent cloud response to aerosols: insights from volcanic eruptions
This study confronts a large-scale observational constraint of cloud response to aerosols, derived from the Holuhraun-2014 volcanic eruption, against six global climate models. It reveals that models significantly underestimate aerosol-induced cloud cover responses, a major source of uncertainty in climate projections.
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Cao et al. (2025) A novel land surface temperature retrieval method using channel correlation for atmospheric parameter modeling from SDGSAT-1 data
This paper introduces a novel wide-band atmospheric correction Temperature and Emissivity Separation (TES) algorithm for SDGSAT-1 Thermal Infrared Spectrometer (TIS) data, which retrieves Land Surface Temperature (LST) without requiring auxiliary atmospheric or land surface parameter input. The method demonstrates high accuracy and robustness across various global ground stations, outperforming existing algorithms.
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Cao et al. (2025) Influencing Factor Analysis of Vegetation Spatio-Temporal Variability in the Beijing–Tianjin–Hebei Region Based on Interpretable Machine Learning
This study integrated multi-source data and machine learning methods to simulate and analyze Normalized Difference Vegetation Index (NDVI) changes in the Beijing–Tianjin–Hebei (BTH) region over the past two decades, identifying climate and human activities as key drivers with varying spatio-temporal importance across land use types.
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Qi et al. (2025) Retrieving forest LAI from Landsat via 3D look-up table generated by realistic LiDAR scenes
This study developed a novel 3D Look-Up Table (3D-LUT) approach for retrieving forest Leaf Area Index (LAI) from Landsat imagery by reconstructing realistic 3D forest scenes using LiDAR data to parameterize a 3D Radiative Transfer Model, achieving high accuracy across various forest types.
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Sun et al. (2025) Understanding the Decreased ENSO Predictability since the Early 2000s Based on Data-Driven and Dynamical Models
This study evaluates the interdecadal change in El Niño–Southern Oscillation (ENSO) prediction skill over the past four decades using dynamical and deep learning models, revealing a significant decline since the early 2000s primarily due to worse prediction of ENSO phase transitions, weakened subsurface precursors, and increased bias in zonal advection.
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Woo (2025) Estimating actual evapotranspiration from widely available meteorological data with a hybrid CNN–LSTM
This study develops a hybrid Convolutional Neural Network – Long Short-Term Memory (CNN–LSTM) model to estimate daily actual evapotranspiration (ETa) directly from widely available meteorological and soil data, demonstrating high accuracy (R²=0.92, RMSE=0.38 mm d⁻¹) across 167 FLUXNET sites and global applicability with ERA5 forcings.
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Jia et al. (2025) An Integrated Correction Method for Geometric, Atmospheric, and Terrain Effects in Hyperspectral Remote Sensing Images
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Xu et al. (2025) A multi-objective optimization framework for urban flood mitigation using machine learning and optimization algorithms
This study introduces a multi-objective optimization framework that leverages a machine learning model as a computationally efficient surrogate for 1D-2D coupled hydrodynamic models. The framework enables the optimal design of urban flood mitigation schemes, achieving significant cost savings and enhanced flood protection.
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Izydorski et al. (2025) Hydrological Analysis of the 2024 Flood in the Upper Biała Lądecka Sub-Basin in South Poland
This study developed a hydrological model based on the SCS-CN methodology and GIS to estimate flood hydrographs in the upper Biała Lądecka River basin, Poland, and analyzed a dam failure, aiming to corroborate climate change impacts on floods and aid future flood forecasting and adaptation.
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He et al. (2025) Increased sensitivity of ecosystem to meteorological drought reduces carbon uptake in the Yellow River Basin
This study investigated the spatiotemporal evolution of ecosystem drought sensitivity and its impact on carbon uptake in the Yellow River Basin from 1982 to 2018, revealing a significant increase in sensitivity across 57.12% of the basin, primarily driven by climate change, which reduced annual carbon uptake by 0.11 ± 0.05 Tg C during post-drought recovery.
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Wu et al. (2025) Changes of Eurasian Cold Winters and Their Associated Key Variables Based on CMIP6 Global Climate Models
This study evaluates the historical simulation skill of CMIP5 and CMIP6 global climate models for Eurasian cold winters and associated atmospheric variables, finding CMIP6 outperforms CMIP5. It also projects a decrease in cold winter probability and a "warm Arctic and cold Eurasia" pattern under future climate change scenarios.
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Tobin et al. (2025) Validation of Downscaled SoilMERGE with NDVI and Storm-Event Analysis in Oklahoma and Kansas
This study evaluated a prototype 500 m downscaled version of the 0.125-degree SoilMERGE root zone soil moisture product using machine learning, demonstrating that downscaled products, particularly those using Extreme Gradient Boosting, significantly outperform the default product in predicting vegetation greenness and storm-event streamflow.
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Petropoulos et al. (2025) RegreSSM: A novel software tool for downscaling the SMAP L3 soil moisture operational product utilizing the Ts/VI feature space and Sentinel-3 data
This study introduces RegreSSM, a novel Python-based software tool for downscaling the SMAP L3 surface soil moisture product from 36 km to 1 km resolution using Sentinel-3 optical and thermal data and the Ts/VI feature space. The tool provides a user-friendly, automated, and reproducible workflow, demonstrating satisfactory soil moisture retrieval accuracy over the Iberian Peninsula.
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Yuan et al. (2025) Weaker absorption of Asian dust than previously estimated based on observation-constrained simulation
This study developed a revised complex refractive index for East Asian dust based on 22 soil samples, revealing significantly weaker absorption than previously estimated. Implementing these new parameters in a climate model substantially improved simulations of dust optical properties and reversed the estimated top-of-atmosphere shortwave direct radiative effect from warming to cooling.
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Ono et al. (2025) Multi-year water and carbon flux contrasts between high-yielding and conventional rice cultivars
This study used eddy covariance to compare multi-year water and carbon fluxes between a high-yielding indica rice (Oonari) and a conventional japonica rice (Koshihikari) in farmers' fields, finding Oonari exhibited significantly higher gross primary production and generally higher evapotranspiration, leading to greater water use efficiency.
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Gobron et al. (2025) A unified framework for trend uncertainty assessment in climate data records: demonstration on global mean sea level
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Kuang et al. (2025) A High Spatiotemporal Resolution Soil Moisture Retrieval Approach Leveraging Deep Regression Networks and Multisource Remote Sensing Data
This paper presents a novel approach for retrieving soil moisture at high spatiotemporal resolution by leveraging deep regression networks and multisource remote sensing data.
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Li et al. (2025) Development of Maize Planting Method Based on Site-Specific Soil Moisture for Improving Seedling Traits in the Northern China Dryland
This study proposes and evaluates a variable sowing depth method based on soil moisture distribution for dryland maize, demonstrating significant improvements in emergence rate, seedling uniformity, and early growth traits compared to conventional planting.
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Blackshaw (2025) Future U.S. TC Wind Hazards
This entry provides data for a study investigating how tropical cyclone design winds in the United States are sensitive to future climate change.
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He et al. (2025) Drying Tropical America Under Global Warming: Mechanism and Emergent Constraint
This study investigates the mechanisms driving precipitation changes in Tropical America (TAM) using CMIP6 models, revealing that amplified equatorial Pacific warming induces a Gill-type atmospheric response that suppresses TAM rainfall. It finds that climate models likely underestimate future TAM drying, projecting a regional mean annual rainfall decline of 46 mm per 1 K of global warming, which is 1.5 times higher than raw projections.
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Liu et al. (2025) Unraveling the impacts of climate factors on leaf area index of Chinese grasslands using interpretable machine learning models
This study utilized three interpretable machine learning models to investigate the impact mechanisms of preseason climate and extreme weather events on grassland Leaf Area Index (LAI) in China from 2001 to 2020, finding that preseason climate was the most important driver, with extreme events and CO2 fertilization also significantly influencing LAI dynamics.
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Lamsal (2025) Modeling the Food-Water Nexus: A Spatio-temporal Accounting of Agricultural Land and Water Use in the United States
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Zhao et al. (2025) Correction: Zhao et al. Validating Data Interpolation Empirical Orthogonal Functions (DINEOF+) Interpolated Soil Moisture Data in the Contiguous United States. Agriculture 2025, 15, 1212
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Ghazi (2025) Smart Irrigation Systems: A Comprehensive Review of IoT, AI, and Sustainable Agriculture Technologies. A Review Article
This review synthesizes recent advances in multi-source sensing, IoT/LPWAN connectivity, and hybrid edge–cloud AI frameworks for real-time irrigation and fertigation optimisation. It finds that AI- and sensor-driven scheduling commonly reduces water use by 15–40% while maintaining or improving yield and nutrient-use efficiency across various agricultural systems.
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Roe et al. (2025) The Atmosphere as a Heat Engine Operating at Maximum Power
This paper proposes that Earth's atmosphere operates at a maximum-power state, where the power generated by poleward heat flux is maximized, and derives analytic solutions that show reasonable agreement with observed annual-mean temperature, atmospheric heat transport, and boundary layer dissipation.
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Bozorgi et al. (2025) Assessing the performance of multi-timescale drought indices for monitoring agricultural drought impacts on wheat yield
This study evaluates the performance of three multi-timescale drought indices (SPEI, SPET, and SEDI) in monitoring rainfed wheat yield variability across four Spanish regions. The findings identify the Standardized Evapotranspiration Deficit Index (SEDI) as the most robust indicator for agricultural drought impact assessment.
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Katipoğlu et al. (2025) Prediction of soil moisture via feature selection, model optimization, and climate data integration
This study evaluates the performance of four machine learning algorithms in predicting soil moisture across the Konya Closed Basin, Türkiye, using long-term climate data from 1950 to 2022. The results identify Deep Neural Networks (DNN) as the most accurate model for estimating soil moisture dynamics in the region.
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Peng et al. (2025) A New Convective Initiation Definition and Its Characteristics in Central and Eastern China Based on Fengyun-4A Satellite Cloud Imagery
This study enhances a satellite-based Convective Initiation (CI) algorithm for central and eastern China by using Fengyun-4A, radar, and precipitation data to differentiate true CI from false CI events, proposing a reliable new CI definition based on distinct pre-CI cloud characteristics.
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Asoka et al. (2025) Tropical Cyclones Unevenly Shape Drought Propagation
This study evaluates the performance of the ISBA-CTRIP land surface model in simulating global hydrological cycles and land-atmosphere interactions. The research demonstrates the model's ability to accurately reproduce river discharge and soil moisture dynamics across diverse climatic zones.
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Sinclair (2025) A Global Climatology of Tropical Cyclone Diabatic Rossby Wave Sources and Their Extratropical Flow Response
This study establishes a 46-year global climatology of Rossby wave forcing by tropical cyclones, demonstrating that TC recurvature is not a prerequisite for downstream Rossby wave excitation and that similar forcing occurs in other ocean basins.
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Touseef et al. (2025) Hydrological modeling of karst terrain in southwestern Pakistan using modified SWAT
This study developed and evaluated a modified Soil and Water Assessment Tool (SWAT) model incorporating a novel three-zonal conceptualization (epikarst, vadose, and phreatic zones) to improve hydrological modeling in the data-scarce karst Nari River Basin, southwestern Pakistan. The enhanced model demonstrated approximately 10 % higher Nash-Sutcliffe Efficiency (NSE) during calibration and validation, better capturing flood peaks and low-flow events compared to the original SWAT model.
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Niaz et al. (2025) Assessing seasonal drought persistence using a Bayesian logistic regression approach
This study investigates meteorological drought patterns and intraseasonal predictability using a Bayesian Logistic Regression approach on 52 years of precipitation data from Ankara Province, Türkiye, revealing that drought frequency and persistence range from 40% to 90% and identifying areas vulnerable to drought persistence between successive seasons.
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Chen et al. (2025) Dynamic Changes in Quasi‐Biweekly Variability Drive Increased Extreme Rainfall and Whiplash Events Over Asian Monsoon Regions
This study reveals a significant intensification of quasi-biweekly rainfall variability in the Asian monsoon region over the past four decades, primarily driven by dynamical changes in vertical velocity perturbations, with thermodynamic moisture increases playing a secondary role. Climate models project stronger future variability only when thermodynamic moistening outweighs dynamical uncertainty, highlighting the need for improved model representation of this circulation.
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wu (2025) Long-term Variation in Lake Ice in a Large Endorheic Lake Experiencing Shrinkage in a Semi-arid Region, Mendeley Data, V3
This study investigates the long-term variations in lake ice cover in a large, shrinking endorheic lake situated in a semi-arid region.
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Sonuç et al. (2025) Fine-Resolution Multivariate Drought Analysis for Southwestern Türkiye Under SSP3-7.0 Scenario
This study utilizes high-resolution (2.5 km) convection-permitting climate simulations to project a significant intensification of compound agricultural droughts in southwestern Türkiye under the SSP3-7.0 scenario. The findings indicate a regime shift where soil moisture becomes increasingly sensitive to rising temperatures rather than precipitation deficits by the late 21st century.
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Zhan et al. (2025) Unveiling the Floodplain River‐Lake Hydrological Interactions by SWOT Observations
This study utilizes Surface Water and Ocean Topography (SWOT) mission data to analyze the Poyang Lake-Yangtze River system, revealing divergent seasonal water surface slopes and associated backwater effects, thereby offering a globally applicable framework for river-lake hydrodynamics.
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Mlot et al. (2025) Integrating Artificial Intelligence and Multi-Source Data for Precision Deficit Irrigation in Vineyards: The ViñAI Tool Case Methodology
This study develops and validates ViñAI, an AI-driven decision-support tool utilizing an Extreme Gradient Boosting (XGBoost) model, to optimize regulated deficit irrigation in vineyards by integrating open-access environmental data.
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Manyari et al. (2025) Accuracy Assessment of Remote Sensing-Derived Evapotranspiration Products Against Eddy Covariance Measurements in Tensift Al-Haouz Semi-Arid Region, Morocco
This study evaluated five high-resolution global evapotranspiration (ET) products against eddy covariance measurements in a semi-arid Moroccan region over 14 years. It found that PMLv2 performed best, followed by WaPOR and SSEBop, while ETMonitor and MOD16 showed significant underperformance.
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Dong et al. (2025) Stratosphere‐Troposphere Exchange of Water Vapor Based on Observations and Reanalyses
This study provides the first global estimates of stratosphere-troposphere exchange (STE) of water vapor (H2O) using an observational mass budget approach, revealing a small net global flux of 0.10 ± 0.04 Pg/yr into the stratosphere and identifying significant biases in reanalysis products (MERRA2, ERA5) compared to observations.
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Kheimi et al. (2025) Multi-boosting and machine learning for soil substrate water content prediction
This study proposes and evaluates six machine learning algorithms and one mathematical model to predict Substrate Water Content (SWC) using volumetric water content, time since last irrigation, and porosity as inputs. The XGBoost ensemble model demonstrated superior performance with the lowest Root Mean Square Error (0.009 m³·m⁻³) and highest Nash-Sutcliffe coefficient (0.987).
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Li et al. (2025) Assessing the impacts of dry-heat extremes on grassland gross primary productivity across mainland China using multi-source remote sensing data
This study investigates how background aridity influences grassland gross primary productivity (GPP) responses to dry-heat extremes across mainland China, and assesses the trade-off between GPP loss rate (G lr) and loss intensity (G li). It finds that compound drought-heatwave events cause greater GPP losses in semi-arid/arid zones but less in humid/semi-humid zones, with soil moisture as the dominant regulator, and that dry-heat extremes significantly decouple the G lr-G li trade-off, exacerbated by increasing aridity.
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Li et al. (2025) Global Low Clouds Evolution and Their Meteorological Drivers Across Multiple Timescales
This study investigates the timescale-dependent relationships between low cloud amount (stratocumulus, cumulus, stratus) and meteorological conditions over global land and ocean, revealing that these interactions are nonlinear and vary significantly across short (≤1 year), medium (1–8 years), and long (>8 years) timescales, as well as in their long-term trends.
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Buarque et al. (2025) Insights into the North Hemisphere daily snowpack at high resolution from the new Crocus–ERA5 product
This study introduces and evaluates the new daily Crocus–ERA5 snow product for the Northern Hemisphere (1950–2022), demonstrating its improved accuracy in snow depth and cover compared to its predecessor, Crocus-ERA-Interim, particularly in Eurasia, despite persistent biases in some Arctic regions.
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Cai et al. (2025) Multi-Objective Optimization for Irrigation Canal Water Allocation and Intelligent Gate Control Under Water Supply Uncertainty
This study proposes an integrated framework combining interval-based uncertainty analysis, intelligent optimization, and advanced control for open-channel irrigation systems, demonstrating significant improvements in water allocation efficiency and gate control performance under uncertain water supply conditions.
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Huang et al. (2025) ENSO's Impacts on Southeastern Australia's Future Rainfall Risk
This study assesses the El Niño-Southern Oscillation (ENSO)-rainfall relationship in large ensembles over the Murray-Darling Basin and quantifies projected rainfall risk using a novel framework. It finds that while El Niño-related rainfall shows no prominent change, La Niña-driven rainfall impacts, characterized by increased variability and higher amounts, may worsen under a warming climate.
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Guan et al. (2025) Discrepant Pathway in Regulating ET Under Change in Community Composition of Alpine Grassland in the Source Region of the Yellow River
This study investigated evapotranspiration dynamics across five alpine grassland transition types in the Source Region of the Yellow River from 1986 to 2018, revealing that the directionality of community compositional transitions more strongly dictates hydrological responses than absolute vegetation states.
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Cheng et al. (2025) A more appropriate framework for graphical attribution of hydrological change in the water-energy partitioning space
This study critically evaluates existing graphical attribution methods (OGA and BGA) for hydrological change in the water-energy partitioning space, identifies limitations of OGA, and proposes the two-path Budyko-based graphical attribution (BGA) as a more appropriate framework, applying it to 15 catchments in the Chinese Loess Plateau. The improved method found that the direct effect of the dryness index (∅) and the regulating effect of the land-atmosphere system contributed, on average, 18 % and 82 %, respectively, to runoff change.
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Zhu et al. (2025) Multi-source remote sensing retrieval and spatiotemporal distribution characteristics of soil moisture content in typical karst farmlands of southwestern China
This study establishes an optimized machine learning framework using the XGBoost algorithm and eight key environmental variables to accurately retrieve soil moisture in complex karst farmlands. The resulting model significantly outperforms standard ERA5-Land reanalysis data and reveals distinct spatiotemporal moisture patterns influenced by monsoon cycles and proximity to water systems.
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Mahto et al. (2025) IoT and AI Integration for Climate‑Smart Farming: A Predictive and Adaptive System for Smallholder Farmers
This research presents an integrated Internet of Things (IoT) and Artificial Intelligence (AI) based climate-smart farming system for smallholder farmers, which continuously monitors environmental conditions, predicts crop responses, and generates adaptive management recommendations. Field experiments demonstrated significant improvements in water efficiency and crop productivity.
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Liu et al. (2025) Assessing groundwater sustainability across high mountain Asia using remote sensing
This study integrates remote sensing, Earth system modeling, and artificial intelligence to quantify historical groundwater storage (GWS) trends and project future evolutions in High Mountain Asia, revealing widespread GWS declines (24.2 gigatonnes per year, 2003-2020) driven by climate (47%), human activities (38%), and cryospheric processes (15%), with projections indicating accelerated depletion by the century's end despite temporary cryospheric buffering.
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Spezza et al. (2025) Intercomparison of Gauge-Based, Reanalysis and Satellite Gridded Precipitation Datasets in High Mountain Asia: Insights from Observations and Discharge Data
This study comprehensively evaluates five gridded precipitation datasets (ERA5, HARv2, GPCC, APHRODITE, PERSIANN-CDR) over High Mountain Asia from 1983–2007, finding that reanalysis products generally outperform others in capturing spatial patterns and hydrological consistency, though no single dataset is optimal for all applications.
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Vaquero et al. (2025) Assessing Catastrophic Historical Floods in a Small Stream: The Case of Tripero River (Villafranca de los Barros, Spain)
This study reconstructs five catastrophic historical floods (1865-1952) in the Tripero stream, Spain, by integrating historical documentary evidence with meteorological reanalysis data, revealing diverse synoptic patterns and meteorological origins for these events.
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Kamm (2025) Sur les échanges entre échelles dans les modèles d’océan : parametrisations des tourbillons mésoéchelles par apprentissage machine
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Duan et al. (2025) A New Addition to Global Soil Moisture Mapping: CFOSAT Scatterometer Algorithm Development and Validation
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Kumari et al. (2025) Climatological characteristics of ITCZ over the South Asian monsoon domain: Using multivariate probabilistic approach
This study investigates the climatological characteristics and seasonal migration of the Intertropical Convergence Zone (ITCZ) over the South Asian monsoon domain using a novel multivariate probabilistic approach. The method, combining precipitation and mean meridional mass flux, effectively captures the ITCZ's seasonal shifts and regional variations, providing a clearer understanding of the South Asian Summer Monsoon phases.
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Chen et al. (2025) A physics-informed neural network for predicting depth-averaged velocities of flows with submerged vegetation: Integrating analytical formulas with deep learning
This paper presents a physics-informed neural network (PINN) that integrates an algebraic momentum-balance relation with a data-driven framework to predict depth-averaged velocities in flows with submerged vegetation, demonstrating superior accuracy, generalization, and robustness compared to traditional analytical formulas and purely data-driven models.
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Aranyossy et al. (2025) Multi-annual predictions of hot, dry and hot-dry compound extremes
This study evaluates the multi-year predictability of hot, dry, and hot-dry compound extremes using CMIP6 decadal hindcast experiments for forecast years 2–5. It finds that hot-dry compound and hot extremes are skillfully predicted over most land regions, while skill for dry extremes is more limited, with most predictability stemming from external forcings and long-term trends rather than initialisation.
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Zhao et al. (2025) A 1 km daily high-accuracy meteorological dataset of air temperature, atmospheric pressure, relative humidity, and sunshine duration across China (1961–2021)
This study presents a 1 km daily high-accuracy meteorological dataset for China (1961–2021), including air temperature, atmospheric pressure, relative humidity, and sunshine duration, generated using a novel hierarchical reconstruction framework that leverages thousands of ground observations and topographic attributes to provide a reliable foundation for climate, hydrological, and ecological studies.
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Yang et al. (2025) Crucial drivers and interaction mechanisms of ecosystem water use efficiency in the Yellow River Basin, China
This study analyzed the spatiotemporal trends and complex interaction mechanisms of ecosystem water use efficiency (WUE) in the Yellow River Basin (YRB) from 2001 to 2020, revealing an overall increase in WUE driven by synergistic and antagonistic effects among key factors like water conditions, leaf area index, human activities, radiation-temperature, and geographic environment.
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Zheng et al. (2025) Comparative Study on Entrainment‐Mixing Mechanisms Between Cumulus Cores and Edges Based on Aircraft Observations
This study quantitatively analyzes entrainment-mixing mechanisms in cumulus cloud cores versus edges using aircraft observations, revealing that cores exhibit a higher homogeneous mixing degree and lower Damköhler number compared to edges, with these differences modulated by environmental and cloud conditions.
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Ding et al. (2025) Sharpening Mesoscale Convective Systems Induced by Enhanced Moisture–Convection Feedback Over East Asia During 2000–2021
This study examines changes in the spatial structure of observed summer mesoscale convective systems (MCSs) over East Asia from 2000 to 2021, finding that MCSs have become sharper with increased maximum precipitation intensity due to an enhanced moisture-convection feedback driven by intensified synoptic moist static energy forcing.
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Peng et al. (2025) Research on dynamic prediction of vegetation coverage by precipitation-evapotranspiration in arid regions based on CNN-LSTM hybrid model
This study developed a CNN-LSTM hybrid model to dynamically predict vegetation coverage in arid regions, integrating SPEI-based drought classification and precipitation-evapotranspiration data. The model achieved a Pearson correlation coefficient of 0.95 with measured data, accurately capturing vegetation dynamics from 2000 to 2022.
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Song et al. (2025) Global Gross Primary Productivity Estimation Using Passive Microwave Observations From China's Fengyun‐3D Satellite
This study develops and validates EDVI-GPP, a novel microwave-based global gross primary productivity (GPP) estimation method, demonstrating its reliability and reduced cloud effects compared to existing products, with a global GPP estimate of 123.77 ± 1.33 Pg C yr⁻¹ for 2020–2022.
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Guniganti et al. (2025) A hybrid hydrologic modeling framework-role of spatial resolution, calibration approaches and error modeling
This study develops a hybrid hydrologic modeling framework to assess the roles of spatial resolution, multi-site calibration strategies, and machine learning-based error modeling in semi-distributed streamflow prediction for the Narmada River Basin, India. It finds that a medium spatial resolution combined with a novel Sequential Ungauged Basin calibration approach and Random Forest error correction provides a computationally efficient and skillful framework, particularly improving high flow predictions and demonstrating strong potential for ungauged basins.
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Evett et al. (2025) The Bushland, Texas, alfalfa, soybean, sunflower, and winter wheat evapotranspiration, growth, and yield dataset collections
This paper presents comprehensive, quality-controlled datasets on evapotranspiration (ET), growth, and yield for alfalfa, soybean, sunflower, and winter wheat, collected over multiple years using large weighing lysimeters and extensive instrumentation at Bushland, Texas. These high-resolution datasets are designed for calibrating and testing crop simulation models and analyzing water productivity in the Southern High Plains.
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He et al. (2025) Spring extreme heat in the Indochina Peninsula enhances the prediction skill of summer precipitation in Central China
This study investigates how spring extreme heat in the Indochina Peninsula (ICP) influences summer precipitation in Central China (CC), proposing that ICP extreme heat enhances CC summer precipitation through a land-atmosphere feedback loop, soil moisture memory, and subsequent anticyclonic circulation. The findings demonstrate that incorporating these land surface processes significantly improves the prediction skill of summer precipitation in Central China.
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Song et al. (2025) Radiative and Precipitation Processes Make it Easier to Match the Temperature Record and Harder to Constrain Future Warming
This study examines a negative correlation between radiative forcing due to aerosol-cloud interactions and shortwave cloud feedback within a perturbed parameter ensemble. It finds that while this correlation helps Earth System Models reproduce historical temperature records, it simultaneously limits the ability to constrain future warming projections using these records.
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Luo et al. (2025) Identification and spatiotemporal analysis of braided rivers in the Yarlung Tsangpo basin using an enhanced U-Net approach
This study develops an enhanced deep learning model, MSU-Net, to accurately identify and map complex braided river systems in the Yarlung Tsangpo River Basin. Using a fusion of Sentinel-1 and Sentinel-2 satellite data from 2018 to 2023, the research quantifies the spatiotemporal dynamics of these channels and their relationship with climatic drivers.
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Gonzalez et al. (2025) We Need to Simulate More Northern ITCZs and Less Southern ITCZs Over the East Pacific Ocean in Coupled Climate Models
This study developed an algorithm to classify daily Intertropical Convergence Zone (ITCZ) states and found that Coupled Model Intercomparison Project 6 (CMIP6) models significantly underestimate northern hemisphere ITCZs and overestimate southern hemisphere ITCZs over the east Pacific, challenging the traditional "double ITCZ bias" interpretation. Reanalyses also exhibit distinct ITCZ state biases and errors in interannual variability.
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Rao et al. (2025) Intensification of Compound Extremes Over India Under 1.5°C and 2°C Global Warming Levels: Insights From Bias‐Corrected CMIP6 Simulations
This study assesses future compound climate extremes in India under 1.5°C and 2.0°C global warming levels using high-resolution CMIP6 projections. It finds that cold-related extremes will become rare, while warm-related extremes, particularly Warm-Dry and Warm-Wet events, will significantly increase across India, leading to sharply rising population exposure.
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Wu et al. (2025) STC-DeepLAINet: A Transformer-GCN Hybrid Deep Learning Network for Large-Scale LAI Inversion by Integrating Spatio-Temporal Correlations
This paper introduces STC-DeepLAINet, a Transformer-GCN hybrid deep learning network, for high-precision, large-scale Leaf Area Index (LAI) inversion by effectively integrating spatio-temporal correlations. The proposed network significantly outperforms existing methods and generates reliable LAI products crucial for agricultural and ecological research.
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Jalal et al. (2025) Spatiotemporal dynamics and future projections of aridity in a semi-arid region based on multi-model CMIP6 scenarios
This study investigated the spatiotemporal variability and projected changes in aridity across western Iraq using historical data (1993–2023) and multi-model CMIP6 future climate projections (2020–2100). It revealed a predominantly arid to hyper-arid climate, with significant intensification of aridity, particularly in southern regions under high-emission scenarios, underscoring increasing climatic stress on water resources.
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Kong et al. (2025) Vegetation carbon use efficiency response to drought in the Manas River Basin of Xinjiang
This study investigated the spatiotemporal dynamics and drought response of vegetation Carbon Use Efficiency (CUE) in the Manas River Basin from 2001 to 2020, revealing a multi-year mean CUE of 0.50 and a dominant 3-month lag effect in CUE response to drought, with forests showing higher resistance and longer lags than croplands.
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Zhang et al. (2025) Sunset Bores Over the Southern North China Plain
This study investigates the formation of atmospheric bores near sunset and their critical role in initiating and sustaining late-afternoon mesoscale convective systems (MCSs) over the Southern North China Plain, highlighting their importance for accurate diurnal cycle prediction.
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Aslan et al. (2025) Hybrid wavelet–ANN modelling for LAI forecasting under climatic variability: comparative case studies from the mediterranean basin
This study developed and evaluated a hybrid Wavelet–Artificial Neural Network (W-ANN) model for forecasting Leaf Area Index (LAI) in the Mediterranean Basin under climatic variability. The W-ANN model significantly outperformed a conventional Artificial Neural Network (ANN), demonstrating 15–85% higher accuracy and revealing divergent LAI trends across four contrasting urban locations.
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Adduri et al. (2025) Development of hybrid machine learning and deep learning techniques for sea level rise projection in Dubai
This study developed and evaluated a hybrid deep learning model (Conv1D-LSTM) for near-term sea level rise (SLR) projection along Dubai's coastline, forecasting an increase in sea levels by 2030 and identifying high-risk areas.
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Tang et al. (2025) A novel hybrid approach for mapping global surface solar radiation with DSCOVR/EPIC: Combining deep learning with physical algorithm
This study develops a novel hybrid approach integrating deep learning with physical algorithms, utilizing DSCOVR/EPIC observations to map global surface solar radiation (Rg) and its direct (Rdir) and diffuse (Rdif) components. The method demonstrates superior accuracy and spatial scalability compared to existing products, providing globally consistent high-resolution radiation data.
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Kang et al. (2025) Quantifying Moisture Source Contributions to Diverse Precipitation Events over the Tibetan Plateau
This study quantifies moisture source contributions to extreme, moderate, and light precipitation events over the Tibetan Plateau from 1979 to 2020, revealing that extreme and moderate events are primarily driven by the Indian monsoon, while light events are dominated by westerlies, with external moisture sources contributing significantly across all types.
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Gudmundsson et al. (2025) Past and future change in global river flows
This review synthesizes the current understanding of past and projected changes in global river flow, revealing that anthropogenic climate change is a significant driver of observed regional trends (e.g., increased flows in high-latitudes, decreased in mid-latitudes/subtropics, earlier seasonal flows in snow-dominated regions), with these changes projected to intensify. It highlights the complex interplay of climate change and direct human interventions, emphasizing the need for improved monitoring, modeling, and attribution frameworks.
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Proutsos et al. (2025) Impact of hydrometeorological factors and extreme events on black pine forests growth in the Mediterranean
This study investigates the impact of hydrometeorological factors and extreme events on black pine (Pinus nigra) growth across 38 sites in the Mediterranean basin. It reveals that while warmer winters and summer precipitation positively influence growth, summer heat, high potential evapotranspiration, and droughts significantly limit it, with distinct regional differences between the Western and Eastern Mediterranean.
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Wei et al. (2025) Hysteretic response of Western North American fire weather to CO₂ removal
This study investigates the hysteretic response of Western North American fire weather to idealized CO₂ ramp-up and ramp-down scenarios, finding that fire weather peaks 7-11 years after maximum CO₂ and intensifies by approximately 5.5% during CO₂ ramp-down compared to ramp-up at identical CO₂ levels. This hysteresis is primarily driven by drier conditions, warmer temperatures, and reduced humidity, linked to shifts in atmospheric circulation and Pacific sea surface temperatures.
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Chen et al. (2025) Regional Forest Wildfire Mapping Through Integration of Sentinel-2 and Landsat 8 Data in Google Earth Engine with Semi-Automatic Training Sample Generation
This study developed an FS-SNIC-ML workflow integrating multi-source optical imagery fusion, semi-automatic sample generation, and object-based machine learning to accurately map burned forest areas in mountainous regions and identify wildfire driving factors. The workflow achieved high classification accuracies, with Random Forest performing best, and identified key environmental drivers of wildfire hotspot density.
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Golubets et al. (2025) Evaluation of Cloud Fraction Data for Modelling Daily Surface Solar Radiation: Application to the Lake Baikal Region
This study evaluates the performance of various open-access cloud cover products for modelling daily surface solar radiation (SSR) in the topographically complex Lake Baikal region. It finds that the AVHRR satellite product provides the most reliable SSR estimates, outperforming ground-based observations and reanalysis data.
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Valera-Prieto et al. (2025) Breaking the jam: Bridge wood-jam break during a flash flood in a Mediterranean river, insights from hydrodynamic modeling and post flood observations
This study reconstructs an extreme flash flood in the Francolí River to analyze how large wood (LW) transport and bridge clogging influence hydraulic behavior. The research demonstrates that wood-jam formation and sudden breakage cycles create transient surges and significantly increase upstream water levels, factors often overlooked in standard flood modeling.
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Yin et al. (2025) Meteorological observation research based on an improved EfficientNetV2 model
This study proposes a novel deep learning model, EfficientNetV2-CBAM-PANet, to improve meteorological image recognition in complex weather scenarios by enhancing feature extraction and robustness. The model achieved a recognition accuracy of 97.6% on a self-constructed dataset, demonstrating strong classification capability across various weather conditions.
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Q et al. (2025) Synergistic Retrieval of Soil Moisture in Arid Regions Using GF ‐3 SAR and Sentinel‐2 Optical Data
This study performs a cross-scale evaluation of the ISBA land surface model and the mHM hydrological model to assess their consistency in simulating the European terrestrial water cycle. The findings reveal that while mHM provides superior river discharge simulations, ISBA demonstrates higher accuracy in capturing surface soil moisture dynamics.
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Bisht et al. (2025) Development of a River Dynamical Core for E3SM to simulate compound flooding on Exascale-class heterogeneous supercomputers
This paper introduces the River Dynamical Core (RDycore), an open-source 2D shallow water equation library for the Energy Exascale Earth System Model (E3SM), designed to enable kilometer-scale flood simulations on exascale supercomputers with significant GPU acceleration. It was validated for various problems and demonstrated successful coupling with E3SM for simulating compound flooding events.
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Cao et al. (2025) Enhancing Machine Learning Models for Nowcasting and Short‐Term Forecasting of Precipitation With a Novel Probability‐Matching Loss Function
This paper proposes a novel probability-matching (PM) based loss function for machine learning precipitation forecasting to overcome the limitations of mean squared error (MSE) loss, demonstrating improved skill, reduced bias across rainfall intensities, and better preservation of small-scale variability.
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Li et al. (2025) Advancing Agricultural Drought Level Prediction in Guangdong Utilizing ERA5-Land and SMAP-L3 Data
This study introduces a feature recalibration encoder for LSTM-based models to improve agricultural drought forecasting in Guangdong Province. The research demonstrates that direct prediction of the Soil Water Deficit Index (SWDI) using satellite data (SMAP-L3) provides the most stable and accurate results for medium-to-long-term (7–14 days) drought level forecasting.
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Ci et al. (2025) Multi-timescale evapotranspiration fusion: A novel autoencoder with automated machine learning-based approach for enhanced estimation accuracy
This study developed AGFusionET, a novel multi-timescale fusion model combining autoencoders and automated machine learning (AutoML), to integrate 20 heterogeneous evapotranspiration (ET) products. It generated a global, high-resolution (0.05 degrees) ET dataset for 1982–2023, demonstrating superior accuracy (Kling-Gupta Efficiency of 0.88, Root Mean Square Error of 12.12 mm/month) compared to benchmark products.
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Xing et al. (2025) Quantifying the spatial extent and attenuation of lake thermal regulation at diurnal scales under extreme heat
This study used WRF modeling and directional buffer analysis to quantify the spatial extent and attenuation of Poyang Lake's thermal regulation during extreme heat, revealing pronounced diurnal asymmetry in cooling (40 km, -1.16 °C, 0.28 °C per 10 km) and warming (70 km, +0.97 °C, 0.13 °C per 10 km) effects.
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Farzad et al. (2025) A systematic review and comprehensive evaluation of artificial intelligence approaches for prediction flood susceptibility
This systematic review comprehensively evaluates artificial intelligence (AI) approaches for flood susceptibility prediction, revealing a significant increase in Machine Learning (ML) and Deep Learning (DL) model usage since 2018 and 2020, respectively, with most research originating from Asian countries and relying primarily on satellite data.
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Feldl et al. (2025) Explaining the Transient and Equilibrium Longwave Feedback with Moist Adiabatic Theory and Its Deviations
This study develops a theoretical framework to understand how patterns of surface temperature change influence the global longwave clear-sky radiative feedback, revealing that the pattern effect is driven by distinct regional physical processes, primarily involving surface temperature and relative humidity feedbacks.
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Costa (2025) Chill To Spill: Unlocking Yosemite’s Water Flow
This project investigates how snowmelt, precipitation, and dam operations interact to influence river overflow and water availability in the Upper Merced River watershed. It found that the speed of snowmelt and dam regulation are critical factors in flood risk and water management, often more so than snowpack volume alone.
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Ma et al. (2025) Propagation of different types of compound drought-hot events: Spatiotemporal patterns and response relationship
This study investigates the spatiotemporal patterns and propagation characteristics of three types of compound drought-hot events (meteorological, agricultural, and hydrological) in Northeast China. The research reveals strong interdependencies between these events, with propagation lag times significantly influenced by land use, soil organic matter, and anthropogenic water management.
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Zhuang et al. (2025) An innovative approach to ZDR data assimilation using an ensemble Kalman filter: a proof-of-concept study
This study develops and evaluates an innovative Mean Diameter Update (MDU) approach for ZDR data assimilation using a local ensemble transform Kalman filter. By explicitly updating the mass-weighted mean diameter (Dm) of raindrops, the MDU approach leverages the strong ZDR-Dm correlation, leading to significantly improved accuracy in microphysical state analyses and short-term rainfall forecasts in both pseudo and real observation experiments.
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Zhang et al. (2025) Moisture from US Corn Belt fuels more intense convective storms
This study reveals that evapotranspiration from shallow groundwater, croplands, and irrigation in the US Corn Belt significantly amplifies mesoscale convective system frequency by 24-35%, extends storm lifetime by up to 10%, and accelerates storm movement, thereby intensifying convective storms.
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Al-Sammarraie et al. (2025) From data to decision: How wearable plant sensors help improving proactive irrigation strategies and water use efficiency
This review synthesizes the role of wearable plant sensors, Internet of Things (IoT), and Artificial Intelligence (AI) in transforming agricultural irrigation strategies. It concludes that these integrated technologies provide accurate, real-time plant physiological and environmental data, enabling proactive water management, significantly improving water use efficiency, and enhancing agricultural sustainability.
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Bright et al. (2025) Using point dendrometers to improve forest transpiration estimation accuracy at stand scales
This study investigates whether increasing the number of sampled trees for stand-level transpiration estimation, by augmenting sap flow measurements with data from point dendrometers, can reduce uncertainty. It found that expanding the tree sample size using point dendrometer-derived sap flow estimates reduced the uncertainty of stand-level transpiration by 31–37%, demonstrating a cost-effective method to improve accuracy.
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Ji et al. (2025) Holistic assessment of seasonally frozen ground changes on the Qinghai-Tibet Plateau
This study developed a sinusoidal heat transfer model to quantify the elevational dynamics of seasonally frozen ground (SFG) on the Qinghai-Tibet Plateau, revealing a widespread decline in freezing depth and duration from 1980 to 2018, with temperature and snowpack jointly controlling these changes.
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Xie et al. (2025) Untangling the inhibitory effect of polluted dust on weak precipitation based on raindrop size distribution observation
This study investigates the distinct impacts of polluted dust and anthropogenic aerosols on precipitation microphysical processes in Shanghai, China, using raindrop size distribution observations. It reveals that polluted dust inhibits weak precipitation by suppressing large raindrop formation, while anthropogenic aerosols promote convective precipitation, increasing large raindrop concentration and delaying rainfall peaks.
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Tang et al. (2025) Emergent constraints reveal underprediction of future global water availability under anthropogenic forcing
This study identifies the drivers of global water resource changes, attributing the observed upward trend primarily to greenhouse gas forcing, and employs an emergent constraint method to reduce uncertainties in future projections, revealing a significant underestimation of future global water availability.
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Wildberger et al. (2025) Generating isoline maps from spatially-exhaustive climate reanalysis data
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Wang et al. (2025) GPP-net: a robust high-resolution GPP estimation network for Sentinel-2 using only surface reflectance and photosynthetically active radiation
This study introduces GPP-net, a deep learning network for robust, high-resolution gross primary productivity (GPP) estimation using only Sentinel-2 surface reflectance and photosynthetically active radiation (PAR). GPP-net demonstrates superior accuracy and generalization across diverse vegetation types and extreme climate conditions, significantly reducing reliance on traditional land cover and coarse meteorological data.
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Su et al. (2025) Predicting net primary productivity response to multiple extreme climate drivers in Inner Mongolia
This study simulated historical (1982–2020) and projected future (2023–2100) net primary productivity (NPP) in Inner Mongolia under extreme climate events, identifying key climate factors and vulnerable regions.
