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Andria et al. (2025) Thermodynamic Versus Large‐Scale Controls on Extreme Precipitation: Temporal Scale Dependence and Clausius‐Clapeyron Scaling Redefined
This study introduces a framework using the Metastatistical Extreme Value Distribution to precisely define precipitation extremes and investigate their dependence on local thermodynamics and large-scale atmospheric circulation across different temporal scales. It finds that hourly precipitation extremes are primarily controlled by thermodynamics, with rarer events intensifying more sharply, while daily extremes are predominantly influenced by large-scale circulation, challenging conventional Clausius-Clapeyron scaling assumptions.
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Küçükoğlu et al. (2025) Global evolution of inland water levels: drying-speed analysis using ICESat-2 ATL13
This study analyzed global inland water level dynamics for over one million lakes and reservoirs from 2018-2025 using ICESat-2 ATL13 data, revealing that more than half of monitored bodies experienced decreasing water levels and demonstrating the capability of satellite altimetry for near-term global-scale monitoring.
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Vila et al. (2025) Potential of thermal imaging for yield and soil water content prediction in leafy vegetables
This study developed predictive models for yield and soil water content in lettuce and arugula by integrating thermal images. The models, based on Crop Water Stress Index (CWSI) and normalized temperature difference (ΔT), demonstrated good performance for yield (R² up to 0.82) and soil water content (R² up to 0.92), providing critical thresholds for efficient irrigation management.
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Sanow et al. (2025) Let Us Change the Aerodynamic Roughness Length as a Function of Snow Depth
This study demonstrates that aerodynamic roughness length (z0) for shallow, seasonal snowpacks is a dynamic variable dependent on snow depth (ds), significantly impacting sublimation modeling.
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Rabie et al. (2025) Remote Sensing, GIS, and Machine Learning in Water Resources Management for Arid Agricultural Regions: A Review
This study optimized geospatial data pipeline automation for landscape monitoring in Italy using GeoAI and machine learning on Landsat imagery, demonstrating that the Support Vector Machine (SVM) algorithm achieved the highest classification accuracy for detecting land cover changes over a five-year period.
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Ferreira et al. (2025) Improving ETa Estimation for Cucurbita moschata Using Remote Sensing-Based FAO-56 Crop Coefficients in the Lis Valley, Portugal
This study assessed pumpkin crop water status and evapotranspiration dynamics in the water-scarce Lis Valley, Portugal, by integrating in-situ soil moisture and electrical conductivity measurements with Sentinel-2 derived vegetation indices. It found that this integrated approach enhances precision irrigation strategies and confirmed the applicability of the FAO-56 method for *Cucurbita moschata* under Mediterranean conditions.
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Zhang et al. (2025) Multi-stage flood utilization framework to support ecological flow protection and groundwater recovery mechanisms
This study introduces a multi-stage flood utilization framework that integrates ecological flow protection with groundwater recharge to address water scarcity. The framework significantly improves downstream ecological flow protection by eliminating flow interruptions and enhances groundwater recovery in water-scarce regions.
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Lakshmi et al. (2025) Remote Sensing-Based Monitoring of Agricultural Drought and Irrigation Adaptation Strategies in the Antalya Basin, Türkiye
This study assessed agricultural drought dynamics in the Antalya Agricultural Basin, Türkiye, from 2001 to 2023 using multiple remote sensing indices, revealing recurrent moderate summer droughts driven by minimal precipitation and high temperatures, and proposing adaptation strategies for irrigation efficiency aligned with national water management goals.
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Louis et al. (2025) A new approach in monitoring regional water use efficiency in response to climate variability: a case study in Hungary
This study develops and evaluates a novel biomass soil moisture index (NWUESM) as a simpler and less expensive alternative to the standard regional water use efficiency (RWUEEC) indicator, demonstrating its superior accuracy at 60 cm soil depth for monitoring water use efficiency in northeastern Hungary.
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Guo et al. (2025) Bidirectional Reflectance Sensitivity to Hemispherical Samplings: Implications for Snow Surface BRDF and Albedo Retrieval
This study systematically analyzes the sensitivity of snow surface Bidirectional Reflectance Distribution Function (BRDF) and albedo retrieval to different multi-angular sampling configurations. It proposes an Angular Information Index (AII) to quantify angular information content, demonstrating that sampling configuration significantly impacts BRDF and albedo accuracy, especially at longer wavelengths.
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Lee et al. (2025) Development of an automated hyperspectral system to monitor hyperspectral reflectance and sun-induced chlorophyll fluorescence with directional and hemispheric view geometries simultaneously
This study develops and evaluates an automated hyperspectral system (enhanced RotaPrism) for simultaneous monitoring of hyperspectral reflectance and sun-induced chlorophyll fluorescence (SIF) using both directional (conical) and hemispherical view geometries. It demonstrates the system's reliability and reveals significant, seasonally variable differences in spectral reflectance and SIF between these viewing geometries across rice paddy and mixed forest ecosystems.