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Naim et al. (2025) Enhancing Drought Identification and Characterization in the Tensift River Basin (Morocco): A Comparative Analysis of Data and Tools
This study evaluates satellite and reanalysis products for drought monitoring in the Tensift River Basin, identifies optimal probability distributions for SPI and SPEI, and compares their performance against reported drought events to enhance early-warning tools for water resource management.
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Bouaziz et al. (2025) A Century of Data: Machine Learning Approaches to Drought Prediction and Trend Analysis in Arid Regions
This study systematically evaluated four machine learning models for multi-scale Standardized Precipitation Index (SPI) tracking and prediction in southeastern Tunisia using a century-long dataset, finding Support Vector Regression (SVR) to be superior and revealing upward trends in longer-term SPIs indicating decreasing drought severity.
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Fu (2025) Robotic autonomous systems for unconstructed land reclamation and preparation
This paper proposes that integrating robotics and artificial intelligence into modern agriculture can transform under-utilized lands into productive and sustainable areas by enhancing precision, efficiency, and real-time monitoring.
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Wang et al. (2025) Extreme Climate Drivers and Their Interactions in Lightning-Ignited Fires: Insights from Machine Learning Models
This study integrates extreme climate indices with meteorological, vegetation, soil, and topographic data using machine learning to develop probabilistic models for lightning fire occurrence. The approach significantly improves prediction accuracy (up to 87.4%) over traditional methods, identifying extreme temperature and precipitation indices as key drivers and offering an interpretable framework for risk assessment.
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Valenzuela et al. (2025) Atmospheric Water Vapor and Precipitation Coupling in Southwestern South America
This study analyzes 15–27 years of precipitable water vapor (PWV) and co-located precipitation data across southwestern South America to understand their long-term coupling across diverse latitudinal and climatic gradients. It reveals that the strength and functional form of PWV–precipitation coupling vary systematically with latitude and precipitation regime, with tropical Andes showing a power-law relationship and extratropical regions following a logistic form.
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Wang et al. (2025) Monitoring Environmental Degradation and Restoration of Wetlands and Arid Lands Using Remote Sensing and Big Geospatial Data
This editorial synthesizes recent advancements in using remote sensing and big geospatial data to monitor environmental degradation and restoration in wetlands and arid lands, highlighting diverse methodological innovations and ecological insights from 21 contributing papers.
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Wang et al. (2025) Depth-Specific Prediction of Coastal Soil Salinization Using Multi-Source Environmental Data and an Optimized GWO–RF–XGBoost Ensemble Model
This study developed an integrated modeling framework combining ensemble machine learning and spatial statistics to investigate depth-specific soil salinity dynamics in the Yellow River Delta, revealing distinct vertical control mechanisms where surface salinity is dominated by vegetation and soil water, while deeper layers are influenced by total dissolved solids, pH, and groundwater depth.
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Su et al. (2025) Integrated effects of rainwater harvesting and organic fertilization on soil structure and aggregates in a rainfed orchards on the Loess Plateau
This study investigated the integrated effects of a novel Water Harvesting, Irrigation, and Organic Fertilization (WHIOF) system on soil structure and aggregates in a rainfed apple orchard on the Loess Plateau. The WHIOF system significantly improved soil physical properties and aggregate stability by enhancing soil organic matter, surface electrochemical properties, and interparticle attractive forces at both macroscopic and microscopic scales.
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Liu et al. (2025) Critical snowpack thresholds and escalating risks for extreme decreases in vegetation productivity across Northern Hemisphere ecosystems
This study investigates the impact of varying snowpack changes on extreme decreases in vegetation productivity (EDVP) across the Northern Hemisphere, revealing that over 30% snowpack decrease events are linked to EDVP in about 10% of the NH, primarily due to snowpack's moisture effect.
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Shukla et al. (2025) Ranking and comparison of temperature-, mass transfer- and radiation based daily reference evapotranspiration models by using compromise programming index (CPI) and global performance indicator (GPI)
This study evaluates 30 empirical reference evapotranspiration (ET o ) models against the FAO56 Penman–Monteith method in two contrasting Indian agro-climatic zones using multi-criteria decision-making tools, finding that model performance is highly region-specific and requires localized validation.
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Luo et al. (2025) Temporal and Spatial Changes in Soil Drought and Identification of Remote Correlation Effects
This study analyzes the spatiotemporal evolution of soil drought in the Yangtze River Basin from 2000 to 2022, identifying precipitation as the primary climatic driver and the Interannual Pacific Oscillation (IPO) as the leading atmospheric circulation influence.
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Marshall et al. (2025) High-resolution mountain topography can inform global snow vulnerability estimates
This study leverages fine-scale digital elevation models (DEMs) and historical freezing level height data to estimate historical and projected changes in global mountain snow-receiving area (SRA). It finds significant SRA declines from 1982–2020 and projects substantial, often nonlinear, further losses under future warming scenarios, highlighting the importance of fine-resolution data for accurate assessments.
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Enakiev et al. (2025) Evapotranspiration in the tomato variety “
This study analyzed evapotranspiration (ET) changes and climatic factor influence on 'Nikolina F1' tomatoes under surface drip irrigation over three years, finding total seasonal irrigation rates of 275-280 mm and significant contributions from irrigation (up to 58%) and precipitation to ET.
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Clemenzi et al. (2025) Attributing European runoff changes to climatic drivers under future conditions
This study attributes future European runoff changes to precipitation and potential evapotranspiration using a Budyko-based framework across 35,408 basins. It finds that while precipitation is the dominant driver under most scenarios, potential evapotranspiration becomes equally significant in central and southern Europe under high-emission scenarios by the late century.
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Dash et al. (2025) Prophet-Based Artificial Intelligence Versus Seasonal Auto-Regressive Models for Flood Forecasting with Exogenous Variables
This study compares SARIMAX and Prophet models for streamflow forecasting, demonstrating Prophet's superior accuracy and ability to capture non-linear dynamics over SARIMAX, particularly for short-term horizons, for flood risk management.
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Masoudimoghaddam et al. (2025) Synergistic flood forecasting: combining physics-based and spatio-temporal deep learning models
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Fang et al. (2025) A global 400-m high-resolution soil moisture dataset derived from multi-sensor remote sensing observations
This study developed and validated a global 400-meter resolution soil moisture (SM) dataset by downscaling the 9-kilometer SMAP product using VIIRS land surface temperature and leaf area index. The resulting 400-meter product demonstrated improved accuracy and better captured spatial variability compared to the 1-kilometer and original 9-kilometer SMAP products when validated against in situ observations.
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Lin et al. (2025) Accelerating increases in soil heatwaves and cropland exposure in China
This study presents a nationwide assessment of historical soil heatwave changes across Mainland China, revealing that since 1960, soil heatwave frequency, duration, and intensity have increased at all depths, with significant acceleration in recent decades and increasing exposure for croplands.
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Wang et al. (2025) Enhanced El Niño–Southern Oscillation impact on Somali jet under greenhouse warming
This study reveals that the influence of El Niño–Southern Oscillation (ENSO) on the Somali Jet (SMJ) is projected to strengthen significantly in the 21st century under greenhouse warming, leading to increased risks of extreme rainfall events over South Asia.
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Boudaghpour (2025) Hydrological impacts of climate-driven flood risks: analyzing the AqQala historical bridge collapse over the Gorganrud river
This study developed a flood risk forecasting model for the AqQala Historical Bridge over the Gorganrud River to analyze hydrological impacts of climate-driven flood risks under various bridge collapse scenarios. It found that while 25-year floods pose minimal risk, 50- and 100-year floods significantly increase inundation risk when structural failure exceeds 70%, highlighting the urgent need for adaptive flood management and resilient infrastructure planning in the face of climate change.
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Roh et al. (2025) Vertical motions in clouds from EarthCare satellite and a global storm-resolving modeling
This study provides an initial assessment of the newly launched EarthCARE satellite's Doppler radar observations by comparing them with high-resolution global simulations using NICAM. The findings demonstrate EarthCARE's capability to provide physically consistent information on hydrometeor vertical motions and its potential to constrain and refine cloud microphysics and dynamics in global weather and climate models.
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Lopes et al. (2025) Dynamically Downscaled European Water Budget Quantities in the Presence of Sea Surface Temperature Uncertainty
This study investigates how the choice of sea surface temperature (SST) products influences land-based water budget quantities in EURO-CORDEX regional climate simulations. It finds that different SST products lead to notable differences in precipitation and freshwater flux, particularly in heavy precipitation events, and generally improve terrestrial water budget statistics compared to reanalysis-based fluxes.
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Sayedi et al. (2025) Anthropogenic drivers and their impact on the hydrological regime of Nepal: a review
This review synthesizes current evidence on anthropogenic drivers of hydrological change in Nepal, revealing that climate change impacts (glacial retreat, altered snowmelt, changing monsoon) combined with human activities (urbanization, agriculture, hydropower) intensify wet-season flood risks and dry-season water scarcity, offering lessons for other data-sparse mountain regions.
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Kumar et al. (2025) Correction: Rainfall variability for crop water management under changing climate in Himachal Pradesh
This article is a correction notice addressing errors in author names and affiliations for a previously published paper titled 'Rainfall variability for crop water management under changing climate in Himachal Pradesh'.
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Liu et al. (2025) Microtopography Governs Tidal Inundation Frequency in the Luanhe Estuarine Salt Marsh: A Decadal Assessment Integrating Sentinel Data and UAV Photogrammetry
This study investigates the fine-scale spatial variations in tidal inundation in the Luanhe Estuary, revealing a strong nonlinear relationship between Apparent Inundation Frequency and microtopographic elevation, and an overall upward trend in inundation probability from 2016 to 2025.
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Huneke et al. (2025) The ACCESS-CM2 climate model with a higher resolution ocean-sea ice component (1/4°)
This paper introduces and evaluates ACCESS-CM2-025, a new configuration of the Australian Community Climate and Earth System Simulator coupled model with a higher resolution (0.25°) ocean-sea ice component. The study finds that while the higher resolution improves the representation of ocean mesoscale variability, ENSO, and North Atlantic deep convection, many biases from the lower-resolution version (ACCESS-CM2-1) persist, particularly those linked to the atmospheric model component.
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Singh et al. (2025) LH-moment framework for regional flood frequency analysis based on Log-Pearson Type III distribution
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2025...
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Sogno (2025) Remote Sensing of African Surface Water Dynamics. Analyzing trends, patterns, and drivers in the context of global change
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Sabegh et al. (2025) Enhancing reference evapotranspiration prediction with biological ensemble support vector regression and MODIS data integration
This study developed a novel Biological Ensemble Support Vector Regression (BE-SVR) model, integrating meteorological and MODIS remote sensing data, to enhance reference evapotranspiration (ET0) prediction in semi-arid regions, demonstrating superior accuracy compared to conventional and optimized SVR models.
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Hu et al. (2025) Transferring visual knowledge in large-scale CNNs facilitates interpretable and cost-effective wetland cover mapping under dynamic environments
This study proposes a method utilizing transferred visual knowledge in large-scale Convolutional Neural Networks (CNNs) to achieve interpretable and cost-effective wetland cover mapping, particularly in dynamic environments.
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Man et al. (2025) Uncertainty in Antarctic precipitation projections under global warming
This study quantifies uncertainties in future Antarctic precipitation projections using CMIP6 models and identifies their underlying sources, revealing that these uncertainties are substantial and linked to global/Antarctic surface temperatures, atmospheric circulation (especially Pacific South American modes), and tropical sea surface temperatures via atmospheric teleconnections.
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Hiebl et al. (2025) Correcting Breaks in Temperature and Humidity Observations: Implications for Climate Variability Analysis in Austria
This study quantifies and corrects a significant discontinuity in daily mean air temperature and relative humidity estimations across Austrian climate stations, caused by a 2-hour shift in evening observation timing in 1971. By employing station-specific multi-linear regressions, the method successfully eliminates spurious cooling and drying biases, substantially improving the accuracy of climate data and trend analyses.
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Hwang et al. (2025) Satellite Cloud-Top Temperature-Based Method for Early Detection of Heavy Rainfall Triggering Flash Floods
This study proposes an early-warning system for heavy rainfall based on the temporal dynamics of satellite-derived Cloud-Top Temperature (CTT). The method, which quantifies CTT rise-peak-fall-trough patterns, achieved an 87.5% probability of detection with 1.3–8.6 hours lead time, providing 1–3 hours more lead time than radar nowcasting.
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Afif (2025) Drought prediction data for IoT
This paper presents a synthetic daily drought prediction dataset spanning two years, designed to simulate realistic soil and rainfall conditions for research in drought prediction and Internet of Things (IoT) applications.
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Tricht et al. (2025) Peak glacier extinction in the mid-twenty-first century
This study projects the future disappearance of over 200,000 individual glaciers globally under various warming scenarios, revealing a peak extinction period between 2041 and 2055 where up to 4,000 glaciers could vanish annually. It introduces the concept of "peak glacier extinction" to highlight the societal and cultural implications of individual glacier loss.
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Morice et al. (2025) An observational record of global gridded near-surface air temperature change over land and ocean from 1781
This study introduces GloSATref, a novel global gridded surface air temperature (GSAT) dataset combining land surface air temperature (LSAT) and marine air temperature (MAT) observations, extending the instrumental record back to 1781. It demonstrates that using MAT, with new bias adjustments, allows for an earlier and more comprehensive reconstruction of global temperature changes compared to traditional sea surface temperature (SST)-based datasets.
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Elsaidy et al. (2025) Groundwater drought assessment in a Mediterranean coastal catchment through a multi-index approach
This study assesses spatio-temporal groundwater drought dynamics in the Bruna River catchment, Central Italy, using a multi-index approach and a regional hydrological model, finding that groundwater droughts are detectable even with short records and are strongly linked to prolonged meteorological deficits.
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Narinesingh et al. (2025) Modeling Northern Hemisphere Heat Extremes in Current and Warmer Climates: Intensity, Duration, and Physical Drivers
This study examines the intensity, duration, and physical drivers of Northern Hemisphere summer heat extremes using observations, reanalyses, and CMIP6 models, finding that models generally capture observed variations but exhibit biases in duration where diabatic effects dominate, and project increased intensity in several regions under future warming scenarios.
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Priyanka et al. (2025) Machine learning approach for crop planning and resource allocation in the Bargarh Canal Command
This study developed an advanced machine learning framework, integrating predictive modeling, clustering, and genetic algorithms, to optimize crop planning and resource allocation in the Bargarh Canal Command, Eastern India. The framework, with XGBoost demonstrating superior performance, provides data-driven insights for enhancing crop yield and net returns under climate variability and resource constraints.
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Huang et al. (2025) Spatiotemporal Characteristics of Drought in Yili River Basin, Northwest China in 1980–2020
This study analyzed the spatiotemporal characteristics and driving mechanisms of drought in the Yili River Basin, Northwest China, from 1980 to 2020, revealing a general intensification of droughts, particularly in spring and summer, driven by meteorological factors and large-scale atmospheric circulation patterns.
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Zhou et al. (2025) Contrasting runoff response times regulated by vegetation and climate changes in typical dry and wet basins
This study investigated how runoff timing, particularly flood timing, has changed over four decades in three Chinese river basins under the combined influence of climate change and large-scale afforestation, finding that afforestation delays flood timing more in semi-arid regions, while humid regions are more influenced by precipitation changes.
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Ryan et al. (2025) A worldwide climatology of extreme air masses
This study quantifies regional exposure to extreme hot and cold air masses (EHAMs/ECAMs) by tracking their frequencies, movements, trends, and sources/sinks globally, revealing a net increase in extreme events due to EHAMs increasing faster than ECAMs decrease.
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Ding et al. (2025) Parameterization and irrigation optimization for foxtail millet using the aquacrop model
This study establishes the first comprehensive parameter set for foxtail millet in the AquaCrop model and develops optimized irrigation strategies to enhance water use efficiency in water-limited regions, achieving a mean yield of 0.573 kg·m⁻² and water productivity of 1.46 kg·m⁻³ with significantly improved yield stability.
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Fang et al. (2025) Reference evapotranspiration in Guangxi, China: Spatiotemporal patterns and multi-scale driving mechanisms
This study analyzed the spatiotemporal patterns and multi-scale driving mechanisms of reference evapotranspiration (ET0) in Guangxi, China, from 1960 to 2024, revealing an overall declining trend in ET0 primarily driven by meteorological factors, especially sunshine duration, with significant threshold effects.
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Asadi-RahimBeygi et al. (2025) Near-term climate extremes in Iran based on compound hazards analysis
This study investigates climate-related hazards from precipitation and temperature in Iran for hindcast (1991–2019) and near-term forecast (2023–2028) periods using the Near-term Climate Prediction (NTCP) project. Results project a significant rise in drought frequency and heat wave events, with high-risk areas increasing by approximately 10% and encompassing over 36% of Iran’s total area by 2028, necessitating urgent adaptation planning.
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Wong et al. (2025) Measuring impacts of California agri-environmental programs using field-scale satellite data
This study evaluates the ex-post agri-environmental outcomes of California's grant programs for water-saving and soil health practices in almonds, grapes, and walnuts using satellite data and causal inference. It reveals varied and sometimes counterintuitive effects, with some interventions increasing water use and others showing modest greenness improvements, highlighting potential discrepancies with program goals.
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Mahmoud et al. (2025) Determining evapotranspiration of Faba bean and Chickpea using the soil water balance method under field conditions in in the Gezira Scheme, Sudan
This study quantified crop evapotranspiration (ETc) and developed growth-stage-specific crop coefficients (Kc) for faba bean and chickpea in the Gezira Scheme, Sudan, providing site-specific parameters to improve irrigation scheduling and water-use efficiency.
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Tan et al. (2025) Evaluating multi-source precipitation data for streamflow simulation using the SWAT model in the Alpine Manas River Basin, Northwest China
This study evaluated the accuracy of four multi-source precipitation products (CMFD, MSWEP, ERA5-Land, IMERG) in Xinjiang's arid regions and their applicability for streamflow simulation in the alpine Manas River Basin using the SWAT model. While CMFD showed optimal precipitation performance, MSWEP and ERA5-Land achieved superior streamflow simulation accuracy, indicating that meteorological superiority does not guarantee hydrological efficacy.
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Colliander et al. (2025) A review of forward modelling and retrieval approaches for forest soil moisture and vegetation optical depth using L-band radiometry
This review evaluates current L-band radiometry retrieval approaches for forest soil moisture (SM) and vegetation optical depth (L-VOD), highlighting persistent systematic uncertainties and the lack of adequate validation data for forest ecosystems. It emphasizes the need for new algorithms and enhanced validation to fully leverage L-band radiometry for forest monitoring.
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Nascimento et al. (2025) How do geological map details influence the identification of geology-streamflow relationships in large-sample hydrology studies?
This study investigates how the level of detail in geological maps (global, continental, regional) influences the identification of geology-streamflow relationships across 4469 European catchments using a multi-scale, nested-catchment approach. It finds that while large-scale analyses show inconsistent map performance, increasing geological detail at intermediate and small scales consistently strengthens correlations with streamflow signatures, particularly for baseflow, aligning better with hydrological process understanding.
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Zhao et al. (2025) Quantifying glacier and snow shrinkage: future water stress in Northern Tien Shan, Central Asia
This study assesses the impacts of glacier and snow shrinkage on hydrological processes and water stress in the Northern Tien Shan using a refined VIC-CAS model. Projections indicate significant decreases in snowmelt runoff, annual streamflow, and soil moisture by the late 21st century, intensifying water scarcity and hydrological droughts.
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Wei et al. (2025) A global long-term daily multilayer soil moisture dataset derived from machine learning
This study generated a global, daily, seamless multilayer soil moisture dataset (SWSM) for 2002–2021 at 0.05° spatial resolution using an XGBoost machine learning approach, demonstrating high accuracy against in situ observations across three soil depths. The resulting dataset addresses the scarcity of continuous, high-resolution, deep soil moisture products and provides physically consistent insights into soil moisture controls.
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Ali et al. (2025) Multi-sensors Remote Sensing and Machine Learning Techniques applications in Agriculture
This paper provides a comprehensive survey of the applications of advanced remote sensing techniques integrated with machine learning and other innovative technologies (IoT, AI, robotics) in modern agricultural practices, highlighting their advantages and limitations.
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Deng et al. (2025) Anthropogenic influences on rainfall seasonality changes and underlying physical mechanisms in global land monsoon regions
This study investigates how anthropogenic forcing influences rainfall seasonality changes and their underlying physical mechanisms in global land monsoon regions. It finds that enhanced rainfall seasonality in several key monsoon areas is confidently attributable to human activities, driven by varying contributions of greenhouse gases and aerosols affecting vertical moisture advection.
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Gao et al. (2025) Integrating Infiltration Holes into Ridge–Furrow Systems Enhances Drought Resilience and Yield of Maize in Semi-Arid China
This study aimed to optimize the double ridge–furrow mulching system (DRFM) by incorporating infiltration holes to enhance its infiltration capacity and mitigate soil water deficits under heavy rainfall on the Loess Plateau. The optimized system (DWCR) significantly improved deep soil water storage, maize growth, yield, and water use efficiency, particularly under drought conditions.
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Serin et al. (2025) AI-Driven Smart Farming for Automated Plant Health Monitoring and Nutrient Deficiency Detection
This paper presents an AI-driven Internet of Things (IoT) system for automated plant health monitoring and early nutrient deficiency detection, integrating multi-sensor data with camera-based leaf analysis. The system achieved 89.0% accuracy in classifying plant health and nutrient deficiencies using a DenseNet 121 model, enabling autonomous irrigation and localized cooling.
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Pullanagari et al. (2025) Mapping of sun-induced fluorescence (SIF) in kiwifruit canopy using a 3D radiative transfer modeling and airborne hyperspectral imaging
This study developed a hybrid 3D radiative transfer model (LESS-KRR) to accurately map sun-induced fluorescence (SIF) in complex kiwifruit canopies using airborne hyperspectral and LiDAR data, demonstrating superior performance over empirical methods for precision agriculture applications.
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Fang et al. (2025) Rainfall Characteristics and Effective Precipitation Analysis of Rice Growth Period in Typical Areas in East China
This study analyzed rainfall characteristics and effective precipitation during the rice growth period in East China using long-term data and a soil-water balance method. It then developed a Support Vector Regression (SVR) model to predict effective precipitation utilization coefficients, demonstrating its potential for optimizing irrigation scheduling and water-saving crop cultivation.
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Luo et al. (2025) GIS-integrated flood risk assessment for metro systems based on bayesian cosine maximization method: a case study in Beijing
This study develops a GIS-integrated Bayesian cosine maximization method (BCMM) for objective and spatially accurate flood risk assessment in metro systems, applying it to Beijing's metro. It identifies central urban areas and Tongzhou District as extremely high-risk zones, with over 50% of stations on key lines facing high or extremely high flood risks, which significantly increases under a 200-year extreme rainfall scenario.
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Fang et al. (2025) The Increased Influence of Eastern European Blocking Highs on Extreme Rainfall Over Pakistan in Recent Decades
This study investigates the long-term impact of Eastern European blocking highs on extreme precipitation in Pakistan, revealing an intensification of this effect after the 1990s primarily due to Arctic warming-induced alterations in atmospheric circulation.
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Zhu et al. (2025) Exploring interactive effects of water stress and ecological restoration on vegetation eco-regimes using interpretable machine learning based on kernel NDVI
This study developed a novel methodology using 18 vegetation indicators, an ecological restoration index, and interpretable machine learning to assess vegetation eco-regime changes across China. It found that precipitation, surface solar radiation, and ecological restoration are the dominant factors influencing these dynamics, with significant shifts in eco-regimes observed since 2002.
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Khan et al. (2025) Climate-driven flood hazard assessment in data-scarce mountainous basins using a GIS-based machine learning and hydrodynamic modelling under CMIP6 SSP scenarios
This study developed a hybrid framework combining explainable AI (SHAP-XGBoost, Random Forest) and coupled hydrologic-hydraulic modeling (HEC-HMS–HEC-RAS) to assess climate-driven flood hazards in data-scarce mountainous basins under CMIP6 SSP scenarios. It found a substantial increase in flood hazard under future scenarios, particularly SSP585, where high and very high hazard zones expanded significantly.
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Saari et al. (2025) Synchronisation of Extreme Precipitation and Sea Surface Temperature Events in the Northern Hemisphere: A Complex Network Approach
This study analyzed spatiotemporal patterns and connections between inland extreme precipitation events (EPEs) and extreme sea surface temperature events (ESSTEs) in the Northern Hemisphere from 1930 to 2020, revealing distinct network characteristics and increasing trends for both event types after 1980.
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Koubodana et al. (2025) Spatial and Temporal Trend Analysis of Flood Events Across Africa During the Historical Period
This study analyzed the spatial and temporal distribution of historical flood events across Africa from 1927 to 2020, revealing a significant upward trend in flood frequency, fatalities, affected populations, and economic damage, primarily driven by extreme precipitation indices.
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Zhai et al. (2025) SMCR: A first satellite-derived all-weather daily/1-km Soil Moisture Climatological Record (1980–2023)
This paper develops the first satellite-derived global all-weather daily/1-kilometer Soil Moisture Climatological Record (SMCR) spanning 1980–2023, achieving an average unbiased root mean square error of 0.051 m³/m³ against in-situ observations.
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Devkota et al. (2025) Hydrological performance of ERA5 land in the data-scarce Himalayan region of the Langtang catchment
This study evaluates the hydrological performance and bias correction sensitivity of ERA5-Land reanalysis data for runoff simulation in the data-scarce Langtang catchment in the Himalayas. It finds that correcting only ERA5-Land temperature data significantly improves runoff simulations (Nash-Sutcliffe Efficiency = 0.73, Root Mean Square Error = 2.89 m³/s), indicating temperature is more critical for hydrological modeling accuracy in snow-fed Himalayan regions.
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Zhang et al. (2025) A novel framework for pixel-wise estimation of irrigation water use by integrating remote sensing and reanalysis data
This study develops a novel soil water balance framework that integrates satellite-derived soil moisture and evapotranspiration with reanalysis data to estimate irrigation water use at 1 km resolution across China. The resulting 20-year dataset reveals that China's irrigation water use increased from 339 to 395 km³/year between 2001 and 2020, driven primarily by the expansion of irrigated croplands.
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Silakhori et al. (2025) Investigating the nexus of climate change, land use change, and desertification using machine learning, stochastic models, and projected climate change scenarios
This paper investigates the complex interplay between climate change, land use change, and desertification, employing machine learning and stochastic models with projected climate change scenarios.
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Ewnetu et al. (2025) Spatial and Temporal Evaluation of Gridded Precipitation Products over the Mountainous Lake Tana Basin, Ethiopia
This study evaluated the reliability of six satellite and reanalysis rainfall estimates (SREs) against gauge observations in the complex terrain of the Ethiopian highlands from 2005 to 2019, finding that SRE accuracy improves with temporal aggregation, and MSWEP, CHIRPS, and IMERG offer the most balanced performance despite a general tendency to misrepresent rainfall intensity.
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Wang et al. (2025) Study on the Spatiotemporal Variation of Vegetation Characteristics in the Three River Source Region Based on the CatBoost Model
This study developed a machine learning-based framework to integrate MODIS and GIMMS NDVI data, reconstructing a 1 km monthly NDVI dataset for the Three River Source Region (TRSR) from 1982 to 2014, which revealed an overall increasing trend in vegetation greenness with significant spatial heterogeneity.
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Pınarlık (2025) Climograph-Supported Assessment of Temperature–Precipitation Trends Using Classical and Innovative Statistical Methods in the Yeşilırmak Basin, Türkiye
This study analyzed annual and seasonal temperature and precipitation trends in Türkiye's Yeşilırmak Basin over 38 years, revealing statistically significant warming, particularly in summer and autumn, and demonstrating the superior sensitivity of Innovative Trend Analysis (ITA) for trend detection compared to Mann–Kendall (MK).
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Riaz et al. (2025) Using remote sensing in agriculture for sustainable development goals in developing countries
This chapter explores the critical role of remote sensing technologies in fostering agricultural sustainability within developing countries, aiming to achieve Sustainable Development Goals, particularly "zero hunger," by enhancing productivity and food security.
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Hao et al. (2025) Evaluating ecosystem water use efficiency and recovery dynamics during flash droughts: insights from observations and model simulations
This study investigates ecosystem water use efficiency (WUE) and its components (gross primary production, GPP; actual evapotranspiration, AET) during flash droughts using observations and the ELMv2 model. It finds that ELMv2 captures shifting GPP/AET dynamics but systematically underestimates GPP, AET, and WUE, and prolongs GPP recovery, highlighting the need to integrate plant hydraulics for improved flash drought predictability.
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Isoaho et al. (2025) An automated Google Earth Engine application for detecting the impacted area of treeless boreal peatland restoration – A tool for practitioners and decision-makers
This study developed a user-friendly Google Earth Engine application to detect the hydrological impact of treeless boreal peatland restoration using optical satellite imagery, finding that Near-Infrared (NIR) and Shortwave Infrared 1 (SWIR1) bands are the most effective indicators.
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Jithendra et al. (2025) Two-stage rainfall forecasting and crop classification using puma-optimized ANFIS–SVM
This study developed an ANFIS-SVM-PO framework for agricultural recommendations, achieving 99.15% accuracy in rainfall prediction and 90% accuracy in optimal crop classification using an Indian agricultural dataset.
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Lu et al. (2025) Blue and green water simulation in the river basin using remote sensing data fusion and dual-variable hydrological calibration
This study developed and evaluated a dual-variable hydrological model calibration method, integrating remote sensing fusion evapotranspiration (ET) data with observed runoff, to improve the accuracy and reduce uncertainty in blue water and green water simulations in the Xiangjiang River Basin. The method significantly enhanced the simultaneous simulation accuracy of both blue and green water compared to traditional single-variable calibration.
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Kotzur et al. (2025) Forests and woodlands resistant to drought revealed in remotely sensed foliar moisture content using probabilistic models
This study investigated the sensitivity of forest and woodland foliar moisture content (FMC) to climatic water balance across south-eastern Australia using satellite data and probabilistic models. It identified drought-resistant areas with lower FMC response to extreme drought, highlighting their potential as climate refugia and natural fire breaks.
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Chen et al. (2025) Integrated modeling of crop growth with 2D soil water flow and solute transport considering dynamic root spatial distribution under film mulched drip irrigation
This study developed and validated a two-dimensional coupled model (WSP-2D) for simulating soil water flow, solute transport, and crop growth under film-mulched drip irrigation, incorporating dynamic root distribution and water flux through mulched film planting holes. The model accurately captured field observations and demonstrated the critical importance of dynamic root growth and surface water flux for accurate simulations of soil water, salt, and crop yield.
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Thudium et al. (2025) Contributions of Sunshine Duration and Atmospheric CO2 to Surface Air Warming in Central Europe from 1915 to 2024 and Empirical Relationship Between Atmospheric CO2 and Global Emissions
This study quantifies the impact of increased surface solar radiation on climate warming in Central Europe and empirically examines the relationship between global CO2 air concentrations and emissions. It finds that increased surface solar radiation accounts for 20-30% of warming, with CO2 concentration accounting for the remaining 70-80%, and estimates an empirical CO2 lifetime of 58 years.
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Ennouri et al. (2025) Advancing change detection and climate risk assessment through remote sensing
This chapter introduces how remote sensing technologies, particularly satellite constellations like Landsat and Sentinel, are advancing change detection and climate risk assessment by enabling large-scale, timely, and reliable monitoring through long image time series analysis.
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Grundner et al. (2025) Reduced cloud cover errors in a hybrid AI-climate model through equation discovery and automatic tuning
This study presents a two-step method combining symbolic regression for an interpretable cloud cover parameterization and an automatic tuning pipeline to improve a hybrid AI-climate model. The approach significantly reduces cloud cover biases in the ICON global atmospheric model, particularly over the Southern Ocean and subtropical stratocumulus regions, while maintaining physical consistency and robustness under warming scenarios.
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Zhang et al. (2025) Non-stationary influence of the North Atlantic Oscillation on summer precipitation in the Central-Eastern Himalayas
This study reveals the non-stationary influence of the Summer North Atlantic Oscillation (SNAO) on summer precipitation in the Central-Eastern Himalayas (CEH), demonstrating a "weak–strong–weak" evolution of this linkage primarily modulated by the interaction of SNAO-driven circulation anomalies with the Himalayan topography.
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Bhatti et al. (2025) Forecasting spring frost events in agriculture using machine learning: A case study from southeastern Massachusetts, United States
This study developed machine learning (Random Forest) models to improve spring frost forecasting for cranberry agriculture in southeastern Massachusetts. The new models significantly outperformed the traditional Franklin model by reducing temperature prediction errors and false alarms, providing a more accurate and efficient early warning system for growers.
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Khan et al. (2025) Harnessing Earth observation for sustainable agriculture: integrating research, technology, and decision support systems
This chapter introduces the transformative role of Earth observation (EO) technologies in achieving Sustainable Development Goals, particularly in agriculture, by exploring their evolution from traditional monitoring to dynamic, interactive decision support systems. It highlights the challenge of converting EO data into actionable knowledge for various stakeholders to foster sustainable development.
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Janani et al. (2025) Dynamic SG-SKRDX hybrid framework for precision weather forecasting and crop suitability in the Cauvery Delta
This study introduces the Dynamic SG-SKRDX hybrid framework for precision weather forecasting and adaptive crop recommendation in the Cauvery Delta Region. The framework integrates an SVR-GRU (SG) model for predicting future weather conditions with a dynamic ensemble of machine learning models (SKRDX) for recommending suitable crops, demonstrating superior accuracy and robustness for sustainable agriculture.
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Li et al. (2025) Comprehensive flood risk identification and assessment in small mountainous watersheds using GF-7 satellite imagery and hydrological-hydrodynamic modeling
This study developed a comprehensive flood risk indicator (HRS&T) by integrating high-resolution Gaofen-7 (GF-7) satellite imagery and hydrological-hydrodynamic modeling to assess flood risk in small mountainous watersheds. The research demonstrated that incorporating land-surface types, terrain slope, and flood duration significantly improves the accuracy of flood risk assessment, particularly for areas prone to secondary hazards like debris flows and landslides.
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Chen et al. (2025) Propagation of meteorological to agricultural flash droughts using a novel weekly index
This study introduces a novel weekly Flash Drought Index (FDI) based on soil moisture to effectively monitor and assess agricultural flash droughts in China's Yangtze River basin. It reveals an increasing frequency and severity of these events from 1961 to 2020, and quantifies their propagation characteristics, including lag times and probabilities, from meteorological droughts.
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Suárez‐Gutiérrez et al. (2025) Temperature variability projections remain uncertain after constraining them to best performing Large Ensembles of individual Climate Models
This study evaluates the historical performance of eleven climate models in simulating temperature variability and uses the best-performing models to constrain future projections, finding that significant uncertainties in the intensity and sign of temperature variability changes persist globally.
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Huq et al. (2025) Advancements in remote sensing and GIS for water resource management and SDG monitoring
This chapter explores advancements in remote sensing and Geographic Information Systems (GIS) technologies, focusing on their application in water resource management and the monitoring of Sustainable Development Goals (SDGs).
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Oh et al. (2025) Noise-induced tipping of Atlantic Meridional Overturning Circulation under climate mitigation scenarios
This study investigates whether climate mitigation can prevent Atlantic Meridional Overturning Circulation (AMOC) collapse, revealing that even under CO2 stabilization, stochastic noise and delayed mitigation can trigger a multi-century AMOC collapse due to internal atmospheric variability near its stability threshold.
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Mahdipour et al. (2025) Improving thunderstorm prediction with neural networks using numerical weather and satellite data: a novel data fusion and validation approach
This paper introduces a novel data fusion and validation approach using neural networks, numerical weather, and satellite data to improve short-term thunderstorm prediction, aiming to enhance air traffic management and reduce weather-related delays.
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Fu et al. (2025) Agricultural Drought Early Warning in Hunan Province Based on VPD Spatiotemporal Characteristics and BEAST Detection
This study pioneers the application of the BEAST algorithm at a provincial scale to detect abrupt changes in vapor pressure deficit (VPD), revealing its spatial-temporal patterns, phenology-aligned shifts, and proposing a VPD-based agricultural drought early warning threshold for Hunan Province.
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Anvari et al. (2025) A nonstationary framework for hydrological drought assessment in Iran
This study introduces a Nonstationary Standardized Runoff Index (NSRI) using the GAMLSS framework to enhance hydrological drought assessment in Iran's Halil-Rud Basin. It finds that nonstationary models, incorporating hydroclimatic covariates, consistently outperform stationary models by more accurately capturing spatiotemporal drought variability and moderating extreme drought estimates, especially in human-impacted downstream regions.
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Weng et al. (2025) Widespread land surface cooling from paddy rice cultivation revealed by global satellite mapping
This study developed a global, long-term, high-resolution paddy rice dataset (GlobalRice500) and revealed that paddy rice cultivation significantly cools the land surface, reducing daytime land surface temperature by 0.21–0.27 °C during the growing season compared to other croplands.
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Zhao et al. (2025) Highest quality remote sensing reflectance database compiled from 20+ years of MODIS-aqua measurements
This study compiles a highest-quality remote sensing reflectance (Rrs) database from over 20 years of MODIS-Aqua measurements using novel quality control criteria. The resulting database significantly improves data consistency with in situ observations and reveals altered long-term Rrs trends in substantial oceanic regions compared to standard products.
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Alamillo et al. (2025) Post-Fire Vegetation Recovery Response: A Case Study of the 2020 Bobcat Fire in Los Angeles, California
This study assessed post-fire vegetation recovery following the 2020 Bobcat Fire in Los Angeles, California, by analyzing changes in evapotranspiration (ET) and Normalized Difference Vegetation Index (NDVI) across different burn severity levels and vegetation classes. It found that while ET and NDVI generally increased, reflecting partial functional recovery, most burned forest and shrub areas have shifted to grassland, indicating a potential ecosystem shift.
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Marshall et al. (2025) A systematic review of the NASA Land Information System (LIS): Two decades of advancements in hydrological modeling, data assimilation, and operational earth system applications
This systematic review synthesizes two decades of advancements in the NASA Land Information System (LIS), demonstrating its impact on Earth system science through improved land surface estimates, enhanced forecast skill via model coupling, and successful transition to operational applications.
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Mascherpa et al. (2025) SmartWT: An open IoT sensor, datalogger and GPRS data transmission device for monitoring water levels in rice fields, with application to AWD irrigation
This study introduces SmartWT, an open-source IoT system for remote, continuous monitoring of ponding water levels in rice fields using an ultrasonic sensor within a Water Tube. It demonstrates the device's robustness, accuracy (typically less than 0.01 m error), and long battery life (120 days) in challenging paddy environments, thereby facilitating the Alternate Wetting and Drying (AWD) irrigation technique.
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Liu et al. (2025) Carbon-Water Coupling and Ecosystem Resilience to Drought in the Yili-Balkhash Basin, Central Asia
This study investigates ecosystem water use efficiency (WUE) and its resilience in the Yili-Balkhash Basin, revealing a significant decline in WUE within high-productivity forest ecosystems due to a decoupling of carbon and water cycle drivers during drought, and identifying immediate thermal stress and one-month ecological memory as key determinants of resilience.
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Seifian et al. (2025) Investigating meteorological drought propagation to soil moisture drought: insights from Iran’s diverse climate regions
This study investigates the propagation of meteorological drought to soil moisture drought across Iran's diverse climate regions using the Standardized Precipitation Index (SPI) and Soil Moisture Deficit Index (SMDI) with copula functions, revealing a 28–40% probability of their co-occurrence and regional variations in their relationship.
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Iyanda et al. (2025) Leveraging Artificial Intelligence and Iot for Precision Crop Management: Enhancing Physiological Responses in Stress-Prone Regions
This review examines the integration of Internet of Things (IoT) technologies with Artificial Intelligence (AI) models for precision agriculture, demonstrating how these systems can increase crop yields by 15–20% and enhance resource efficiency in stress-prone regions like Nigeria.
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Rickard (2025) Deciphering soil structure: linking soil physics, water dynamics, carbon storage, and agricultural resilience in long-term experiments
This study evaluates the performance of the ISBA land surface model and the mHM hydrological model in simulating soil moisture and river discharge. The results indicate that while both models accurately capture soil moisture dynamics, mHM demonstrates superior accuracy in river discharge simulation.
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Ma et al. (2025) Response and mechanisms of sub-daily precipitation over the Tibetan Plateau to regional climate change
This study investigates the impact of regional climate change over East Asia (RCC) and the Tibetan Plateau (PCC) on sub-daily precipitation over the Tibetan Plateau using dynamical downscaling and a storyline attribution approach. It finds that RCC significantly reduces overall precipitation but enhances afternoon convective precipitation, while PCC exerts a smaller and less statistically significant impact.
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Guilhen et al. (2025) Assessment of surface water storage in the Amazon floodplains by hydrological modelling and earth observation data
This study quantifies the spatiotemporal dynamics of floodplain water storage in the Amazon Basin from 2000–2018 using an integrated framework of hydrological modeling and multi-sensor Earth observation data, revealing a mean annual surface water storage of 1800 ± 854 km³ with significant sub-basin contributions.
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Duque et al. (2025) Simulating Closed‐to‐Open Mesoscale Cellular Convection Over the Southern Ocean: Part I. Evaluation Using SOCRATES and CAPRICORN Observations
This study evaluates a convection-permitting WRF model's performance in simulating low-level clouds over the Southern Ocean during post-frontal conditions, finding it effectively reproduces key morphological and microphysical distinctions between closed and open mesoscale cellular convective clouds, though with some underestimation at higher latitudes.
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Yang et al. (2025) Effects of extreme climate on hydrological dynamics in dryland apple orchards: a modeling study
This study integrates a dynamic leaf area index (LAI) sub-module into the process-oriented STEMMUS model to accurately simulate the ecohydrological responses of dryland apple orchards to climate extremes. The findings demonstrate that while increased precipitation volume enhances soil water storage, both 2°C warming and high-intensity precipitation patterns significantly reduce canopy transpiration and water use efficiency (T/ET).