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Instituto Interamericano de Tecnología y Ciencias del Agua IITCA-UAEM et al. (2025) Sensibilidad de índices de sequía dependiendo de la longitud de registros climatológicos
This study evaluates the sensitivity of various meteorological drought indices to the length of climatological records, aiming to determine which indices exhibit less uncertainty when applied with short time series. It identifies several indices (CPI, PNI, RDI, SPI, SPEI, ZI) as less sensitive to record length, with SPEI requiring at least 10 years and others 20 years, while EDI needs over 30 years for improved certainty.
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Shahbazi et al. (2025) The compounding effects of agricultural expansion and snow drought on lake urmia’s drying crisis
This study investigated the combined impacts of agricultural expansion and climate variability, including snow drought, on river inflows to Lake Urmia from 1985 to 2020. Findings reveal that agricultural water use was the dominant factor, accounting for approximately 66% of the total impact on reduced river inflows, amplified by a persistent snow drought.
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Ghezali et al. (2025) Combined CA-ANN, CMIP6 GM and SCS-CN modeling of future impacts of climate change and urbanization on potential natural groundwater recharge at city scale
This study modeled the long-term impacts of climate change and urbanization on potential natural groundwater recharge in Algiers (1986-2100) under SSP2-4.5 and SSP5-8.5 scenarios. Findings project a significant decline in groundwater recharge, primarily driven by climate change, with urbanization having a lesser but still notable impact.
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Zhao et al. (2025) Multidimensional Copula-Based Assessment, Propagation, and Prediction of Drought in the Lower Songhua River Basin
This study assesses, propagates, and predicts multidimensional drought (meteorological, hydrological, agricultural) in the lower Songhua River basin under future climate change scenarios using a coupled modeling framework. It reveals a significant increase in multidimensional drought risk, with varying propagation patterns and thresholds across different climate scenarios.
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Zhang et al. (2025) Significant Shifts in Continental Precipitation Sources in the 21st Century
## Identification - **Journal:** Water Resources Research - **Year:** 2025...
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Long et al. (2025) Reconstruction of drought propagation pathways: A global analysis of multitype propagation chains and nonlinear mechanisms
This study reconstructs global drought propagation pathways by combining copula functions, a Bayesian framework, and multi-scale drought indices, revealing that abnormal evapotranspiration often initiates drought and quantifying the nonlinear roles of natural and anthropogenic drivers using interpretable machine learning.
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Unknown (2025) Forests’ misty breath sustains crops in distant lands
This research highlights that crops in 155 countries are partly sustained by moisture originating from forests in other nations, with some regions receiving up to 40% of their annual cropland precipitation from these cross-border flows. It underscores the global interconnectedness between forest health and food security, suggesting forest conservation as a climate change adaptation strategy for agriculture.
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Zhang et al. (2025) Future Meiyu Precipitation Change and Atmospheric River Impacts Across East Asia: Regional Disparities and Risks
This study projects future changes in East Asian Meiyu precipitation and its coupling with atmospheric rivers (ARs) under CMIP6 SSP245 and SSP585 scenarios, finding significant increases in both mean/extreme rainfall and AR intensity, primarily driven by thermodynamic moistening, with notable regional variations.
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He et al. (2025) Hydrological response to land use change under low carbon-optimal economic scenario
This study developed a framework integrating land-use simulation (CA Markov) and hydrological modeling (SWAT) with spatiotemporal regression (GTWR) to assess hydrological responses to land-use change under a low-carbon, economic-optimal scenario in the Dongjiang River Basin. It found that by 2035, land-use changes, primarily farmland conversion to forest, grassland, and construction, lead to increased surface runoff and evapotranspiration, decreased soil percolation and groundwater recharge, with significant spatiotemporal heterogeneity and implications for drought and flooding risks.
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He et al. (2025) A three-dimensional Budyko framework with terrestrial water storage dynamics: mechanistic modeling and diagnostic ecohydrological applications across global basins
This study introduces a novel three-dimensional (3D) Budyko framework that mechanistically incorporates terrestrial water storage changes (TWSC) as a dynamic third dimension. The framework significantly improves water partitioning prediction accuracy globally, especially in tropical basins, and establishes the Water Storage Change Index (WSCI) as a powerful diagnostic indicator for anthropogenic impacts on the water cycle and ecological water stress.
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Rodrigues et al. (2025) Estimating Reservoir Evaporation Under Mediterranean Climate Using Indirect Methods: A Case Study in Southern Portugal
This study assessed reservoir evaporation patterns in southern Portugal using offshore meteorological data and benchmarked five indirect evaporation methods against the Energy Budget method, finding good correlation and small biases for all methods, especially in the dry semester.
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Jiao et al. (2025) Multi-Layer Soil Moisture Profiling Based on BKA-CNN by Integrating Sentinel-1/2 SAR and Multispectral Data
This study developed a BKA-CNN model integrating Sentinel-1 SAR and Sentinel-2 multispectral data to estimate multi-layer soil moisture (SM) in the Shandian River Basin, achieving high accuracy (R² up to 0.799) across depths from 3 cm to 50 cm, with superior performance compared to single-source data and traditional machine learning models, and demonstrating robust generalization.
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Güngüneş et al. (2025) Phenology-Aware Machine Learning for Wheat Yield Prediction under Climate Variability: Central Anatolia, Türkiye
This study developed a phenology-aware machine learning framework for wheat yield prediction in Türkiye's Central Anatolia Region, demonstrating that Gradient Boosting consistently achieved high accuracy (R² 0.96-0.99) by integrating phenological segmentation of agro-climatic and soil parameters. The research highlights the critical role of local calibration to account for management practices like irrigation.
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Liu et al. (2025) Dynamic processes and mechanisms of the Quasi-Biweekly Oscillation of summer precipitation in the middle and lower reaches of the Yangtze River
This study investigates the dynamic processes and mechanisms of the Quasi-Biweekly Oscillation (QBWO) of summer precipitation in the middle and lower reaches of the Yangtze River (MLYR), revealing that low-level vorticity development, driven by vortex tube stretching and multiscale interactions, maintains the QBWO.
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Wu et al. (2025) Hybrid Variational Modal Decomposition-Extreme Learning Machine-Adaptive Boosting Model for Monthly Runoff Prediction
This study developed a novel hybrid VMD-ELM-AdaBoost model for monthly runoff prediction, demonstrating superior accuracy and efficiency compared to benchmark models, particularly in data-limited basins, by effectively addressing non-stationarity and improving generalization.
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Mliyeh et al. (2025) Assessing drought dynamics in a semi-arid basin: a multi-index approach using hydrological and remote-sensing indicators
This study integrates remote-sensing products with traditional hydrological indices to comprehensively monitor drought dynamics in a semi-arid basin, demonstrating that remote-sensing indicators, particularly AWEI and NDWI, strongly correlate with hydrological drought indices and reveal significant declines in surface water extent.
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Cai et al. (2025) JSPSR: Joint Spatial Propagation Super-Resolution Networks for Enhancement of Bare-Earth Digital Elevation Models from Global Data
This research introduces the Joint Spatial Propagation Super-Resolution network (JSPSR) to convert global Digital Elevation Models (DEMs) into bare-earth DEMs with enhanced spatial resolution and vertical accuracy. The method significantly outperforms existing techniques, achieving an RMSE of approximately 1.1 meters for 3-meter and 8-meter resolution bare-earth DEMs derived from 30-meter global DEMs.
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Feng et al. (2025) A Novel Method for Reservoir Outflow Simulation Based on Spearman Rank Correlation Between Satellite‐Derived Water Elevations and Model‐Simulated Reservoir Storage
This study proposes a novel calibration scheme for Reservoir Outflow Models (ROMs) utilizing Spearman rank correlation between satellite-derived surface water elevations and ROM-simulated storage to accurately reconstruct historical reservoir outflows, demonstrating comparable performance to gauged data calibration and superior data efficiency compared to hybrid methods.
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Zeroualı et al. (2025) Next-generation runoff prediction: Merging RFE, SHAP insights, and satellite data with innovative deep learning techniques
This study developed and evaluated three advanced hybrid deep learning models (RFE-GRU-BiLSTM, RFE-GRU-CNN, and RFE-CNN-GRU-BiLSTM) for daily runoff prediction in north-central Algeria. The research found that these models, incorporating Recursive Feature Elimination and SHAP analysis, significantly improve predictive accuracy and interpretability, with lagged discharge identified as the primary driver of runoff.
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Shrestha et al. (2025) Impact of Urbanization on Flooding and Risk Based on Hydrologic–Hydraulic Modeling and Analytic Hierarchy Process: A Case of Kathmandu Valley of Nepal
This study analyzed the impact of urbanization and land use/land cover changes on flooding and flood risk in Nepal's Kathmandu Valley, finding that future urbanization is projected to significantly increase flood inundation extent, volume, and the area of high-risk zones.
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Sun et al. (2025) High-resolution streamflow simulation, trend and drought analysis in China (1980–2022): A large-scale routing model based on improved geomorphic functions
This study developed improved geomorphic functions for China and integrated them with the Variable Infiltration Capacity (VIC) model to reconstruct high-resolution streamflow across China from 1980 to 2022. The research revealed an overall increasing streamflow trend in eastern regions, declining trends in western regions, and detailed spatiotemporal characteristics of hydrological droughts, including a contraction in drought extent after 2013.
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Ali et al. (2025) Aquifer-specific flood forecasting using machine learning: A comparative analysis for three distinct sedimentary aquifers
This study comparatively analyzes four machine learning models (TFT, Informer, LSTM, XGBoost) for multi-horizon (1-4 days) flood forecasting across three distinct sedimentary aquifers (Limestone, Chalk, Greensand) in the Thames Basin, UK. The research reveals that model accuracy is highly dependent on aquifer-specific hydrogeological characteristics, with Limestone showing very high accuracy (R² = 0.98–0.99) and Greensand exhibiting poor predictability (R² ≤ 0).
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Jia et al. (2025) How vegetation greening mitigates climate-driven aridification in mid-latitude Asia
This study investigates the spatiotemporal characteristics and drivers of drought variability across mid-latitude Asia from 1982 to 2018, revealing a biogeophysical dichotomy where climate-driven aridification intensifies in the central-western sector, while vegetation greening mitigates drought in the southeastern regions through hydrological and land-atmosphere interactions.