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Pereira et al. (2025) Linking ROMS with watershed models for simulating hydrodynamics and thermohaline dynamics in a coastal lagoon affected by extreme weather events
The study develops and validates a linked numerical modeling system (ROMS-TETIS-SUTRA) to simulate the hydrodynamics and thermohaline structure of the Mar Menor lagoon. It demonstrates that extreme flash floods, such as the September 2019 event, trigger long-lasting vertical stratification and significantly reduce the lagoon's water renewal time.
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Guehi et al. (2025) A revisit of Niger river’s major flood events and the role of convective systems with satellite data and hydrological modeling
This study develops and applies a novel method combining satellite data and hydrological modeling to quantify the impact of individual mesoscale convective systems (MCSs) on Niger River floods in Niamey, revealing that a small number of MCSs are responsible for a significant portion of extreme flood events, particularly the record 2020 flood.
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Faraji et al. (2025) AI-Driven Flood Mapping and Precision Rice Monitoring in Morocco Using Sentinel Satellite Data
This study presents a multi-sensor approach combining Sentinel-1 SAR and Sentinel-2 optical imagery to accurately map rice fields and monitor crop phenology in the Gharb plain, demonstrating its utility for precision agriculture and sustainable water management.
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Blake et al. (2025) The impact of convection-permitting model rainfall on the dryland water balance
This study quantifies the impact of convection-permitting climate model rainfall on the dryland water balance in the Horn of Africa, demonstrating that explicitly resolving convection improves rainfall characteristics, leading to significantly higher soil moisture, transpiration, surface runoff, and potential groundwater recharge compared to models that parameterize convection.
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Chen et al. (2025) How Do the Asian Highlands Affect Phase-Preferred Rossby Waves and Synchronous Heat Extremes in the Midlatitudes?
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López-Pérez et al. (2025) A Spatial Framework for Assessing Irrigation Water Use in Overexploited Mediterranean Aquifers
This study develops the Spatial Irrigation Adequacy Index (SIAI) to evaluate irrigation performance by comparing satellite-derived actual evapotranspiration with modeled crop water requirements. The framework identifies distinct patterns of water use across Mediterranean aquifers, revealing near-optimal irrigation in vineyards, systemic over-irrigation in apple orchards, and persistent water deficits in citrus groves.
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Sokol et al. (2025) Estimating changes in precipitation event characteristics with projected temperature increase in the 21st century
This study investigates how precipitation event characteristics (duration, frequency, total, mean, and maximum precipitation) change with increasing ground temperature in the Czech Republic during the warm season of the 21st century, using observed, reanalysed, and high-resolution climate model projections. It finds that while precipitation totals increase with temperature up to a certain point before decreasing, the duration of precipitation events does not exhibit any significant change with rising temperature.
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Barreiro-Fonta et al. (2025) Assessing Climate Change and Reservoir Impacts on Upper Miño River Flow (NW Iberian Peninsula) Using Neural Networks
This study utilizes artificial neural networks to project the impacts of climate change on the Upper Miño River, finding that while high-emission scenarios intensify hydrological extremes, reservoir operations can significantly mitigate these effects by redistributing seasonal water availability.
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Sathvara (2025) A Comprehensive Review of Remote Sensing and Artificial Intelligence-Based Smart Agriculture for Assessing Climate Change Impacts
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Paquin et al. (2025) The Ouranos CRCM5-CMIP6 ensemble: A dynamically downscaled ensemble of CMIP6 simulations over North America
This paper presents Ouranos' contribution to the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) by providing a dynamically downscaled ensemble of CMIP6 simulations over North America using the Canadian Regional Climate Model (CRCM5). It describes the model configuration, validates its performance against reanalysis data, and details the generated climate projections under various future emission scenarios, making the high-resolution dataset publicly available.
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Hadded et al. (2025) Enhancing groundwater resources through managed aquifer recharge: A SWAT application in arid southeastern Tunisia
This study applied the Soil and Water Assessment Tool (SWAT) to simulate hydrological processes and quantify the impact of Managed Aquifer Recharge (MAR) structures, including traditional Water Harvesting Techniques (WHTs), on groundwater recharge in arid southeastern Tunisia, demonstrating that MAR significantly enhances aquifer recharge.
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Ahmadi et al. (2025) Evaluating the combined effects of climate and land use change on hydrological response in a mixed land use watershed
This study developed a coupled modeling platform (SWMM and SWAT+) to assess the combined impacts of climate and land use change on the Stroubles Creek watershed, finding increased flood risks and reduced drought resilience in urban areas due to rising temperatures, more intense rainfall, and expanding impervious surfaces.
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Zhu et al. (2025) Assessing the Use of the Standardized GRACE Satellite Groundwater Storage Change Index for Quantifying Groundwater Drought in the Mu Us Sandy Land
This study evaluates the effectiveness of a GRACE satellite-derived standardized groundwater index (GRACE_SGI) for monitoring groundwater drought in the Mu Us Sandy Land. The research identifies a significant intensifying trend in groundwater depletion and a temporal lag of up to 12 months between meteorological and groundwater drought at annual scales.
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Hirasawa et al. (2025) Forcing Susceptibility and Climate Sensitivity to Midlatitude Marine Cloud Brightening
This study uses three Earth system models to evaluate marine cloud brightening (MCB) by injecting sea salt aerosol (iSSA) in 14 ocean regions, identifying a novel midlatitude emission strategy that produces more uniform cooling and precipitation responses, effectively offsetting greenhouse gas warming.
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Chen et al. (2025) Monitoring sub-canopy inundation dynamics in global croplands: An unexplored application of SWOT satellite data
This study develops the first method for year-round monitoring of sub-canopy inundation dynamics (non-inundated, partially-inundated, fully-inundated) in global croplands using SWOT satellite KaRIn coherent power, demonstrating its robustness across diverse climate zones and improving upon existing remote sensing limitations.
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Dimarco et al. (2025) FireRisk-Zone-LR: A Logistic Regression-Based Wildfire Hazard Zoning Framework for Mediterranean Forests in Tangier, Morocco
This study developed an open-access, reproducible geospatial workflow integrating satellite-derived indicators with logistic regression to assess wildfire susceptibility in northern Morocco, finding that Normalized Difference Vegetation Index (NDVI) is a significant predictor with the model achieving an AUC of 0.72.
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Dodangeh et al. (2025) Analyzing climatic anomalies and ecological impacts on wetlands environmental conditions using LSTM and remote sensing imagery
This study employs deep learning models and remote sensing to detect climatic anomalies in the Anzali Wetland (Iran) from 2019-2023 and assess their ecological impacts. The findings reveal significant environmental degradation, including reduced water availability, poor vegetation health, and increased emissions, linked to these anomalies.
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Hughes (2025) Monthly hydro-economic model of the Murray-Darling Basin
This entry describes and provides data sets for a monthly time-step hydro-economic model of the Australian Murray-Darling Basin, developed for predicting irrigation water demands and analyzing water markets under climate change.
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Bouswir et al. (2025) Assessment of empirical and physically-based approaches to simulate surface resistance for improved evapotranspiration modeling of winter wheat in semi-arid region, Morocco
The study evaluates two different methods for parameterizing surface resistance ($r_c$) in the Penman-Monteith model to improve evapotranspiration (ET) estimation for winter wheat in Morocco. It finds that while mechanistic models (Jarvis) excel under full irrigation, empirical models based on Land Surface Temperature (LST) are more effective at capturing rapid water stress dynamics under deficit irrigation.
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Stošić et al. (2025) Multi-scale precipitation trends across Serbia: insights from ITA and MMK methods
This study analyzed multi-scale precipitation trends in Serbia from 1961-2020 using Innovative Trend Analysis (ITA) and Modified Mann-Kendall (MMK) methods, finding ITA to be significantly more sensitive in detecting diverse temporal and spatial trends, particularly revealing more negative trends than MMK.
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Ouyang et al. (2025) A deep learning method for identifying waterlogging depth on urban roadways from surveillance camera images
This paper introduces a deep learning method that integrates Cascade Mask R-CNN with ellipse detection to precisely identify waterlogging depth on urban roadways from surveillance camera images, achieving high accuracy and low absolute errors compared to manual measurements.
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Kang et al. (2025) Modification of Similarity Relationships and Parameterization of Submesoscale Motions Under Spectral Regimes Over Uniform Flat Terrain
This study investigates the impact of submesoscale motions and turbulence intermittency on flux estimation in the atmospheric boundary layer, identifying four distinct turbulence regimes. It establishes revised surface-layer similarity relationships by removing submesoscale motions and proposes an empirical parameterization for submesoscale wind speed standard deviations, significantly improving turbulence representation.
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Bandrés et al. (2025) Chemistry and sources of atmospheric aerosols deposited in the Central Pyrenees in the period 2016–2023, with a focus on African dust events occurred during cold season
This study geochemically characterizes and identifies primary sources of atmospheric aerosols deposited in the Central Pyrenees from 2016 to 2023, revealing that African dust accounts for 45% of total annual deposition, with intense cold-season events significantly impacting the cryosphere.
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Duque et al. (2025) Simulating Closed‐to‐Open Mesoscale Cellular Convection Over the Southern Ocean: Part II. Perturbed Physics Experiments
This study investigates the drivers of mesoscale cellular convective (MCC) cloud organization and the transition from closed-to-open cells over the Southern Ocean. It finds that enhanced cloud ice production and microphysical latent cooling are key factors in MCC organization and break-up, while sea surface temperature influences cloud morphology but is not the primary driver of the closed-to-open cell transition.
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Nunno et al. (2025) Actual evapotranspiration dynamics in Italy: trend detection and regional clustering analysis
This study analyzed actual evapotranspiration (AET) dynamics across Italy from 1950 to 2024 using ERA5-Land data, revealing a north-south AET gradient driven by water availability and widespread statistically significant positive AET trends, particularly in northern and central regions.
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Baño-Medina et al. (2025) A regional high resolution AI weather model for the prediction of atmospheric rivers and extreme precipitation
This study develops and evaluates a stretched-grid AI-driven weather model with high resolution over the Western United States for predicting atmospheric rivers and extreme precipitation. The model significantly reduces precipitation errors, performs competitively with regional numerical weather prediction (NWP) models, and effectively captures extreme events, outperforming coarser global models.
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Ghazouani et al. (2025) Assessment of AquaCrop Inputs from ERA5-Land and Sentinel-2 for Soil Water Content Estimation and Durum Wheat Yield Prediction: A Case Study in a Tunisian Field
This study compares AquaCrop model performance using various input combinations, including ERA5-Land reanalysis and Sentinel-2 derived crop cover, demonstrating its feasibility for durum wheat yield estimation in data-scarce Mediterranean regions.
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Tayer et al. (2025) Mapping resilience: A framework for analysing surface-water dynamics and persistent pools in non-perennial rivers using remote sensing, rainfall and river discharge data
This study developed a scalable, data-driven framework integrating remote sensing, rainfall, and discharge data to analyze multi-decadal surface water and persistent pool dynamics in non-perennial rivers. Applied to the Gilbert River (Australia, 1986–2023), the framework revealed a dynamic mosaic of 29 persistent pools, with increasing rainfall and discharge leading to more numerous, larger, and less fragmented pools over time.
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Mwinjuma et al. (2025) Corrigendum to “Comparisons of SPI and SPEI in capturing drought dynamics: A global assessment across arid and humid regions” [Atmospheric Research, 329(2026), 108475]
This corrigendum addresses and rectifies errors found in Figures 6 and 9 of the original article, "Comparisons of SPI and SPEI in capturing drought dynamics: A global assessment across arid and humid regions," which were caused by a coding fault.
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Rodrigues et al. (2025) Correction: Rodrigues et al. Estimating Reservoir Evaporation Under Mediterranean Climate Using Indirect Methods: A Case Study in Southern Portugal. Hydrology 2025, 12, 286
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
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Bolot et al. (2025) No decrease of tropical convection in individual deep convective systems with global warming
This study challenges the consensus that tropical convection decreases with global warming by showing that individual deep convective systems intensify, with the overall decrease in tropical convection attributed to a reduction in the total number of such systems.
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Bragina et al. (2025) Predictability of temperature anomalies in Southeast Europe and the Eastern Mediterranean in June 2024 based on seasonal forecasts of the INM RAS Earth system model
This study investigates the predictability of the June 2024 heatwave in Southeast Europe and the Eastern Mediterranean using seasonal forecasts from the INM RAS Earth system model (INMCM6M), identifying reduced soil moisture and increased meridional warm air transport as key contributing factors.
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Xiao et al. (2025) High-resolution ensemble retrieval of cloud properties for all-day based on geostationary satellite
This study introduces CloudDiff, a novel generative diffusion model, for high-resolution (1 km) and all-day ensemble retrieval of cloud properties (Cloud Optical Thickness, Cloud Effective Radius, Cloud Top Height, Cloud Phase) from geostationary satellite data, providing uncertainty quantification and significantly improving retrieval accuracy and reliability compared to deterministic methods.
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Lazin et al. (2025) Climate-Informed flood damage assessment in the cropland area across the midwestern USA
This study developed a climate-informed convolutional neural network (CNN) model to assess flood damages in corn and soybean croplands across the midwestern USA, projecting future damages ranging from a 40% decrease to a 120% increase by mid-century under CMIP5 climate scenarios.
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ding (2025) Long-term seasonal surface environment dataset of DKH
This paper presents a long-term seasonal surface environment dataset comprising Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Fractional Vegetation Cover (FVC), and Temperature Vegetation Dryness Index (TVDI) for the Dushanzi-Kuqa Highway region, derived from MODIS satellite data.
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Avila-Velasquez et al. (2025) How Good Are Drought Forecasts? Skill of multi-model Seasonal Forecast of Meteorological Droughts in a semi-arid Mediterranean Basin
This study develops and evaluates a multi-model seasonal forecasting system for meteorological drought indices (SPI and SPEI) in the semi-arid Jucar River Basin, Spain, integrating Copernicus Climate Change Service (C3S) forecasts with artificial intelligence post-processing to demonstrate high forecast skill for water management.
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Bai et al. (2025) Near Real-Time Reconstruction of 0–200 cm Soil Moisture Profiles in Croplands Using Shallow-Layer Monitoring and Multi-Day Meteorological Accumulations
This study developed a machine learning-based model to reconstruct deep-layer soil moisture (0–200 cm) using shallow-layer data and meteorological features. The approach achieves high predictive accuracy (R² up to 0.98), providing a low-cost alternative to expensive deep-probe monitoring for precision irrigation.
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Fotso‐Nguemo et al. (2025) Severity of Compound Precipitation and Temperature Extreme Events Over Africa at 1.5°C and 2°C Global Warming Levels
This study investigates how increasing global warming levels affect the variability of compound dry/warm and wet/warm extreme events over Africa. It finds that a 0.5 °C increase in global warming (from 1.5 °C to 2 °C) significantly increases the frequency, duration, and affected area of these compound events, particularly near the Equator and in specific sub-regions of Africa.
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Yan et al. (2025) Estimation of pear tree leaf area index using fused UAV multispectral and RGB imagery
This study developed a UAV-based multi-source and adaptive weighted ensemble framework to precisely estimate pear tree Leaf Area Index (LAI) during the fruit expansion stage, demonstrating that fusing multispectral and RGB imagery with an Optimized Integrated Algorithm (OIA) significantly enhances estimation accuracy and robustness.
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Li et al. (2025) Joint calibration of Manning’s roughness and seepage in canals using NSGA-II for precision hydrodynamic modeling
This study introduces a novel framework using the NSGA-II algorithm to jointly calibrate Manning’s roughness and seepage parameters in irrigation canals, significantly improving hydrodynamic modeling precision and providing a quantitative basis for targeted maintenance and enhanced water resource utilization.
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Liu et al. (2025) An improved global river vector dataset based on multi-source river data fusion
This study introduces GSriver, a new global river vector dataset generated through a multi-source data fusion framework, which significantly enhances spatial accuracy while preserving complete topological information. Validation shows GSriver substantially improves positional accuracy compared to existing global datasets.
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Ahmed et al. (2025) Attention-Based Ensemble Learning for Crop Classification Using Landsat 8–9 Fusion
This study developed an Attention-guided Stacked Ensemble Network (ASEN) leveraging fused Landsat 8–9 imagery and SHAP-based feature selection for crop classification, achieving 98.43% overall accuracy and an 89.29% F1-score for six major crops in Central Punjab, Pakistan.
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Mohanty et al. (2025) Precipitation bursts in northern Australia with and without preceding heatwaves
This study investigates the interaction between heatwaves and precipitation bursts in Northern Australia, revealing that heatwaves precondition the atmosphere, leading to stronger, more prolonged, and dynamically driven rainfall bursts compared to independent events. The research highlights the critical role of atmospheric circulation in shaping these compound extremes and their distinct responses to large-scale climate modes.
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Sun et al. (2025) Dynamic monitoring of maize field vegetation cover using sentinel-1 and sentinel-2 data and transfer learning algorithms
This study developed a transfer learning model integrating multi-temporal Sentinel-1 and Sentinel-2 data with a pixel dichotomy model and temporal variation features to dynamically monitor maize fractional vegetation cover (FVC) during cloudy and rainy periods. The model demonstrated superior performance compared to classical machine learning methods, providing a robust solution for continuous crop monitoring under optical data limitations.
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Küchler (2025) Retrieval of the atmospheric water vapour content from Sentinel-5 Precursor measurements using the AMC-DOAS algorithm
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Khalil et al. (2025) A novel optimized machine learning ensemble approach for future drought assessment
This study proposes a novel multi-model ensemble (MME) framework integrating Learning Vector Quantization (LVQ) and Optimized Learning Vector Quantization (OLVQ) to enhance the reliability of precipitation forecasts from General Circulation Models (GCMs). The findings demonstrate that both LVQ and OLVQ significantly outperform traditional ensemble methods, with OLVQ providing further substantial improvements in reducing prediction errors and increasing correlation.
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Bai et al. (2025) The Response of Maximum Freezing Depth in the Permafrost Region of the Source Region of the Yellow River to Ground Temperature Change
This study quantifies the historical (1981–2014) and projected future (through 2100) evolution of maximum freezing depth (MFD) in the Yellow River source region, revealing a significant shallowing trend linked to rising ground temperatures, with substantial future degradation under climate warming scenarios.
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Anees et al. (2025) Use of Artificial Intelligence in Modern Agricultural Practices
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Puxley et al. (2025) A Continental United States Climatology of Precipitation Whiplash Using a New Event‐Based Definition
This study developed a novel algorithm to define and analyze spatially coherent precipitation whiplash events across the continental United States (CONUS) between 1915 and 2020, finding that the largest drought-to-pluvial and pluvial-to-drought events are increasing in both frequency and total area impacted, particularly in the Southeast and Northeast.
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Adedeji et al. (2025) Effects of Spatial Resolution on Assessing Cotton Water Stress Using Unmanned Aerial System Imagery
This study evaluated the performance of UAS-derived Water Deficit Index (WDI) and Crop Water Stress Index (CWSI) across cotton growth stages and examined how spatial resolution influences stress detection and yield prediction. It found that WDI outperformed CWSI, and a 0.5 m spatial resolution provided the optimal balance between accuracy and computational efficiency for assessing cotton water stress and predicting yield.
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Trivedi et al. (2025) An assessment of Antarctic sea-ice thickness in CMIP6 simulations with comparison to the satellite-based observations and reanalyses
This study assesses the spatio-temporal variations and biases in Antarctic sea-ice thickness (SIT) and volume (SIV) simulated by 39 CMIP6 models against satellite observations and reanalyses, finding that models replicate seasonal cycles but generally underestimate absolute thickness and project seasonally asymmetric declines under future warming.
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He et al. (2025) Improving precipitation simulations in CIESM through a new entrainment rate parameterization
This study develops and implements a new entrainment rate parameterization (HL) for deep convection in the CIESM1.1.0 climate model, demonstrating improved simulations of convective and large-scale precipitation in tropical and subtropical regions compared to the existing Gregory parameterization.
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Abdelfattah et al. (2025) Synergistic Use of Remote Sensing, Geophysics, and Hydrogeology for Sustainable Groundwater Management in Arid Landscapes
This research assesses the groundwater potential and aquifer characteristics in Egypt's Gallaba Plain using an integrated approach of remote sensing, geophysics, and hydrogeology. It identifies two highly productive, semi-confined Nubian Sandstone aquifers with significant potential for agricultural and urban use, despite the brackish water quality requiring managed application.
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Li et al. (2025) Improved Madden–Julian oscillation simulation using the modified moist physical parameterizations for a global climate model
This study investigates the impact of modified moist physical parameterizations on the global climate model, CIESM, regarding Madden–Julian oscillation (MJO) simulation. The new moist physical schemes significantly improve MJO propagation characteristics and mean-state fidelity by enhancing zonal asymmetry in moist static energy tendency and optimizing cloud-convection interactions.
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Qiu et al. (2025) Quality control of the composite radar quantitative precipitation estimation product for Great Britain
This study develops a novel quality control (QC) framework for radar Quantitative Precipitation Estimation (QPE) products in Great Britain to systematically detect and correct underestimation (e.g., beam blockage) and overestimation errors. The framework significantly improves the accuracy of radar QPE, reducing RMSE by 29% and increasing the correlation coefficient by 31% compared to rain gauge observations, while preserving real extreme rainfall events.
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Zhang et al. (2025) Integrating deep learning and multi-objective optimization for floodwater utilization: a coordinated surface water-groundwater regulation framework for groundwater recovery
This study developed a novel deep learning and multi-objective optimization framework to integrate flood control, water storage, and groundwater recovery through coordinated surface water-groundwater regulation. It demonstrated that optimizing reservoir operations and implementing managed aquifer recharge effectively reduced flood risk and water scarcity while promoting groundwater recovery.
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Kale et al. (2025) Water evaporation forecasting using a deep learning model based on Perrin sequence CNN and minimization techniques
This study introduces a novel deep learning model, the Perrin Sequence Convolutional Neural Network (PS-CNN), for accurate water evaporation forecasting by integrating the Perrin mathematical sequence into its convolutional layers to capture complex spatio-temporal patterns. Tested on a 15-year climate dataset from India, the PS-CNN significantly outperforms traditional and existing deep learning methods, achieving a Mean Absolute Error of 0.17 mm/day and a Coefficient of Determination (R²) of 0.93.
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Cava et al. (2025) Energy Partitioning and Air Temperature Anomalies Above Urban Surfaces: A High-Resolution PALM-4U Study
This study applies a coupled atmospheric model (MOLOCH and PALM-4U) to Bologna during a summer 2023 heatwave to quantify meter-scale thermal variability and energy exchanges within the urban canopy. It reveals pronounced diurnal and vertical atmospheric dynamics and distinct surface-dependent thermal contrasts, highlighting the roles of asphalt/roofs as heat sources/reservoirs and vegetation as a cooling mechanism.
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Barbaux (2025) Températures maximales au 21ème siècle
## Identification - **Journal:** theses.fr (ABES) - **Year:** 2025...
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Lin et al. (2025) Soil salinity estimation based on satellite hyperspectral and synthetic aperture radar remote sensing image fusion
This study proposes a novel multi-scale, multi-depth Wasserstein Generative Adversarial Network with Gradient Penalty (MSD-WGAN-GP) to fuse hyperspectral (HSI) and Synthetic Aperture Radar (SAR) images, significantly improving soil salinity estimation accuracy by mitigating the coupling effects of soil moisture and surface roughness.
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Rossi et al. (2025) Annual detection of wetlands using optical indices and supervised
This study comparatively evaluates three supervised classification algorithms (Random Forest, Support Vector Machine, and Classification and Regression Tree) for wetland detection in the Tangier Tetouan Al Hoceima region using Sentinel-2 imagery and spectral indices, finding that Random Forest offers higher temporal stability and a multi-model strategy enhances detection robustness.
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Zhang et al. (2025) A multiple spatial scales water use simulation for capturing its spatial heterogeneity through cellular automata model
This study proposes a multi-scale water use simulation framework, integrating a cellular automata model with Generalized Likelihood Uncertainty Estimation, to address spatial heterogeneity and uncertainty in water resource planning across China. The framework reveals that both model structure and spatial scale significantly impact water use heterogeneity and uncertainty, offering a flexible tool for adaptive water resource management.
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Gandham et al. (2025) The emerging contribution of Tigris Euphrates basin dust emissions to extreme dust activity over the Arabian Peninsula
This study quantitatively assesses, for the first time, the influence of dust emissions from the Tigris–Euphrates river basin (TE) on extreme dust activity over the Arabian Peninsula (AP) using a regionally tuned WRF-Chem model, demonstrating that suppressing TE emissions significantly reduces extreme dust events by over 50% and alters regional dust event distributions and radiative balance.
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Piccarreta et al. (2025) Trend analysis of hourly rainfall in the Mediterranean: a case study of the Basilicata Region, Southern Italy (2001–2024)
This study analyzed hourly rainfall data (2001–2024) in Basilicata, Southern Italy, revealing a spatially coherent regional signal of increasing frequency and intensity of moderate sub-daily rainfall extremes in summer and autumn, alongside a decline in winter extremes, indicating a seasonal redistribution of precipitation.
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Nguyen et al. (2025) Four-decades assessment of hydrological drought using streamflow reconstruction in poorly-gauged basins in Vietnam
This study evaluates the effectiveness of MSWEP and CHIRPS precipitation products for reconstructing streamflow and assessing hydrological drought in three poorly-gauged basins in Vietnam. The results demonstrate that these global datasets, combined with the SWAT model, accurately replicate historical drought events and monthly streamflow patterns over a 30-year period.
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Lai et al. (2025) Identification of spatiotemporal changes and driving factors of ecological drought during 1982–2024 across the mainland China
This study utilizes the Standardized Ecological Water Deficit Index (SEWDI) to assess ecological drought across mainland China from 1982 to 2024, revealing a general increasing trend in drought severity since 2000. The research identifies evapotranspiration, soil moisture, and irrigation water as the primary drivers of these spatiotemporal changes.
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Xia et al. (2025) A multi-temporal scale framework for comprehensive quantification and attribution of anthropogenic impacts on runoff
This study developed a multi-temporal scale framework integrating machine learning and hydrological models with multi-source data to quantify and attribute anthropogenic impacts on runoff. Applied to the Lan River Basin, the framework identified optimal temporal scales for modeling and elucidated the dual role of human activities on runoff, providing a transferable approach for attribution analysis.
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Agency et al. (2025) Drought impact on ecosystems NUTS2 and NUTS3 (2000-2024)
This indicator monitors meteorological drought impacts on ecosystem productivity across Europe from 2000 to 2024 by combining remote sensing vegetation indices with soil moisture deficit data at NUTS2 and NUTS3 administrative levels. It quantifies drought pressure and impact areas and intensities annually.
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Wang et al. (2025) Drivers of agricultural ecosystem resilience under compound atmospheric-soil drought in Northeast China
This study quantitatively evaluates agricultural ecosystem resilience in Northeast China under compound atmospheric-soil drought, revealing significant spatial heterogeneity and an overall improvement trend in agricultural areas compared to non-agricultural regions, driven by precipitation and large-scale cropland with nonlinear effects from compound drought severity on resilience evolution.
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Giani et al. (2025) Origin and Limits of Invariant Warming Patterns in Climate Models
This paper presents a simple theory based on local energy balance to reconcile the apparent contradiction between approximately invariant surface warming patterns in typical end-of-century climate projections and evolving patterns in idealized CO2 increase experiments. It shows that pattern invariance arises under specific conditions met in future projections, while idealized experiments exhibit evolution due to spatially inhomogeneous ocean heat uptake.
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Goutard (2025) Importance de l’eau liquide et variabilité de l’albédo sur les surfaces en glace des glaciers de montagne
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Zhang et al. (2025) TALESF: A Novel Terrain-Adjusted Local-Enhanced Spatiotemporal Fusion Method for Generating Spatiotemporally Seamless, High-Resolution NDVI
Not available in the provided text. The paper introduces TALESF, a novel terrain-adjusted local-enhanced spatiotemporal fusion method for generating spatiotemporally seamless, high-resolution Normalized Difference Vegetation Index (NDVI).
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Augenstein et al. (2025) Influence of the North Atlantic Oscillation on annual spatio-temporal lightning clusters in western and central Europe
This study investigates the spatio-temporal characteristics and trends of thunderstorm activity in western and central Europe using lightning data from 2001 to 2021, revealing a significant decrease in thunderstorm activity and a shift towards smaller, more separated convective systems, which is linked to an accumulation of negative North Atlantic Oscillation phases.
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Ullah et al. (2025) The Internet of Nature Things (IoNT): Pioneering a New Frontier in Environmental Monitoring and Sustainable Ecosystem Management
This paper introduces the concept of the Internet of Nature Things (IoNT) as an emerging paradigm to advance environmental monitoring and facilitate sustainable ecosystem management.
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Yan et al. (2025) Time-Extended Bayesian Retrieval of Dual-Polarization Radar Data Enhancing Short-Term Precipitation Forecasts
This study developed a time-extended Bayesian retrieval method for dual-polarization radar data to address performance degradation in convective structure retrieval caused by temporal biases in Numerical Weather Prediction (NWP) models. The method significantly improved initial moisture fields and enhanced short-term precipitation forecasts (0–6 hours) by effectively resolving these temporal biases.
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Han et al. (2025) FluxHourly: global long-term hourly 9 km terrestrial water-energy-carbon fluxes
This study presents FluxHourly, a global long-term (2000–2020) hourly 9 km dataset of terrestrial water-energy-carbon fluxes, generated by integrating model simulations, in-situ measurements, and machine learning to enable analysis of ecosystem responses to climate extremes at unprecedented spatiotemporal scales.
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Hwang et al. (2025) Synergistic Effects of Infrared and Microwave Radiance in All-Sky Data Assimilation for a Heavy Precipitation Case Over the Korean Peninsula
[This paper investigates the combined effects of infrared and microwave radiance in all-sky data assimilation to improve forecasts for a heavy precipitation event over the Korean Peninsula.]
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Gu et al. (2025) FloodTransformer: Efficient real-time high-resolution flood forecasting
This paper introduces FloodTransformer, a hybrid AI-hydrodynamic framework for accurate, real-time, and high-resolution flood forecasting, demonstrating superior performance in water depth and inundation classification compared to existing models.
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Toropov et al. (2025) The New IGRICE Model as a Tool for Studying the Mechanisms of Glacier Retreat
This study introduces IGRICE, a new global glacier model with an explicit surface mass balance, validated against observational data in the Caucasus and Svalbard, demonstrating good agreement and identifying regional drivers of glacier degradation.
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Cao et al. (2025) Quantifying Dynamic Water-Saving Thresholds Through Regulating Irrigation: Insights from an Integrated Hydrological Model of the Hetao Irrigation District
This study developed an integrated SWAT-MODFLOW model for the Hetao Irrigation District to quantify dynamic inter-annual and intra-annual water-saving thresholds, revealing that wet/normal years allow up to 20% savings while dry years are limited to 5%, with autumn irrigation offering the highest intra-annual potential and significant spatial variability in crop yield responses.
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Mirandilla et al. (2025) Phenological Monitoring and Discrimination of Rice Ecosystems Using Multi-Temporal and Multi-Sensor Polarimetric SAR
This study developed a method using multi-temporal polarimetric dual-polarization SAR data from Sentinel-1B and ALOS PALSAR-2 to monitor and discriminate irrigated and favorable rainfed rice ecosystems. The integration of backscatter and polarimetric parameters from both sensors achieved an 81.81% overall accuracy in distinguishing these two rice types.
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Wang et al. (2025) On how ecosystem pattern changes can affect water-related ecosystem services, a case study in the Huolin River Basin, China
This study quantified the hydrological impacts of ecosystem pattern changes (degradation and conservation of forests, grasslands, and wetlands) on four water-related ecosystem services (water conservation, water yield, flood regulation, and drought mitigation) in the Huolin River Basin, China, using the SWAT model. It found that ecosystem degradation generally reduced water conservation and increased water yield and the frequency of floods and droughts, while conservation measures produced opposite, beneficial effects.
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Pu et al. (2025) MCI GPP: ensembling a global model- and climate-independent gross primary productivity for 2001–2023
This study develops a novel model- and climate-independent (MCI) global gross primary productivity (GPP) product for 2001–2023 by ensembling 12 diverse GPP datasets using random forest regression and spatio-temporal tensor models. The MCI GPP product demonstrates superior accuracy against independent observations and reveals a significant global GPP increase of 5.7 Pg C yr⁻¹ per decade.
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Fu et al. (2025) Reconstruction of the July 12, 2022, flash flood event in Southwest China: integrating emergency management analysis with hydrological modeling
This study reconstructs the July 12, 2022 flash flood in China's Heishui River watershed using field investigations and hydrological–hydrodynamic simulations, identifying heavy rainfall and human factors as causes, and demonstrating the value of post-disaster data for modeling ungauged basins.
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Dong et al. (2025) Prediction of water consumption and affecting factor analysis using Inception-V4 network and enhanced single candidate optimization: a case study
This study developed a novel hybrid ESCO-Inception-V4 model for long-term water consumption forecasting in Shanghai, demonstrating superior accuracy (RMSE 0.0519, MAE 0.0407) compared to benchmark models and identifying key influencing factors. The model predicts a steady increase in water demand for Shanghai over the next 15 years.
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Liang et al. (2025) Optimized classification reveals typical summer precipitation anomaly patterns and associated circulation features over the Yangtze-Huai river Valley
This study developed an optimized classification scheme to identify and characterize nine distinct summer precipitation anomaly patterns (PAPs) over the Yangtze-Huai River Valley (YHRV) from 1951 to 2020. The research reveals their associated large-scale atmospheric circulation features and a significant temporal shift towards fewer drought patterns, providing a refined basis for improved seasonal forecasting and disaster prevention.
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Tang et al. (2025) REBAI: Development and validation of a novel indicator for burned area detection using Sentinel-2 images
This study develops and validates the novel Red-Edge Burned Area Index (REBAI) using Sentinel-2 data, demonstrating its superior and stable performance in accurately detecting small-scale and subtle burned areas across diverse global forest ecosystems compared to existing spectral indices.
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Cao et al. (2025) Standardized compound drought and heatwave index: A new compound drought and heatwave events monitoring index considering evapotranspiration effects
This study developed the Standardized Compound Drought and Heatwave Index (SCDHI) by integrating the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Temperature Index (STI) using Gaussian copula modeling. The SCDHI significantly improved the monitoring accuracy and capability for compound drought and heatwave events (CDHEs), particularly in assessing vegetation responses, compared to existing indices.
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Özdel et al. (2025) Climate-Driven Futures of Olive (Olea europaea L.): Machine Learning-Based Ensemble Species Distribution Modelling of Northward Shifts Under Aridity Stress
This study assessed the impact of climate change on olive (Olea europaea L.) distribution in Türkiye using machine learning-based species distribution models, predicting a significant northward and upward shift in suitable areas, a drastic decline in highly suitable land, and increased aridity pressure by the end of the century.
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Vora et al. (2025) Dataset for “Data-driven Regional Analysis of Seasonal Hydrology within Contiguous United States: Sources of Asynchrony between Seasonal Precipitation and Soil Moisture Patterns”
This study conducts a data-driven regional analysis across the Contiguous United States to investigate the sources of asynchrony observed between seasonal precipitation and soil moisture patterns.
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Amouch et al. (2025) Recent and future trend of drought in the Context of climate change: Southwest of Morocco (Guelmim-Sidi Ifni and Agadir)
This study analyzes historical drought patterns and projects future dry and wet episodes in southwest Morocco using the Standardized Precipitation Index (SPI) and the HadGEM2 climate model, revealing significant past droughts and forecasting extended future dry periods.
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Davies et al. (2025) Application of the Davies four-stage conceptual model for life-threatening rainfall extremes on the April 2024 United Arab Emirates and Oman floods
This study investigates the environmental conditions leading to the April 2024 UAE and Oman floods, applying a four-stage conceptual model to numerical weather prediction (NWP) models and observations. It finds that a combination of deep Moist Absolute Unstable Layers (MAULs) and high saturation fractions are key indicators for predicting extreme rainfall events, distinguishing between organised and isolated convection.
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Schalkwyk et al. (2025) Observations of a dryline interacting with a back-building mesoscale convective system in the Kalahari region of southern Africa
This study analyzes a unique set of observations to describe the environment and development of a back-building mesoscale convective system (MCS) interacting with a dryline in the southern Kalahari region. It reveals that the MCS developed in a highly unstable, weakly sheared environment, with back-building and strong low-level inflow extending its lifespan and contributing significantly to regional rainfall.
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Wang et al. (2025) Towards a Global Water Use Scarcity Risk Assessment Framework: Integration of Remote Sensing and Geospatial Datasets
This study developed a storage-aware water scarcity risk assessment framework, integrating satellite remote sensing, geospatial data, and machine learning with the IPCC EHV paradigm, to evaluate global water scarcity dynamics over the past two decades, identifying high-risk regions and increasing vulnerability in Asia and Africa.
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Tulu et al. (2025) RGB-to-synthetic-thermal image translation using generative AI to support crop water stress assessment
This study developed and evaluated generative AI models to translate standard RGB images into synthetic thermal images for crop water stress assessment. The Pix2PixGAN model demonstrated high correlation (r > 0.95) with measured thermal data and accurately reflected water stress gradients, offering a cost-effective alternative to specialized thermal sensors for irrigation scheduling.
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Lin et al. (2025) Enduring local impact of springtime snow cover over the Third Pole
This study confirms the enduring local impact of springtime Third Pole snow cover (TPSC) on summer precipitation and proposes a self-sustaining mechanism where initial excessive precipitation releases atmospheric latent heat, driving anomalous circulation that favors subsequent precipitation.
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Zhao et al. (2025) A 10-m resolution dataset of plastic-mulched farmland distributions on the Chinese Loess Plateau (2019–2021)
This study generated the first 10-meter resolution plastic-mulched farmland (PMF) distribution maps for the Chinese Loess Plateau (2019–2021) by developing a novel framework that couples automatic training sample generation and classifier transfer methods. The resulting maps achieved satisfactory accuracies (F1-scores 0.80–0.86) and demonstrated good agreement with agricultural census data (R² ≥ 0.87).
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Szwarcman et al. (2025) Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications
## Identification - **Journal:** IEEE Transactions on Geoscience and Remote Sensing - **Year:** 2025...
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Zhang et al. (2025) A pixel-aligned co-registration and DSM-grid fusion framework for UAV multispectral and thermal imagery and point-cloud data: 3D Characterization of crop canopy water status
This study proposes a pixel-aligned co-registration and DSM-grid fusion framework to integrate UAV multispectral, thermal imagery, and point-cloud data for 3D prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT), demonstrating its effectiveness for precision agricultural management.
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Jiang et al. (2025) Spatial and temporal variation of raindrop size distribution in Great Britain
This study investigates the spatial and temporal characteristics of raindrop size distributions (DSDs) across Great Britain using disdrometer data from 2017-2019. It reveals distinct geographical and topographical influences on DSD parameters, significant differences between stratiform and convective rain types, and unique seasonal variations in extreme convective events compared to subtropical/tropical regions.
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Toride et al. (2025) Roles of MJO and Tropical–Extratropical Interactions in Subseasonal Conditions Related to Landfalling Atmospheric Rivers
This study investigates the role of the Madden–Julian oscillation (MJO) and other tropical–extratropical interactions in generating landfalling atmospheric rivers (ARs) over the West Coast of North America. It finds that weakly coupled tropical–extratropical interactions are the primary drivers of AR activity, with the MJO contributing only a minor role, mainly to subtropical vapor transport for Alaska ARs.
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Qian et al. (2025) Comparative analysis of flood characteristic changes under Meiyu and typhoon rainfall in Zhejiang Province, China (1964–2021)
This study comparatively analyzed flood characteristic changes in Meiyu-dominated (XF) and typhoon-dominated (HJT) watersheds in Zhejiang Province (1964–2021), revealing stable flood trends in the XF watershed but a decoupled response of decreased frequency and amplified intensity in the HJT watershed, highlighting the importance of rainfall mechanisms under climate change.
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Witze (2025) This AI model ‘studied’ physics — and learnt to forecast extreme weather
This paper explores a novel hybrid approach combining artificial intelligence (AI) models with conventional physics-based climate models and mathematical tools to forecast extreme weather events more effectively. This method demonstrates the ability to predict extreme heatwaves as accurately as traditional models but significantly faster, addressing AI's limitations with unprecedented events.
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Wang et al. (2025) Quantifying nonlinear synergistic effects of environmental changes on runoff change using segmented hydrological modeling
This study introduces a segmented hydrological modeling approach to quantify the nonlinear synergistic effects of climate change and human activities on runoff variation in the Wei River Basin. It finds that human activities are the dominant and escalating driver of runoff reduction, with the synergy between these factors significantly amplifying the overall decline.
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Liu et al. (2025) Smart Irrigation Scheduling for Crop Production Using a Crop Model and Improved Deep Reinforcement Learning
This paper proposes an intelligent irrigation scheduling method, Temporal–Spatial Attention Soft Actor–Critic (TSA-SAC), which integrates a crop growth model (DSSAT) with an improved deep reinforcement learning agent to optimize cotton yield and water use efficiency. The method achieved a 39.0% improvement in water use efficiency and an 8.4% increase in yield compared to fixed-schedule irrigation strategies.
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Xue et al. (2025) Emulating vegetation phenology in Xinjiang using GOSIF data: Investigating cumulative and lagged responses to drought
This study investigates the spatiotemporal evolution of vegetation phenology (Start and End of Season) in the Xinjiang arid zone from 2001 to 2020 using SIF and MODIS data, quantifying the cumulative and lagged effects of drought on phenology and assessing drought resilience across different vegetation types.
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Pimple et al. (2025) AI-Driven Agriculture Monitoring System
This paper presents an AI-driven agriculture monitoring system that integrates real-time crop disease detection, soil moisture prediction, automated irrigation, and weather-risk alerts. The system leverages machine learning and sensor data to provide farmers with precise, timely insights, aiming to enhance crop health, productivity, and agricultural sustainability.