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Niu et al. (2025) Tail-Aware Forecasting of Precipitation Extremes Using STL-GEV and LSTM Neural Networks
This study introduces a hybrid modeling framework combining Generalized Extreme Value (GEV) distribution fitting with deep learning (LSTMs) to forecast monthly maximum precipitation extremes. The approach, which uses a tail-weighted loss function, demonstrates strong predictive performance in identifying anomalously high precipitation months.
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Manara et al. (2025) MODIS (2001-2022) snow cover variability over the Italian territory
This study analyzes the spatial distribution and temporal evolution of snow cover variables (duration, onset, end) across Italy from 2000-2022 using MODIS data, complemented by ERA5-Land reanalysis for longer-term trends (since 1950s), revealing a strong elevation dependence and a significant overall negative trend in snow cover extent, particularly in the Alps.
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Sanchez-Lorente et al. (2025) Air–sea interaction heat and momentum fluxes based on vessel's experimental observations over Spanish waters
This paper presents a quality-controlled dataset of air–sea interaction heat and momentum fluxes derived from experimental observations aboard four research vessels over Spanish waters from 2011 to 2023, providing crucial regional data for climate studies and validation of models.
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Abbas et al. (2025) Southward to northward shifting trends of monsoonal precipitation and their connections with atmospheric circulations over Pakistan: A comparative study of 1961–1990 and 1991–2020
This study analyzes the southward-to-northward shifting trends of monsoonal precipitation and their connections with atmospheric circulations over Pakistan from 1961 to 2020. It reveals a significant northward shift in precipitation, with regional increases in core monsoon areas and decreases in coastal and southern regions, largely influenced by the Indian Ocean Dipole (IOD) and El Niño-Southern Oscillation (ENSO).
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Achite et al. (2025) Multivariate uncertainty analysis of severity–duration–magnitude–frequency curves using Khoudraji copula and bootstrap method
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2025...
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Zhang et al. (2025) Regional greening intensifies transpiration water consumption but enhances the positive feedback process between vegetation and precipitation
This study quantifies the hydrological impacts of regional greening in the Yellow River Basin, revealing that while vegetation restoration intensifies transpiration and overall evapotranspiration, it also significantly enhances the precipitation recycling ratio and generates additional precipitation, suggesting an underestimation of regional vegetation carrying capacity.
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Peng et al. (2025) Global Evidence of the Unimodal Response of Ecosystem Respiration to Soil Moisture
This study analyzes ecosystem respiration (ER) response to soil moisture at 135 global FLUXNET sites, revealing a prevalent unimodal response with an optimum soil moisture level, challenging monotonic assumptions in models, and demonstrating ER's adaptation to local water availability.
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Jehanzaib et al. (2025) The influence of plantation forest legacy on blanket bog hydrology
This research investigates the hydrological impact of legacy plantation forestry (clear-felled areas) on streamflow in a small (0.21 square kilometers) blanket bog catchment in Ireland. The study found that streamflow increased by 106% annually in the degraded catchment compared to an intact blanket bog, with significant seasonal variations.
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Wang et al. (2025) Divergences in typhoon and non-typhoon extreme rainfall trends and their spatial variations at multiple timescales in typical region of southeastern China
This study analyzed divergent long-term trends and spatial patterns of typhoon-related and non-typhoon-related extreme rainfall across multiple timescales in Fujian, southeastern China, revealing distinct temporal and spatial variations for each type of rainfall.
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Wang et al. (2025) Runoff Estimation in the Upper Yangtze River Basin Based on CMIP6 and WRF-Hydro Model
This study integrates CMIP6 climate model data with the WRF-Hydro model to project future runoff changes in the Upper Yangtze River Basin under SSP2-4.5 and SSP5-8.5 scenarios, finding significant warming, modest precipitation increases with seasonal redistribution, and a substantial increase in extreme flood risks, particularly from the Jialing River.
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Lei et al. (2025) PANet: a physics and action informed network for water level prediction in canal systems
This study introduces PANet, a multi-step water level prediction model for canal systems that integrates Integral Delay theory and action-informed structures to simultaneously model hydrological delays, Markovian properties, and the impact of operational interventions. PANet achieves superior accuracy and stability, significantly outperforming mainstream deep learning models in multi-step forecasting tasks.
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Lin et al. (2025) Heatwaves exacerbate the effects of early-season drought on peak vegetation growth in the Northern Hemisphere
This study investigates how compound drought-heatwave events exacerbate the effects of early-season drought on peak vegetation growth in the Northern Hemisphere, finding that CDHWs intensify negative impacts in arid/semi-arid biomes and positive impacts in wetter/colder biomes.
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Heyvaert et al. (2025) Land data assimilation of satellite‐based surface soil moisture: Impact on atmospheric simulations over the contiguous United States
This study investigates the effectiveness of surface soil moisture (SSM) data assimilation (DA) in enhancing land initialisation within coupled land-atmosphere models. It finds that SSM DA improves atmospheric predictions, particularly 2-meter air temperature, with greater impact in regions exhibiting stronger land-atmosphere coupling.