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lu (2025) Heatwaves in China
This dataset provides calculated heatwave indices and their trigger times for heatwave events in China.
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Wang et al. (2025) Season-Aware Ensemble Forecasting with Improved Arctic Puffin Optimization for Robust Daily Runoff Prediction Across Multiple Climate Zones
This paper proposes a Season-Aware Ensemble Forecasting (SAEF) method that integrates multiple machine learning models with seasonal segmentation and an optimized weighting algorithm to improve daily runoff forecasting accuracy and provide physically interpretable insights into seasonal hydrological dynamics across diverse climates.
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Zhang (2025) An Automated Labeling Method for Lakes from Remote Sensing Images Guided by Visual and Structural Features
This paper introduces an automated method for labeling lakes in remote sensing images using visual and structural features, and provides a corresponding dataset for its evaluation.
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Rami et al. (2025) Urban surface modeling using town energy balance model: A review
This paper presents a systematic literature review of the Town Energy Balance (TEB) model's applications across various climates and urban morphologies, concluding that TEB is a valuable tool for understanding urban energy and planning sustainable cities despite requiring detailed data and further improvements in energy and water aspects.
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Zhang et al. (2025) Spatiotemporal dynamics of crop water use and groundwater depletion under the winter wheat–summer maize rotation system in Henan Province, China
This study quantified the spatiotemporal dynamics of crop water use and groundwater depletion in Henan Province (1961-2020) under a winter wheat–summer maize rotation, revealing that while winter wheat is the primary driver of depletion, summer maize's groundwater use has sharply increased since 2010, intensifying water stress in the central–northern plains.
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Li et al. (2025) Multistep-ahead prediction of daily water temperature for Poyang Lake, China, using monthly monitoring data
This study proposes a novel framework integrating a physically based (PB) model with deep learning (DL) models to address data scarcity for daily water temperature (WT) forecasting in large lakes. The framework successfully converts monthly WT observations into daily simulations and extends predictions to ungauged areas, demonstrating competitive multistep-ahead daily WT forecasts for Poyang Lake.
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Zhao et al. (2025) A Spatial-Temporal Seamless Evapotranspiration Product Based on TSEB-SM Model and DNN-ETo Method Across China
This paper aims to develop a novel spatial-temporal seamless evapotranspiration product for China by integrating the TSEB-SM model with a Deep Neural Network (DNN) approach for reference evapotranspiration.
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Fan et al. (2025) El Niño modulated Holocene hydroclimate extremes as a dipolar pattern of droughts in northeastern China and floods in southwestern China
This study reconstructs Holocene hydroclimate from Yangzong Lake, southwestern China, revealing a dipolar pattern of increasing floods in the southwest and droughts in the northeast, modulated by El Niño events, which resolves discrepancies in past monsoon-ENSO relationships.
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Michel et al. (2025) Temporal attention multi-resolution fusion of satellite image time-series, applied to Landsat-8/9 and Sentinel-2: all bands, any time, at best spatial resolution
This paper proposes a general formulation for fusing Satellite Image Time Series (SITS) from multiple sensors with varying spatial resolutions and acquisition times. It introduces TAMRF-SITS, a novel deep learning architecture and training strategy, which predicts all spectral bands from all input sensors at the best spatial resolution and any requested acquisition time, outperforming or matching existing ad-hoc methods across various tasks while relaxing unrealistic assumptions.
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Shen et al. (2025) Physically Consistent Runoff Simulation in Mountainous Catchments Using a Time-Varying Gated Hybrid XAJ–LSTM Model
This study developed a time-varying gated hybrid model (XAJ–LSTM) integrating the Xinanjiang (XAJ) model with a Long Short-Term Memory (LSTM) network to improve rainfall-runoff prediction accuracy and physical consistency in mountainous catchments, demonstrating superior performance over individual models.
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Elabidine et al. (2025) Harnessing CNN for flood mapping: Insights from Landsat-8 imagery
This study developed a practical method for flood mapping in the Zat sub-basin, Morocco, utilizing a U-Net Convolutional Neural Network on freely available Landsat-8 satellite imagery and SRTM elevation data, achieving high accuracy in detecting flooded areas.
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Zheng et al. (2025) Corrigendum to “Micro-sprinkler irrigation with optimal irrigation regimes maintain grain yields while increasing carbon emission efficiency and water productivity of winter wheat on the North China Plain” [Agric. Water Manag. 321 (2025) 109933]
This corrigendum corrects numerical errors in a study that investigated the impact of micro-sprinkler irrigation with optimal regimes on winter wheat grain yields, carbon emission efficiency, and water productivity on the North China Plain, aiming to maintain yields while enhancing these efficiencies.
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Donohue et al. (2025) Structured dataset of reported cloud seeding activities in the United States (2000–2025) using an LLM
This study presents a structured dataset of reported cloud seeding activities in the United States from 2000 to 2025, extracted from 832 historical NOAA reports using a multi-stage PDF-to-text pipeline combined with an LLM, achieving an estimated 98.38% accuracy. The dataset addresses a critical data gap and demonstrates a scalable framework for unlocking historical environmental data using large language models.
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Wen et al. (2025) Spatial distribution and variation trends of soil freezing front on the Qingzang Plateau revealed by machine learning models
This study simulated the spatiotemporal variations of soil freezing front depth (SFFD) on the Qingzang Plateau (QP) from 1982 to 2019 using machine learning models, revealing a significant and accelerating shallowing trend across both permafrost and seasonally frozen ground regions.
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Hu et al. (2025) Long-term changes in the drought propagation characteristics in the Yangtze River Basin
This study systematically assesses the long-term evolution of drought propagation characteristics in the Yangtze River Basin (1950–2022) using a dynamic sliding-window framework and copula-based joint probability analysis. It reveals significant spatiotemporal changes in drought propagation time and risk, highlighting the influence of vegetation type and hydrological interventions.
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Xia et al. (2025) Began+: Leveraging bi-temporal SAR-optical data fusion to reconstruct clear-sky satellite imagery under large cloud cover
This paper introduces Began+, a novel deep learning framework that fuses bi-temporal SAR and optical data to reconstruct clear-sky satellite imagery, effectively addressing large cloud cover and restoring temporal changes. It demonstrates superior performance in synthesizing high-quality Landsat-8 and Sentinel-2 images and open-sources two global datasets for cloud removal.
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Yang et al. (2025) Unraveling three-decade dynamics and drivers of thermokarst lakes on the Tibetan Plateau
This study analyzed the three-decade dynamics (1990-2022) of 58,538 thermokarst lakes on the Tibetan Plateau using remote sensing and machine learning, revealing that over 82% of lakes expanded, primarily driven by topographic and climatic factors, while 15% shrank due to evaporation and soil temperature trends.
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Gao et al. (2025) 1 km HILDA + based land cover/use map time series of China under 1.5 °C climate of this century
This study generated a 1 km resolution land cover/use map time series for China from 2015 to 2100, under a 1.5 °C climate scenario incorporating Nationally Determined Contributions (NDCs), by integrating the GCAM and Land-N2N models with the accurate HILDA+ baseline map. The resulting dataset provides crucial scientific guidance for land management in addressing climate crises.
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Yousefi et al. (2025) A reinforcement learning approach with explainable AI for spatial flood susceptibility analysis
This study develops and compares reinforcement learning (RL) models, including a novel RL-Stack ensemble, for spatial flood susceptibility mapping in a semi-arid mountainous region, finding that the Proximal Updating (PU) model achieved the highest accuracy and stability, with snow depth identified as the primary hydrological control.
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Niedzielski et al. (2025) Relationship between skills of multimodel hydrologic ensemble predictions and atmospheric circulation patterns: a case study from the Nysa Kłodzka river basin (SW Poland)
This study investigated the relationship between short-term hydrologic ensemble prediction skills and atmospheric circulation patterns in the Nysa Kłodzka river basin, finding that the most skillful predictions were associated with wet humidity and prevailing northerly air mass advection.
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Fang et al. (2025) Modelling river ice processes in a small-steep-regulated river
This study revised the University of Alberta’s River1D model to improve its simulation of complex river ice processes, including aufeis evolution, in small-steep-regulated rivers. The enhanced model was successfully calibrated and validated on the Aishihik River, providing a valuable tool for assessing flow regulation impacts, mitigating ice hazards, and evaluating climate change scenarios.
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Hu et al. (2025) Century-scale attribution and constrained projection of temperature extremes in eastern China
This study performs a multi-scale attribution analysis of temperature extremes from 1901 to 2020, finding that greenhouse gas forcing is the dominant driver of observed warming, with century-scale attribution providing the most robust results for constraining future projections.
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Sangeetha et al. (2025) Correction: A new band selection approach integrated with physical reflectance autoencoders and albedo recovery for hyperspectral image classification
This correction notice addresses revisions to the descriptions of Figures 5 and 7 in the original article, clarifying the specific agricultural classes highlighted in qualitative results for hyperspectral image classification.
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Hou et al. (2025) Characterization of single-cropping rice net irrigation water requirements in China's major rice-producing regions using time-frequency domain methods
This study characterizes single-cropping rice net irrigation water requirements (RIWR) in China's major rice-producing regions from 1951 to 2023 using time-frequency domain methods and projects future changes. It reveals substantial spatiotemporal variability in RIWR, with national annual averages ranging from 524.6 to 791.5 mm and dominant 15–25 year cycles, primarily driven by effective precipitation, sunshine duration, maximum air temperature, and relative humidity.
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Na et al. (2025) Administrative-District-Level Risk Indices for Typhoon-Induced Wind and Rainfall: Case Studies in Seoul and Busan, South Korea
This study developed a district-level typhoon hazard framework for South Korea by integrating high-resolution meteorological fields with structural and hydrological vulnerability indicators, revealing a strong coastal-inland hazard gradient and providing an operational foundation for localized early warning.
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Bak et al. (2025) Development of a web-based No coding machine learning platform for hydrology and environmental management - MoolML
This paper introduces MoolML, a free, web-based, no-coding machine learning platform designed to simplify the development of regression and classification models for hydrology and environmental management, demonstrating its applicability and efficiency with South Korean datasets.
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Blanco et al. (2025) Comparison of Spatial Patterns of Aridity in Argentina Based on Different Climatic Indices and Datasets
This study evaluates the spatial patterns of aridity in Argentina from 1961 to 2020 by comparing various climatic indices and gridded datasets. It finds that aridity indices based on mean temperature or potential evapotranspiration (especially Thornthwaite) are more consistent, with CRU and WORLDCLIM performing better than ERA5, which shows significant errors in certain conditions.
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Hu et al. (2025) Water-mediated vegetation impacts of photovoltaic plants: insights from field and satellite data on the Qinghai-Xizang Plateau
This study investigates the water-mediated impacts of photovoltaic (PV) plants on vegetation restoration on the Qinghai-Xizang Plateau, finding that soil moisture is the primary driver and additional water input significantly enhances restoration, potentially leading to overestimations in current assessments if not accounted for. The research highlights regional differences in these impacts, emphasizing the need for optimized PV layout and water resource management.
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Li et al. (2025) Uncertainty analysis and parameter optimization enhance assessment accuracy in water yield modelling
This study develops an integrated framework for uncertainty analysis, sensitivity analysis, and parameter optimization to enhance the accuracy of water yield modeling using the InVEST model in the Qinling-Daba Mountains region, demonstrating significant improvements in simulation reliability through optimal precipitation dataset selection and Markov Chain Monte Carlo optimization.
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Khalil et al. (2025) Wheat Farming Dynamics in Pothohar: From Rainfed Practices to Irrigation Systems
This study evaluated wheat yield and water productivity under various irrigation methods in Pakistan's Pothohar region, revealing significant yield gaps in current practices. It found that high-efficiency irrigation systems (HEIS) substantially improve water productivity and reduce losses compared to conventional flood irrigation, offering a sustainable solution for the region.
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Zhao et al. (2025) Cumulative Effect of Synoptic‐Scale Storms on Decadal Mixed‐Layer Temperature Variability in the Mid‐Latitude North Pacific
This study investigates the contribution of synoptic-scale storms to decadal mixed-layer temperature variability in the mid-latitude North Pacific. It reveals that frequent storms lead to cumulative mixed-layer cooling, primarily driven by anomalous meridional Ekman heat transport, a process linked to the strength of the Aleutian Low.
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Xie et al. (2025) Determination of dry soil layer and vertical variations in soil thermal properties through interpretation of heat pulse signals using deep learning-based data assimilation
This study evaluates a deep learning-based data assimilation (DA(DL)) method for estimating vertical variations in soil thermal properties from heat pulse measurements to characterize dry soil layer (DSL) dynamics. It demonstrates that DA(DL) outperforms conventional methods in handling nonlinearity and successfully captures DSL evolution under natural evaporation conditions.
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Pang et al. (2025) Flash droughts threaten global managed forests
This study reveals that global forests, particularly managed ones, have experienced increasingly rapid, intense, and prolonged flash droughts over the past four decades, with current forest management practices exacerbating their vulnerability to browning.
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Cui et al. (2025) Study on the climate impacts on the reservoir waterlevel
This study quantifies the nonlinear impact of temperature and precipitation on reservoir water level fluctuations in Sichuan Province, China, by enhancing a benchmark model with quadratic functions. It reveals critical thresholds and dynamic dependencies in the Marginal Rate of Substitution (MRS) between these climatic factors, offering insights for resilient water resource management.
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Wei et al. (2025) Altitudinal pattern of runoff and its change during 1980–2020 in the Yarkant River, Northwest China
This study investigated the spatial and temporal dynamics of glacier and snowmelt runoff and their contributions to total runoff in the Upper Yarkant River Basin from 1980 to 2020. It found significant declines in meltwater runoff, particularly glacier runoff in July, and revealed strong altitudinal gradients where over 80% of runoff originates from elevations above 3100 meters.
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Zhou et al. (2025) Role of Diurnal Cycle of Insolation on the MJO Propagation in the Maritime Continent
This study uses a regional model to test the hypothesis that the diurnal cycle of convection in the Maritime Continent acts as a barrier to Madden-Julian oscillation (MJO) propagation, finding that the absence of the diurnal cycle allows for more continuous eastward MJO propagation.
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Pawar et al. (2025) Adaptive Precision Agriculture Through Iot And Reinforced Machine Learning (Q-Learning): A Sustainable Approach For Optimized Plant Growth
This study integrates IoT-based sensing with Q-learning reinforcement learning to optimize plant growth and resource efficiency in both controlled and natural field conditions. The findings validate IoT-based adaptive monitoring as a practical tool for improving crop yields, minimizing input use, and promoting long-term agricultural sustainability.
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Jiang et al. (2025) Probabilistic assessment of water network influence zones and ecosystem responses following the middle route of the South-to-North Water Diversion Project
This study quantitatively assesses the structural transformation and ecohydrological consequences of the South-to-North Water Diversion project in central and southern Hebei Province, revealing significant increases in water network density and connectivity, and expanded ecological influence zones post-implementation.
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Guo et al. (2025) An improved gap probability estimation method accounting for radiometric effects in airborne LiDAR intensity
This paper introduces PRE_COR, a novel method for estimating vegetation canopy gap probability (P) from airborne LiDAR intensity data by correcting for radiometric effects (distance and incidence angle). The method significantly enhances P estimation accuracy and stability, particularly under large scan angle conditions, outperforming traditional approaches.
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Dubey et al. (2025) Forest-savanna stability in India under human interventions and changing climate
This study investigates the stability of forest-savanna ecosystems in India under human interventions and climate change, projecting a future shift towards savanna-like conditions due to increased precipitation variability, while highlighting the mitigating potential of strategic anthropogenic conservation efforts.
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Liu et al. (2025) Radar First Echo Detection With Spaceborne Precipitation Radars
This study identifies and characterizes "first echoes" in fresh growing clouds using 26 years of satellite Ku-band radar observations, revealing three distinct altitude-related modes in the tropics linked to different microphysical processes, environmental conditions, and aerosol types.
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Cheng et al. (2025) Improving winter wheat yield and water use efficiency using soil moisture sensor-driven precision furrow irrigation
This study evaluated precision irrigation strategies using real-time soil moisture monitoring to enhance winter wheat yield and water use efficiency (WUE) in South Korean rice-wheat double-cropping systems. Field experiments demonstrated that irrigation based on 55 % available soil water significantly improved grain yield and WUE compared to rainfed and saturation-based approaches.
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Yan et al. (2025) Simulation of soil moisture and drought prediction in middle reaches of the Yellow River based on machine learning
This study integrates a Multi-Layer Perceptron (MLP) model with RegCM4 climate data to generate a high-resolution, layered daily soil moisture dataset (MLP_D) for the Middle Reaches of the Yellow River (MRYR) from 2001 to 2100. Analysis of this dataset reveals a decline in deep soil moisture historically and projects a significant increase in future drought frequency and duration under intensifying climate change scenarios.
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Zerouali et al. (2025) Climate Change and Groundwater Sustainability in the Berrechid Aquifer (Morocco): Projections to 2050 Under Regulated Abstraction Scenario
This study investigates the impacts of climate change on the Berrechid aquifer in Morocco and evaluates groundwater-withdrawal management strategies using GMS simulations, providing insights into future aquifer evolution and groundwater availability up to 2050.
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Li et al. (2025) Evaluation of the effectiveness of the large turbulent eddies parameterization in typhoon for typhoon intensity forecasting
This study evaluates the effectiveness of a large turbulent eddies (LEs) parameterization (LP scheme) in the WRF model for typhoon intensity forecasting, revealing that the original scheme can disrupt inflow and lead to poor simulations, but modifications like adding divergence and Richardson number thresholds significantly improve accuracy for Super Typhoon Lekima (2019).
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Clerck et al. (2025) High-spatial-resolution gross primary production estimation from Sentinel-2 reflectance using hybrid Gaussian processes modeling
This study develops a hybrid modeling framework using Sentinel-2 reflectance and Gaussian Process Regression (GPR) trained with SCOPE radiative transfer model simulations to estimate high-spatial-resolution (20 meters) Gross Primary Production (GPP) across 10 plant functional types (PFTs). The PFT-specific GPR models, implemented in Google Earth Engine, demonstrated strong predictive performance in most ecosystems, outperforming MODIS GPP in terms of bias and spatial detail, though showing limitations in evergreen forests.
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Pan et al. (2025) Multi-Scale Assessment and Prediction of Drought: A Case Study in the Arid Area of Northwest China
This study analyzed multi-scale drought evolution in the Arid Area of Northwest China (AANC) from 1962–2021, revealing a shift from wetting to drying after 1997 due to warming-enhanced evapotranspiration. It developed a Stacking ensemble model that significantly improved meteorological drought prediction, projecting increasingly frequent and severe droughts in the AANC until 2035.
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Shen et al. (2025) Forecasting River Ice Breakup and Ice Jam Flooding
This study reviews existing river ice breakup forecasting methods, including data-driven and machine learning techniques, and develops a novel physically based method for rapid, Nowcasting-mode forecasting of ice cover breakup and associated ice jam flooding.
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Zhang et al. (2025) Dynamic monitoring of ecological security patterns in arid zone oases: a remote sensing-based ecological index evolution analysis
This study dynamically monitored ecological security patterns in three arid zone oases in China from 2000-2022 using a remote sensing ecological index (RSEI) and interpretable machine learning, identifying precipitation as the primary driver of eco-environmental quality (EEQ) dynamics and establishing critical thresholds for key factors.
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Emelina et al. (2025) Comparative analysis of drought indices to assess drought conditions in agricultural regions of Northern Eurasia
This study compares the Selyaninov hydrothermal coefficient (HTC) and the Standardized Precipitation Evapotranspiration Index (SPEI) for assessing extreme drought frequency in agricultural regions of European Russia and Central Asia from 1991-2020, finding SPEI to have slightly higher correlation with observed soil moisture.
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Diouf et al. (2025) Assessment of Spatio-Temporal Trends in Rainfall Indices in Senegal: Validation of CMIP6 Models over the Historical Period and Projections Under Future Climate Scenarios
This study investigates historical and projected rainfall extremes in Senegal using bias-corrected CMIP6 data to assess risks to water resources under future climate scenarios. It finds that CMIP6 models effectively capture historical patterns, projecting significant shifts in rainfall regimes with prolonged dry periods in the north and heavier rainfall in the south, necessitating urgent adaptation planning.
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Bakhrel et al. (2025) Identifying Urban Pluvial Frequency Flooding Hotspots Using the Topographic Control Index and Remote Sensing Radar Images for Early Warning Systems
This study integrates Sentinel-1 radar imagery and the Topographic Control Index (TCI) to identify and prioritize urban areas frequently experiencing post-rainfall ponding in Beaumont, Texas, revealing 99 natural flood-vulnerable depressions, with 74 identified as high-priority nuisance flooding hotspots.
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Paredes et al. (2025) rsofun v5.1: a model-data integration framework for simulating ecosystem processes
This study introduces and evaluates `rsofun` v5.1, an R package providing a computationally efficient implementation of the P-model for simulating ecosystem photosynthesis and trait acclimation, coupled with Bayesian model-data integration. Multi-target calibration using ecosystem fluxes and leaf traits demonstrated robust parameter estimation and unbiased predictions that generalize well across diverse environmental conditions.
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Tran (2025) Leveraging Advancements in Earth Observation Products for Disaster Monitoring and Water Resources Management
This dissertation comprises three studies: evaluating satellite precipitation products and hydrological models in Vietnamese river basins, projecting future hydroclimatic disaster risks in coastal Virginia using GCMs, and conducting a comprehensive global assessment of historical drought conditions and recovery using multiple soil moisture datasets.
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Arboleda et al. (2025) Joint evolution of irrigation, the water cycle and water resources under a strong climate change scenario from 1950 to 2100 in the IPSL-CM6
This study investigates the coupled evolution of irrigation, the water cycle, and water resources from 1950 to 2100 under a strong climate change scenario using the IPSL-CM6 model, revealing increased irrigation, intensified water depletion, and identifying regions vulnerable to water stress.
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Chambers et al. (2025) Hot droughts in the Amazon provide a window to a future hypertropical climate
This study synthesizes long-term demographic, ecophysiological, and climate model data to assess the impact of "hot droughts" on central Amazon forests. It finds that hot droughts significantly increase tree mortality, particularly for fast-growing species, by pushing soil moisture below critical thresholds, and projects that these extreme conditions will become typical in a future "hypertropical" climate by 2100.
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Zhu et al. (2025) Increased Thunderstorm Activities Caused by Warming and Wetting Over the Tibetan Plateau
This study generated a new continuous thunderstorm dataset for the Tibetan Plateau (TP) from 2010 to 2024, revealing a significant increase in thunderstorm activity but a weak decrease in intensity, primarily driven by changes in convective available potential energy and moisture.
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Li et al. (2025) Exploring the driving forces of soil salinity reduction using Random Forest and SHAP in water-saving oasis irrigation areas
This study investigated the spatiotemporal dynamics and driving forces of soil salinity reduction in water-saving oasis irrigation areas of the Manas River Basin from 2013 to 2021, revealing a significant decrease in salinity primarily driven by declining groundwater depth.
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Servín‐Palestina et al. (2025) Calibración del sensor de humedad Watermark 200SS para programación de riego en cinco tipos de suelos de Zacatecas, México
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Yuan et al. (2025) Critical Rainfall of Torrential Floods Induced by Heavy Precipitation Based on the FloodArea Model: A Case of Luba River, China
This study addresses the challenge of calculating critical rainfall for torrential floods in data-scarce mountainous small watersheds by employing the FloodArea hydrodynamic model. It successfully simulates a significant flood event in the Luba River Basin, validating the model's effectiveness and deriving critical rainfall thresholds for various flood risk levels to aid disaster prevention.
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Zhang et al. (2025) An Automated Method for Regional-Scale Agricultural Soil Moisture Retrieval Using ISMN Measurements and Sentinel Data in Google Earth Engine
This paper presents an automated method for retrieving agricultural soil moisture at a regional scale, leveraging ISMN measurements and Sentinel satellite data within the Google Earth Engine platform.
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Zhao et al. (2025) Optimizing Cloud Mask Accuracy over Snow-Covered Terrain with a Multistage Decision Tree Framework
This study developed an enhanced multi-threshold cloud detection algorithm using AVHRR data to improve cloud-snow discrimination in optical remote sensing, achieving an overall accuracy of 82.08% and significantly outperforming existing methods, especially in snow-covered mountainous regions.
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Shah et al. (2025) Integrating time–space dynamics for meteorological drought monitoring and trend analysis
This study developed a Composite Integrated Meteorological Drought Index (CIMDI) by integrating SPI, SPEI, and SPTI using a hybrid weighting scheme (steady-state probabilities and mean squared correlation) to provide a more robust and spatially/temporally adaptive meteorological drought assessment in Punjab, Pakistan. CIMDI demonstrated superior statistical performance and identified significant increasing drought trends in several stations.
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Ye et al. (2025) Comparison of Process-Based and Machine Learning Models for Streamflow Simulation in Typical Basins in Northern and Southern China
This study compared the performance of two process-based hydrological models (SWAT, GWLF) and a machine learning model (Random Forest) for monthly streamflow simulation in contrasting humid southern and semi-arid northern Chinese basins, concluding that optimal model selection depends on hydrological context, data availability, and the need for physical realism.
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Cai et al. (2025) Multifaceted Analysis of Green Roof Characteristics in Modulating Urban Microclimate Patterns
This study utilized the ENVI-met simulation platform to systematically analyze how green roof characteristics (vegetation type, Leaf Area Index, substrate moisture) and environmental factors (meteorology, building height) modulate urban microclimate patterns, revealing that intensive green roofs and higher Leaf Area Index offer superior heat mitigation and thermal comfort benefits.
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García-Martínez et al. (2025) Performance curve of two analog sensors for estimating soil moisture
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Malik et al. (2025) High-impact and Low-likelihood compound hot and dry extremes in India
This study investigates the occurrence and implications of high-impact and low-likelihood (HILL) compound hot and dry extremes (CHDEs) in India under observed and projected future climates, finding a significant increase in their frequency, duration, spatial extent, and intensity, particularly during the summer monsoon, driven by climate warming and El Niño events.
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Korzani et al. (2025) Dynamic CA-ffé: A hybrid 1D/2D fast flood evaluation model for urban flash floods
This paper introduces Dynamic CA-ff´e, a novel hybrid 1D/2D urban flash flood model coupling a static 2D cellular automata overland flow model with a dynamic 1D drainage network model. It demonstrates high accuracy in flood prediction comparable to full hydrodynamic models, while achieving significantly faster simulation times (at least 20 times faster).
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Bokuchava et al. (2025) Contribution of Leading Natural Climate Variability Modes to Winter SAT Changes in the Arctic in the Early 20th Century
This study assessed the contributions of Northern Hemisphere natural variability modes to 20th-century Arctic winter temperature changes, revealing that these modes explain a significant portion of the variance, with forced-signal removal proving more effective than detrending for isolating internal dynamics.
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Mirza et al. (2025) Evaluating the utility of Sentinel-1 in a Data Assimilation System for estimating snow depth in a mountainous basin
This study evaluates the temporal and spatial accuracy of Sentinel-1 (S1) C-band radar snow depth retrievals and their utility within a data assimilation (DA) system for mountain snowpack characterization in the East River Basin, Colorado, finding significant inconsistencies and limited potential for S1 to improve snow DA in this region.
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Tóth et al. (2025) Stationarity and non-stationarity in long-term climate time series in Hungary
This study investigated the stationarity of 154 years of monthly precipitation and temperature data in four Hungarian cities, revealing regional differences in precipitation stationarity (stable in Szeged and Nyíregyháza, declining in Budapest and Sopron) but universal non-stationarity and upward trends in temperature across all locations.
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Xu et al. (2025) Applicability and improvement of the Chicago storm pattern from the perspective of dimensionless storm curve shape and co-frequency probability of rainfall depths
This study re-evaluates the applicability of the Chicago storm pattern, finding its fixed shape unsuitable for high-volume rainfall events, and proposes improved storm patterns that offer more realistic representations for urban flood assessment and drainage design optimization.
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Bao et al. (2025) Spatio-Temporal Evolution of Ecosystem Water Use Efficiency and the Impacts of Drought Legacy on the Loess Plateau, China, Since the Onset of the Grain for Green Project
This study characterized the spatio-temporal dynamics and critical legacy effects of moisture stress on ecosystem water use efficiency (eWUE) on China's Loess Plateau from 2001–2020. It found an increasing eWUE despite persistent drying, with eWUE exhibiting spatially divergent sensitivity and a pronounced multi-year drought legacy effect, highlighting the need for optimized water management in reforestation efforts.
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Cosme et al. (2025) Deconstructing Agrivoltaic Microclimates: A Critical Review of Inherent Complexity and a Minimum Viable Monitoring Framework
This systematic review critically analyzes the microclimatic impacts of agrivoltaic systems (AVS), revealing significant variability in effects on atmospheric, radiation, and soil parameters. It proposes a "Minimum Viable Monitoring" (MVM) framework to standardize data collection and leverage AVS heterogeneity for precision agriculture.
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Camilletti et al. (2025) AI Reconstruction of European Weather From the Euro‐Atlantic Regimes
This study develops a non-linear AI model to reconstruct monthly mean anomalies of European temperature and precipitation using Euro-Atlantic Weather Regime (WR) indices, demonstrating its potential for sub-seasonal to seasonal forecasting by capturing complex non-linear relationships and showing improved or comparable skill to a state-of-the-art seasonal forecast system.
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Zhou et al. (2025) Detecting time-varying characteristics of the integrated parameter in the generalized complementary principle based on long-term flux data
This study investigated the temporal variability of the integrated parameter αc in the generalized complementary relationship (GCR) using long-term flux data from seven sites, revealing that incorporating its temporal dynamics significantly improves regional evaporation estimation across various time scales.
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Quintela et al. (2025) Refining European Crop Mapping Classification Through the Integration of Permanent Crops: A Case Study in Rapidly Transitioning Irrigated Landscapes Induced by Dam Construction
This study refines the EU Crop Map 2018 by developing an automated machine learning model integrating Sentinel-1 and Sentinel-2 imagery to distinguish permanent crop types in southern Portugal, achieving 91% overall accuracy and highlighting the critical need for balanced training data.
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Dobiašová et al. (2025) The Impact of Climate Change on Changes in the Onset and Termination of Growing Seasons and the Area of Agriculturally Usable Land in Slovakia
This study aimed to evaluate and revise the spatial extent of vegetation zones and agricultural land in Slovakia under projected climate change. It found that growing seasons are expected to lengthen significantly and shift to higher altitudes by the end of the century, impacting agricultural potential.
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Bai et al. (2025) Influence of ENSO on the seasonal and extreme characteristics of ridges over Pacific-Western North American region
This study investigates the seasonal and extreme characteristics of atmospheric ridges over Pacific-Western North America and their modulation by ENSO, finding that La Niña generally favors more frequent, larger, and more intense extreme ridges, especially in winter, with ENSO-forced shifts in mean flow largely explaining these patterns.
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(USGS) et al. (2025) Projected future groundwater balance for California Central Coast under different scenarios of land-use and climate change
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Li et al. (2025) Surrogate modeling for rapid estimation of spatially-resolved flood damage: Application to a coastal region
This study introduces BayFlood, a Bayesian-optimized machine learning surrogate model for rapid, accurate, and spatially resolved flood damage estimation using river discharge and tidal level inputs. The boosting-ensemble-driven BayFlood achieved the best performance (coefficient of determination = 0.92–0.98; root mean square error = 0.04–0.08) and reduced computational time by two orders of magnitude compared to hydraulic modeling.
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Thomas (2025) Variabilité climatique multiéchelle : une approche systémique combinant multifractales et réseaux complexes
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Abadefar et al. (2025) Integrated approach to identify suitable sites for percolation tanks to enhance groundwater in Kesem sub-basin
This study identified suitable sites for percolation tanks to enhance groundwater recharge in the Kesem sub-basin, Ethiopia, using an integrated Geographic Information System (GIS), remote sensing (RS), the Soil and Water Assessment Tool (SWAT), and Fuzzy Analytical Hierarchy Process (FAHP) framework. The analysis revealed that only 3.26 % of the sub-basin is classified as very highly suitable for percolation tank development.
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Li et al. (2025) Comparing the impact of precipitation pre-processing and streamflow post-processing for daily sub-seasonal streamflow forecasts over the Gan River basin
This study investigates the separate and joint impacts of precipitation pre-processing and streamflow post-processing on daily sub-seasonal streamflow forecasts over the Gan River Basin. It finds that streamflow post-processing is more impactful for lead times within 10 days, while precipitation pre-processing becomes more significant at longer lead times, with specific methods (BJP and SSh) showing superior performance.
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Chen et al. (2025) Robust water level measurement using adaptive prompt staff gauge image segmentation based on EdgeSAM
This study proposes a robust image-based water level measurement method for staff gauges using an adaptive prompt EdgeSAM model, achieving high accuracy and generalization with minimal training data in complex field environments. The method addresses limitations of traditional and existing deep learning approaches by integrating image features with prompt information for precise segmentation and measurement.
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Hiraga et al. (2025) Impacts of Climate Change on Tropical Cyclones and associated Rainfall over South Korea: Storyline and Risk-based approaches
This study quantifies the impact of climate change on Typhoon Hinnamnor and its associated rainfall over South Korea using a storyline approach, and examines changes in the frequency and intensity of tropical cyclones affecting the region through a risk-based approach. It finds that future warming intensifies Hinnamnor-like typhoons and increases the frequency of violent typhoons, despite a decrease in overall tropical cyclone frequency, with significant implications for regional flood risk.
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Zhang et al. (2025) Assessment of WRF-Solar and WRF-Solar EPS Radiation Estimation in Asia Using the Geostationary Satellite Measurement
This study evaluates the short-term performance of the WRF-Solar model and its ensemble version for global horizontal irradiance (GHI) and direct horizontal irradiance (DIR) over East Asia, revealing systematic overestimation for GHI and regional biases for DIR, with limited improvement from the ensemble, necessitating refinements in cloud-aerosol parameterizations.
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Wang et al. (2025) Drought onsets and their driving factors for multiple drought types in the Yangtze River Basin
This study analyzed the spatiotemporal evolution and driving mechanisms of meteorological, agricultural, and hydrological drought onset characteristics in the Yangtze River Basin (YRB), revealing that temperature is the dominant driver with complex nonlinear interactions among factors, especially under extreme conditions, which provides a scientific basis for drought risk prevention.
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Yu et al. (2025) Synergistic Fusion of Sentinel-1 and Sentinel-2 for Global LULC Mapping: The Multimodal Network LULC-Former and Dynamic World+ Dataset
This paper introduces LULC-Former, a multimodal network for global Land Use/Land Cover (LULC) mapping, leveraging the synergistic fusion of Sentinel-1 and Sentinel-2 data, and presents the associated Dynamic World+ dataset.
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Zhu et al. (2025) Applications of Polarization Spectroscopy in Agricultural Engineering: A Comprehensive Review
This comprehensive review synthesizes the principles and applications of polarization spectroscopy analysis (PSA) in agricultural engineering, demonstrating its advantages over conventional methods for non-destructive testing across various agricultural materials and environments. It highlights PSA's potential to enhance precision agriculture through improved monitoring of crop health, pest/disease detection, quality assessment, and environmental evaluation.
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Xu et al. (2025) Controls on actual evapotranspiration in wetland ecosystems across different climates
This study investigated the controls on actual evapotranspiration (ETa) in 40 wetland ecosystems across temperate, cold, and polar climates, revealing that while incoming shortwave radiation is a common driver, other controls like leaf area index and hydrological regimes vary significantly with climate type and timescale. The research identified four distinct response patterns of daily ETa to vegetation dynamics, enhancing understanding of wetland ETa responses to environmental changes.
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Sun et al. (2025) A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data
This study developed a machine learning-based quality control algorithm for heavy rainfall by integrating multi-sensor observations, demonstrating that gradient boosting models significantly outperform conventional threshold-based methods, thereby enhancing data reliability and interpretability.
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Federico et al. (2025) Forecasting convective precipitation over northern Italy: A comparison of lightning and GNSS-ZTD data assimilation
This study evaluates the impact of assimilating lightning and Global Navigation Satellite System – Zenith Total Delay (GNSS-ZTD) data on short-range (up to 6 hours) forecasts of intense convective precipitation over Northern Italy, demonstrating that both data sources, particularly when combined, significantly enhance forecast accuracy.
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Neto et al. (2025) How does the reference period influence meteorological drought analysis and monitoring? A case study in Northeast Brazil using a century of data
This study investigates how different reference periods influence the identification and characterization of meteorological droughts in Northeast Brazil using the Standardized Precipitation Index (SPI) over a century of data, finding that short (10-year) and long (100-year) periods distort drought classification, while 20-year and 40-year periods best match the conventional 30-year baseline.
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Archfield (2025) Spatial connections between the timing of hydroclimatic extremes
This News & Views article discusses a global analysis that reveals unexpected cross-hemisphere synchronizations in the timing of droughts and rainfall-induced flooding, highlighting their significant implications for global food supply chains.
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Kasaei et al. (2025) Compounding of 100-year coastal floods by rainfall in an urban environment
This study investigates the combined impacts of coastal and pluvial flood drivers, challenging the assumption that rainfall has a negligible effect on 100-year coastal flood maps. It finds that pluvial drivers can significantly expand flood zones and occasionally deepen floods, particularly in topographic depressions.
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Miao et al. (2025) Comparing the Linkage between Springtime Central Pacific Cross-Equatorial Winds and Wintertime ENSO Events in Reanalyses and CMIP6 Models
This study assesses the fidelity of the springtime cross-equatorial meridional wind anomaly as an El Niño–Southern Oscillation (ENSO) precursor in CMIP6 models, finding that models underestimate this relationship primarily due to overestimating ENSO persistence, but successfully reproduce the linkage when the springtime ENSO signal is removed.
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Larson et al. (2025) QuadTune version 1: a regional tuner for global atmospheric models
QuadTune is a novel, inexpensive regional tuner for global atmospheric models that uses an uncorrelated quadratic emulator to quickly reduce parametric errors. In an example application, it demonstrated a reduction in Shortwave Cloud Radiative Forcing (SWCF) root mean square error (RMSE) from 12.4 W m⁻² to 10.4 W m⁻².
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Yu et al. (2025) Optimizing Crop Maximum Carboxylation Rate Using Machine Learning to Improve Maize Yield Estimation Under Drought Conditions
This study focuses on optimizing the crop maximum carboxylation rate using machine learning to enhance maize yield estimation under drought conditions.
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Yuan et al. (2025) Data Assimilation in Hydrological Models: Methods, Challenges and Emerging Trends
This study systematically synthesizes research hotspots and cutting-edge trends of data assimilation (DA) in hydrology, categorizing DA techniques by model structure, parameters, and states, and identifying key challenges while proposing future directions like integrating deep learning.
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Radebe et al. (2025) A near-surface groundwater prospectivity model for the Main Karoo Basin of South Africa derived from multivariate machine learning
This study developed a near-surface groundwater prospectivity model for the Main Karoo Basin, South Africa, using multivariate machine learning, demonstrating its effectiveness in identifying high-potential zones, particularly during drought periods. The model, based on the Fast Tree Decision Learning algorithm, achieved high accuracy and showed significant alignment with known groundwater indicators.
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Unknown (2025) Ai-Based Smart Irrigation System
This research proposes an intelligent irrigation model combining deep learning-based soil moisture classification from mobile phone images with dynamic weather forecasting to improve irrigation decisions, achieving up to 92% classification accuracy and 30-40% water savings.
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Creamean et al. (2025) Long-term measurements of ice nucleating particles at Atmospheric Radiation Measurement (ARM) sites worldwide
This paper presents one of the most comprehensive publicly available datasets of immersion-mode ice nucleating particle (INP) concentrations, generated using a single analytical method through the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility. Collected across diverse global environments over long periods, this dataset reveals distinct seasonal and site-specific differences in INP concentrations and types, providing critical constraints for aerosol-cloud interaction models.
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Stoffels et al. (2025) Precipitation, moisture sources and transport pathways associated with summertime North Atlantic deep cyclones
This study investigates how summertime extratropical cyclones influence the water cycle by identifying their moisture sources and transport pathways. It finds that precipitation primarily occurs near the cyclone center during intensification, sourced from high ocean evaporation regions and some continental areas, with moisture residence time remaining constant at approximately 4 days throughout the cyclone's life cycle.
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Ayugi et al. (2025) Physical Predictand Drivers and Characteristics of Aridity Across East Africa
This study investigates the expansion or contraction of aridity in East Africa over 34 years and identifies its dominant climatic drivers. It finds that temperature fluctuations exert a stronger influence on aridity patterns than precipitation, explaining 82.2% of the total variance, and regional aridity significantly correlates with major climate indices.
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Zhao et al. (2025) Limited capability of current satellite solar-induced chlorophyll fluorescence reconstructions to capture stomatal responses to environmental stresses
Current satellite solar-induced chlorophyll fluorescence (SIF) reconstructions have limited capability to capture stomatal responses to environmental stresses, leading to an overestimation of gross primary productivity (GPP) during dry periods due to a decoupling between stomatal responses and the physiological SIF emission yield.
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Yang et al. (2025) Projected hydroclimatic changes in Xinjiang under bias-corrected CMIP6 scenarios
This study projects future hydroclimatic changes in Xinjiang (2031–2060) using bias-corrected CMIP6 scenarios, revealing significant warming, increased potential evapotranspiration, and intensified drought risk, particularly in southern Xinjiang and eastern basins.
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Alsumaiei et al. (2025) Enhancing Soil Water Prediction in Arid Climates Using Multipredictor Machine-Learning Models and SHAP-Based Interpretability
## Identification - **Journal:** Journal of Irrigation and Drainage Engineering - **Year:** 2025...
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Nasser et al. (2025) Design of an Irrigation System Using Infrared Lasers
This paper designs and develops a smart irrigation system that uses infrared laser detection for non-contact soil moisture assessment, automating irrigation and providing GSM-based alerts. The system demonstrates over 95% accuracy in moisture detection and a 1.2-second response time, supporting efficient water management and sustainable farming.