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Liang et al. (2025) May Relative Humidity Reconstruction Based on Populus cathayana Ring-Width Chronology on the Eastern Tibetan Plateau, China
This study reconstructed May relative humidity for the eastern Tibetan Plateau over the past 146 years using tree-ring width, revealing multi-decadal variability and its links to ENSO and the East Asian Summer Monsoon.
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Zhang et al. (2025) Long Short-Term Memory (LSTM) Based Runoff Simulation and Short-Term Forecasting for Alpine Regions: A Case Study in the Upper Jinsha River Basin
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
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Hu et al. (2025) How well does ERA5 reanalysis depict low-level winds associated with nocturnal rainfall in Sichuan Basin
This study evaluates ERA5 reanalysis performance in depicting low-level winds associated with nocturnal rainfall in the complex terrain of the Sichuan Basin (SCB) using wind profiler radar observations. While ERA5 generally reproduces key features, it exhibits significant biases in the vertical structure of low-level winds, underestimating the height of maximum wind speed and failing to capture the observed increase in circulation with height.
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Javadi et al. (2025) Analysis of historical global warming impacts on climatological trends for the partially gauged Hirmand river basin based on multiple data products and bias correction methods
This study evaluated and bias-corrected global climate datasets (CRU, ERA5, GLDAS) for the Hirmand River Basin (1960-2022) using Quantile Delta Mapping (QDM) and Bidirectional Long Short-Term Memory (Bi-LSTM), revealing no significant trends in precipitation or maximum temperature but a significant 4.3 °C increase in minimum temperature over 63 years.
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Bundy et al. (2025) Derecho impacts on United States crop condition ratings and yield
This study quantifies the impacts of derechos on corn and soybean conditions and modeled yield across the United States over a nine-year period (2015–2023), revealing significant declines comparable to hurricane impacts, with vulnerability influenced by wind speed, crop stage, and pre-storm conditions.
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Adeyeri et al. (2025) Asymmetric heatwave intensification under divergent climate change mitigation pathways amplifies urban–rural exposure disparities
This study projects future heatwave characteristics and population exposure under different climate change mitigation pathways (SSP370, SSP585) using bias-corrected climate models, revealing asymmetric heatwave intensification and significant, often comparable, exposure disparities between urban and rural populations globally.
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Kuang et al. (2025) Climate change accelerates the evolution of reorganized river-lake systems on the Tibetan Plateau
This study reveals how climate change has accelerated the reorganization of the Zonag and Yanhu Lake drainage basins on the Tibetan Plateau, leading to their connection with the Yangtze River headwaters since 2019, with significant ecological and socio-economic consequences including increased flood risks and sandstorms.
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Doornbos et al. (2025) Ending Overfitting for UAV Applications - Self-Supervised Pretraining on Multispectral UAV Data
This research investigates whether self-supervised pretraining can address the "small data problem" in UAV-based deep learning for remote sensing. It demonstrates that using an efficient self-supervised learning framework (FastSiam) tailored for multispectral UAV imagery significantly improves model generalization and reduces overfitting, even with extremely limited labelled data, outperforming end-to-end trained models.
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Tumajer et al. (2025) Longer growing seasons will not offset growth loss in drought-prone temperate forests of Central-Southeast Europe
This study uses the VS-Lite growth model calibrated with 2013 tree-ring chronologies from Central-Southeast Europe to predict that while extended growing seasons may temporarily offset drought-induced growth loss until the 2040s-2050s, high-emission climate scenarios will lead to significant long-term growth reduction in drier temperate forests, as growing season extension becomes insufficient to compensate for declining summer growth rates.
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Dixey et al. (2025) Addressing how spatial resolution affects image velocimetry outputs: implications for measurements from space
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Wang et al. (2025) Spatiotemporal dynamics of soil moisture in a watershed in the Loess Plateau during vegetation restoration
This study analyzed the spatiotemporal dynamics and temporal stability of soil moisture in a watershed on the Loess Plateau during vegetation restoration through two years of field monitoring. It revealed distinct seasonal and spatial patterns influenced by rainfall and vegetation, identified strong spatial autocorrelation, and pinpointed a specific location (S25) as the most temporally stable representative point for the entire watershed.
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Ranzi et al. (2025) A Multi‐Century Meteo‐Hydrological Analysis in the Italian Alps: Daily Streamflow (1862–2022) at Different Time Scales
This paper reconstructs and analyzes a 161-year daily streamflow time series for the Adige river in the Italian Alps to identify and quantify the impact of natural and anthropic factors on hydrological changes. The study reveals a significant decline in annual streamflow and runoff coefficient, primarily driven by human activities such as water withdrawals, enhanced evapotranspiration due to temperature increase, and afforestation, rather than precipitation changes.
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Getachew et al. (2025) Exploring the dynamics of past and future climate extremes under CMIP6: implications for rainfed agriculture
This study examined the projected climate extremes trends and changes in occurrences in Ethiopia using eight temperature and precipitation-related indices under CMIP6 scenarios. Results indicated that Ethiopia’s rainfed agriculture system will likely face exacerbated and frequent severe droughts, posing significant challenges to food security.