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Budhathoki (2025) Data-Driven and Process-Based Modeling Approaches for Advancing Irrigation Water and Nitrogen Management in Humid Cropping Systems
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Ear (2025) Modèles distributionnels pour la correction de biais des précipitations journalières : un focus sur les évènements extrêmes
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Khaniya et al. (2025) Using ensemble optimal interpolation with dynamic covariance matrices for assimilation of water level observations in a distributed rainfall-runoff-inundation model
This study investigates two ensemble generation strategies for the computationally efficient ensemble optimal interpolation (EnOI) scheme to produce dynamic covariance matrices for assimilating water level observations into a distributed rainfall-runoff-inundation model, demonstrating that EnOI can provide improved state estimates compared to deterministic simulations, particularly with an adaptive error parameter estimation approach.
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Vesterdal et al. (2025) Current remote sensing applications for sustainable agricultural transitions and nature-based solutions
This review systematically synthesizes current satellite remote sensing applications in agriculture, focusing on their potential to facilitate a transition towards sustainable management practices in nutrient management, environmental impact, and food security. It highlights that while remote sensing offers valuable insights, challenges in data integration, accuracy, and scalability persist.
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Graversen et al. (2025) Enhanced weather persistence due to amplified Arctic warming
This study provides observational evidence that weather persistence, in terms of surface-air temperature anomalies, has increased in the Northern Hemisphere mid-latitudes during recent decades, directly linking this change to amplified Arctic warming and a consequent weakening of mid-latitude westerly atmospheric flow.
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Ma et al. (2025) Spatial and Temporal Variability of Cloud Water Content and Precipitation Efficiency in the Tienshan Mountains
This study investigates the spatiotemporal characteristics of total cloud water (TCW) and precipitation efficiency (PE) over the Tienshan Mountains from 1979–2023, revealing distinct spatial patterns and trends that impact regional water resources and drought risks.
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Xu et al. (2025) From grain to ground: How hydrologic uncertainty drives shifts in crop patterns across the Yellow River Basin
This study developed a robust bi-objective optimization model to optimize agricultural resource allocation in the Yellow River Basin under hydrologic uncertainty, finding that adaptive crop patterns and irrigation technology are crucial for balancing water use efficiency and inter-provincial fairness.
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Piemontese et al. (2025) Global dataset of sand dam features and geographical distribution across drylands
This paper introduces the Global Sand Dams Dataset (GSDD), the first comprehensive global inventory of 1006 sand dam locations and dimensions, developed to address the lack of empirical data on this crucial dryland water infrastructure. The dataset aims to support research on sand dam effectiveness and aid practitioners in planning new installations.
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Marino (2025) REST-COAST Sicily - SWAN+XBeach flood model results
This study presents SWAN+XBeach model results simulating coastal flood areas in Granelli, Sicily, under present and future climate change scenarios (2070, 2100) for 50-year and 100-year return period storms, evaluating the efficacy of Nature-based Solutions (NbS).
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Khorram et al. (2025) Harnessing hyperspectral imaging and machine learning to enhance salinity stress detection in canola
This study applied hyperspectral imaging and machine learning to detect and classify salinity stress in canola, achieving 82.61% accuracy with a ridge classifier using a reduced set of 15 spectral features and novel vegetation indices.
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Lambert (2025) Irrigation increased historical land surface and groundwater loss
A systematic modelling study indicates that historical increases in agricultural irrigation have caused net losses of both land surface runoff and groundwater, independently exceeding the losses attributable to climate change.
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Kumwenda et al. (2025) FIEA: An android tool for sustainable furrow irrigation
This study introduces the Furrow Irrigation Evaluation App (FIEA), an Android-based tool that computes application efficiency and water losses using simplified SCS hydraulic relationships. FIEA was validated against established desktop models, demonstrating high accuracy for application efficiency and runoff, thus providing a field-ready solution for sustainable irrigation management in data-scarce environments.
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Janzing et al. (2025) Hyper-resolution large-scale hydrological modelling benefits from improved process representation in mountain regions
This study enhances the hyper-resolution global hydrological model PCR-GLOBWB 2.0 for mountain regions by implementing structural and parametric changes, including an extended snow module, a glacier module, and adjusted runoff partitioning. It demonstrates that these process-based improvements, alongside high-resolution meteorological forcing, significantly enhance discharge simulations in complex mountain environments.
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Wang et al. (2025) Possible Causes of the Significant Advancement of the Tibetan Plateau Rainy Season under the Background of Climate Warming
The Tibetan Plateau rainy season (TPRS) has significantly advanced from 1961 to 2023, primarily driven by an intensified Lake Baikal ridge, weakened 500-hPa westerlies over East Asia, and enhanced activity of warm, moist air over the southern TP, all linked to regional climate warming and large-scale teleconnections.
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zhang et al. (2025) Ground-based solar irradiance observations from Xianghe (XIA) station
This paper presents a quality-controlled, one-minute resolution dataset of ground-based global horizontal, direct normal, and diffuse horizontal solar irradiance measurements collected over one year at the Xianghe station. The dataset aims to provide reliable observational data for atmospheric and solar energy research.
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Ogunrinde et al. (2025) Probabilistic quantification of global drought risk amplification from temperature-enhanced evapotranspiration under climate change
This study develops a probabilistic framework using the Risk Ratio methodology and CMIP6 models to quantify global drought risk amplification from temperature-enhanced evapotranspiration under climate change. It reveals pervasive global drought intensification, particularly under high-emission scenarios, with over 90% of land grids projected to experience increased severity by the far-future period.
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Liu et al. (2025) Global characteristics of drought propagation from surface water to groundwater
This study globally assesses groundwater drought using GRACE satellite data and quantifies its propagation relationships with meteorological and hydrological droughts, revealing distinct propagation times and characteristics for each drought type.
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Zhou et al. (2025) Response of Vegetation to Extreme Climate in the Yellow River Basin: Spatiotemporal Patterns, Lag Effects, and Scenario Differences
This study projected Leaf Area Index (LAI) responses to extreme climatic factors in the Yellow River Basin (YRB) from 2025 to 2065 using CMIP6 outputs under three SSP scenarios. It found a consistent increasing trend in regionally averaged LAI across all scenarios, with significant spatial and scenario-dependent differences in vegetation responses to extreme climates.
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Shikhov et al. (2025) Climatology and Formation Environments of Heavy Snowfall Events in the Ural Region (Russia)
This study provides the first comprehensive climatology and synoptic-scale environment analysis of hazardous heavy snowfall (HHS) events (≥20 mm 12 h−1) in the Ural region (1981–2025), revealing that 46% of events are linked to cyclones from the Caspian and Aral seas, leading to higher HHS frequency east of the Ural Mountains.
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Dehati et al. (2025) Comparison and validation of spatial reference evapotranspiration datasets over Africa
This study compares eight open-access global reference evapotranspiration (ET0) datasets against in-situ measurements from 165 weather stations across Africa to assess their performance across different climate zones. It finds that high-resolution datasets perform better in temperate and tropical regions, and that input data quality accounts for 60–70% of the variability among datasets.
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Raza et al. (2025) Corrigendum to “Predicting regional-scale groundwater levels at high spatial resolution using spatial Random Forest models” [Int. J. Appl. Earth Obs. and Geoinf. 144C (2025) 104918]
This document is a corrigendum correcting an error in the legends of Fig. 1(c) in the original paper titled "Predicting regional-scale groundwater levels at high spatial resolution using spatial Random Forest models".
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Mahdi et al. (2025) Intensifying Drought Patterns and Agricultural Water Stress in Erbil Governorate, Iraq: A Spatiotemporal Climate Analysis
This study assesses drought impacts in Erbil, Iraq, over 28 years using a soil-water-balance framework and the Standardized Precipitation Index (SPI), identifying recurring drought events, severe stress on winter wheat, and high precipitation variability, underscoring the need for climate-resilient planning.
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Pink et al. (2025) Increased rainfall-runoff drives flood hazard intensification in Central Himalayan river systems
This study provides the first large-ensemble, regional climate-change impact assessment of design floods for the Central Himalayan Karnali River, projecting significant increases in 1% annual exceedance probability flood magnitudes (up to +79% by 2060–2099 under high emissions) primarily driven by increased rainfall-runoff.
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Liu et al. (2025) A Comprehensive Long-Term (1981–2020) Radiation Dataset for China: Ensuring Consistency, Comparability, and Incorporating Uncertainty
This study generated a comprehensive, long-term (1981-2020) radiation dataset for China, including global radiation, diffuse radiation, photosynthetically active radiation (PAR), and diffuse PAR, along with uncertainty estimates, by combining in situ observations with a Gaussian process regression approach, revealing decreasing trends in global radiation and PAR primarily driven by water vapor.
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Chowdhury et al. (2025) Aerosol‐Cloud Interactions and Their Role in Modulating Lightning Activity: Evidence From Extreme Events Over India
This study investigates the role of aerosol-cloud interactions in modulating lightning flash rates over India using the WRF model. It reveals a nonlinear relationship between aerosol loading (CCN concentration and size) and lightning activity, with region-specific optimal thresholds for enhancement, beyond which suppression occurs.
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Pitoro et al. (2025) Dataset of Calibration of Low-cost Soil Moisture Sensors based on IoT approach
This paper presents a dataset for the empirical calibration and performance comparison of two low-cost soil moisture sensors (TDR and capacitive) against gravimetric moisture, collected using a custom IoT-based data acquisition unit.
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Toosi et al. (2025) S 3 -ESRGAN: Enhanced Super-Resolution Generative Adversarial Network for Remote Sensing Imagery Spatial Resolution Improvement—An Application Using Sentinel-2 and UAV Images
## Identification - **Journal:** IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - **Year:** 2025...
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Alif et al. (2025) Design and Implementation of an IoT-Based Smart Drip Irrigation System Using Takagi-Sugeno Fuzzy Logic for Melon Cultivation
This study designed and implemented an Internet of Things (IoT)-based smart drip irrigation system using Takagi-Sugeno fuzzy logic to precisely regulate water supply for melon cultivation in a greenhouse. The system successfully maintained soil moisture within the optimal range of 60%–80%, though plant growth evaluation indicated limitations in promoting overall healthy development, suggesting the need for further refinement considering additional environmental factors.
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Amdar (2025) Jordan_water_data
This paper presents a comprehensive dataset of groundwater abstractions, interbasin transfers, and irrigation water abstractions for the Amman Zarqa basin in Jordan, covering the period from 2018 to 2021.
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Fang et al. (2025) Improving the fine structure of intense rainfall forecast by a designed generative adversarial network
This study proposes a Generative Fusion Residual Network (GFRNet), a generative adversarial network (GAN)-based framework, to integrate multi-source numerical weather prediction (NWP) forecasts and generate 3-hourly quantitative precipitation forecasts for North China up to 24 hours in advance. GFRNet significantly improves the fine structure and intensity control of intense rainfall forecasts compared to traditional NWP and deep learning baseline models.
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Tyagi et al. (2025) Machine Learning-Based Forecasting of Wet-Bulb Temperature and Two-Decade Urban Climate Shifts
This study investigates the spatio-temporal dynamics of Wet-Bulb Temperature (WBT) and Land Surface Temperature (LST) in Delhi, India, from 2005 to 2024, using satellite data and an LSTM model to forecast future WBT trends. The research reveals a significant rise in WBT, projected to exceed 35 °C by 2030 in densely built-up areas, highlighting the urgent need for climate adaptation strategies.
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Zhang et al. (2025) Trends in Atmospheric River Influence on Precipitation over the Jianghuai River Basin in Northern Hemisphere Summer
This study examines the frequency of atmospheric rivers (ARs) and precipitation intensity trends in the Jianghuai River basin from 1970-2019. It finds that despite a declining AR frequency, AR-related precipitation intensity has significantly increased, especially in directly impacted areas, leading to more extreme precipitation events.
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Yu et al. (2025) Long-term land–atmosphere energy and water exchange observational dataset over central Tibetan Plateau
This paper presents a 9-year (2014–2022) hourly observational dataset of land-atmosphere energy and water exchange from four field stations across the central Tibetan Plateau, providing crucial data for understanding regional climate dynamics. The dataset, which includes near-surface meteorological, radiation, turbulent flux, and soil hydrothermal characteristics, reliably reflects the complex interactions across diverse underlying surfaces and temporal scales.
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Zhuang et al. (2025) Design strategies based on hydrological performance of bioretention: experiments and modeling
This study comprehensively evaluated the hydrological performance of bioretention cells through laboratory experiments, numerical simulations, and statistical analyses. It found that topsoil composition and initial soil moisture are critical factors, with performance significantly limited for high-intensity rainfall events exceeding approximately 200 mm, leading to the proposal of climate-based design strategies.
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Zhang et al. (2025) A multi-element coupled drought index for drought characterization in seasonal shallow lake basins
This study develops a Multi-element Coupled Drought Index (MCDI) by integrating six key water cycle elements and their lag times using vine copula, specifically for seasonal shallow lake basins. The MCDI demonstrates superior performance in drought characterization, offering more prompt and accurate detection of drought occurrence and grades compared to traditional indices.
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Tran et al. (2025) Evaluating reanalysis datasets as meteorological input for estimating reference evapotranspiration in Africa and Southwest Asia
This study evaluates the accuracy of ERA5, AgERA5, and GEOS5 reanalysis datasets for meteorological input in Africa and Southwest Asia, finding that GEOS5 is less accurate than ERA5 and AgERA5, and all datasets exhibit specific biases in meteorological variables, leading to high reference evapotranspiration (ETo) uncertainty in tropical regions.
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Chai et al. (2025) Flash droughts exacerbate global vegetation loss and delay recovery
This study reveals that flash droughts cause significantly greater global vegetation loss (9.0%) and delay recovery compared to conventional droughts (5.3%), with increasing frequency of flash droughts being the primary driver of this exacerbation.
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Zhao et al. (2025) Evaluation of climate prediction models in Yunnan, China: traditional methods and AI approaches
This study evaluates five artificial intelligence (AI) models (CNN, LSTM, Transformer, CNN-LSTM, LSTM-Transformer) against a traditional regional climate model (RegCM) for predicting daily temperature, precipitation, and relative humidity in Yunnan, China. The results demonstrate that AI models, particularly LSTM-Transformer and CNN-LSTM, significantly outperform RegCM, offering a data-driven basis for improved climate risk assessment in complex terrains.
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Li et al. (2025) Dynamic Graph Transformer with Spatio-Temporal Attention for Streamflow Forecasting
This study introduces DynaSTG-Former, a novel deep learning architecture designed to enhance multi-step-ahead streamflow forecasting by adaptively integrating diverse spatio-temporal dependencies. The model demonstrated exceptional performance in the Delaware River Basin, significantly outperforming baseline models and providing a robust tool for water management.
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Wang et al. (2025) Aridification enhancing vegetation sensitivities to soil and atmospheric dryness in Northeast Asia
This study investigates how vegetation gross primary production (GPP) sensitivity to soil moisture (SM) and vapor pressure deficit (VPD) shifts across aridity gradients in Northeast Asia, finding that aridification enhances GPP vulnerability, especially to VPD in semi-arid to humid zones.
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Fatima et al. (2025) Machine Learning–Based Bias Correction of Model-Simulated Soil Moisture Using In-situ AWS Observations Over India
This study evaluates statistical and machine learning methods for bias correction of model-simulated soil moisture over India using in-situ observations, finding that machine learning (specifically XGBoost) significantly improves accuracy and correlation across all soil layers.
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Hu et al. (2025) Global retrieval of harmonized microwave land surface emissivity leveraging multi-sensor measurements from GMI, AMSR2 and MWRIs
This study develops an innovative framework to retrieve a global harmonized microwave land surface emissivity (MLSE) database by integrating measurements from five passive microwave sensors (GMI, AMSR2, and three MWRIs) and six geostationary visible/infrared imagers. The framework, employing a simultaneous conical overpass (SCO) recalibration technique, achieves exceptionally strong consistency among the harmonized MLSE subsets, with Pearson R ≈0.95, RMSD <0.011, and mean bias within ±0.005.
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Quang et al. (2025) Semantic water body extraction by the high-quality segment anything model using multiple optical and SAR imagery
This study evaluates the high-quality Segment Anything Model (HQ-SAM) for semantic water body extraction using diverse optical and Synthetic Aperture Radar (SAR) imagery. The HQ-SAM demonstrated high accuracy (above 95%) and outperformed traditional water index-based methods for delineating water bodies in South Korea.
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Huang et al. (2025) Decrease in the spatial inhomogeneity of tropical cyclone rainfall in China
This study reveals a significant 65% decrease in the spatial inhomogeneity of tropical cyclone (TC) rainfall across China from 1998–2023. This redistribution is driven by reduced rainfall in historically high-rainfall areas and increased rainfall in low-rainfall areas, primarily linked to northward TC migration.
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Ercan et al. (2025) Trend analysis of drought in Antalya basin, Türkiye, using classical and innovative approaches
This study spatiotemporally analyzed drought trends in the Antalya basin, Türkiye, from 1969 to 2022 using classical (Mann–Kendall, Sen’s slope) and innovative (Frequency Innovative Trend Analysis) methods with the Standardized Precipitation Index. It revealed an increasing trend in drought events, particularly for longer timescales and in the southern coastal parts of the region.
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Long et al. (2025) Refined River Discharge Estimation Through Integrating Multisource Satellite Remote Sensing and Aerial Photogrammetry
## Identification - **Journal:** IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - **Year:** 2025...
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Kerns et al. (2025) Global Atmospheric River Lifecycle Detection Using Integrated Water Vapor and Vapor Transport
This study introduces the global Atmospheric River Lifecycle Detection (ARLiD) method and a 44-year database that uniquely incorporates both Integrated Water Vapor Transport (IVT) and Total Precipitable Water (TPW) to detect and track atmospheric rivers throughout their entire global life cycles. This dual-variable approach enhances consistency in AR identification, particularly in phases where TPW is a stronger signature than IVT.
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Simon et al. (2025) Arctic regional changes revealed by clustering of sea-ice observations
This study applies k-means clustering to satellite sea-ice concentration data (1979–2023) to identify four distinct Arctic sea-ice seasonal cycle types, revealing a significant decline in permanent sea-ice (3.1% per decade) compensated by increases in open-ocean and seasonal ice types. The research introduces a probabilistic framework to monitor these regional changes and identifies areas of stability, stabilization, and destabilization in the Arctic sea-ice regimes.
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Khandappa et al. (2025) Improving Irrigation Scheduling through Deep Learning-Based Reference Evapotranspiration Estimation
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Farzin et al. (2025) Integrating Deep Learning and Copula Models for Flood–Drought Compound Analysis in Iran
This study developed an integrated framework combining U-Net++, quantile mapping, and Copula models to forecast the combined impacts of drought and flood under future climate change scenarios. The framework demonstrated superior performance in downscaling river flows and projected increased vulnerability to compound extreme events in future periods (2025 and 2071).
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Dogouri et al. (2025) Development of a stochastic multi-objective optimization model for managing the water, food, and energy nexus in agriculture
This study develops a novel stochastic multi-objective optimization model, integrating Chance-Constrained Programming (CCP) with the NSGA-II algorithm, to manage the water-food-energy nexus in the Sefidroud River basin, aiming to minimize agricultural water shortages and maximize hydropower production under uncertainty. The model provides a quantifiable framework for decision-making by analyzing trade-offs between these competing objectives across various confidence levels.
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Sarkar (2025) Precision Agriculture Enhanced with Physics-Informed Neural Networks (pinns)
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Zan et al. (2025) Parameter Uncertainty in Water–Salt Balance Modeling of Arid Irrigation Districts
This study developed a lumped water–salt balance model for arid irrigated regions, integrating farmland and non-farmland areas with a vertical structure, and introduced a novel calibration approach combining random sampling with Kernel Density Estimation (KDE) to address parameter uncertainty. The model satisfactorily simulated groundwater depth and general soil salinity trends, providing a robust tool for water and salt management in data-scarce environments.
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Fan et al. (2025) Grain yield and resource efficiency responses to water-nitrogen coupled input reduction: A global meta-analytical perspective
This meta-analysis systematically evaluated the impact of water-nitrogen coupling management under reduced water and fertilizer inputs on the yield, water use efficiency (WUE), and nitrogen partial factor productivity (NPFP) of maize, wheat, and potatoes. The study found that moderate reductions (approximately 10%) in water and nitrogen inputs significantly increased crop WUE and NPFP while maintaining high yields.
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Jiang et al. (2025) The Global 9 km Soil Moisture Estimation by Downscaling of European Space Agency Climate Change Initiative Data from 1978 to 2020
This study downscales the European Space Agency Climate Change Initiative (CCI) soil moisture data to a 9 km spatial resolution for a 43-year period (1978-2020) using a spatiotemporal fusion model, demonstrating improved spatial detail while maintaining comparable temporal accuracy to the original data.
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Wang et al. (2025) Remote Sensing Inversion and Spatiotemporal Dynamics of Multi-Depth Soil Salinity in a Typical Arid Wetland: A Case Study of Ebinur Wetland Reserve, Xinjiang
This study developed a six-layer (0–100 cm) soil salinity inversion framework integrating multi-year field samples and Landsat imagery for the Ebinur Lake wetland. The framework, particularly using a Convolutional Neural Network with Random Frog Leaping Algorithm-optimized features, accurately retrieved multi-depth soil salinity and revealed distinct salinity migration patterns across different land types.
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Shao et al. (2025) ENT-YOLO: An improved lightweight YOLO for cotton organ detection in mulched drip irrigation systems in southern Xinjiang
This study proposes ENT-YOLO, a lightweight deep learning model based on YOLOv11n, for precise detection and spatial mapping of cotton organs (buds, flowers, bolls) in complex mulched drip irrigation systems in southern Xinjiang. The model achieves high accuracy (79.77 % mAP@0.5) with a compact size (4.2 MB), providing a foundation for intelligent water, fertilizer, and salt management.
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Li et al. (2025) Effects of Cover Crops on Water Use Efficiency in Orchard Systems in the Danjiangkou Catchment, Central China
This two-year field study investigated the effects of legume, gramineae, and mixed cover crops on soil water dynamics, evapotranspiration (ET), and water use efficiency (WUE) in a young cherry orchard in Central China. The study found that while cover crops generally increased total ET, a legume-gramineae mixture effectively buffered drought-induced water loss and significantly improved WUE, particularly during winter and spring.
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Xu et al. (2025) Assessing the clear-sky assimilation of FY-3D/HIRAS water vapor data and its impact on Typhoon forecasting
This study assesses the clear-sky assimilation of Fengyun-3D (FY-3D) Hyperspectral Infrared Atmospheric Sounder (HIRAS) water vapor data into the China Meteorological Administration Global Forecasting System (CMA-GFS). The assimilation is shown to improve analysis fields and enhance short- to medium-range forecast accuracy for various meteorological parameters, including precipitation and typhoon tracks.
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Xing et al. (2025) A deep learning-based composite agricultural drought index for monitoring and impact assessment in Central Asia
This study develops a Composite Agricultural Drought Index (CAEDI) using an unsupervised Convolutional Autoencoder (CAE) to integrate multiple drought indicators with soil moisture as a physical prior. CAEDI effectively monitors agricultural drought in Central Asia, outperforming individual indices and accurately assessing yield losses, particularly during critical crop phenological stages.
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Screen et al. (2025) Causes and consequences of Arctic amplification elucidated by coordinated multimodel experiments
This perspective synthesizes scientific advances facilitated by the Polar Amplification Model Intercomparison Project (PAMIP), elucidating the critical roles of sea-ice loss and associated feedbacks in Arctic amplification and identifying robust, albeit often weak, remote tropospheric responses that contribute to projected climate change.
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Rongali et al. (2025) Snow cover area distribution and its difference assessment in Beas River basin, India using Landsat-8 and MODIS-Terra satellite data
This study aims to assess the distribution and differences in snow cover area (SCA) within the Beas River basin, India, by utilizing and comparing data from Landsat-8 and MODIS-Terra satellites.
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Musy et al. (2025) Modeling a geologically complex volcanic watershed for integrated water resources management in Mt. Fuji, Japan
This study presents high-resolution 3D geological and integrated hydrological models for the complex Mt. Fuji watershed in Japan, along with a reproducible workflow for their construction in data-scarce volcanic regions. The models accurately simulate surface and subsurface hydrology, providing a crucial resource for integrated water resources management and disaster preparedness.
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Balouei et al. (2025) Advanced AI, machine learning, and deep learning tools for climate studies
This chapter reviews advanced Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) tools for climate studies, particularly their application in addressing drought issues by integrating meteorological and remote sensing indices. It discusses the capabilities of these technologies in overcoming limitations of traditional drought monitoring and prediction methods.
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Wang et al. (2025) Estimation of seasonal ecological water demand in arid zone of Northwest China: An approach using the LSTM-random forest regression model
This study developed a coupled LSTM-Random Forest Regression model with a probability modification coefficient to dynamically assess seasonal ecological water demand in arid zones, overcoming the limitations of deterministic models by characterizing uncertainties. Applied to the Shiyang River Basin, the model accurately predicted fractional vegetation cover and revealed significant seasonal variations in ecological water demand.
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Chavez et al. (2025) Simulating Stratiform Precipitation With Embedded Convection in High‐Elevation Valleys Using LES: The Role of Topographic Detail
This study uses large-eddy simulations to investigate how topographic detail influences precipitation distribution in the Mantaro Valley, Peru, finding that subtle subkilometer terrain variations critically modulate convection and stratiform precipitation processes, necessitating high-resolution topography in mountain rainfall models.
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Valcarce-Diñeiro et al. (2025) Artificial intelligence and Earth observation for agricultural applications
This chapter reviews the application of artificial intelligence and Earth observation technologies to address challenges in agriculture, highlighting their potential to provide actionable insights and noting the superior accuracy of deep learning models.
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Maffei et al. (2025) Probabilistic approaches for the prediction of forest fire danger using optical and thermal satellite data
This study develops and evaluates probabilistic approaches using optical and thermal satellite data to predict forest fire danger, demonstrating performance comparable to or better than the Fire Weather Index for extreme fire events.
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Narine et al. (2025) More than ice in ICESat-2: Measuring forests
This paper explores the application of NASA's ICESat-2 satellite data, specifically the ATL08 vegetation product, for measuring various forest attributes beyond its primary ice mission. It demonstrates ICESat-2's capability to provide along-track terrain and canopy height estimates, contributing to forest and environmental monitoring.
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Qu et al. (2025) Accurate tropical cyclone intensity forecasts using a non-iterative spatiotemporal transformer model
This study introduces TIFNet, a non-iterative spatiotemporal transformer model, to generate accurate 5-day tropical cyclone (TC) intensity forecasts. TIFNet integrates high-resolution global forecasts with historical TC evolution, consistently outperforming operational numerical models, especially during rapid intensification and weakening events.
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Wei et al. (2025) Quasi-invariance of tropical meridional surface temperature gradient in a wide range of climates
This study reveals that the annual- and zonal-mean tropical (30°S-30°N) meridional surface air temperature gradient (TMSTG) remains remarkably stable across a vast spectrum of climates, from extremely cold to extremely hot. This quasi-invariance is robustly maintained by the small gradient of incoming solar radiation and the tropical dynamics of weak temperature gradient (WTG) and convective moist adiabat (CMA).
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Herrmann et al. (2025) Atmospheric Blocks Increase the Odds of Extreme Wildfire Danger at High Latitudes in the Northern Hemisphere
This study statistically links atmospheric blocks to extreme fire weather and observed fires across seasons from 1979 to 2020, finding that blocks substantially increase the odds of extreme fire weather and are significantly linked to observed fires in Arctic regions during summer.
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Warter et al. (2025) Assessing the sensitivity of urban aquatic nature-based solutions to hydroclimate variability using stable water isotopes
This study utilizes stable water isotopes ($\delta^{18}O$ and $\delta^{2}H$) to characterize the hydrological functioning and hydroclimatic sensitivity of urban aquatic nature-based solutions (aquaNBS) across four European cities. The findings reveal that pond-based systems are highly sensitive to recent precipitation and evaporation, while stream-based systems exhibit greater resilience due to the mixing of older water sources and groundwater.
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Adhikari et al. (2025) Development of flood detection framework integrating Synthetic Aperture Radar polarimetry and machine learning for semi-urban vegetation systems
This study proposes a novel flood detection methodology for semi-urban vegetation systems by integrating Synthetic Aperture Radar (SAR) polarimetry and machine learning. A Random Forest model, trained on a new Flood Index (FI) derived from Sentinel-1 SAR data, accurately identifies flood extents across diverse global flood events, outperforming existing methods and demonstrating high transferability.
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Zhang et al. (2025) Integration of deterministic initialization, real-time updating and probabilistic postprocessing in hydrological forecasting for enhancing flood risk reduction
This study presents an integrated hydrological forecasting framework combining deterministic modeling, real-time correction, and probabilistic post-processing. It significantly enhances forecast accuracy and provides reliable uncertainty quantification for flood risk reduction in snowmelt-supplied river basins.
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Wang et al. (2025) Energy‐Consumption‐Induced Anthropogenic Heat Release Intensifies Heatwaves and Wildfire Threats in North America: A CESM2‐Based Projection for the Late 21st Century
This study investigates the impact of anthropogenic heat release (AHR) on summer extreme heat events in North America during 2081–2100 under the SSP5-8.5 scenario, revealing that AHR significantly warms surface temperatures, increases extreme heat event frequency, alters radiative fluxes, modifies atmospheric circulation, and exacerbates moisture stress and wildfire risk.
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Ye et al. (2025) Hydrodynamic simulation and multi-objective optimization coupling for efficient decision-making of water transport and distribution in irrigated areas
This study proposes a multi-objective optimization framework for open-channel flow distribution, coupling hydrodynamic simulation with gate regulation and crop water demand under varying hydrological conditions. Applied to the Chahayang Irrigation District, the framework significantly reduces average flow fluctuations by 35%, enhances distribution efficiency by 22%, and decreases gate adjustments by 28% compared to traditional methods.
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Kayusi et al. (2025) Climate modeling, validation and uncertainty mapping methodologies and challenges
This chapter reviews climate modeling, validation, and uncertainty mapping methodologies, highlighting the challenges and types of uncertainties inherent in climate models like Global Climate Models (GCMs), Regional Climate Models (RCMs), and Earth System Models (ESMs).
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Tosco et al. (2025) Comparison of Broadband Surface Albedo from MODIS and Ground-Based Measurements at the Thule High Arctic Atmospheric Observatory in Pituffik, Greenland, During 2016–2024
This study compares satellite-derived (MODIS) and ground-based broadband surface albedo measurements at the Thule High Arctic Atmospheric Observatory (THAAO) in Greenland, finding good agreement in snow-free summer conditions but significant discrepancies during transition seasons and for rapid albedo changes.
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Cai et al. (2025) High-resolution surface and rootzone soil moisture over US cropland: A novel framework assimilating multi-source remote sensing data, machine learning, and the Layered Green and Ampt Infiltration with Redistribution model
This paper introduces a novel framework for monitoring high-resolution surface and rootzone soil moisture over US cropland by assimilating multi-source remote sensing data, machine learning, and a hydrological model. The objective is to provide accurate soil moisture data crucial for water resource management, drought forecasting, and nutrient transport estimation at the field scale.
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Allende-Prieto et al. (2025) Application of PAZ satellite radar images for soil moisture monitoring in urban areas
This study applies high-resolution PAZ satellite radar images to monitor soil moisture in urban sustainable urban drainage systems, establishing strong correlations between soil moisture and air humidity, precipitation, and the normalized difference vegetation index using temporal series data.
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Wang et al. (2025) Vegetation Changes and Its Driving Factors in the Three-River Headwaters Region from 1990 to 2022
This study analyzed vegetation coverage dynamics and land cover changes in the Three-River Headwaters (TRH) region from 1990 to 2022, revealing a general increase in vegetation coverage and specific land cover type expansions, primarily influenced by precipitation, elevation, and temperature.
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Ortega-Terol et al. (2025) Combined use of open Earth Observation data and free software tools for the control of irrigation extractions in the groundwater body of the Mediterranean Júcar River Basin demarcation
This paper describes a methodology combining open Earth Observation data and free software tools to control irrigation extractions in the groundwater body of the Mediterranean Júcar River Basin, integrating various data sources into a unified platform.
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Miah et al. (2025) Irreversibility of extreme precipitation intensity in global monsoon areas under multiple carbon neutrality scenarios
This study investigates the irreversibility of extreme precipitation intensity across seven Global Monsoon Area (GMA) sub-regions under eight distinct carbon neutrality scenarios. It reveals that extreme precipitation intensity exhibits irreversible behavior, failing to return to initial levels even after atmospheric carbon dioxide (CO2) reduction, with regional vulnerabilities significantly influenced by the timing and rate of carbon neutrality.
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Balouei et al. (2025) A novel high-resolution soil-moisture mapping using Sentinel-1-imagery and optimization-based for a new precise remote sensing drought index
This study developed a novel high-resolution (10 m) Optimized Soil Moisture Condition Index (OSMCI) for agricultural drought monitoring in Khuzestan Province, Iran, using Sentinel-1 imagery and optimization algorithms, demonstrating superior accuracy compared to existing coarse-resolution indices.
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Guo et al. (2025) Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change
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Ortega-Hita et al. (2025) Earth Observation satellites as a tool to determine the fire risk around logistic corridors
This study aims to assess wildfire risk around critical logistic corridors using a low-cost method integrating Earth Observation, geographic information systems, and open geospatial data. The main finding indicates that vegetation is the primary driver of overall wildfire risk, which is highest near infrastructures and varies based on proximity and land cover.
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Ju et al. (2025) Impact of groundwater depth on crop coefficient: An improved evapotranspiration model
This study investigated the exponential relationship between groundwater depth and actual crop evapotranspiration (ETc act) for winter wheat, developing a new Groundwater–Meteorology-Based Actual Crop Evapotranspiration Model (GW–M model) that significantly improves ETc act estimation accuracy in shallow groundwater regions.
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Song et al. (2025) Analysis of cultivated land changes and driving factors in the Alar Reclamation Area (1990–2019) based on multi-temporal Landsat data and machine learning algorithms
This study analyzed the spatiotemporal dynamics of cultivated land and its driving factors in the Alar Reclamation Area, southern Xinjiang, China, from 1990 to 2019 using multi-temporal Landsat data and machine learning. It found a significant increase of 729.97 km² in cultivated land, primarily driven by anthropogenic factors such as population growth, agricultural output value, and fixed asset investment, with GDP showing a negative direct effect.
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Yin et al. (2025) Hybrid wetland city map: Improved wetland characterization through the synergy of global land cover products
This study developed a hybrid Wetland City Map (WCM) by fusing three global 10-meter resolution land cover products using a Weighted Voting-Knowledge based Decision Rule method. The WCM significantly improved wetland classification accuracy within 43 Ramsar Wetland Cities, providing crucial high-resolution data for urban wetland monitoring and management.
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Li et al. (2025) Intelligent and interpretable forecasting method for ice-jam flood disaster levels based on fusion model
This study proposes an intelligent and interpretable forecasting framework for ice-jam flood (IJF) disaster levels, integrating generative modeling, feature selection, and ensemble learning to address data scarcity and model interpretability challenges. The developed fusion model significantly improves forecasting performance and provides localized interpretations of risk scenarios.
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Rodge et al. (2025) Frontier technologies and remote sensing applications for climate change mitigation and adaptation
This chapter reviews frontier technologies and remote sensing applications, including satellite remote sensing and IoT, for monitoring and addressing climate change impacts and facilitating mitigation and adaptation strategies.
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Haghighi et al. (2025) Comparative assessment of hydrological and deep learning models for runoff simulation and water storage in irrigated basins
This study evaluates the performance of physically-based and deep learning models in simulating runoff and estimating terrestrial water storage (TWS) in the Hablehroud River Basin, a semi-arid watershed in northern Iran with increasing irrigation demands. The semi-distributed Bidirectional Long Short-Term Memory (BLSTM-S) model demonstrated superior accuracy in both streamflow simulation and monthly TWS estimation, highlighting the value of deep learning in human-modified hydrological systems.
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Liao et al. (2025) Biodiversity regulates the asymmetric influence of forest cover gain and loss on land surface temperature
This study reveals that biodiversity is the dominant driver of spatial heterogeneity in the asymmetric land surface temperature responses to forest cover gain and loss, primarily by stabilizing interannual climate variability and enhancing soil organic carbon, which promotes young tree growth and associated cooling effects.
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Shirzadi et al. (2025) Leveraging imbalanced dataset in urban flood susceptibility prediction: A case study of Sanandaj City
This study investigates the utility of imbalanced datasets for urban flood susceptibility prediction in Sanandaj City, Iran, comparing hybrid machine learning (RFADT) and deep learning (CNN) models, and finds that imbalanced datasets significantly enhance prediction accuracy compared to balanced ones.
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Li et al. (2025) Introducing an explainable neural network framework for nonstationary flood frequency analysis
This study introduces an explainable neural network framework (XNN-NFFA) for nonstationary flood frequency analysis, integrating feedforward neural networks with SHAP to accurately estimate flood distributions and interpret the influence of environmental drivers. The framework demonstrates superior performance over traditional models and identifies key drivers of flood nonstationarity in the upper Yangtze River Basin.
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Han et al. (2025) Vegetation greenness and productivity recovery following the 2022 record-breaking compound soil moisture and atmospheric drought in Yangtze River Basin
This study investigates the daily-scale recovery patterns and drivers of vegetation greenness (NDVI) and productivity (GPP) in the Yangtze River Basin following the 2022 record-breaking compound drought, revealing distinct recovery times and influencing factors for each.
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He et al. (2025) Event- and annual-scale precipitation extremes enhance groundwater recharge at the ecological restoration catchment of hilly and gully region
This study investigated how precipitation extremes and ecological restoration influence groundwater recharge in a Chinese Loess Plateau catchment, finding that both event- and annual-scale extreme precipitation significantly enhance deep groundwater recharge, particularly through preferential flow pathways.
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Hamid et al. (2025) Analyzing the Impact of Reservoir Incorporation, Changing Land Cover, and Future Climate Change on Basin Response Using a SWAT Model
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Hu et al. (2025) Elevation and Vegetation Cover Dominate Inter-Basin Water Use Efficiency Patterns in China
This study investigates the primary environmental factors influencing inter-basin water use efficiency patterns across China, identifying elevation and vegetation cover as the dominant drivers.
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Chaurasia et al. (2025) Overview of geospatial technology and machine learning in agriculture
This introductory chapter highlights the significant contribution of agriculture to India's economy and introduces how contemporary geospatial technologies and machine learning are revolutionizing sustainable agricultural resource management.
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Luo et al. (2025) Assessing uncertainties in modeling the climate of the Siberian frozen soils by contrasting CMIP6 and LS3MIP
This study quantifies the contributions of land surface parameterization and atmospheric forcing to discrepancies in frozen soil simulations in Siberia by contrasting CMIP6 and LS3MIP models. It finds that land-only models (LS3MIP) exhibit larger biases and spread in frozen soil temperatures than coupled models (CMIP6), indicating significant error compensation in coupled systems and underscoring the need for improved snow insulation and soil hydrothermal dynamics in land surface models.
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Zhu et al. (2025) Parametric Sensitivity Analysis of ENSO ‐Related Shortwave Feedback Simulations by PPE Experiments in CAM6
This study investigates how deep convection and cloud physics scheme parameters in CAM6 impact the simulation of shortwave feedback during the El Niño‐Southern Oscillation (ENSO) cycle, identifying key parameters that modulate this feedback through ice and liquid water paths.
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Li et al. (2025) Advances and Challenges in Dew Research on Land Surface: A Review
This review synthesizes advances in understanding dew's ecological, hydrological, and environmental effects, quantification methods, and spatiotemporal variations, highlighting a regional dichotomy in its impacts and persistent challenges in its study. It finds that dew is a crucial hydrological source in arid regions but primarily regulates energy balance in humid/alpine areas, with a general declining trend observed in many arid zones.
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Hibbert et al. (2025) Future Projections of Sea Surface Temperature ( SST ) in the MDR and Wider Caribbean Region: Utilising CMIP6 GCM Ensembles
This study evaluates CMIP6 global climate models' performance in simulating and projecting sea surface temperature (SST) variations in the Caribbean and tropical Main Developing Region, particularly during the Late Rainfall Season. The models robustly reproduce historical SSTs and project significant future warming with a distinct spatial gradient, providing a foundation for climate impact understanding and adaptation planning.
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Wang et al. (2025) Applicability of the Surface Energy Balance System (SEBS) Model for Evapotranspiration in Tropical Rubber Plantation and Its Response to Influencing Factors
This study evaluated the applicability of the Surface Energy Balance System (SEBS) model for estimating evapotranspiration (ET) in a tropical rubber plantation using Landsat-8 imagery and flux tower data, demonstrating high accuracy and identifying key meteorological and physiological drivers of ET.
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Devoie et al. (2025) Modelling near-surface ice content and midwinter melt events in mineral soils
This study presents a numerically efficient, semi-analytical coupled thermal and mass transport model to represent near-surface soil ice content and midwinter melt events in mineral soils. The model capably reproduces field observations of frozen, thawed, or transitioning soils and offers significant computational advantages over existing continuum models, making it suitable for regional hydrologic applications.
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Ueda et al. (2025) Building a Generalized Pre-Training Model to Predict River Water-Level from Radar Rainfall
This paper develops a generalized deep learning model for river water-level prediction applicable to multiple Japanese rivers by pre-training with inundation data from all Class-A rivers, demonstrating higher accuracy and broader applicability compared to pre-training with only similar rivers.