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Yoshe (2025) Assessment of drought vulnerability in the Nile River basin using satellite remote sensing, Africa
This study utilized GRACE terrestrial water storage variations to identify and characterize 12 drought events in the Nile River basin between 2002 and 2023, demonstrating that the GRACE-based drought index significantly correlates with meteorological drought.
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Roussel et al. (2025) Saharan dust impacts on the surface mass balance of Argentière Glacier (French Alps)
This study quantifies the impact of Saharan dust and black carbon on the surface mass balance (SMB) of Argentière Glacier (French Alps) from 2019-2022, revealing that mineral dust significantly reduced annual SMB, particularly in 2022, with impacts up to 1.2 meters water equivalent (m w.e.) at specific locations.
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Cheng et al. (2025) Climatological characteristics of tropical cyclones simulated in the global-regional integrated forecasting system (GRIST) model
This study evaluates the climatological characteristics of tropical cyclones (TCs) simulated by the Global-Regional Integrated Forecasting System (GRIST) model at 60 km and 15 km resolutions. It finds that while the model generally reproduces TC activity well, especially in the North Pacific, it consistently underestimates TC intensity and activity in the North Atlantic due to large-scale circulation biases and poor representation of African easterly waves.
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Xiao et al. (2025) Quantitative identification of drought dominant periods and driving factors in China: integrating from TVDI and pixel-wise EMD
This research quantifies the multi-scale driving mechanisms of drought in China from 2000 to 2022 using the Temperature-Vegetation Drought Index (TVDI) and pixel-wise Empirical Mode Decomposition (EMD), revealing that precipitation drives seasonal drought, potential evapotranspiration dominates interannual drought in arid regions, and maximum temperature is crucial for interdecadal drought, with its influence increasing for longer drought periods.
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Marčiš et al. (2025) Corrigendum to “A combined effect of heat and drought limits the growth of Central European silver fir” [Agricultural and Forest Meteorology 371 (2025) 1–11 / 110610]
Information not available in the provided corrigendum.
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Zelalem et al. (2025) Assessment of deep-water wells drawdown: A case study of legedadi deep well field phase I, Addis Ababa, Ethiopia
This study assessed groundwater sustainability and operational performance in the Legedadi Deep Well Field Phase I, Addis Ababa, Ethiopia, revealing significant groundwater drawdown, low pump efficiencies, high energy consumption, and operational inefficiencies exacerbated by SCADA system failure.
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Shah et al. (2025) Spatiotemporal changes in heat stress exposure in India, 1981-2023
This study provides a comprehensive analysis of the spatiotemporal evolution of heat stress exposure (HSE) across India's districts from 1981 to 2023, revealing a 3.3% increase in average HSE duration and significant variations by time of day, time of year, and region, with critical implications for public health and policy.
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Haghiabi et al. (2025) A comparative study between time series and soft computing models for river discharge forecasting
This study compared six predictive models, including machine learning (SVR, RF, KNN, LSTM) and time series (CARMA, CARMA-GARCH), for monthly river discharge forecasting in the Kashkan River Basin, Iran, under two input scenarios. The Random Forest (RF) model demonstrated the highest accuracy among machine learning methods, while CARMA-GARCH was the best-performing time series model, with time series models generally showing superior performance.
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Umar (2025) Climate change adjustment factor on rainfall depths in river basins of Khyber Pakhtunkhwa, Pakistan
This study assesses the impact of climate change on future intense rainfall events in the river basins of Khyber Pakhtunkhwa, Pakistan, to inform flood management and infrastructure planning. It projects an increase in design rainfall adjustment factors ranging from 1.0% to 24.3% for a 100-year return period under various climate change scenarios.
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Masoudi et al. (2025) Spatial and Statistical Analysis of Climate Change in the Middle East: A Study of Precipitation and Temperature Variability Using NOAA Weather Data and Geostatistical Methods
This study provides the first comprehensive spatial and statistical analysis of precipitation, temperature, and the De Martonne climate index variability across the entire Middle East from 1979 to 2017. It reveals a significant warming trend (average 1.32 °C) and a complex precipitation pattern with an average decline of 7.58%, leading to increased aridity in the region.
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Ali et al. (2025) A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification
This study introduces a hybrid deep learning model combining 3D Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, enhanced by residual connections and a soft spatial attention mechanism, to improve hyperspectral image classification accuracy. The proposed model achieved remarkable overall accuracies of 99.66 % on the Indian Pines dataset and 99.58 % on the Salinas dataset, outperforming current leading methods.
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Lei et al. (2025) Global monthly CMIP6-downscaled high-resolution (1 km) near-surface air temperature dataset (1950–2100)
This study developed MoCHAT, a novel global 1 km monthly near-surface air temperature dataset (1950-2100) by downscaling 16 CMIP6 GCMs using the delta method, providing mean, maximum, and minimum temperatures under three SSP scenarios with high accuracy (mean absolute errors between 1.60 K and 2.38 K).
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Zhu et al. (2025) Coupling Mechanism and Management of Groundwater Dynamics and Land Use in Arid Inland Basins (Wuwei, China)
This study analyzed multi-source data from China's Wuwei Basin (2000-2020) to understand the coevolution of groundwater and land use, revealing a post-2010 shift towards ecological land use, spatially variable groundwater declines due to anthropogenic pressures, and a significant attenuation of natural groundwater recharge.
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Ji et al. (2025) Response characteristics of vegetation net primary production to cascade hydropower development and climate change in the dry-hot valleys of the Jinsha River
This study quantifies the ecological impacts of cascade hydropower development on Net Primary Production (NPP) in the dry-hot valleys of the Jinsha River, finding an overall increase of approximately 12% in NPP compared to pre-development levels due to altered local hydrology and climate. Reservoir impoundment significantly enhanced NPP during the dry season by alleviating water stress, while in the rainy season, its influence shifted to intensifying heat-related factors.
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Raoult et al. (2025) Parameter Estimation in Land Surface Models: Challenges and Opportunities With Data Assimilation and Machine Learning
This paper reviews the current state, challenges, and opportunities in parameter estimation for land surface models (LSMs) using data assimilation (DA) and machine learning (ML), particularly focusing on carbon-water-vegetation interactions. It highlights how ML can enhance computational efficiency and address poorly represented processes, advocating for international collaboration to improve LSM predictive capabilities.
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Hwang et al. (2025) Explainable deep learning-based simulation for evaluating climate-driven future groundwater level changes in South Korea
This study developed explainable deep learning models to simulate future groundwater level changes in South Korea under Shared Socioeconomic Pathways, revealing that high-emission scenarios lead to more unstable and significantly declining groundwater levels, particularly in alluvial aquifers.
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Moon et al. (2025) An Analysis of Drought Characteristics in Busan Based on the Standardized Precipitation Evapotranspiration Index Reflecting Climate Change Trends
This study projected future drought characteristics in Busan, Republic of Korea, by calculating the Standardized Precipitation Evapotranspiration Index (SPEI) under RCP 4.5 and 8.5 climate change scenarios. The analysis revealed a significant increase in the frequency and intensity of extreme short-term droughts, particularly a 1,560% rise in 1-month extreme droughts, alongside shifts in seasonal drought patterns and reduced variability across most seasons.
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Zheng et al. (2025) Spatiotemporal variation of snow cover over the Tibetan Plateau based on the MODIS fractional snow cover product: 2000–2023
This study investigates the spatiotemporal variations of snow cover and its climatic responses across the Tibetan Plateau from 2000 to 2023 using MODIS fractional snow cover data, revealing a widespread decline in snow cover driven by distinct temperature and precipitation factors across different circulation regimes.
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Aghababaei et al. (2025) Development and Comparison of Methods for Identification of Baseflow-Dominant Periods in Streamflow Records
This study defines baseflow-dominant (BFD) periods and develops an expert-labeled dataset from 182 USGS stream gages to evaluate automated BFD identification methods. It demonstrates that a machine learning model (RF-BFD) significantly outperforms other approaches, achieving an F1 score of 0.92 and 92% accuracy, thereby establishing benchmarks for improved large-scale hydrological assessments.
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Maniraho et al. (2025) Optimizing Agricultural Drought Monitoring in East Africa: Evaluating Integrated Soil Moisture and Vegetation Health Index (SM-VHI)
This study comprehensively analyzes the integrated Soil Moisture–Vegetation Health Index (SM-VHI) for drought detection and agricultural monitoring in East Africa, confirming its effectiveness as a reliable remote-sensing tool with strong potential to inform agricultural practices and policy for food security.
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García-Ten et al. (2025) Evaluation of CMORPH V1.0, IMERG V07A and MSWEP V2.8 Satellite Precipitation Products over Peninsular Spain and the Balearic Islands
This study evaluates the suitability of three satellite-derived precipitation products (CMORPH V1.0, IMERG V07A, and MSWEP V2.8) across Peninsular Spain and Balearic Islands using AEMET gauge data as reference, finding MSWEP generally preferable over IMERG, while CMORPH is not recommended, with performance varying significantly by seasonality, intensity, altitude, and orography.
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Villani et al. (2025) Large dams and small reservoirs: co-modeling water storage strategies in a Mediterranean catchment under a changing climate
This study co-models water storage strategies (small agricultural reservoirs and a large dam) in a Mediterranean catchment under climate change, finding that future annual average water stored in both reservoir types is projected to decline by 6.3% by the end of the century due to reduced inflows and enhanced evaporation.
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Shirgholami et al. (2025) Tracing the Dust: Two Decades of Dust Storm Dynamics in Yazd Province from Ground-Based and Satellite Aerosol Observations
This study investigated the spatiotemporal dynamics of dust storms in Yazd, Iran, from 2003 to 2022, revealing an increasing trend in dusty days and aerosol load, primarily affecting central and northern regions, driven by a combination of natural and anthropogenic factors.
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Yang et al. (2025) Phenology-Guided Wheat and Corn Identification in Xinjiang: An Improved U-Net Semantic Segmentation Model Using PCA and CBAM-ASPP
This study developed an improved U-Net semantic segmentation model, integrating Principal Component Analysis (PCA), a Convolutional Block Attention Module (CBAM), and an enhanced Atrous Spatial Pyramid Pooling (ASPP) module, guided by phenological analysis, to accurately identify wheat and corn in Xinjiang, achieving an overall accuracy of 90.91%.