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Zhou et al. (2025) Advancing Riverine–Lacustrine Ecosystem Vulnerability Prediction Using Multi-Sensor Satellite Data, Attention-Based Deep Learning, and Evolutionary Metaheuristics
This study developed a satellite-based Deep Attention Network framework, optimized by Genetic Algorithm and Grey Wolf Optimizer, to map and interpret ecosystem vulnerability in the Ebinur Lake Basin, identifying distinct degradation drivers and pathways for targeted management.
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Yan et al. (2025) Dynamically Updated Irrigation Canal Scheduling Rules Based on Risk Hedging
This study develops a novel "Bi-level, Two-stage" (BT) model for dynamically updated irrigation canal scheduling, integrating historical data-derived Target Residual Lump-Sum Water Quota (TRLSWQ) and hydrometeorological forecasts. The BT model significantly improves irrigation efficiency and water utilization by reducing water shortage indices and increasing water quota utilization compared to conventional methods, effectively hedging against future water shortage risks.
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Kumar et al. (2025) Integrating climate data for agricultural resilience using geospatial approaches
This chapter explores the integration of climate data using geospatial approaches, specifically remote sensing and Geographic Information Systems (GIS), to enhance agricultural resilience against climate change-induced natural disasters. It highlights how these technologies improve disaster response, prevention, and the creation of timely, precise assessments for agricultural communities.
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özden (2025) Data-Driven Decision Support in Environmental Management: Hybrid GNN-PINN Modeling of Subsurface Soil Temperature
This paper describes a comprehensive dataset of daily meteorological and subsurface soil temperature records from 15 stations across Turkey, spanning five years, specifically designed to support research in environmental decision support systems and the development of hybrid Physics-Informed Neural Networks (PINNs) and Graph Neural Networks (GNNs).
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Fustos et al. (2025) Controls over debris flow initiation in glacio-volcanic environments in the Southern Andes
This study investigates the geomorphological, geotechnical, and hydrometeorological controls over debris flow initiation in glacio-volcanic environments of the Southern Andes, revealing that the combination of high water accumulation capacity, effective precipitation capture by slopes, and specific soil properties (volcanic soils over low-permeability glacial deposits) are critical for triggering these events. The research highlights the importance of monitoring soil moisture and surface deformation for predicting these hazards, especially under changing climate conditions.
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He et al. (2025) Extreme precipitation
This paper addresses the critical need for accurate and timely nowcasting of extreme precipitation events, highlighting the limitations of traditional numerical weather prediction models and advocating for data-driven Earth observation approaches to enhance disaster management.
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Wang et al. (2025) Regional Evapotranspiration Estimation and Partitioning Model Based on Energy Balance: A Case Study of the Tibetan Plateau
This study develops a novel regional two-source evapotranspiration (ET) model based on energy balance principles to simulate and partition ET into soil evaporation and vegetation transpiration over the Tibetan Plateau at 1 km daily resolution without requiring land surface temperature. The model demonstrates strong performance and superior skill in ET partitioning when validated against flux tower observations and existing regional products.
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Shi et al. (2025) A sub-seasonal to seasonal climate forecast informed irrigation scheduling tool for the Contiguous United States
This study develops a real-time irrigation scheduling tool for cornfields across the Contiguous United States (CONUS) by integrating sub-seasonal to seasonal (S2S) climate forecasts with the Soil Water Atmosphere Plant (SWAP) model. The S2S-informed scheduling improves water use efficiency and net profit compared to default SWAP schedules, offering national applicability.
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Joo et al. (2025) Analysis of Application of Design Standards for Future Climate Change Adaptive Agricultural Reservoirs Using Cluster Analysis
This study aimed to classify meteorologically homogeneous regions to assess climate change impact and vulnerability, identifying the Gaussian Mixture Model (GMM) as the optimal clustering method. The research determined optimal cluster numbers (k=4 or k=5) based on effective storage capacity, and subsequently identified standard reservoir designs for agricultural infrastructure.
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Hasan (2025) Global Synthetic Crop Yield, Meteorological, and Climate Teleconnection Dataset for Machine Learning Benchmarking
This paper presents a high-fidelity synthetic dataset of global crop yield, local meteorological conditions, and large-scale climate teleconnection indices from 1990 to 2023. The dataset was generated to benchmark machine learning architectures, particularly Spatial-Temporal Graph Neural Networks (ST-GNNs), by explicitly modeling physical correlations between global climate drivers and regional weather patterns.
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Shukla et al. (2025) Soil mapping and categorization using fusion of satellite imagery and machine learning
This chapter introduces the critical role of digital soil mapping (DSM) in creating detailed soil maps for sustainable land use, particularly in emerging nations, by leveraging satellite imagery and machine learning to overcome limitations of traditional methods.
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Netzel et al. (2025) Standardized Climatic Water Balance for Poland (1951–2023) Supporting Forestry and Drought Monitoring
This study presents a high-resolution (100 m) gridded dataset of the Standardized Climatic Water Balance (SCWB) for Poland, spanning 72 years (1951–2023), developed to assess long-term water availability and drought conditions. The publicly available dataset, derived from over 2,000 meteorological stations, provides crucial data for environmental monitoring, drought risk assessment, and climate adaptation planning.
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Ma et al. (2025) Model Calibration and Data Assimilation for the National Water Model: A Case Study over California
This study retrospectively evaluated and enhanced the Noah-MP hydrological model for flood forecasting in three California river basins, demonstrating that an enhanced Noah-MP produced forecasts comparable to, and often more accurate than, operational forecasts, particularly for lead times exceeding 2 days.
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Xu et al. (2025) Seasonality of the South Pacific Meridional Mode: Role of Oceanic Meridional Advection Feedback Beyond Thermodynamics Dominance
This study investigates the seasonality of the South Pacific Meridional Mode (SPMM), revealing that while thermodynamical wind-evaporation-SST feedback drives SPMM-SST growth, meridional advection, primarily from wind stress-driven Ekman transport, acts as a dominant damping mechanism, critically shaping its seasonal cycle.
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Zhu et al. (2025) KADL: Knowledge-Aided Deep Learning Method for Radar Backscatter Prediction in Large-Scale Scenarios
This paper proposes a novel knowledge-aided deep learning (KADL) method for predicting large-scale radar backscatter, demonstrating superior accuracy (root mean square error of 4.74 dB), robustness, and generalization compared to existing empirical and purely data-driven models by integrating physical knowledge.
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Waleed et al. (2025) An overview of Google Earth Engine for disaster risk management
This paper provides an overview of Google Earth Engine's utility in disaster risk management, emphasizing its role in leveraging Earth observation data for real-time monitoring and adaptive strategies amidst increasing global disaster frequency.
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Singh et al. (2025) Crop health monitoring using geospatial methods and deep learning
This paper introduces the critical role of integrating deep learning with geospatial technologies (GIS, remote sensing, GPS) to revolutionize crop health monitoring and management, aiming to overcome the limitations of traditional field surveys.
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Chandrappa et al. (2025) A dynamic real-time soil moisture identification system with interpolation capabilities for improved crop yield and water conservation
This study develops a real-time soil moisture identification system using interpolation models to predict soil moisture across varying depths and horizontal locations with minimal sensors, aiming to optimize irrigation for improved crop yield and water conservation.
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Li et al. (2025) Evaluating the Performance of the STEMMUS-SCOPE Model to Simulate SIF and GPP Under Drought Stress Using Tower-Based Observations of Maize
This study simulated solar-induced fluorescence (SIF) and gross primary productivity (GPP) using the STEMMUS-SCOPE model at a semi-arid irrigated farmland to assess its accuracy and capability in analyzing water stress impacts. The STEMMUS-SCOPE model demonstrated higher accuracy than the SCOPE model, particularly under drought conditions, and reliably characterized the SIF-GPP relationship under water stress.
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Xu et al. (2025) RMC: advancing daily runoff forecasting with a unified cross-scale deep learning approach
This paper introduces Res-Mamba-Causal (RMC), a novel deep learning architecture designed to improve daily runoff forecasting by unifying multi-scale hydrologic feature modeling. RMC consistently outperforms existing models like LSTM and Transformer across various performance metrics on four U.S. watersheds, demonstrating enhanced accuracy and the ability to capture complex hydrologic dynamics.
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Sucozhañay et al. (2025) Streamflow drought identification and characterization in a tropical Andean basin: effect of threshold methods
This study evaluates the **eartH2Observe Tier-1** global water resources ensemble, consisting of 10 hydrological and land surface models forced by a consistent reanalysis dataset. The research demonstrates that while the ensemble mean generally provides the most reliable global estimates, significant model spread exists, particularly in runoff and snow-dominated regions.
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Haile (2025) Ecosystem Water Use Efficiency
This dataset provides soil moisture profiles and key monthly climate variables, derived from GLDAS and ERA5-Land, specifically curated for research on ecosystem water use efficiency.
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Chandrakar et al. (2025) Dampening of the Precipitation Response to Aerosol Pollution From Turbulence in Cumulus Clouds
This study, using aircraft observations and a cloud model, demonstrates that turbulence-enhanced drop collision-coalescence in warm cumulus clouds not only accelerates rain onset but also significantly dampens precipitation susceptibility to aerosol loading, a critical finding for Earth system models.
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Kacimov et al. (2025) Phreatic saturated and saturated-unsaturated seepage towards transpiring strip of vegetation: Analytic and HYDRUS2D modeling of biodrainage
This study develops and utilizes 2D analytical and numerical models to simulate saturated and saturated-unsaturated seepage towards a transpiring strip of vegetation, quantifying its impact on water table drawdown and flow dynamics. It advocates for biodrainage via root water uptake as a sustainable and efficient hydroecological tool for managing shallow aquifers in agro and urban environments.
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Liu et al. (2025) PINN framework for urban flood depth prediction: integrating data-driven insights with physical constraints
This study developed SWEPINN, a Physics-Informed Neural Network integrating shallow water equations and data-driven insights, to provide efficient, accurate, and interpretable urban flood depth predictions, outperforming traditional data-driven models.
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Kumari et al. (2025) Geospatial and machine learning-based mapping and analysis for agricultural sustainability
This chapter explores the application of geospatial technologies and machine learning for enhancing agricultural sustainability, highlighting their role in precision farming, land use mapping, and environmental monitoring to address global food security challenges.
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Garg et al. (2025) Estimation of wheat yields and water savings with deficit irrigation in water-stressed NW India
This study evaluates efficient irrigation scheduling strategies for spring wheat in water-stressed NW India using the AquaCrop-OSPy model calibrated with Particle Swarm Optimization. It demonstrates that 1.7–38.1% irrigation water savings can be achieved with less than 5% yield loss, significantly improving irrigation water productivity and aiding groundwater conservation.
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Zhang et al. (2025) Long history paddy rice mapping across Northeast China with deep learning and annualresult enhancement method
This study developed a deep learning and annual result enhancement (ARE) method to generate annual paddy rice maps for Northeast China from 1985 to 2023 using multi-sensor Landsat data, demonstrating significantly improved accuracy over traditional methods. The research revealed a substantial expansion of paddy rice cultivation in the region, providing valuable data for agricultural monitoring and policy.
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Das et al. (2025) Integration of geospatial technology and machine learning for precision agriculture
This chapter introduces the significant potential of integrating geospatial technology (GIS, GPS, remote sensing) with machine learning frameworks to enhance analysis and decision-making in precision agriculture. It highlights how this synergy leverages spatial context and computational power to gain insights into crop growth, soil properties, and environmental factors.
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javari (2025) Vegetation Thresholds and Phase Transitions in Urban Heating of Arid Megacities
This paper presents a comprehensive dataset, stored in Excel files within a ZIP archive, designed for analyzing vegetation thresholds and phase transitions in urban heating of arid megacities, including raw measurements, preprocessed variables, and model inputs/outputs.
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Huang (2025) Future challenges and opportunities in data-driven Earth observation
This chapter reviews the current landscape of Earth observation (EO) technologies in disaster management, addressing advancements, data volume challenges, and future opportunities presented by emerging technologies like quantum computing, blockchain, and 5G.
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Xie et al. (2025) Estimation of annual runoff using supraglacial channel geometry derived from UAV surveys of Qiyi Glacier, northern Tibetan Plateau
This study developed a novel remote sensing method using UAV-derived supraglacial channel geometry to accurately estimate the annual meltwater discharge of Qiyi Glacier, finding that geometric parameters like lateral deviation, gradient, and width can predict annual discharge with high accuracy.
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Otmane et al. (2025) Contribution of hydrological modeling to the estimation of groundwater deficit and/or excess in a karstic aquifer: the case of Wadi Sebdou catchment (Tafna, NW, Algeria)
This study aimed to estimate the groundwater deficit and characterize the hydrodynamic behavior and self-renewal capacity of the karstic aquifer in the Wadi Sebdou basin, northwestern Algeria. Using the GARDÉNIA hydrological model, it quantified an average groundwater deficit of 1.36 × 10^6 cubic meters per year, compensated by regional karstic system inputs, and established the aquifer's consistent hydrodynamic response to recharge.
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anaya (2025) Obas
This document describes the methodology for creating a pre-processed Sentinel-2 multispectral data cube, designed as input for a Convolutional Neural Network to predict burned areas.
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Liu et al. (2025) Soil Moisture Monitoring Method and Data Products: Current Research Status and Future Development Trends
This review paper integrates mechanistic classification and applicability discussions to provide a coherent understanding of current soil moisture monitoring approaches and comparatively analyzes publicly accessible dataset products. It concludes that no single monitoring method or dataset product is universally applicable due to varying limitations, highlighting the need for multi-source data fusion and advanced modeling in future research.
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Tripathy et al. (2025) Cloud-based flood mapping with Sentinel-1 SAR on Google Earth Engine: Insights from the global flood mapper
This paper introduces a cloud-based flood mapping system utilizing Sentinel-1 SAR data on Google Earth Engine to provide rapid, scalable, and accurate flood information for disaster management. It aims to overcome the limitations of traditional flood monitoring methods by leveraging advanced geospatial technologies.
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Rahman et al. (2025) Machine Learning Approaches for Assessing Avocado Alternate Bearing Using Sentinel-2 and Climate Variables—A Case Study in Limpopo, South Africa
This study aimed to assess and predict avocado alternate bearing patterns in commercial orchards using satellite remote sensing and climatic variables. The TabPFN machine learning model effectively predicted alternate bearing with high accuracy, demonstrating that a combination of Sentinel-2 vegetation/flowering indices and key climatic factors during the flowering period can support proactive orchard management.
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Omar et al. (2025) Deep learning and geospatial technology-based decision support systems for smart agricultural and irrigation applications
This paper introduces the critical need for advanced agricultural practices to address global challenges like food security and climate change, proposing deep learning and geospatial technologies as a viable solution for developing smart agricultural and irrigation decision support systems.
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Shah et al. (2025) Constrained negentropy optimisation (CoNE-opt): Using independent components to merge satellite data products
This paper introduces Constrained Negentropy Optimisation (CoNE-opt), a novel method for merging uncertain geophysical datasets by maximizing non-Gaussianity. CoNE-opt outperforms traditional merging techniques, particularly in the presence of high error cross-correlation and outliers, as demonstrated by superior performance in merging global satellite-derived surface soil moisture products.
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Zito et al. (2025) SolarFertigation: A Unified Cloud Platform for Smart Fertigation and Agrivoltaic System Integration
This paper presents the architecture of SolarFertigation, an intelligent solar-powered fertigation system designed for precise, adaptive management in agrivoltaic contexts. While demonstrating effective data transmission and system integration, initial field validation revealed a significant crop yield reduction, exceeding 80%, under photovoltaic panels compared to open-field conditions.
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Wen et al. (2025) Tornadoes
This chapter introduces the critical role of Earth observation data in enhancing tornado disaster management, from precursor detection to post-disaster assessment, while acknowledging the challenges posed by integrating diverse datasets.
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Miś (2025) Thermal and precipitation conditions during the thermal growing season in Central and Northern Europe
This study comprehensively analyzed thermal and precipitation conditions during the thermal growing season (TGS) in Central and Northern Europe from 1950 to 2022, revealing a consistent and statistically significant warming and lengthening of the TGS, while precipitation trends showed high spatial and interannual variability with no clear overall long-term direction.
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Jat et al. (2025) Maize as an alternative to resource-intensive rice: Empirical insights from on-farm participatory study under diverse agricultural scenarios in the Indo-Gangetic Plains of Northwestern India
This study empirically evaluates maize as a sustainable alternative to resource-intensive rice in the Indo-Gangetic Plains of Northwestern India. It finds that while maize yields are slightly lower, its cultivation significantly enhances profitability, water productivity, and environmental sustainability compared to rice.
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Lalo et al. (2025) Future North Atlantic tropical cyclone intensities in modified historical environments
This study investigates future North Atlantic tropical cyclone (TC) intensities under various climate change scenarios by applying warming signals to 618 historical TC events using a deep-learning intensity model, revealing regional shifts in intensity despite conservative overall projections. An interactive dashboard is also provided to explore the simulated data.
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Yang et al. (2025) Hybrid high-dimensional vine copula–Bayesian network framework for flood risk analysis in reservoir–lake systems: Addressing multisource uncertainties
This study developed a hybrid high-dimensional vine copula–Bayesian network framework for flood risk analysis in complex reservoir–lake systems, demonstrating its effectiveness in the Chaohu Lake Basin by identifying dominant risk sources and quantifying their impact on lake water levels.
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Huang et al. (2025) Introduction to data-driven earth observation for disaster management
This chapter introduces the critical role of data-driven Earth observation (EO) in modern disaster management, emphasizing its capacity to provide timely and accurate information across all phases—preparedness, response, recovery, and mitigation—to enhance decision-making and minimize disaster impacts.
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Egbuikwem et al. (2025) Optimizing in-season nitrogen management through satellite-guided fertigation in field-scale maize production
This study developed and evaluated a practical framework using PlanetScope satellite imagery and the Holland–Schepers sensor algorithm to guide in-season, variable-rate fertigation in maize, demonstrating significant improvements in nitrogen use efficiency and comparable yields with reduced nitrogen input.
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Ahmed et al. (2025) Agricultural drought monitoring in Africa based on Self-Organizing Agricultural Drought Index
This paper proposes a novel Self-Organizing Agricultural Drought Index (SOADI) using a Self-Organizing Map (SOM) technique to improve agricultural drought monitoring in Africa, demonstrating its robustness and accuracy across diverse agro-climatic zones.
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Chatrabhuj et al. (2025) Geo-artificial intelligence for smart irrigation management systems
This chapter introduces Geo-artificial intelligence (Geo-AI) as an integration of geospatial technologies with AI for analyzing and processing geography-based data. It highlights Geo-AI's application in developing smart systems for environmental management, planning, and particularly, the optimization of irrigation management systems.
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Gemechu et al. (2025) Assessing the Impact of Multi-Decadal Land Use Change on Agricultural Water–Energy Dynamics in the Awash Basin, Ethiopia: Insights from Remote Sensing and Hydrological Modeling
This study assesses the impact of multi-decadal land use and land cover (LULC) changes on agricultural water–energy dynamics in Ethiopia's Awash Basin using the WRF-Hydro/Noah-MP modeling framework, revealing that early agricultural expansion increased surface runoff while later woodland recovery promoted subsurface flow and groundwater recharge.
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Woo et al. (2025) Application of a Combined Synthetic-Perturbation Method for Turbulent Inflow in Time-Varying Urban LES
This study proposes and evaluates a combined digital-filter-based synthetic turbulence generator (STG) and cell perturbation method (CPM) as an inflow turbulence strategy for large-eddy simulations (LES) of urban boundary layers, demonstrating its ability to improve turbulence realism and vertical mixing while reducing computational cost for time-varying conditions.
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Ghaus et al. (2025) Effects of Forest Thinning on Water Yield and Runoff Components in Headwater Catchments of Japanese Cypress Plantation
This study investigated the hydrological impacts of 40% forest thinning with contour-aligned log placement in Japanese cypress headwater catchments, finding a temporary increase in annual water yield and enhanced low-flow discharge without increasing stormflow risk.
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Wu et al. (2025) Time Series Consistency of Passive Microwave Sensors (1978–2023) Brightness Temperature Data for Snow Depth Estimation
This paper investigates the time series consistency of passive microwave sensor brightness temperature data from 1978 to 2023, aiming to improve the reliability and accuracy of snow depth estimation over this extended period.
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Kukunuri et al. (2025) Synthetic data generation using microwave modeling with efficient application of machine learning for bare land soil moisture retrieval: a case study
This study develops a multilayer microwave model to generate synthetic backscatter data for bare land, accounting for varying soil properties, and demonstrates its use in training machine learning models like Gaussian Process Regression for soil moisture retrieval without requiring extensive real-world ground truth data.
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Liu et al. (2025) Unravelling the Dominant Influence of ENSO Over IOD on Australian Springtime Climate Variability Using a Pacemaker Modelling Approach
This study utilizes the Conformal Cubic Atmospheric Model (CCAM) to isolate the independent impacts of ENSO and IOD on Australian springtime rainfall and temperature, concluding that ENSO is the dominant driver of variability.
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Berihun et al. (2025) Correction: Modeling soil moisture and evapotranspiration dynamics from variably irrigated vegetable fields
This article is a correction notice addressing a typographical error in an author's name in a previously published paper titled "Modeling soil moisture and evapotranspiration dynamics from variably irrigated vegetable fields."
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Kayhomayoon et al. (2025) Improving the performance of daily pan evaporation (Evp) prediction using the ensemble empirical mode decomposition combined with deep learning models
This study presents a novel hybrid approach for daily pan evaporation (Evp) prediction, combining gamma test and genetic algorithm (GTGA) for optimal input selection, ensemble empirical mode decomposition (EEMD) for noise reduction, and deep learning models (LSTM and CNN). The EEMD-CNN hybrid model demonstrated superior performance, significantly enhancing prediction accuracy for water resource management in arid and semi-arid regions.
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Yang et al. (2025) A novel Improved Geographically Weighted Random Forest (IGWRF) model for low-resolution soil moisture data downscaling in Africa
This study proposes an Improved Geographically Weighted Random Forest (IGWRF) model to downscale 9 km SMAP soil moisture data to 1 km in Kenya, effectively addressing spatial heterogeneity and nonlinear relationships. The IGWRF model significantly outperforms traditional Random Forest and Geographically Weighted Random Forest, providing high-accuracy, high-resolution soil moisture data crucial for agricultural management and drought monitoring in Africa.
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Chakma et al. (2025) Copula-based multivariate analysis of hydrological drought over jiabharali sub-basin of Brahmaputra River, India
This study conducted a copula-based multivariate analysis of hydrological drought in the Jiabharali sub-basin of the Brahmaputra River, India, from 2000 to 2023, revealing that drought severity and duration are strongly correlated and their joint return periods increase with longer time scales, indicating extreme joint drought events.
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Takahashi et al. (2025) Land‐Ocean Differences in Tropical Deep Convective Clouds: Intercomparison of DYAMOND Simulations and CloudSat Observations
This study compares tropical deep convective clouds and their environments in DYAMOND simulations with CloudSat observations across three tropical regions. It finds that while DYAMOND models capture environmental differences, they exhibit biases in representing convective intensity and precipitation dynamics, particularly overestimating convection in the Tropical Warm Pool.
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Gül et al. (2025) Modified standardized precipitation index for skewed hydro-meteorological data
This study introduces the Modified Standardized Precipitation Index (M-SPI), which uses the median for standardization, to improve drought and wetness representation in skewed precipitation data compared to the traditional mean-based Standardized Precipitation Index (SPI). Comparative analysis using precipitation data from Türkiye shows M-SPI offers smoother transitions and more stable persistence, particularly in regions with skewed distributions.
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Williams et al. (2025) Sea‐Surface Temperature Patterns, Radiative Cooling, and Hydrological Sensitivity
This study connects clear-sky longwave radiative cooling to tropical sea-surface temperature patterns, explaining why hydrological sensitivity is approximately 25% larger in uniform warming scenarios compared to abrupt-4xCO2 runs due to differing tropical clear-sky longwave radiative cooling changes.
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Norman et al. (2025) PortUrb: a performance portable, high-order, moist atmospheric large eddy simulation model with variable-friction immersed boundaries
This paper introduces "portUrb," a performance-portable, high-order, moist atmospheric Large Eddy Simulation (LES) model designed for urban building geometries using variable-friction immersed boundaries. The model demonstrates accuracy and robustness across various atmospheric boundary layer, supercell, and urban flow scenarios, closely matching experimental observations and other model comparisons.
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Ebaju et al. (2025) Spatiotemporal Changes of Drought Conditions Over the Hindu‐Kush Himalayan Region During the Recent Century: Insights for Climate Adaptation
This study reveals contrasting drought patterns across the Hindu Kush Himalayan (HKH) region from 1901 to 2022, showing significant drought reduction in the western HKH and intensifying aridity in the eastern parts. It attributes these divergent trends to distinct climatic drivers, including precipitation, temperature, and teleconnections like the Arctic Oscillation and ENSO, highlighting the need for spatially targeted adaptation strategies.
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feng (2025) Underrated yet vital: Vegetation restoration-driven non-growing season land surface roughness in the Eastern Hobq Desert, Northern China
This paper presents a comprehensive dataset of land surface roughness and near-surface characteristics from 11 sites in the Eastern Hobq Desert, Northern China, emphasizing the critical, often underestimated, role of vegetation restoration in influencing non-growing season land surface roughness.
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Fibbi et al. (2025) Assessment of three remote sensing methods for estimating actual evapotranspiration in a Mediterranean region
This study assesses three remote sensing methods (NDVI-Cws, MODIS, LSA SAF) for estimating actual evapotranspiration (ETa) in Tuscany, Italy, over 20 years (2005-2024) using a triple collocation approach. It finds that LSA SAF and NDVI-Cws estimates show strong spatial and temporal concordance and indicate widespread increasing ETa trends, while MODIS estimates are less concordant, especially for forests, and poorly reflect these trends.
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Young et al. (2025) Decreasing Snow Cover and Increasing Temperatures Are Accelerating in New England, USA, with Long-Term Implications
This study evaluates temperature increases and snow cover declines in New England, finding significant warming since the late 1980s, particularly in winter and at night, alongside a rapid decrease in snow cover, with an accelerating trend in recent years.
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Sigmond et al. (2025) Jet stream response to future Arctic sea ice loss not underestimated by climate models
This study proposes a new, more robust emergent constraint based on lower stratospheric winds to assess climate models' ability to project the winter jet stream response to Arctic sea ice loss. The findings indicate that climate models do not systematically underestimate this response, reducing projection uncertainty by 62% and increasing confidence in future poleward jet stream shifts.
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Chanie (2025) Evaluation of CMIP6 model performance and future climate projections over the Genale dawa river basin, Ethiopia
This study evaluated 12 CMIP6 models for historical climate simulation (1985–2014) and future projections (2021–2080) of precipitation and temperature over the Genale Dawa River Basin, Ethiopia. It found that the multi-model ensemble outperformed individual models and projected significant warming (up to 1.8 °C for Tmax and Tmin) and pronounced seasonal precipitation shifts (e.g., March decline, November increase) under high-emission scenarios.
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Kumar et al. (2025) All-sky radiance assimilation of INSAT-3DS imager water vapour channel in the weather research and forecasting model
This study evaluates the impact of assimilating all-sky water vapour radiance observations from the recently launched INSAT-3DS satellite into the Weather Research and Forecasting (WRF) model. It demonstrates that all-sky assimilation significantly increases the number of assimilated observations and improves short-range weather forecasts, particularly for moisture and temperature fields, compared to clear-sky assimilation.
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Klaus et al. (2025) Brief communication: How extreme was the thunderstorm rain in Vienna on 17 August 2024? A temporal and spatial analysis
This study quantifies the exceptional nature of the 17 August 2024 thunderstorm in Vienna, which delivered 107 mm of rain in two hours with an estimated 700-year return period, demonstrating that such extreme events are often missed by gauge networks and their frequency is likely increased by climate change.
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Galton‐Fenzi et al. (2025) Multi-model estimate of Antarctic ice-shelf basal mass budget and ocean drivers
This study provides the first multi-model mean estimate of Antarctic ice-shelf basal mass budget and ocean drivers by comparing nine circum-Antarctic ocean simulations, revealing that the multi-model mean melt rate (0.64 m/year) is lower than satellite-derived estimates (0.88 m/year) but highlights the critical, combined influence of thermal driving and friction velocity on melting.
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Dietrich et al. (2025) Uncertainties in the determination of water storage changes of a shallow groundwater site using profile probe-measured volumetric water contents
This study evaluates the suitability of a capacitive soil moisture profile probe for estimating soil water storage changes at a shallow groundwater site, comparing probe data (with default and soil-specific calibrations) against reference lysimeter measurements. It found that soil-specific calibration improved accuracy, especially for organic soils, and profile probes can quantify water storage changes well, particularly over longer periods, despite limitations during rapid hydrological events or when water levels are above the surface.
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Guillemois (2025) Τrajectοires des paysages bοcagers de la Νοrmandie au Grand Οuest : apprοche géο-histοrique et mοdélisatiοn des cοnnectivités hydrοlοgiques pοur cοntribuer à la gestiοn intégrée des bassins versants
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Liu et al. (2025) Cumulative and Lagged Drought Effects Shape Start and End of Season on the Mongolian Plateau
This study investigated the temporal depth of drought influence on dryland phenology across the Mongolian Plateau, revealing that spring green-up is delayed by multi-month winter–spring moisture deficits (6–9 months prior), while dormancy is advanced by near-term summer–autumn dryness (1–2 months prior), with varying sensitivities across ecoregions.
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Sinha (2025) MausamResearch
This paper presents research related to drought assessment.
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Rajesh et al. (2025) Satellite-based estimation of potential evapotranspiration using the Thornthwaite–Mather model for sub-regional water resource assessment
This study estimates potential evapotranspiration (PET) using satellite-derived land surface temperature (LST) and the Thornthwaite–Mather model for Samastipur district, Bihar, India, finding that bias-corrected LST significantly improves PET estimation accuracy and reveals stable long-term PET trends despite seasonal temperature shifts.
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Agosta et al. (2025) A un año de la DANA de Valencia, ¿preludio de una nueva era climática?
This paper analyzes the unprecedented torrential rainfall event in Valencia on October 29, 2024, which recorded 771 mm in Turís, demonstrating that while the atmospheric configuration was a traditional DANA, its extreme virulence was driven by climate change-induced abnormally warm Mediterranean Sea temperatures and record atmospheric humidity.
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Wang et al. (2025) Root zone storage capacity reveals ecohydrological turning points in Tibetan Plateau permafrost regions
This study estimates root zone storage capacity (SR) across the Tibetan Plateau using an observation-based water balance approach, revealing its spatial heterogeneity and identifying a critical ecohydrological turning point in permafrost regions linked to active layer thickness. The findings highlight how permafrost degradation restricts vegetation water access, providing a quantitative basis for assessing ecosystem vulnerability.
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Silpa et al. (2025) Internet of Things (IoT)-Linked Approaches for Soil Health Monitoring
This review provides an in-depth examination of Internet of Things (IoT) technologies for assessing soil physical, chemical, and biological properties, highlighting their architectural frameworks, diverse applications, and the benefits and challenges they present for sustainable agricultural monitoring.
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Davidson et al. (2025) Ephemeral Channel Expansion: Predicting Shifts Toward Intermittency in Vulnerable Streams Across Semi-Arid CONUS
This research identifies significant trends toward novel stream intermittency across semi-arid regions of the Conterminous United States (CONUS) from 1980 to 2024, finding that over half of analyzed stream gages show increased flow cessation, primarily controlled by December and January precipitation.
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Gilmour et al. (2025) Mesoscale Convective Systems over South America: Representation in Kilometer-Scale Met Office Unified Model Climate Simulations
This study assesses the representation of mesoscale convective systems (MCSs) in multiyear convection-permitting regional climate model simulations over South America against satellite observations. It finds that while simulations capture MCS climatology well, they show biases in frequency (overestimated in Amazon, underestimated in La Plata basin) and precipitation characteristics (overestimated intensity, underestimated area), leading to an underestimation of MCS contribution to total rainfall.
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Lim et al. (2025) Improved observed full raindrop size distributions and their normalization using double and triple moments
This study develops an optimized method for constructing full raindrop size distributions (DSDs) by statistically merging measurements from a 2D-Video Distrometer (2DVD) and a Meteorological Particle Spectrometer (MPS) based on instrumental uncertainty. The new method significantly improves DSD accuracy, reduces variability, and provides more stable generic functions compared to existing merging techniques.
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Wang et al. (2025) A global hourly gross primary production dataset from 2001 to 2020
This paper introduces a novel global hourly gross primary production (GPP) dataset for 2001–2020, generated at a 0.1° spatial resolution using a modified radiation scalar two-leaf LUE (RTL-LUE) model. This dataset significantly improves the capture of short-term GPP variations and extreme environmental stresses, offering enhanced insights into terrestrial carbon dynamics.
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Quan et al. (2025) Solar Geoengineering Strategies Based on Reinforcement Learning
This paper investigates the use of reinforcement learning (RL) to optimize stratospheric aerosol injection (SAI) strategies within an idealized global climate model, demonstrating that RL can learn stable, plausible, and time-dependent deployment strategies to maximize benefits and minimize side-effects.
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Marchal (2025) Some challenges in predicting heavy precipitation with convective-scale ensembles
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Brighenti et al. (2025) Cryosphere and lithology influence the hydrological gradients of high elevation Alpine catchments
This study investigates how receding cryosphere and lithology influence the hydrological, thermal, and chemical gradients in two high-elevation Alpine catchments. It finds that rock glaciers are major hydrological regulators, contributing significantly to runoff and buffering water temperature, while predisposing lithology in glacierized catchments can lead to geochemical hotspots with elevated trace element concentrations.
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Scarpin et al. (2025) Peanut yield and grade prediction in Georgia, USA: integrating management, climate, and remote sensing data with explainable AI
This study integrates management, climate, and remote sensing data with explainable AI to predict peanut yield and grade in Georgia, USA, finding that Cubist-rule and support vector machine models, particularly with management and soil/remote sensing data, achieve the lowest prediction errors and reveal irrigation and vegetation indices as key drivers.
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Cohen-Manrique et al. (2025) Emerging trends in IoT for aquatic systems: a systematic literature review
This systematic literature review analyzes 458 articles published between 2015 and 2025 to identify emerging IoT-based strategies for surface and groundwater monitoring and management. It finds LoRa as the most adopted transmission technology and highlights the growing relevance of remote IoT, satellite-assisted sensing, and digital twins, proposing an integrated IoT architecture for aquatic systems.
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Gaglo et al. (2025) Sensitivity of a Sahelian groundwater-based agroforestry system to tree density and water availability using the land surface model ORCHIDEE (r7949)
This study developed and evaluated a new configuration of the ORCHIDEE land surface model to simulate a Sahelian groundwater-based agroforestry system, revealing that increased tree density enhances carbon sequestration but reduces crop yield, and that interannual water variability differentially impacts tree and crop productivity.
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Liu et al. (2025) Physics-Driven Machine-Learning Retrieval and Uncertainty Quantification of Crop Leaf Area Index
A physics-driven machine-learning framework, coupling the PROSAIL radiative transfer model with a genetic-algorithm-optimised multilayer perceptron, is developed for operational Leaf Area Index (LAI) retrieval and end-to-end uncertainty quantification, demonstrating improved accuracy and generalisation across different crop sites.
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Chantaveerod et al. (2025) Adaptive Physically Based Contour Framework for Robust and Efficient Catchment Estimation on Large-Scale Terrain Using Super-Resolution DEMs
This paper introduces an adaptive physically based contour framework designed for robust and efficient catchment estimation across large-scale terrains, leveraging super-resolution Digital Elevation Models.
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Ma et al. (2025) Geographical analysis of the factors influencing pyrocumulonimbus and their regional differences over temperate southeast Australia
This study statistically analyzed pyroCb drivers and occurrence patterns in temperate southeastern Australia from 1980 to 2020, revealing significant regional variations and identifying atmospheric variables, particularly the continuous Haines (C-Haines) index, as the most influential factors.
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Helfer et al. (2025) Enhanced baseflow separation in rural catchments: event-specific calibration of recursive digital filters with tracer-derived data
This study enhanced baseflow separation in a small rural catchment by developing an innovative event-specific calibration methodology for Recursive Digital Filters (RDFs) using silica tracer data, demonstrating that dynamic calibration significantly improves accuracy, particularly for the Eckhardt's filter.
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Gupta et al. (2025) AI Driven Spatio-Temporal Modeling for Climate-Resilient Crop Yield Prediction in Indian Agro Ecosystems
This paper focuses on developing AI-driven spatio-temporal models to predict climate-resilient crop yields within Indian agro ecosystems.
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Lutz et al. (2025) Estimating Plant Physiological Parameters for Vitis vinifera L. Using In Situ Hyperspectral Measurements and Ensemble Machine Learning
This study developed and evaluated an ensemble machine learning framework, integrating hyperspectral reflectance data with first derivative preprocessing, to accurately predict key photosynthetic parameters and water potential in grapevines, demonstrating its potential for precision viticulture.
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Bongio et al. (2025) A Statistically Based Method to Estimate Long‐Term Daily Air Temperature at High Elevations
This study develops a statistical methodology to reconstruct daily air temperature time series at the Jungfraujoch (3571 m a.s.l.) in Switzerland from 1900, using observations from lower-altitude stations. The reconstructed series provides a robust, computationally efficient benchmark for evaluating temperature anomalies and studying elevation-dependent warming, achieving performance comparable to existing high-resolution datasets with fewer data requirements.
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Hajani (2025) Uncertainty in stationary and nonstationary IFD curves with future projections in Australia
This study updates Intensity-Frequency-Duration (IFD) curves for six Australian stations using 45 years of annual maximum rainfall data, comparing stationary and non-stationary models with ENSO to quantify uncertainty in future rainfall extreme projections.
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Juidías et al. (2025) Satellite-Derived Spectral Index Analysis for Drought and Groundwater Monitoring in Doñana Wetlands: A Tool for Informed Conservation Strategies
This research introduces the Water Inference Moisture Index (WIMI), a new spectral index derived from Sentinel-2 imagery using machine learning, to monitor surface water dynamics in the Doñana wetlands. The study reveals a concerning trend of surface water disappearance and declining water retention capacity, even during normal rainfall years, indicating increased stress on groundwater resources.
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Prima et al. (2025) Trees control hillslope subsurface flow: Insights from stemflow and throughfall experiments, geophysical surveys, and numerical modeling
This study investigated the effects of rainfall partitioning on subsurface water dynamics across multiple spatial scales on a forested hillslope in Central Italy. Findings reveal dual-permeability soil behavior, with throughfall promoting matrix infiltration and stemflow enhancing rapid macropore flow, which connects to deeper lateral pathways, controlling hillslope-scale groundwater fluctuations.
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Sharifi et al. (2025) Technical note: GRACE-compatible filtering of water storage data sets via spatial autocorrelation analysis
This study develops a methodology to determine an optimal spatial filtering approach for water storage compartment (WSC) datasets to ensure spatial compatibility with GRACE/GRACE-FO terrestrial water storage anomaly (TWSA) products. It identifies an isotropic Gaussian filter with a 250 km width as optimal for combined WSCs, enabling consistent subtraction from GRACE-TWSA for groundwater storage estimation.
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Mishra et al. (2025) Future Projections of Marine Heatwaves in the Northern Indian Ocean Using the HighResMIP Models: Role of Horizontal Resolution and Percentile Thresholds
This study investigates how model horizontal resolution and percentile thresholds influence marine heat wave (MHW) characteristics in the northern Indian Ocean using HighResMIP simulations. It finds that higher resolution improves MHW simulation and projects significant increases in MHW duration, intensity, and frequency, including the emergence of severe and extreme events, under the SSP5-8.5 scenario.
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Zhang et al. (2025) Incorporating water and temperature factors enhanced the performance of the stomatal conductance model for soybeans cultivated under plastic film mulching with drip irrigation in the northeast black soil region
This study aimed to enhance stomatal conductance model performance for soybeans under plastic film mulching with drip irrigation (PFMDI) in northeast China by incorporating water response (f(θ)) and leaf-air temperature difference (ΔT) factors. The corrected Unified Stomatal Optimization (USO) model, particularly with the f(θ) factor, demonstrated significantly improved accuracy and applicability across diverse hydrothermal conditions.
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Qin et al. (2025) Response of SOC stocks in Northeast China to climate warming and precipitation changes
This study investigated the spatio-temporal dynamics and driving mechanisms of soil organic carbon (SOC) stocks in Northeast China under climate warming and precipitation changes, revealing a net SOC loss between 1985 and 2020 and projecting significant future declines, particularly in croplands, due to warming and drought.
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Shao et al. (2025) Bridging Uncertainty in SWMM Model Calibration: A Bayesian Analysis of Optimal Rainfall Selection
This study establishes a Bayesian SWMM calibration framework to investigate how different rainfall types influence the uncertainty of urban hydrological model parameters, finding that higher intensity, one-year return period rainfall events and double-peak patterns generally yield more accurate and less uncertain parameter estimations.
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Marques et al. (2025) Integrating UAV Multi-Temporal Imagery and Machine Learning to Assess Biophysical Parameters of Douro Grapevines
This study investigates the use of UAV multispectral data and machine learning to estimate grapevine leaf area index, pruning wood biomass, and yield across mixed-variety vineyards, demonstrating that ML algorithms, especially with geometric features, provide accurate and scalable monitoring solutions.
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Li et al. (2025) Dominant drivers of spatiotemporal variations in carbon and water use efficiency across the Yellow River Basin revealed by interpretable machine learning
This study quantified the nonlinear spatiotemporal variations of ecosystem carbon and water use efficiency (CWUE) across the Yellow River Basin (YRB) from 1982 to 2018 and identified the spatially heterogeneous dominant driving factors using interpretable machine learning. The findings reveal that CWUE generally increased with high sustainability, primarily driven by leaf area index (LAI) for water use efficiency (WUE) and temperature for carbon use efficiency (CUE).
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Müller et al. (2025) The ICON-based Earth System Model for climate predictions and projections (ICON XPP v1.0)
This paper introduces and evaluates ICON XPP, a new Earth System model configuration designed for next-generation climate predictions and projections. The model generally reproduces basic coupled climate properties and performs comparably to CMIP6 models, despite exhibiting some regional biases.
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Zhang et al. (2025) Irrigation cooling effect reduced by water-saving practices
This study reveals that the widespread adoption of water-saving irrigation (WSI) practices across China has significantly weakened irrigation's daytime cooling effect on land surface temperature while intensifying nighttime cooling, driven by shifts in surface energy partitioning.
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Eitel et al. (2025) A global analysis of SAR altimetry signals over different landcover types
This study analyzes how Sentinel-3 SAR altimetry waveforms respond to different land cover types and what physical characteristics are encoded in the signal. It demonstrates that a feature-enhanced one-dimensional convolutional neural network (1D-CNN) can effectively extract land cover information from these signals, revealing their sensitivity to surface variations despite large footprints.
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Zhang et al. (2025) Dominant Role of Meteorology and Aerosols in Regulating the Seasonal Variation of Urban Thermal Environment in Beijing
This study quantified the independent and interactive effects of aerosols, meteorological conditions, and urban features on Land Surface Temperature (LST) in Beijing using multisource data and a random forest model, revealing nonlinear responses where meteorological and aerosol factors have a greater impact than urban landscape, with significant seasonal aerosol cooling and specific urban feature influences.
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Fan et al. (2025) Remote sensing proxies underestimate fire-induced gross primary productivity loss and overestimate recovery in forests
This study evaluated five global satellite-based Gross Primary Productivity (GPP) products and three complementary proxies against eddy covariance measurements at ten fire-affected sites, revealing systematic biases that underestimate fire-induced GPP loss and overestimate recovery, particularly in forests.
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Yang et al. (2025) Increasing irrigation water requirements across croplands in the Contiguous United States throughout the 21st century
This study estimates crop-specific irrigation water requirements (IWR) across the Contiguous United States (CONUS) throughout the 21st century under moderate (SSP245) and severe (SSP585) warming scenarios. Results project a continuous increase in IWR across CONUS, with annual average IWR depth rising by 10.3 % under SSP245 and 26.4 % under SSP585 by the late 21st century, highlighting growing challenges for agricultural water management.
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Zhou et al. (2025) Multi-sensor assessment of phenology-based field-level cover cropping detection using satellite vegetation time series from Harmonized Landsat-8 and Sentinel-2, MODIS, and PlanetScope
This study evaluated the performance of multi-sensor satellite vegetation time series (HLS, MODIS, PlanetScope) for phenology-based field-level cover cropping detection in Indiana. It found that Harmonized Landsat-8 and Sentinel-2 (HLS) outperformed MODIS and MODIS-calibrated PlanetScope, with original PlanetScope showing the highest accuracy, and identified key factors influencing detection accuracy.
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Lozano‐Parra (2025) Exploring the aridity risk in agricultural lands of southwest Spain: the Extremadura region
This study quantified future climate variations and aridification trends in irrigated and rainfed agricultural areas of southwestern Spain using CMIP6 projections. It found a strong aridification trend characterized by significant precipitation decline, temperature increase, and a widespread shift towards semi-arid and arid conditions, necessitating urgent adaptive measures.
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Then et al. (2025) Modified Mann-Kendall with higher-order statistics for trend analysis
This study proposes Mann–Kendall with Third-Order Cumulant (MKC3) to improve trend analysis by addressing nonlinearity and autocorrelation, comparing its performance against existing Mann–Kendall variants through simulations and a case study of rainfall trends in Peninsular Malaysia. The findings provide practical guidance for selecting the most suitable trend test based on data characteristics.
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Ghosh et al. (2025) Novel R-CNN and transformer models for pollution impacts and land cover changes around iconic heritage sites in developing countries: a case study
This study quantifies pollution impacts and land cover changes around the Meenakshi Amman Temple in Madurai, India, using novel R-CNN and Transformer models to characterize particulate matter and predict future environmental indicators. It demonstrates the significant benefits of transitioning to electric vehicles and proposes an AI-mediated method for pollution assessment and remediation.
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Hajdini et al. (2025) Data Analysis and Evaluation of the Impact of Meteorological Parameters on the (eto) Calculated by Fao Penman-Monteith Equation
This study analyzed the influence of meteorological parameters on daily reference evapotranspiration (ETo) calculated by the FAO Penman-Monteith equation in Prrenjas, Albania, finding strong positive correlations with wind speed, sunshine hours, and solar radiation, and a strong negative correlation with relative humidity.
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Daoud et al. (2025) Comprehensive hazard susceptibility assessment in Port Sudan city using AHP: emphasizing flash flood risk, soil moisture, and salinity dynamics
## Identification - **Journal:** Geomatics Natural Hazards and Risk - **Year:** 2025...
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Zehrung et al. (2025) Standardising the “Gregory method” for calculating equilibrium climate sensitivity
This study systematically assesses 32 data processing pathways for the "Gregory method" to estimate Equilibrium Climate Sensitivity (ECS) using 44 CMIP6 models, revealing that while the multi-model ECS range is robust, individual model estimates can vary significantly based on processing choices, leading to a recommended standardized method for improved reproducibility.
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Felix et al. (2025) Probing Early and Long-Term Drought Responses in Kauri Using Canopy Hyperspectral Imaging
This study assessed the effectiveness of multitemporal canopy-scale hyperspectral imaging for detecting water stress in kauri trees under controlled and field conditions, demonstrating its capacity for early and sensitive stress detection within one week of drought initiation.
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Guth et al. (2025) Benchmarking Elevation Plus Land Surface Parameters Finds FathomDEM and Copernicus DEM Win as Best Global DEMs
This study benchmarks six global 1-arc-second Digital Elevation Models (DEMs) against high-resolution lidar-derived reference Digital Terrain Models (DTMs) across 1510 test tiles, evaluating both elevation and derived Land Surface Parameters (LSPs). It finds FathomDEM to be the best overall, with Copernicus DEM as the top unrestricted option, and highlights significant variability in LSP accuracy depending on terrain characteristics and derivative order.
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Alcalde et al. (2025) Remote Sensing Standardized Soil Moisture Index for Drought Monitoring: A Case Study in the Ebro Basin
This study evaluates the satellite-derived Standardized Soil Moisture Index (SSI) for drought monitoring across various timescales in the Ebro Basin, demonstrating its robustness and superior spatial resolution compared to precipitation-based indices, and its capability to monitor hydrological droughts without relying on in situ measurements.
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Loconsole et al. (2025) Soil Moisture Sensing Technologies: Principles, Applications, and Challenges in Agriculture
This review comprehensively evaluates invasive and non-invasive soil moisture sensing technologies, discussing their principles, applications, strengths, and limitations in agriculture. It highlights recent innovations and identifies key challenges to widespread adoption, particularly for smallholder farmers, while proposing strategies for future development.
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Jiang et al. (2025) CropLayer: a 2 m resolution cropland map of China for 2020 from Mapbox and Google satellite imagery
This study presents CropLayer, a 2 meter resolution cropland map of China for 2020, developed from Mapbox and Google satellite imagery. It achieves high accuracy (pixel-level 88.73%, block-level 96.5%) and strong consistency with official statistics, with 30 out of 32 provincial units showing area estimates within ±10% deviation, significantly outperforming existing datasets.
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Kim et al. (2025) Deep learning-based prediction of cold surge frequency over South Korea
This study develops a hybrid deep learning framework combining a coupled general circulation model with a Long Short-Term Memory neural network to improve seasonal prediction of winter cold surge frequency over South Korea, demonstrating significantly enhanced prediction skill and revealing a temporal shift in dominant teleconnection drivers.
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Niu et al. (2025) Grassland NDVI in Ngari Prefecture, Tibet Autonomous Region Remains Dominantly Increasing After Filtering Out Climatic Effects (2000–2024)
This study quantifies the influence of non-climatic factors on grassland greening in Ngari, Tibet, from 2000 to 2024, demonstrating a net positive effect after statistically removing climatic drivers.
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Chen et al. (2025) Validating physical and semi-empirical satellite-based irradiance retrievals using high- and low-accuracy radiometric observations in a monsoon-influenced continental climate
This study validates physical and semi-empirical satellite-based irradiance retrievals against both high- and low-accuracy ground observations in a monsoon-influenced continental climate. The findings reveal that using low-accuracy observations for validation introduces significant, non-unidirectional deviations in validation outcomes, comparable to commonly accepted error margins, thereby posing risks to scientific assertions.
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Uusinoka et al. (2025) Scale invariance in kilometer-scale sea ice deformation
This study uses high-resolution MOSAiC ship radar data and a deep learning optical flow technique to investigate kilometer-scale sea ice deformation, revealing a lower limit of approximately 100 meters for scale-invariance in winter pack ice that disappears in summer.
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Toguyeni et al. (2025) U-NET Deep Learning-based Downscaling to Generate High-resolution Seasonal Forecasts for Small Watersheds: A Case Study of the Nouhao Sub-basin, Burkina Faso
This study develops a U-Net Deep Learning framework to downscale coarse 1° (~100 km) seasonal forecasts of precipitation and temperature into high-resolution 0.05° (~5 km) data for Burkina Faso, demonstrating substantial skill improvements (up to sixfold for precipitation and twenty-fold for temperature) compared to raw forecasts.
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Prabhahar et al. (2025) Harnessing Automated Irrigation Technologies to Enhance Sustainability of Agriculture: A Pathway to Food Security
This study evaluates the impact of automated irrigation technologies on water-use efficiency, energy optimization, and crop productivity, finding that high-level automation significantly improves water savings (up to 87%), yield (30%), and economic returns compared to traditional methods.
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Xiao et al. (2025) Optimization of coordinated autumn and spring irrigation under water resource constraints in cold and arid region
This study developed an optimized water allocation scheme for autumn and spring irrigation in the Hetao Irrigation District under water resource constraints, integrating remote sensing data and the SHAW model to enhance water use efficiency and mitigate soil salinization. It recommends differentiated irrigation quotas and areas for various soil salinity levels and crop types, leading to a balanced water distribution for sustainable agricultural development.
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Jing et al. (2025) Estimation of Fractional Vegetation Cover From Fully Polarimetric SAR Data via Multidimensional Feature Selection and Model Optimization
## Identification - **Journal:** IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - **Year:** 2025...
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Gong et al. (2025) A changepoint approach to automated estimation of soil moisture drydown parameters from time series data
This study introduces an automated, changepoint-based method to analyze in-situ soil moisture time series, autonomously detecting wetting events and estimating drydown parameters. The method successfully extracts physically interpretable information, demonstrating that these parameters correlate with climatic regimes and soil texture across diverse field sites.
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Zarei et al. (2025) Integrative modeling for enhanced flood risk forecasting and management in Semi-Arid area of Iran
This study developed an integrated multi-model framework (WRF-HC-HMS-HEC-RAS) for enhanced pre- and post-flood risk forecasting and management in a semi-arid Iranian basin, successfully predicting 48-hour floods and mapping post-event damage.
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Ohneiser et al. (2025) Impact of seeder-feeder cloud interaction on precipitation formation: a case study based on extensive remote-sensing, in situ and model data
This study provides an unprecedented detailed investigation of a seeder-feeder cloud system over the Swiss Plateau, demonstrating significant precipitation enhancement from seeder-feeder interaction, with an estimated 20% to 40% of precipitation originating from the feeder cloud.
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Baruah et al. (2025) Interpretable machine learning for predicting rating curve parameters using channel geometry and hydrological attributes across the United States
This study developed interpretable machine learning models to predict power-law rating curve parameters (α, β) across the CONtiguous United States (CONUS) stream network, demonstrating their sensitivity to channel geometry and hydrometeorological factors for improved flood risk assessment.
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Cao et al. (2025) Hydroeconomic optimization for canal-well conjunctive irrigation and drainage management in an arid region with salinization
This study developed an integrated hydroeconomic optimization framework to manage canal-well conjunctive irrigation and drainage in arid regions facing water scarcity and salinization. It identified that a 10% reduction in the surface-to-groundwater irrigation area ratio combined with an enhanced groundwater drainage capacity (α = 0.21 year⁻¹) is a sustainable strategy, simultaneously increasing agricultural net benefits, mitigating salinization, conserving water, ensuring food security, and maintaining groundwater sustainability over a 15-year horizon.
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Liu et al. (2025) Urbanization is projected to increase local surface temperature by 2100
This study developed a 1-km resolution global land surface temperature dataset for 2020–2100, integrating climate change and urbanization effects. It projects that urbanization will contribute an average local warming of 0.1 °C by 2100, with 10–16% of urban areas experiencing extreme warming exceeding 1 °C.
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Decharme (2025) A process-based modeling of soil organic matter physical properties for land surface models – Part 1: Soil mixture theory
This study proposes a process-based framework, grounded in soil mixture theory, to accurately model soil organic matter physical properties in land surface models, demonstrating significant improvements over empirical approaches through validation against experimental and in situ datasets.
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Zhu et al. (2025) Large-scale irrigation area mapping: Status and challenges
This paper provides a comprehensive synthesis of large-scale irrigation area mapping methodologies and datasets, benchmarking ten global and regional products against EUROSTAT 2020 gridded statistics in Europe. It concludes that integrating ground-based statistics with geospatial information significantly improves mapping accuracy, especially in humid regions, while highlighting the critical need for standardized, accessible, and high-quality ground-truth and statistical data.
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Laghari et al. (2025) Predicting spatiotemporal changes in flood prone regions using PSO-ML coupling under climate change scenarios
This study developed Particle Swarm Optimization-Machine Learning (PSO-ML) models, integrated with General Circulation Model (GCM) data, to predict spatiotemporal flood risk in Shanxi Province, China, under climate change scenarios. The PSO-ML models significantly improved prediction accuracy, projecting a southward shift and increase in flood-prone areas by 2100, with land use, elevation, and slope being the most influential factors.
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Shin et al. (2025) Machine learning-based retrieval of aerosol size and hygroscopicity using horizontal scanning LiDAR and PM data
This study develops a machine learning-based approach to retrieve aerosol size and hygroscopicity by integrating horizontal scanning LiDAR and in-situ PM data, revealing that coarse hygroscopic aerosols dominate the coastal urban region and significantly impact optical properties despite low mass concentrations.
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Wang et al. (2025) Improving river surface flow velocity measurement by coupling optimal search line algorithm with space-time image velocimetry
This study introduces an automatic optimal search line selection algorithm to enhance the accuracy of Space-Time Image Velocimetry (STIV) for river surface flow velocity measurement. The proposed method significantly reduces errors in velocity estimation compared to traditional fixed-line STIV, improving its robustness and applicability across various flow conditions.
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Kole (2025) An Iot–machine Learning–decision Support System Framework for Smart Agriculture: Design, Implementation, and Performance Evaluation
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Samantray et al. (2025) Studying the dynamics of cloudburst events over Indian Himalayan region using model simulation
This study utilized a high-resolution Weather Research and Forecasting (WRF) model to simulate and analyze six cloudburst events in the Indian Himalayan Region (IHR) during the 2022 monsoon season. The model effectively replicated the spatial and temporal distribution of heavy rainfall, demonstrating a strong correlation with India Meteorological Department (IMD) data and providing insights into the dominant atmospheric conditions driving these extreme events.
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Rahlves et al. (2025) Investigating the multi-millennial evolution and stability of the Greenland ice sheet using remapped surface mass balance forcing
This study introduces and evaluates an SMB remapping procedure for stand-alone ice sheet models to efficiently simulate the multi-millennial evolution of the Greenland ice sheet. The remapping method effectively captures first-order climate-ice sheet feedbacks, preserving ablation zone structure and reducing biases compared to conventional parameterizations, leading to more realistic long-term mass loss projections.
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Lang et al. (2025) Significant uncertainties from overlooking aerosol-cloud coexistence in surface solar radiation estimates using passive satellite observations
This study systematically evaluates the significant uncertainties introduced by overlooking aerosol-cloud coexistence in surface solar radiation estimates derived from passive satellite observations. It finds that ignoring this coexistence leads to substantial errors in direct and diffuse solar radiation, highlighting the need for improved methodologies.
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Faqiri et al. (2025) Rainfall and Groundwater Relationship Assessment in the North River Basin, Afghanistan
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Chao et al. (2025) Evaluation of satellite soil moisture products (AMSR2, SMAP L3/L4) across mainland China using in situ data (2020–2024)
This study evaluated the soil moisture monitoring performance of three satellite products (AMSR2, SMAP L3, and SMAP L4) across mainland China using 3293 in-situ stations and Monte Carlo simulations. It found that SMAP L4 consistently performed best, followed by SMAP L3, while AMSR2 exhibited the largest errors, with performance differences primarily driven by sensor properties, algorithm complexity, and land cover heterogeneity.
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Dutrievoz (2025) Les isotopes de la vapeur d'eau en Antarctique, traceurs des processus de la couche limite et de la dynamique à grande échelle
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Zakharova et al. (2025) Satellite altimetry over frozen rivers. Satellite altimetry and hydrodynamic model reproduce the ice jam conditions
This study demonstrates that satellite altimetry, combined with hydrodynamic models, can accurately reproduce river ice jam conditions and explains that altimetric signals over frozen rivers can reflect from either the ice-water or ice-air interface, accounting for observed variability and inconsistencies in winter water surface elevation retrievals in Arctic rivers.
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Marchionni et al. (2025) Urban greening and water strategies are key to adapt Australian cities to climate change and urban growth
This paper explores how Australian cities integrate urban greening with water-sensitive urban design (WSUD) for climate adaptation, demonstrating that effective water management is crucial for the long-term success of urban greening in building climate-resilient cities.
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Zakeri et al. (2025) High-resolution snow water equivalent estimation: a data-driven method for localized downscaling of climate data
This study develops a data-driven k-nearest neighbor method to downscale low-resolution climate data into daily high-resolution (500 m) snow water equivalent (SWE) estimates for mountainous regions. The approach successfully generates SWE data that closely matches reanalysis data, demonstrating that performance is highly dependent on the choice and accuracy of the climate model inputs.
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Creaco (2025) Excess Rainfall-Based Derivation of Intensity–Duration–Frequency Curves
This paper introduces an innovative method to derive Intensity-Duration-Frequency (IDF) curves from excess rainfall (ERIDF curves), utilizing a simplified hydrological model and annual maxima analysis. This approach demonstrates improved preservation of return periods in rainfall-runoff transformation for hydraulic design compared to conventional methods.
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Liu et al. (2025) First Observation of Thunderstorm Occurrences in the Lower Atmosphere by All‐Sky Meteor Radars
This study pioneers the use of all-sky meteor radars, typically for mesospheric/ionospheric observations, to detect and track lower-atmospheric thunderstorms, demonstrating their capability to accurately capture thunderstorm development.
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Barbagallo et al. (2025) Integrating Satellite and Field Data for Glacier Melt Modeling in High-Mountain Asia: A Case Study on Passu Glacier
This study developed an integrated remote sensing and ground-based approach to model bare ice melt on Passu Glacier, High-Mountain Asia, accurately estimating a total melt volume of 16 million cubic meters water equivalent with a 9% uncertainty against field measurements.
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Kuunya et al. (2025) Soil moisture sensors for sustainable water management in field crop production: A review of advances and application challenges
This review synthesizes advances and challenges of soil moisture sensors for sustainable water management in field crop production, concluding that while sensors significantly improve water use efficiency and yields, their widespread adoption is hindered by socio-economic and technical barriers.
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Xie et al. (2025) Asymmetric Impacts of Extreme Positive and Negative Indian Ocean Dipole Events on Late‐Summer Monsoon Rainfall in Western South Asia
This study reveals that both extreme positive and negative Indian Ocean Dipole (IOD) events asymmetrically enhance rainfall over western South Asia during August–September, even when ENSO effects are removed, through distinct moisture convergence mechanisms driven by anomalous winds.
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Harris et al. (2025) Global observations of land-atmosphere interactions during flash drought
This study uses global satellite observations to investigate land-atmosphere coupling processes during flash droughts from 2000–2020, revealing that precursor land surface conditions significantly influence drought intensity and associated heat extremes, offering valuable information for subseasonal-to-seasonal (S2S) forecasts.
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Ayoob et al. (2025) Possible consequences of land cover and land use dynamics on the land surface temperature: A case study of lower Zab River Basin
This research investigates the dynamics of land cover/land use (LCLU) and their influence on Land Surface Temperature (LST) in the Lower Zab River Basin (LZRB), Iraq, from 2002 to 2023. Findings reveal significant increases in urban and agricultural areas, a reduction in water bodies, and a strong correlation where bare lands exhibit the highest LSTs while water bodies show the lowest, emphasizing the critical impact of LCLU changes on regional thermal conditions and water resources.
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Liang et al. (2025) GNSS-IR retrieval of soil moisture at hourly resolution taking into account corrections for inter-orbit phase bias of satellites
This study proposes an hourly-resolution soil moisture retrieval method by fusing multi-system GNSS observations and correcting for inter-orbit phase biases using a low-order polynomial fitting approach. The method, validated with machine learning models, achieved a correlation coefficient of 0.95 and a 40.1 % reduction in root mean square error for hourly soil moisture retrieval.
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Bolivar et al. (2025) How Do Tropical Cyclones Directly Simulated in High-Resolution Climate Models Differ from Statistically Dynamically Generated Storms?
This study compares high-resolution climate models and statistical–dynamical downscaling (SDD) models for simulating landfalling tropical cyclones (TCs). It finds that while SDD offers computational advantages and improves some metrics, it also introduces unphysical behaviors and overrepresents landfalling storms, highlighting distinct biases in both techniques that can be better understood through their comparison.
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Zhang et al. (2025) Groundwater volume loss and land subsidence in the North China plain investigated using wide-area InSAR surveying and mechanical modeling
This study integrates wide-area InSAR data with a mechanical model to map aquifer deformation and groundwater storage loss (GWSL) across the North China Plain, revealing severe subsidence, quantifying aquifer elastic recovery, and providing the first 2-km resolution GWSL dataset. It highlights a shift in subsidence from urban to agricultural areas, with urban deformation stabilizing while agricultural subsidence intensifies.
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He et al. (2025) Machine learning prediction of future land surface temperature from SAR optical fusion under urban expansion in Changsha, China
This study developed an innovative SAR–optical collaborative framework to reconstruct cloud-free land surface temperature (LST) and predict future LST under urban expansion in Changsha, China, demonstrating high accuracy and revealing a strong synchrony between built-up expansion and LST increase.
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Chawanda et al. (2025) CoSWAT Model v1: A high-resolution global SWAT+ hydrological model
This study developed CoSWAT Model v1, a high-resolution global SWAT+ hydrological model, and an open-source framework for its reproducible setup and execution. It demonstrates the feasibility of global SWAT+ modeling at 2 km resolution, showing reasonable evapotranspiration patterns but limited river discharge performance without calibration or reservoir implementation.
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Parazoo et al. (2025) Solar Induced Fluorescence as an Application Ready Early Warning Indicator of Flash Drought
This paper synthesizes recent advancements in high-resolution solar-induced fluorescence (SIF) mapping, arguing that these machine learning-derived, spaceborne SIF products are ready for immediate operational use by the drought monitoring community for flash drought early warning.
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Benito et al. (2025) Optimizing Water Efficiency in Urban Farming with an Automated Smart Drip Irrigation System
This study developed and evaluated an Automated Smart Drip Irrigation System for urban farming, demonstrating its ability to significantly reduce water consumption while maintaining or improving crop health and yield compared to traditional irrigation methods.
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Benli et al. (2025) The Application of Remote Sensing to Improve Irrigation Accounting Systems: A Review
This systematic review assesses the state of remote sensing for irrigation water accounting, revealing a strong focus on technological advancements and management benefits but significant gaps in institutional integration, policy, and application in water-scarce regions of the Global South. It concludes that despite efficiency improvements, operational adoption is hindered by institutional, regulatory, and methodological barriers, necessitating interdisciplinary approaches and stakeholder engagement.
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Zoratipour et al. (2025) Deriving hourly and daily crop water stress index through the lens of proximal sensing in sugarcane fields
This study evaluated proximal sensing for hourly and daily Crop Water Stress Index (CWSI) detection in sugarcane fields in an arid region, identifying solar radiation and wind speed as key hourly drivers and soil moisture as a primary daily influence. It proposes a CWSI threshold of 0.4–0.5 for initiating irrigation to optimize water management.
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Huang et al. (2025) Multi-objective collaborative optimization of water resources in Hebei irrigation areas: maximizing the benefits of the water-energy-grain nexus driven by the NSGA-III algorithm and verified by digital twins
This study developed a digital twin-enabled framework for multi-objective collaborative optimization of water resources in Hebei irrigation areas, integrating an enhanced NSGA-III algorithm and a four-dimensional nexus model to maximize benefits of the water-energy-grain nexus. The framework achieved significant water conservation, energy reduction, and grain yield increase while reversing groundwater over-extraction.
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Bonan et al. (2025) Beyond surface fluxes: Observational and computational needs of multilayer canopy models – A walnut orchard test case
This study evaluates the Community Land Model's multilayer canopy model (CLM-ml v2) against comprehensive, multi-level observations from a walnut orchard, demonstrating its strong performance for most atmospheric conditions but highlighting limitations in simulating within-canopy mixing during strongly stable regimes.
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Zhan et al. (2025) Exacerbated Variability and Extremes in Streamflow Across Half of China From 1961 to 2018
This study investigated trends in seasonal streamflow variability and extremes across China from 1961 to 2018, revealing significant increases in approximately half of the country, particularly in the Northwest River Basin and during summer.
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Jiao et al. (2025) Mapping stability and instability hotspots in Jiangsu’s vegetation: an explainable machine learning approach to climatic and anthropogenic drivers
This study investigated the spatiotemporal patterns and climatic drivers of vegetation stability across Jiangsu Province, China, using an explainable machine learning approach. It found that while most areas showed enhanced stability, 15.77% experienced increasing instability, primarily driven by background solar radiation and its temporal variability, followed by vapor pressure deficit.
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Rahman et al. (2025) Groundwater science in the age of AI: emerging paradigms and challenges
This review synthesizes recent advances in artificial intelligence (AI) for sustainable groundwater management, demonstrating how emerging AI methods enhance forecasting accuracy, contaminant detection, and real-time decision support across key domains. It uniquely consolidates groundwater-specific applications, identifies research gaps, and introduces new paradigms to outline a future research agenda for transparent groundwater governance.
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Ellis et al. (2025) Distinguishing Precipitation by Process as a Prerequisite for Understanding Hydroclimate Change: An Example from the Southeastern Lake-Effect Region of the Great Lakes Basin
This study reveals that analyzing total cool-season precipitation in the southeastern Great Lakes region masks opposing trends in synoptic-scale and mesoscale lake-effect precipitation over a 47-year period, demonstrating that process-based stratification is essential for accurately understanding hydroclimate change.
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Rogger (2025) TREED (v1.0) vegetation model code and input
This publication presents the TREED (v1.0) vegetation model code and its associated input data, enabling researchers to simulate vegetation dynamics for present-day validation, past climate scenarios like the Paleocene-Eocene Thermal Maximum (PETM), and studies on eco-evolutionary lags.
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Du et al. (2025) Evapotranspiration and Its Components Partitioning Based on an Improved Hydrological Model: Historical Attributions and Future Projections
This study developed an improved hydrological model, integrating water balance and water-carbon coupling, to estimate and attribute historical changes and project future trends of evapotranspiration (ET) and its components (evaporation, E; transpiration, T). It found significant historical increases in ET and E, primarily driven by precipitation, and projected future increases in ET and its components under most Shared Socioeconomic Pathways (SSP) scenarios.
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Xu et al. (2025) Balancing agricultural expansion and groundwater sustainability: Insights from GRACE and hydrological models
This study developed an integrated downscaling framework using GRACE observations and hydrological models to generate high-resolution groundwater storage anomaly (GWSA) estimates for the Sanjiang Plain. It found that while regional dry-wet conditions are the dominant drivers of GWSA dynamics, agricultural cropping pattern shifts, particularly paddy expansion in the eastern region, increasingly exert negative impacts on groundwater sustainability, with a critical threshold identified for maize-to-rice conversion.
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ElGhawi et al. (2025) Hybrid‐Modeling of Land‐Atmosphere Fluxes Using Integrated Machine Learning in the ICON‐ESM Modeling Framework
This paper develops Hybrid-JSBACH4, a novel hybrid modeling approach that integrates data-driven neural network parameterizations, trained on eddy-covariance flux measurements, into the mechanistic JSBACH4 land surface model. This integration significantly improves the simulation of land-atmosphere water and carbon fluxes by reducing biases in transpiration and gross primary production compared to the original JSBACH4.
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Zhang et al. (2025) On the Use of Knowledge‐Informed Machine Learning and Multisource Data for Spatially Explicit Estimation of Irrigation Water Withdrawal
## Identification - **Journal:** Earth s Future - **Year:** 2025...
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Wu et al. (2025) Assessing the contribution of impoundment–induced climate change to vegetation growth in the Xiluodu reservoir area of the Jinsha River
This study developed an integrated framework to assess the indirect contribution of impoundment-induced climate change to vegetation growth in the Xiluodu Reservoir area, revealing that impoundment significantly altered local climate and was conducive to vegetation growth.
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Brunetti et al. (2025) A Unified Physically Based Model to Simulate Water and Carbohydrates Allocation Along the Soil‐Fruit Axis
This study integrates the HYDRUS hydrological model with the SUGAR model to mechanistically link soil processes and fruit development, demonstrating accurate predictions of tomato fruit water and carbohydrate dynamics under varied irrigation, and identifying phloem flux and active carbohydrate uptake as key drivers of fruit growth and sugar accumulation.
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Ishag et al. (2025) Using Remote Sensing and Machine Learning to Determine Past, Current and Future Crop Water Use From the Nubian Sandstone Aquifer
This study quantifies the increase in irrigated land and associated crop water use from the Nubian Sandstone Aquifer System (NSAS) between 2000 and 2024, revealing a significant doubling of total irrigated area and a substantial increase in water abstraction, with critical implications for sustainable water management.
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Ayantobo et al. (2025) Atmospheric Rivers Sustain and Reshape Hydrological Responses Across Chinese River Basins
This study quantifies the role of Atmospheric Rivers (ARs) in driving hydrological responses across Chinese river basins from 1950 to 2023, revealing a significant south-to-north contrast where southern basins experience strong, long ARs that sustain wetness and amplify floods, while central and northern basins show declining AR influence since the 1980s.
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CAGUIAT et al. (2025) Machine learning modeling of reference evapotranspiration in Central Luzon, Philippines
This study evaluates various machine learning algorithms for estimating reference evapotranspiration (ETo) in Central Luzon, Philippines, using limited ground-based weather data. It demonstrates that machine learning, especially Gaussian Process Regression, can accurately predict ETo with only two or three input variables, offering a robust alternative to data-intensive empirical models.
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Krishna et al. (2025) Effect of mulching and irrigation methods on soil moisture, growth, and yield of tomato (Solanum lycopersicum L.)
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Zhang et al. (2025) A Simple Intermediate Coupled MJO‐ENSO Model: Multiscale Interactions and ENSO Complexity
This paper develops a simple intermediate coupled MJO-ENSO model to understand their bidirectional feedback and its role in modulating ENSO complexity, successfully capturing observed MJO and ENSO features and their critical interactions.
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Tang et al. (2025) Mass Change Index for Characterizing Hydrological Extremes Every Few Days From Satellite Gravity Measurements
This paper introduces the Mass Change Index (MCI), a new hydrological index derived from GRACE Follow-On (GRACE-FO) satellite data, enabling the assessment of instantaneous extreme wet and dry events every few days. It demonstrates MCI's superior ability to detect the severity and timing of the 2020 Yangtze River flood and the 2022 MLYRB drought compared to traditional monthly GRACE-FO and streamflow indices.
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Khuong et al. (2025) A new framework for quantifying the impacts of climate variability and human activities on streamflow variation with an application to the upper Da river basin
This paper introduces a new framework, combining a physics-based hydrological model and an Extended Impact Factor Formula (EIFF), to quantify the impacts of climate variability and human activities on streamflow at annual and seasonal scales in transboundary river basins. Applied to the upper Da River basin, the framework revealed that human activities have lessened annual streamflow downstream since 2009, reducing flood season flow while enhancing dry season flow, with climate variability generally dominating impacts in the initial period.
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Ilyas et al. (2025) Climate-responsive cropland dynamics in Indus Basin: A comprehensive SDM assessment with intra-seasonal variability
This study predicts the spatial and temporal shifts in the suitability of five major crops in the Indus Plain under various climate change scenarios using an ensemble Species Distribution Modeling (SDM) approach. It reveals significant alterations in crop suitability, primarily driven by elevation and phenology-specific climatic variables, which necessitate adaptive strategies for food security.
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Wang et al. (2025) Using Machine Learning to Discover Parsimonious and Physically‐Interpretable Representations of Catchment‐Scale Rainfall‐Runoff Dynamics
This paper explores the development of physically interpretable machine learning models for dynamical systems, demonstrating that Mass-Conserving-Perceptron (MCP) based networks with a distributed-state mechanism can achieve both physical interpretability and good predictive performance in catchment-scale streamflow modeling with minimal complexity.
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UkrNDIPVT et al. (2025) Analysis of Ukraine’s Agrocenoses Transformation Under Climate Change Using Ndvi Remote Monitoring
This study systematically analyzed the long-term dynamics of crop biomass formation in Ukraine (2001-2020) using satellite-derived NDVI, establishing quantitative relationships between vegetation status and key meteorological factors. It found that a comprehensive approach combining temperature and cumulative climatic water balance accurately predicts crop status, highlighting regional vulnerabilities and informing climate-smart agricultural practices.
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Juzbašić et al. (2025) Impact of global warming on precipitation extremes based on the design frequencies over South Korea
This study evaluates and analyzes future changes in precipitation extremes over South Korea using bias-corrected regional climate models and generalized extreme value theory. It finds that return values for extreme precipitation are projected to significantly increase, especially for longer return periods and under high-emission scenarios, necessitating the use of future projections for flood defense planning.
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Jin et al. (2025) Surface energy balance under paddy-upland rotation in the lakeside area of Erhai Lake, Southwest China
This study investigated surface energy balance (SEB) characteristics and compared Large Aperture Scintillometer (LAS) and Eddy Covariance (EC) systems in a paddy-upland rotation area near Erhai Lake, Southwest China, revealing crop- and atmospheric stratification-dependent advantages of LAS in improving sensible heat flux quantification and overall energy balance closure.
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Kim et al. (2025) Hysteresis response of Northern Hemisphere winter temperature variability under different CO₂ removal pathways
This study investigates the hysteresis and reversibility of Northern Hemisphere winter daily temperature variability (Tstd) under different CO₂ removal pathways, finding that Tstd partially recovers but exhibits regional hysteresis and irreversibility, particularly in high-concentration scenarios, driven by changes in local temperature gradients.
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Zhang et al. (2025) Challenges and Strategies for Flood Forecasting in a Changing Environment
This document describes the Digital Object Identifier (DOI) system, its purpose in providing persistent identification and reliable access to digital objects, and highlights recent operational milestones, including reaching over 3 billion proxy resolutions in a single month.
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Xue et al. (2025) A Greening Future Elevates Flash Drought Risk in Northern Mid‐to‐High Latitudes
This study investigates the mechanism by which vegetation regulates flash drought frequency and its future changes, finding that dense vegetation, particularly in northern mid-to-high latitudes, increases flash drought risk by decoupling precipitation from soil moisture through enhanced transpiration, accelerating soil moisture depletion.
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Talha et al. (2025) Robust Ensemble Machine Learning for Flash Flood Susceptibility Mapping Across Semiarid Regions
This study aimed to enhance flash flood susceptibility mapping in Morocco's Assaka watershed using an ensemble of machine learning models, finding that the integrated approach significantly improved accuracy and identified key high-risk zones around Guelmim city and major river infrastructure.
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Ma et al. (2025) Assessment of artificial lakes' impact on glacier conservation: Evidence obtained from stable isotope
This study quantifies the contribution of artificial lake evaporation to snowfall in the Yulong Snow Mountain glacial region using stable isotopes and atmospheric trajectory models, revealing that these lakes significantly decelerate local glacial retreat by providing substantial moisture during snowfall periods.
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He et al. (2025) Hybrid Lake Model (HyLake) v1.0: unifying deep learning and physical principles for simulating lake-atmosphere interactions
This study introduces HyLake v1.0, a novel hybrid lake model that unifies physics-based surface energy balance equations with a Bayesian Optimized Bidirectional Long Short-Term Memory-based (BO-BLSTM-based) surrogate to simulate lake surface temperature (LST) dynamics. The model demonstrates superior performance in simulating lake-atmosphere interactions and strong generalization and transferability to ungauged sites and with unlearned forcing datasets compared to traditional and other hybrid models.
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Ablila et al. (2025) Development and Validation of a New Remotely Sensed Combined Drought Anomaly Index (CDAI) for Monitoring Agriculture Drought Over Morocco
This study developed a new Combined Drought Anomaly Index (CDAI) for monitoring agricultural drought in Morocco, integrating multiple remote sensing-based indices using Principal Component Analysis. The CDAI was validated against cereal yield and in-situ precipitation data, demonstrating strong correlations and superior performance in early drought detection compared to existing indices.
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Agboly (2025) Improving Agricultural Water Use in the Texas High Plains: A Strategic Approach to Crop Switching and Optimal Land Allocation
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Zhao et al. (2025) Response of Tipping Elements to Different Strategies of Stratospheric Aerosol Injection
This study assesses the effectiveness of various Stratospheric Aerosol Injection (SAI) strategies, including single-objective and multi-objective approaches at different latitudes, in mitigating risks associated with climate tipping elements. It finds that while SAI generally reduces risks, the optimal strategy depends on the specific tipping element, highlighting trade-offs between temperature stabilization goals and regional risk reduction.
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Kim et al. (2025) Changes in the Frequency of Flood Events Across the United States Detectable by the Middle of This Century
This study applies a statistical attribution-and-projection approach to thousands of streamgages across the conterminous United States (CONUS) to assess how the frequency of flood events is expected to change under multiple scenarios, finding increased frequency in the eastern US, slight decreases in the Southwest and Great Plains, and shifts in seasonality by mid-century.
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Han et al. (2025) A Decadal Hybrid GCM Simulation Using Deep‐Learning‐Based Cloud and Convection Parameterization Generalized to a Warm Climate
This study demonstrates that a global climate model (GCM) with neural-network-based cloud and convection parameterization, trained solely on present-day climate data, can successfully perform a stable, decade-long simulation of a warm climate with a +4 K sea surface temperature anomaly, matching the performance of conventional and superparameterized models.
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Wang et al. (2025) Method for selecting typical floods based on an unfavorable indicator and flood classification
This study proposes a multi-indicator method integrating an entropy-weighted unfavorable indicator with a two-dimensional return-period classification to scientifically identify and select typical unfavorable flood events. Applied to the Tongguan Station, the method enhances the representativeness and accuracy of flood event selection for disaster response planning.
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Li et al. (2025) Evaluating multi-source precipitation datasets for hydrological applications in ungauged alpine region of Tibetan Plateau
This study evaluates the accuracy of five multi-source gridded precipitation datasets for hydrological applications in an ungauged alpine region of the Tibetan Plateau using the physically-based WRF-Hydro/Glacier model, finding that reanalysis datasets (ERA5-Land, TPReanalysis) provide the most realistic streamflow simulations.
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Eshaghi et al. (2025) A remote sensing-based framework for agricultural drought risk monitoring and assessment: introducing SADFI for disaster risk assessment in Northeastern Iran
This study developed a remote sensing-based framework for agricultural drought risk monitoring and assessment in Northeastern Iran, introducing the novel Standardized Agricultural Drought Frequency Index (SADFI). The research evaluated spatio-temporal drought patterns from 2001 to 2023 using satellite-derived indices, revealing significant spatial variations in drought vulnerability across the region.
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Shafizadeh‐Moghadam et al. (2025) Pronounced decline in Iran's terrestrial water storage from GRACE and GRACE-FO associated with climate and unsustainable land-use change
This study quantifies the spatiotemporal decline in Iran's terrestrial water storage (TWSa) from 2002-2022 using GRACE/GRACE-FO data, attributing it to a complex interplay of climatic variability and unsustainable land-use changes, particularly intensified irrigation.
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Liu et al. (2025) Corrigendum to “Integrating multi-temporal information for monitoring plant spectral diversity with PlanetScope and Sentinel-2 satellite imagery”. [Ecol. Indic. 180 (2025) 114348]
This document is a corrigendum that corrects specific citations and a discussion point within the original article titled "Integrating multi-temporal information for monitoring plant spectral diversity with PlanetScope and Sentinel-2 satellite imagery".
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Guo et al. (2025) Diurnal Dynamics of Vegetation Photosynthesis Under Drought Stress in the Qilian Mountains
This paper investigates the diurnal dynamics of vegetation photosynthesis in the Qilian Mountains, specifically focusing on how these dynamics are affected by drought stress.
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Horne et al. (2025) Stress testing water allocations across large river basins
This study develops a novel, rapid-assessment method to stress test water allocations in large, complex river basins, demonstrating substantial spatial variation in climate sensitivity and highlighting the critical role of water allocation policy in mediating climate impacts.
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Dorjsuren et al. (2025) Land cover change and hydro-climatic system interactions in the high mountains and around lakes of the Great Lakes Depression Region of Mongolia
This study investigated hydro-climatic and land cover changes in Mongolia's Great Lakes Depression Region, revealing increasing air temperature, decreasing precipitation, river discharge, and lake levels, with land cover changes strongly linked to these climatic shifts and human activities.
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Wu et al. (2025) Long‐Term Lake Ice Evolution in a Large Endorheic Lake Undergoing Accelerated Shrinkage in a Semiarid Region of China
This study investigates the long-term evolution of lake ice in Lake Daihai, a shrinking endorheic lake, by integrating six decades of data and a numerical model. It reveals accelerated lake shrinkage and ice thinning, primarily driven by atmospheric warming, salinization, and morphological changes, highlighting the need for integrated assessment frameworks.
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Jiang et al. (2025) Substantial Contribution of Woody Components to Rainfall Interception in Chinese Forests: Insights From a Refined Analytical Model
This study refined the Gash model to distinguish rainfall interception by woody components from leaves, finding that woody components contribute significantly to total interception, particularly in needle-leaf forests and during non-growing seasons.
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Fong et al. (2025) Advancing evapotranspiration estimation with remote sensing and artificial intelligence – A review
This review paper comprehensively synthesizes the state-of-the-art in evapotranspiration (ET) estimation by integrating remote sensing (RS) data with artificial intelligence (AI) techniques, including machine learning, deep learning, explainable AI, and emerging geospatial foundation models. It highlights how RS addresses data limitations of conventional methods and how AI enhances accuracy and efficiency for sustainable water management.
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Hale et al. (2025) What's Next for Snow: Insights From the NASA Terrestrial Hydrology Program Community Snow Meeting
This paper summarizes the outcomes of a Community Snow Meeting sponsored by NASA THP, outlining the current state of snowpack monitoring techniques, identifying critical knowledge gaps, and recommending next steps for global-scale snow research and development.
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Liu et al. (2025) Drought risk assessment and future scenario prediction in agricultural cropping zones of China
This study developed a novel drought risk assessment framework based on agricultural cropping zones in China, coupling a Geographical and Temporal Neural Network Weighted Regression (GTNNWR) model with the Standardized Precipitation Evapotranspiration Index (SPEI). The framework accurately assessed historical drought patterns and predicted a substantial intensification of drought risk across different cropping zones under various future climate change scenarios.
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Deng et al. (2025) Optimizing winter wheat resilience with drought-crop modeling in the Tarim River Basin
This study quantifies drought-induced yield-reduction risks for winter wheat in the Tarim River Basin by integrating drought indices (SPEI, SMDI) with the DSSAT-CERES-Wheat model. It proposes adaptive water management strategies, including optimized sowing dates and supplemental irrigation (30–60 mm) at the jointing stage, to enhance regional winter wheat productivity and provide guidance for efficient agricultural water use in arid regions.
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Pham (2025) Fusion de données spatialement et temporellement résolues : application à l'imagerie de proxidétection en viticulture
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Margaryan et al. (2025) Glacier regime of the Drakhtik River (Lake Sevan basin) in the conditions of climate change
This study investigates the spatio-temporal changes in the ice regime of the Drakhtik River (Lake Sevan basin) from 1956/57 to 2024/25, revealing a significant reduction in ice cover duration and maximum ice thickness due to rising air temperatures.
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Zheng et al. (2025) Quantitative assessment of building losses in China’s coastal regions due to sea-level rise under extreme climate conditions
This study quantitatively assessed land inundation and building loss across 14 coastal provinces of China under extreme sea level events and sea-level rise, revealing a total potentially inundated area of 49 366.22 km² and significant building losses concentrated in economically developed regions under a 100-year return period extreme climate scenario.
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Bittmann et al. (2025) Influence of Urbanization and Climate on Surface Water Diversions in a Semi‐Arid Basin
This study examined temporal trends in canal diversions to irrigation districts experiencing varying urbanization levels in the Lower Boise River Basin, finding that urbanization negatively impacted total diversion volumes, while interannual variability was more strongly correlated with climate variables.
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Méndez et al. (2025) Impacts of Spatial and Temporal Station Availability on Gridded Precipitation Products in Central America
This study evaluates the performance of four gridded precipitation products (CHIRPS, GPCC, CRU, ERA5-Land) against in situ station data across Central America, finding CHIRPS to be the most accurate and highlighting the critical impact of station density on precipitation trend detection in data-sparse regions.
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Shi et al. (2025) The Effects of Sea‐State‐Dependent Surface Fluxes on CESM2 Climate Simulations
This study implements and evaluates a sea-state-dependent surface flux scheme, incorporating prognostic ocean waves via WAVEWATCH III, into CESM2, demonstrating significant improvements in mean atmospheric circulation and upper ocean biases.
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Dolcetti et al. (2025) Fully non-contact discharge measurement in shallow streams via physics-based water-surface image analysis
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Verdone et al. (2025) Topography Controls the Response of Beech Trees to Atmospheric Demand During Soil Moisture Droughts
This study investigated how topography and atmospheric demand influence beech tree transpiration during soil moisture droughts in a central Italian hillslope, finding that trees on upper slopes experienced reduced sap flow during summer heat, unlike those on lower slopes.
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Abbasi et al. (2025) Increased Streamflow Intermittence in Europe Due To Climate Change Projected by Combining Global Hydrological Modeling and Machine Learning
## Identification - **Journal:** Earth s Future - **Year:** 2025...
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Kant et al. (2025) Case study on exceptionally heavy rainfall episode over Northwest Uttar Pradesh & adjoining Uttarakhand (India) during Monsoon 2024
This study analyzes an exceptionally heavy rainfall event over Northwest Uttar Pradesh and Uttarakhand in July 2024, detailing its complex meteorological causes, associated impacts, and the effective performance of India Meteorological Department's forecasts, while also evaluating various reanalysis precipitation products.
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Chehreh et al. (2025) An impact-oriented framework for a deep learning–based composite drought index considering potential economic losses
This study proposes a novel deep learning-derived drought index, shifting from traditional comparative validation to an impact-oriented evaluation using drought-induced economic losses as the primary performance metric, and offers a framework for index selection based on predictive reliability and data effort.
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Yuan et al. (2025) An Evaluation of Soil Temperature Predictions Based on the Long Short-Term Memory Model and Remote Sensing Data
This paper evaluates the performance of soil temperature predictions that are based on a Long Short-Term Memory (LSTM) model and utilize remote sensing data.
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You et al. (2025) Predicting Ecosystem Respiration Under Climate Extremes Requires Varying Parameters
This study investigated the predictability of conventional ecosystem respiration (ER) models in a semi-arid grassland under climatic extremes. It found that models calibrated with fixed parameters from normal years performed poorly during extreme drought and wet years due to significant and asymmetric parameter divergence, highlighting the need for varying parameters to accurately predict ER under climate change.
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Ichino et al. (2025) Reconstruction of solar radiation for Tokyo since 1720 using weather descriptions from historical diaries
This study reconstructs daily solar radiation for Tokyo from 1720 to 1912 using qualitative weather descriptions from historical diaries, validating the method against modern observations and sunshine duration records. The reconstruction reveals significant low-insolation episodes coinciding with cool summers and famine events during the 18th and 19th centuries, demonstrating the feasibility of converting qualitative historical data into quantitative climate records.
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Lozinskyi et al. (2025) Transgressive Variability of 1000-Grain Weight of the Main Spike of Soft Winter Wheat in F2–4 Populations Under Hybridization of Different Ecotypes
This study investigated the transgressive variability of 1000-grain weight in F2-F4 hybrid populations of soft winter wheat, derived from crosses of different ecotypes, to identify promising breeding forms. It found significant positive transgressions in 1000-grain weight in several hybrid combinations across generations, demonstrating the potential for selecting superior genotypes for yield improvement.
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Hérincs et al. (2025) Synoptic analysis of cyclone Ianos based on surface and satellite observations and high-resolution reanalysis data
This study provides a comprehensive synoptic analysis of Medicane Ianos, the strongest medicane in recent years, using multi-source observations and high-resolution reanalysis data. It confirms Ianos's tropical cyclone characteristics, including a deep warm core and hurricane-force winds, and details its tropical transition process.
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Zidikheri et al. (2025) Increasing Atmospheric Surface Spread in an Ensemble Model Using Land Cover Fraction Perturbations
This study investigates perturbing land surface fraction values in an operational ensemble numerical weather prediction model to address underspread near the land surface, demonstrating that this method significantly increases ensemble spread for key surface variables and improves forecast skill.
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King et al. (2025) Detectability of Post‐Net Zero Climate Changes and the Effects of Delay in Emissions Cessation
This study investigates the detectability of climate changes under net zero carbon dioxide emissions pathways and the impact of delays in achieving emissions cessation. It finds that detectable climate changes persist for centuries after net zero, and even a 5-year delay in emissions cessation leads to significantly different and detectable climate outcomes globally.
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Kareem et al. (2025) Runoff prediction under climatic variability using SWAT and machine learning models: a case study of the Hunza River basin
This study evaluates and compares six models (five machine learning and the physically-based SWAT model) for monthly runoff prediction in the glacier-fed Hunza River Basin (Pakistan) from 2007 to 2022. The research found that the XGBoost machine learning model significantly outperformed the other models, including SWAT, in predictive accuracy under climatic variability, though all models struggled with extreme runoff events.
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Kumar et al. (2025) Artificial intelligence for enhancing soil organic matter, nutrient cycling and water productivity: A comprehensive review of soil-health-led intensification approaches
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Pall et al. (2025) Characterising the range and outliers in CMIP6 multi-model climate projections of extremes
This study explores the projected ensemble ranges of mean and extreme temperature and precipitation changes from CMIP6 models, revealing that ensemble minimums and maximums are often dominated by one or two specific models, and this domination can vary significantly depending on whether the future is framed as a fixed time slice or a specific global warming level.
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Liu et al. (2025) Deep Reinforcement Learning for irrigation optimization: Advantages, opportunities, and challenges
This paper systematically reviews the applications of Deep Reinforcement Learning (DRL) in irrigation optimization, highlighting its strengths in handling dynamic, high-dimensional environmental data for adaptive and long-term strategies, while also identifying key challenges like data scarcity, model interpretability, and difficulties in field deployment.
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Senthilkumar (2025) A cloud-connected iot model for crop monitoring and automated irrigation management
This study developed a cloud-connected Internet of Things (IoT) model for real-time crop monitoring and automated irrigation management. Experimental deployment demonstrated a significant reduction in water usage (28–45%) and improved crop health compared to manual irrigation methods.
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Khalil et al. (2025) Inter-rater reliability adaptive weighting (IRRAWE) - a novel ensemble scheme for improved precipitation projections using CMIP6 climate models
This study introduces IRRAWE, a novel spatio-temporal weighting scheme for CMIP6 multimodel ensembles, which significantly improves precipitation projections by achieving higher correlation and lower Normalized Root Mean Square Error compared to simple model averaging.
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Jahanbakhsh et al. (2025) Monitoring and forecasting agricultural drought in Golestan Province, Iran (2001–2028): an integrated approach using remote sensing and machine learning
This study developed an integrated framework using remote sensing and machine learning to monitor and forecast agricultural drought in Golestan Province, Iran. The framework predicts that severe and extreme drought will expand to cover approximately 13,000 square kilometers (62% of the province) by 2028, with croplands and bare lands being most vulnerable.
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Ioannidis et al. (2025) A Climatology of Heatwaves Over Greece for the Period 1960–2022
This study presents a climatology of Heatwave Events (HEs) over Greece from 1960 to 2022, revealing significant positive trends in heatwave frequency, duration, and severity across most of the country, particularly in northern and western regions, with notable shifts occurring between the 1970s and 2010s.
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Jiao et al. (2025) Spatial-temporal pattern and attribution factors of Yellow River’s streamflow seasonality and inter-annual variability
This study quantifies the spatial-temporal patterns and attribution factors of streamflow seasonality and inter-annual variability in the Yellow River Basin since 1960. It reveals a 29% decline in seasonal variability and a sharp post-2000s intensification of inter-annual variability (weighted coefficient of variation reaching 0.24), primarily driven by anthropogenic water extraction rather than climate.
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Amitai et al. (2025) Projected cooling in a subtropical lake despite climate warming
This study uses a 3D hydrodynamic model driven by regional and global climate projections to reveal an unexpected abrupt cooling of subtropical Lake Kinneret around 2065, despite regional atmospheric warming, attributed to enhanced evaporative cooling and subsequent stratification degradation.
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Lyddon et al. (2025) Climate Change Likely to Intensify Storm‐Driven Compound Flooding in an Exemplar UK Estuary
This study uses novel high-resolution, physically consistent climate projections to assess future storm-driven compound flooding in the Dyfi estuary, UK, under a high-emissions scenario (RCP8.5). It finds that by 2080, river discharge extremes will intensify, and compound flood events will become more frequent and concurrent, leading to increased inundation.
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Wang et al. (2025) Spatiotemporal evolution and atmospheric circulation response of DWAA events in the Yellow River Basin, China, under climate warming
This study investigates the spatiotemporal evolution of Dry–Wet Abrupt Alternation (DWAA) events in the Yellow River Basin (YRB) from 1960 to 2023 and their relationship with atmospheric circulation under climate warming, revealing a northward shift and eastward expansion of high-frequency zones and increased influence of atmospheric factors after a 1997 temperature shift.
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Linick et al. (2025) Environmental and noise signals in terrestrial gravimetry
## Identification - **Journal:** Open MIND - **Year:** 2025...
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Arefyev (2025) Dendrochronological Reconstruction of Waterlogging of Birch Forests in Steppe Watersheds in Southern Omsk Oblast
This study dendrochronologically reconstructed the timing and causes of waterlogging in insular steppe birch forests in the Odessa district of Omsk Oblast, Western Siberia, revealing that warm, snowy winters leading to anaerobic root rotting are the primary cause of extreme stress and mortality since 2007.
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Babker et al. (2025) Comparative Evaluation of Gridded Precipitation Datasets in Capturing Hydrological Extremes in a Mesoscale Heterogeneous Catchment in Austria
This study evaluates the performance of four gridded Precipitation Products (SPARTACUS v2.1, IMERG-F v07, CHIRPS v2.0, and ERA5-Land) in representing extreme precipitation and their reliability as hydrological model forcings over the Kamp catchment, Austria, finding that SPARTACUS v2.1 performed best in detecting extremes and simulating streamflow, while CHIRPS v2.0 and ERA5-Land showed poor performance.
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Zhang et al. (2025) Runoff simulation based on landscape pattern classification and machine learning
This study developed a transferable classification-coupling framework integrating landscape structure and machine learning to improve runoff simulation accuracy and stability in the Middle Yellow River Basin. It demonstrated that coupling landscape patterns with meteorological variables significantly enhances model performance, with XGBoost achieving the highest accuracy (e.g., NSE = 0.966, NRMSE = 0.037) and strong generalization to ungauged basins.
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Yan et al. (2025) Research on Grassland Classification Method in Water Conservation Areas of the Qinghai–Tibet Plateau Based on Multi-Source Data Fusion
This study developed a novel grassland classification method for the Qinghai–Tibet Plateau by integrating multi-source remote sensing data with machine learning algorithms. The XGBoost model demonstrated the best performance (accuracy of 0.829), revealing that climate and topography are key drivers of alpine grassland distribution.
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Xia et al. (2025) A Spatiotemporal Pathformer‐Based Deep Learning Framework for Watershed Flood Forecasting
This study introduces a spatiotemporal Pathformer-based deep learning framework for multi-step-ahead flood forecasting, designed to dynamically adapt to flood magnitude and duration. The model demonstrates superior predictive accuracy and stability compared to LSTM and Transformer models, particularly during extreme flood events, by effectively mitigating time-lag errors and prediction bottlenecks.
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Tian et al. (2025) A dataset of 500 m annual MPDI data for the Weihe River Basin from 2000 to 2024
This study developed a 25-year (2000–2024) annual Modified Perpendicular Drought Index (MPDI) dataset for the Weihe River Basin at 500 m spatial resolution using the Google Earth Engine (GEE) platform, providing a robust tool for dynamic drought monitoring and water resource management.
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Tiwari et al. (2025) A two-step iterative data assimilation and calibration approach for improving large-scale hydrological processes in the Ganga basin
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Hassan et al. (2025) Intelligent Drip Irrigation Systems Utilising Internet of Things and Laser Fiber Optic Sensors for Soil Moisture Content (REVIEW)
This paper highlights the limitations of traditional soil moisture measurement methods and proposes IoT-based smart irrigation utilizing laser-optical fiber sensors as a superior alternative for precise, real-time, multi-depth monitoring, leading to enhanced water use efficiency, plant health, and crop yields in sustainable agriculture.
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Morovati (2025) Evaluating Use of Multiple Hydrologic Storage Indicators to Enhance Streamflow Forecasting
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Bobde et al. (2025) Future Intensification of Compound Heatwaves and Socioeconomic Exposure in Africa
This study projects a robust intensification of compound heatwaves (co-occurring daytime and nighttime heatwaves) across Africa with increasing global warming levels, leading to significantly higher population and economic exposure, particularly in Western, central, and eastern regions, and a dramatic increase in the frequency of historically rare events.
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Chahed (2025) Hydro‐Climatic Modelling for Water Resources: From Processes to Adaptive Management and Governance
This paper synthesizes advances in hydro-climatic modelling, particularly within the CMIP framework, to demonstrate how improved model design and uncertainty management inform practical water resource strategies and adaptive governance, while also identifying persistent challenges and promising new approaches.
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Swain et al. (2025) Africa’s booming rice cultivation is fueling regional warming
This study investigates the link between the rapid expansion of rice cultivation in Africa and regional warming, finding that a 436% increase in cultivation area (1960-2018) is associated with a 603 million tonne rise in agricultural methane emissions, contributing to a 1.3 °C increase in surface air temperature anomaly.
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Mishra et al. (2025) An Optimal Transport Framework for Water‐Energy Coupling in Soil‐Vegetation‐Atmosphere Continuum
This study introduces an optimal transport framework based on a least action principle to explain soil moisture-evapotranspiration (SM-ET) coupling across diverse hydroclimates. Global validation using remote sensing data reveals widespread convergence to this least action state, enabling accurate estimation of active root zone depth and characteristic SM transit timescales.
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Li et al. (2025) Deciphering Intra‐Annual Isotope Fluctuations of River Water in an Arid‐Alpine Watershed: Source Discrimination and Evaporation Quantification
This study investigated the intra-annual variations of Golmud River water isotopes in 2019 to identify recharge sources and quantify evaporation impacts, revealing that glacial meltwater and groundwater are primary recharge sources and that watershed-scale evaporation loss averages approximately 9%.
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RASOANAIVO (2025) Geospatial Framework and Hydrological Processing Pipeline for Environmental Systems and Climate Risk Assessment in Saudi Arabia (Riyadh Basin Demonstration)
This paper presents a fully reproducible geospatial and hydrological processing workflow for basin-scale environmental and climate-risk analysis, demonstrated for the Riyadh Basin, designed for national-scale deployment in Saudi Arabia.
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Meng et al. (2025) Application of the InVEST model to quantify the annual water yield of Yellow River Basin
This study evaluates the annual water yield in the Yellow River Basin for 2020 using the InVEST model, finding a total yield of 74.35 billion cubic meters influenced by precipitation and evapotranspiration.
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Thorp et al. (2025) The pyfao56 automatic irrigation scheduling algorithm
This paper develops and demonstrates a new automated irrigation scheduling algorithm within the pyfao56 Python package, allowing flexible simulation of diverse irrigation management strategies based on 25 user-specified parameters, validated with a 2018 Arizona cotton field study. The methodology provides a flexible tool for simulating realistic irrigation management schedules with practical relevance for various field applications.
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Jiang et al. (2025) Retention-driven water flow regulation: A network perspective
This study develops a network-based framework to conceptualize water retention as a time-spanning flow network, distinguishing three time-structured pathways and quantifying their contributions to hydrological regulatory functions. Applied to the Gongshui Watershed, the framework demonstrates that ecosystem retention significantly dampens hydrological variability and enhances dry-season water security, with connectivity maintenance pathways substantially improving water supply accessibility.
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Zhao et al. (2025) SEV-Field: A Crop Field Extraction Framework for High-Resolution Imagery via Semantic Segmentation and Boundary Connection
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Bodunrin et al. (2025) Analyzing the Spatial and Temporal Dynamics of Rainfall and Drought in The Vall River Basin, South Africa
This study analyzed the spatial and temporal dynamics of rainfall and drought in the Vaal River Basin, South Africa, from 1983 to 2023 using the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), revealing an intensification of drought frequency and severity, particularly a severe hydrological drought in 2016, driven by climate change.
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Bhatt et al. (2025) A Framework for Evaluating Uncertainty From Multiple Sources in Probable Maximum Precipitation Estimation by the Hershfield Method Using Imprecise Probability
This paper proposes a novel framework based on imprecise probability theory to quantify and attribute uncertainty in Hershfield method-based Probable Maximum Precipitation (PMP) estimates, identifying key uncertainty sources and their contributions in case studies across Indian and US river basins.
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Rastogi et al. (2025) Evaluating Extreme Storm Events in an Ensemble of High‐Resolution Projections
This study investigates extreme storm characteristics (size, depth, volume, intensity) over the conterminous US using various downscaling techniques and CMIP6 GCMs, finding consistent future intensification across seasons and ensemble-dependent changes in storm size.
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Biegel et al. (2025) Unrecognised water limitation is a main source of uncertainty for models of terrestrial photosynthesis
This study investigates how models accounting for temporal structure impact the prediction of ecosystem photosynthesis (GPP), comparing mechanistic, memoryless deep learning (MLP), and recurrent neural network (LSTM) models. It finds that while both deep learning models outperform the mechanistic one, the LSTM leverages learned temporal dependencies to achieve lower error during periods of drought and frost, and performs better than MLP in dry environments.
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Chunya et al. (2025) The application of remote sensing technology in high-yield farmland: A review
This paper reviews the current applications of remote sensing in high-standard farmland construction across its planning, construction, and monitoring stages, identifying key advantages, limitations, and future development directions.
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Li et al. (2025) Ensembling differentiable process-based and data-driven models with diverse meteorological forcing datasets to advance streamflow simulation
This study systematically evaluates and utilizes ensembles of a data-driven Long Short-Term Memory (LSTM) network and a physics-informed differentiable HBV ($\delta$HBV) model with diverse meteorological forcing datasets to advance streamflow simulation. The research demonstrates that cross-model-type ensembles consistently outperform single-model approaches and set new accuracy benchmarks, particularly enhancing spatial generalization due to complementary error characteristics and the structural constraints of $\delta$HBV.
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Lv et al. (2025) Analysis of driving factors of soil salinity in Southern Xinjiang irrigation areas under dry-sowing and wet-emerging conditions
This study applied Multiscale Geographically Weighted Regression (MGWR) for the first time to quantitatively analyze the spatially heterogeneous driving factors of topsoil salinity (0–30 cm) in the Xiaohaizi Irrigation District under dry-sowing and wet-emerging conditions, revealing groundwater salinity as the dominant positive driver and proposing zone-specific management strategies.
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yuhang et al. (2025) A machine leaning model for hydrological drought prediction: Model development and application
This study developed a hybrid Boruta-CNN-LSTM model to accurately forecast hydrological drought at the catchment scale, demonstrating its superior performance in predicting spatiotemporal drought variations in the Huai River Basin.
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Zhao et al. (2025) Quantifying the Hydrological Impact of Ecological and Water Conservation Projects Within a Major Tributary Basin of the Middle Reaches of the Yellow River, China
This study quantified the basin-scale hydrological impacts of Returning Agricultural Land to Forest (RAF) and Returning Agricultural Land to River (RAR) projects in the Qin River Basin using the SWAT model, revealing distinct and spatially varied effects on runoff components, emphasizing the importance of slope conditions for effective water management.
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Cotrim et al. (2025) A Framework for Storm Classification and Hydrograph Generation From Total Water Level in Europe
This study characterizes total water level (TWL) storms across Europe by classifying them based on shape and developing a method to determine their duration. The aim is to improve the construction of hydrographs and flood maps by considering regional variability and uncertainties in storm characteristics.
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Akl et al. (2025) Global Groundwater Drought Assessment Revisited: A Holistic Re‐Evaluation of the GRACE‐Groundwater Drought Index Across Major Aquifers
This study holistically re-evaluates the GRACE-Groundwater Drought Index (GGDI) across 37 major aquifers by integrating multi-model GRACE-derived groundwater storage anomaly (GRACE-GWA) estimates. It reveals that variability among these estimates introduces substantial uncertainty into groundwater drought indicators and aquifer memory, compromising the reliability of single-model GGDI assessments.
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Zhang et al. (2025) Projections of actual and potential evapotranspiration from downscaled high-resolution CMIP6 climate simulations in Australia
This study evaluates actual and potential evapotranspiration (AET and PET) projections for Australia using dynamically downscaled high-resolution CMIP6 climate simulations, finding that these models provide reasonably accurate estimations and project scenario-dependent changes with significant implications for water security and agriculture.
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Biondi et al. (2025) Storm severity assessment: Application of severity diagrams to events in Calabria (southern, Italy)
This study applies and adapts severity diagrams to assess and visualize storm severity in Calabria, southern Italy, by explicitly incorporating areal extent, duration, and intensity into return period estimation. The research demonstrates that severity diagrams effectively capture the complexity of rainfall events, providing a synthetic visualization and enabling systematic classification for enhanced flood risk analysis and civil protection.
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erincrockett (2025) erincrockett/ForestDrought: ForestDrought
This Zenodo record provides access to the "ForestDrought" software (version v1.0.0), a tool likely intended for research and analysis related to forest drought.
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Hoteit et al. (2025) New Climate Change Center of Saudi Arabia: Advancing Understanding and Prediction for the Arabian Peninsula Climate
This paper presents the roadmap of Saudi Arabia's newly established Climate Change Center (CCC), outlining its mission to address the underrepresentation of the Arabian Peninsula's climate in global research and develop advanced modeling and forecasting tools to improve predictions and projections for the region.
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Liu et al. (2025) From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction
This study introduces a deep learning framework to benchmark 11 Transformer-based architectures against a baseline Long Short-Term Memory (LSTM) model and evaluate pretrained Large Language Models (LLMs) and Time Series Attention Models (TSAMs) for diverse hydrologic prediction tasks, revealing that LSTM excels in regression but attention-based models surpass it in complex tasks like autoregression and zero-shot forecasting.
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Xue et al. (2025) Development and assessment of a C-vine copula-based composite drought index coupling multiple hydrological cycle variables in the North China Plain
This study developed two C-vine copula-based composite drought indices (CDI-P and CDI-R) by coupling multiple hydrological variables to comprehensively monitor drought in the North China Plain. The CDIs effectively capture drought events with low false alarm and omission rates, revealing increasing trends in drought duration, intensity, and severity since 2000.
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Li et al. (2025) PAGrid: A probabilistic area-weighted gridding method for seamless mapping of sentinel-3 swath data
This study proposes a probabilistic area-weighted gridding method (PAGrid) to overcome spatial discontinuities in gridded Sentinel-3 OLCI swath data. PAGrid efficiently generates more consistent and continuous gridded time series by approximating area-weighting through randomized spatial perturbations, significantly outperforming conventional center point-based gridding.
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Chen et al. (2025) Coupling Differentiable Modules of Reservoir Operation and Rainfall‐Runoff Processes for Streamflow Simulation
This paper tests the integration of reservoir operation and rainfall-runoff processes using differentiable parameter learning (dPL) within hydrological models. The study demonstrates that dPL significantly improves model efficiency, with a differentiable loosely coupled model (LCM) showing superior performance in simulating both inflow and outflow, particularly for ungauged catchments.
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Hashim et al. (2025) Automated Rose Farming with IoT and Machine Learning: A Real-Time Predictive Irrigation System
This study developed an IoT-based automated rose farming system integrating machine learning for real-time environmental monitoring and intelligent irrigation control. The system achieved 100% classification accuracy for irrigation needs and demonstrated successful end-to-end operation, offering a cost-effective and scalable solution for smart floriculture.
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Puche et al. (2025) Assessing temporal and spatial generalization of LSTMs for streamflow modeling in French watersheds with and without European training data
This study evaluates the temporal, spatial, and spatio-temporal generalization capabilities of Long Short-Term Memory (LSTM) networks for streamflow modeling across 310 French watersheds, also investigating the impact of including 501 additional European basins in the training data. LSTMs perform best in temporal generalization (median Kling-Gupta efficiency (KGE) = 0.78), but performance slightly decreased when European training data was added.
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Sobkowiak et al. (2025) Impacts of Climate Change on Water Resources: Assessment and Modeling—Second Edition
This paper likely emphasizes the critical role of water resources for sustainable development and human well-being, setting the stage for an analysis of their availability, management, or vulnerability in a specific context.
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Al-Hilali et al. (2025) Effects of climate change on precipitation and runoff in semi-arid regions: future scenarios and implications for water
This study investigates the impact of climate change on precipitation and runoff in Northwestern Iraq under a high emission scenario (RCP8.5), using regional climate and hydrological models. It projects a significant decrease in annual precipitation and a substantial reduction in annual runoff in the Al-Khoser River Basin by the end of the century, highlighting the need for water harvesting solutions.
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Gallais et al. (2025) Snow Sublimation Significantly Decreases Following Stand‐Replacing Fire With Minor Water Balance Impacts From Forest Thinning in a Water Limited Forest
This study quantifies the impact of wildfire and forest thinning on water availability for runoff (WAfR) in semi-arid montane environments. It found that stand-replacing fires significantly decrease actual evapotranspiration (AET) due to changes in canopy composition, while thinning has a less pronounced effect on water fluxes.
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Arnell et al. (2025) High‐Impact Low‐Likelihood Climate Scenarios for Risk Assessment in the UK
This paper develops two sets of High-Impact Low-Likelihood (HILL) climate scenarios for the UK, comprising transient changes to 2100 and extreme monthly/seasonal anomalies, to complement existing climate projections and facilitate practical risk assessment for adaptation and resilience planning. These scenarios provide physically plausible storylines and indicative quantifications for "worst-case" climate outcomes beyond conventionally assumed ranges.
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Wang et al. (2025) CitrusNet: A vision transformer-CNN approach for citrus detection from multi-source imagery with multi-scale feature integration
This paper introduces CitrusNet, a novel deep learning model combining Vision Transformers and Convolutional Neural Networks with multi-scale feature integration, to accurately detect citrus fruits across diverse multi-source imagery, outperforming state-of-the-art models.
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Yang et al. (2025) Local Off‐Grid Weather Forecasting With Multi‐Modal Earth Observation Data
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
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Wanniarachchi et al. (2025) Enhancing the effectiveness of satellite precipitation products with topographic and seasonal bias correction
This study introduces the Heavy Rain Peak Adjustment (HRPA) method for satellite precipitation bias correction, comparing its effectiveness against the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The HRPA method significantly enhances the accuracy of Global Satellite Mapping of Precipitation-Near-Real-Time (GSMaP-NRT) data, particularly for heavy precipitation events and at lower elevations, outperforming SARIMA in reducing errors and improving correlation with observed data.
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Jialin et al. (2025) Spatiotemporal dynamics of meteorological and soil droughts and their effects on water use efficiency in the Ningxia-Inner Mongolia Irrigation District
This study analyzed the spatiotemporal dynamics of meteorological and soil drought and their effects on water use efficiency (WUE) in the Ningxia-Inner Mongolia Irrigation District, finding that meteorological drought is the dominant factor influencing WUE, with significant lagged effects.
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Shen et al. (2025) Two recent mega dams reshape Yangtze river hydrology with comparable impact to Three Gorges Dam
This study quantifies the hydrological impacts of two recently completed mega-dams (Wudongde and Baihetan, 2MDs) upstream of the Three Gorges Dam (TGD) in the Yangtze River basin, revealing that their collective effects on water levels and flood peak reductions are comparable to those of the TGD, necessitating coordinated basin-wide management.
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Jones et al. (2025) Snow Accumulation Increases With Forest Structural Diversity in Low‐Relief Catchments
This study investigates the relationships between forest canopy structure and below-canopy snow depth in two low-relief Mississippi headwater catchments, finding that co-dominant tree density and canopy structural diversity are key predictors for a deeper snowpack.
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Li et al. (2025) Peat Hydraulic Structure Maintains the Stability of Permafrost Slope Peatlands in the Central Qinghai‐Tibet Plateau
This study investigates the water balance mechanisms enabling permafrost slope peatlands on the central Qinghai-Tibet Plateau to sustain waterlogging under low net precipitation. It finds that exceptionally low peat hydraulic conductivity is the primary control on hydrological stability, facilitating millennial-scale waterlogging and continued peat accumulation.
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Small et al. (2025) The 1957–1976 Summertime Drought Gap in the Southeastern United States
This study examined drought frequency in the Southeastern United States from 1931–2024, identifying an exceptional 20-year drought-free period (1957–1976) linked to specific climatological conditions, which is unlikely to reoccur under current climate.
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Henry et al. (2025) Marine Cloud Brightening to Cool the Arctic: An Earth System Model Comparison
This study is the first multi-model comparison of Arctic marine cloud brightening (MCB) via sea-salt aerosol (SSA) injections, demonstrating that this geoengineering technique can substantially cool the Arctic, maintain sea ice, and preserve the Atlantic Meridional Overturning Circulation without causing robust precipitation changes outside the Arctic.
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Liu et al. (2025) Impact of Aerosols on Weather Forecasts in China During Winter 2016–2017
This study developed and applied CMA's first chemistry-weather integrated model (GRAPES_Meso5.1/CUACE CW V1.0) to investigate aerosol impacts on weather forecasts during the 2016–2017 winter season across China, finding that incorporating aerosol feedbacks significantly improves temperature and precipitation forecast accuracy, particularly in polluted regions.
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Chaiyana et al. (2025) Evaluating trade-offs among cotton yield, groundwater extraction, and future projections for sustainable water management in the Texas High Plains
This study developed a novel data-driven framework integrating remote sensing and in-situ observations to quantify groundwater extraction (GWE) and evaluate trade-offs with crop water productivity (WPc) in irrigated cotton fields across the Texas High Plains (THP) from 2008 to 2030. The findings reveal that much of the central and northern THP exhibited unsustainable water use patterns (overuse and inefficiency) from 2008 to 2023, with GWE projected to increase from 2892 billion liters to 3439 billion liters by 2030, posing a significant risk to groundwater sustainability.
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Lükő et al. (2025) Evaluating Winter Turbulent Heat Fluxes in a Hydrodynamic‐Ice Model of the Great Lakes
This study evaluates the performance of operational hydrodynamic and ice models in simulating turbulent heat fluxes in the Great Lakes across open water, partial ice, and ice-covered conditions during winter. It finds that while early winter open water fluxes are well modeled, accuracy decreases significantly during ice-covered periods, primarily due to errors in simulated ice thickness.
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Beobide-Arsuaga et al. (2025) Increasing central and northern European summer heatwave intensity due to forced changes in internal variability
This study investigates how forced changes in internal variability under global warming will affect European summer heatwaves, finding that it intensifies heatwaves in central and northern Europe due to increased moisture limitations, while weakening them in southern Europe due to reduced extreme temperature variability in a stable moisture-depleted environment.
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Geng et al. (2025) Widespread decline of gross primary productivity due to compound heat and drought in the Wei river Basin, China
This study investigates the impact of compound heat and drought on Gross Primary Productivity (GPP) in the arid and semi-arid Wei River Basin, China, revealing that compound events significantly increase GPP decline, with grassland and cultivated vegetation being most vulnerable.
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Debangshi et al. (2025) Precision irrigation with artificial intelligence–integrated ground‐penetrating radar reduces water stress in corn
This study evaluates an AI-Radar irrigation system against conventional subsurface drip irrigation in central Kansas to optimize water use. It found that the AI-Radar system significantly reduced crop water stress and applied 23.5%–25.1% less irrigation water.
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Wang et al. (2025) A Deep State Space Model for Rainfall‐Runoff Simulations
This study introduces the Frequency Tuned Diagonal State Space Sequence (S4D-FT) model for rainfall-runoff simulations, benchmarking it against LSTM and a physically-based model across 531 watersheds in the contiguous United States. Results indicate S4D-FT generally outperforms LSTM, especially in snowmelt-driven or intermittent flow regimes, but shows limitations in flashier, high-magnitude flow conditions.
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Feng et al. (2025) Implementation and Evaluation of Emission‐Driven Land‐Atmosphere Coupled Simulation in E3SMv2.1
This paper introduces and evaluates BGCLNDATM_progCO2, the first emissions-driven land-atmosphere coupled biogeochemistry configuration in E3SMv2.1, performing historical emission-hindcasts from 1850 to 2014. While it overestimates atmospheric CO2 by 11–23 ppm, it remains within the spread of CMIP6 models and largely retains physical climate properties, laying the groundwork for advanced carbon-climate feedback projections.
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Meena et al. (2025) Emerging trends in precision horticulture integrating digital technologies for smart crop management
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Güz et al. (2025) Dynamic coupling of hydrogeology and human decision-making for sustainable groundwater management in Konya Closed Basin, Türkiye
This study develops and applies a coupled socio-hydrological model, integrating a process-based groundwater flow model (MODFLOW-UZF) with a system dynamics model (VENSIM), to simulate hydrogeological processes and human decision-making for sustainable groundwater management in the Konya Closed Basin, Türkiye. The research demonstrates that spatially and temporally disaggregated policy measures significantly enhance groundwater resource management by more accurately capturing human-hydrological feedback compared to stand-alone models.
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Wang et al. (2025) Isolating ENSO Influence on Eastern China Summer Rainfall Variability with Month-Dependent EOF Method: Observation–Model Synthesis
This study identifies two leading modes of interannual summer rainfall variability over eastern China using a month-dependent empirical orthogonal function (EOF) method, linking them to antecedent and concurrent El Niño–Southern Oscillation (ENSO) events, and interprets these modes using an atmospheric general circulation model.
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Choi et al. (2025) Estimation of Catchment‐Scale Evapotranspiration With the Simple Method Based on the Maximum Entropy Production Principle
This study introduces a novel Maximum Entropy Production (MEP) based method for estimating catchment-scale evapotranspiration, demonstrating its promising performance when validated against a global Penman-Monteith-Leuning product and annual water balance.
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Nguyen et al. (2025) Bias correction of precipitation from convection-permitting models at the point scale: a case study in Switzerland
This study evaluates five quantile mapping (QM) approaches to bias correct and downscale sub-hourly precipitation from convection-permitting model (CPM) simulations (2.2 km resolution) to the station scale in Switzerland. It finds that while QM reduces biases in annual precipitation indices, conventional QM often overcorrects, introducing dry biases in extreme quantiles and altering climate change signals, with a combination of spatial pooling and a moving window showing the most promise.
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Singh et al. (2025) Remote Sensing and Machine Learning for Irrigation Management in Potato Cultivation: A Systematic Review
This systematic review synthesizes research from 49 peer-reviewed studies (2000–2025) to evaluate the application of remote sensing and machine learning for irrigation management in potato cultivation, highlighting current trends, limitations, and future research directions to enhance sustainable water use.
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Khorrami et al. (2025) Drought-induced changes in groundwater-surface water exchange at Lake Mead area
This study investigates the hydrological response of the coupled surface-groundwater system to the 2020–2022 drought in the Lake Mead region using InSAR and elastic load modeling. It quantifies significant total water storage loss, including substantial groundwater depletion, and reveals hydraulic connectivity between the lake and aquifers, emphasizing the need for integrated water management.
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Wang et al. (2025) The extent of drought determines daily area burned in Canadian forests
This study developed the first effective models to predict daily area burned (DAB) in Canadian forests, identifying a strong relationship between DAB and the spatial extent of fire-conducive weather, particularly fuel aridity. It found significant increases in DAB, a concentration of fire activity within the season, and more extreme DAB events across Canada from 1940 to 2023.
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Orth et al. (2025) Regional Emergence of Water‐Related Browning in a Greening World
This study reveals that declining water availability and increasing atmospheric water demand are key drivers of regional browning trends in tropical carbon sinks, contributing significantly to inter-annual variability in Leaf Area Index, with Earth system models showing variable agreement.
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Linga et al. (2025) Global Irrigation Modeling Relies More on Pragmatic Than Empirical Assumptions
This study analyzes 102 assumptions across nine global irrigation models (GIMs) to distinguish between empirically grounded and pragmatic assumptions, finding that 70% are pragmatic, which suggests a larger uncertainty space in GIMs than typically addressed.
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Chen et al. (2025) Drivers and thresholds of carbon and water flux dynamics in a semi-humid urban forest ecosystem
This study quantified carbon and water fluxes in a semi-humid urban forest using eddy covariance, identifying their magnitudes, key environmental drivers, and nonlinear response thresholds under both drought and non-drought conditions.
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Tedesche et al. (2025) Changing Snow Regime Classifications Across the Contiguous United States
This study developed a new spatial snow regime classification system to track climate-driven changes in snow phenology across the Contiguous US (CONUS) from 1981–2020, revealing widespread decreases in snow cover duration and snow-dominated areas, with shifts towards rain-dominated and transitional regimes moving up in latitude and elevation.
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Mehta et al. (2025) Rainfall temporal variability and drought analysis by means of the Standardized Precipitation Index in Ganganagar District, Rajasthan, India
This study investigated the temporal behavior and severity of droughts in the Ganganagar district of Rajasthan, India, using a 122-year record of monthly precipitation analyzed through the Standardized Precipitation Index (SPI) at multiple timescales. The analysis revealed increasing pre-monsoon wetness and post-monsoon dryness, with frequent drought events, highlighting complex seasonal shifts and persistent hydrological risks.
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Bajaj et al. (2025) Exposure of global agricultural lands to extreme weather using CMIP6 projections of future climate
This study assesses the global exposure of agricultural lands, including croplands and pasturelands of varying sizes, to extreme weather in a 2 °C warmer world, finding a significant increase in extreme heat exposure that varies by land use type and farm size.
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Gao et al. (2025) The key drivers of streamflow recession variability and their implications for robust parameterization of recession processes
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2025...
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Huang et al. (2025) Evaluation of E3SM Simulated Aerosols and Aerosol‐Cloud Interactions Across GCM and Convection‐Permitting Scales
This paper introduces an Earth system modeling testbed using E3SMv2 at convection-permitting scales (3.25 km) to predict aerosols and aerosol-cloud interactions (ACIs). While increased resolution improves some aspects like heavy precipitation and certain ACI relationships, it also amplifies biases in light drizzle and poorly represents aerosol composition, indicating that resolution alone is insufficient for broad improvements without concurrent advancements in physical and chemical process representations.