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You et al. (2025) Decadal trends in global grassland growth peaks and their drivers since the 1980s
This study reveals a widespread increase in global grassland growth peaks from 1982 to 2021, but identifies a significant reversal between 1998 and 2009 across 64% of regions, primarily driven by a global-scale decadal drought.
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Zhu et al. (2025) In-Flight Radiometric Calibration of Gas Absorption Bands for the Gaofen-5 (02) DPC Using Sunglint
This study presents a novel method for the in-flight radiometric calibration of gas absorption bands on the Gaofen-5 (02) satellite's Directional Polarimetric Camera (DPC), demonstrating robust performance with total uncertainties of 3.01% for oxygen and 3.45% for water vapor bands.
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Monte et al. (2025) Skilful seasonal predictions of droughts in the Mediterranean region
This study investigates the skill of seasonal prediction systems (SPSs) in forecasting meteorological drought in the Mediterranean region using SPI3 and SPEI3 indices. It demonstrates that optimized multi-model ensembles (MME) significantly enhance drought prediction skill, outperforming individual systems and climatology across most of the region.
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Zhou et al. (2025) A Database of High-Resolution Meteorological Drought Comprehensive Index Across China for the 1951–2022 Period
This study developed a high-resolution (0.1 degrees) daily Meteorological Drought Comprehensive Index (MCI) dataset for China from 1951 to 2022, alongside multi-timescale Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) datasets. The MCI dataset demonstrates superior accuracy in reflecting shallow soil moisture changes in eastern China compared to single-time-scale indices, providing a robust tool for drought monitoring and analysis.
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Kashyap et al. (2025) Ecological droughts increased in India with changing Indian summer monsoon and human interventions
This study investigates ecological droughts, their drivers, and implications in India during the Indian Summer Monsoon (2000–2019) using remote sensing and machine learning. It finds that ecological droughts are increasing in ecologically fragile pristine forests and croplands, primarily driven by meteorological aridity (23.9%) and ocean warming (18.2%), leading to declining vegetation health.
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Archer et al. (2025) Characteristics of gauged abrupt wave fronts (walls of water) in flash floods in Scotland
This study characterizes abrupt wave front floods (AWFs) in Scotland using gauged river level and discharge data, revealing their widespread occurrence, downstream intensification, and the inadequacy of traditional flood forecasting models to represent their distinct hazard to human life.
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Chakraborty et al. (2025) Game-theoretic diagnostic framework for monsoon climate stability: quantifying internal dynamics and external forcing
This study introduces a novel game-theoretic framework to diagnose the intrinsic stability of a local climate system versus the influence of external forcing. It reveals a profound divergence between a theoretical Evolutionary Game Theory (EGT) model, which predicts a neutral equilibrium, and a standard Markovian model that accurately forecasts the empirically observed, skewed climate, demonstrating the overwhelming governance of external drivers.
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Wang et al. (2025) Spatially synchronized structures of global hydroclimatic extremes
This study develops DOMINO-SEE, a multilayer event-based complex climate network framework, to analyze global synchronizations of meteorological droughts, pluvials, and drought-pluvial 'seesaw' extremes using 67 years of precipitation reanalysis data. It reveals pronounced spatial asymmetries in teleconnected synchronizations, dominated by oceanic regions and southern mid-latitudes, and highlights significant cross-hemisphere seesaw patterns affecting global breadbasket regions.
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Sourp et al. (2025) Assessment of Snow Cover Fraction Parameterizations for High Resolution Snowpack Reanalyses
This study assesses the performance of various snow cover fraction (SCF) parameterizations within a 100 m resolution data assimilation framework in a mountainous region, finding that a simple asymptotic parameterization consistently performs best for improving high-resolution snow depth estimates through remote sensing SCF assimilation.
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Jiang et al. (2025) Assessing the Simulation of Ephemeral-Snow Duration in the Tibetan Plateau
This study evaluates the capability of a land surface model to simulate ephemeral snow duration over the Tibetan Plateau's interior basins using optimized atmospheric forcing data, revealing systematic biases including overestimation of short snow durations and underestimation of long durations, and proposing a physical scheme adjustment for improvement.
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Chen et al. (2025) Generating high accuracy multi-layer soil moisture at daily scale in the black soil region of China
This study developed and validated a high-accuracy, multi-layer soil moisture dataset (DMSM) for the black soil region of China, spanning 16 years (2008–2023) at a daily, 2 km resolution. The dataset, generated using an enhanced Community Land Model 3.5, demonstrated excellent performance against in-situ observations and effectively captured soil moisture dynamics, providing crucial data for agricultural management.
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Chang et al. (2025) Historical evolution and future trend of meteorological drought in the upper Yangtze River basin
This study analyzed historical (1961-2018) and projected future (2019-2099) meteorological drought trends in the upper Yangtze River basin using SPI and SPEI and CMIP6 models, finding a historical intensification of droughts post-2000 and a projected transition to significantly drier conditions with more frequent, longer, and more intense droughts after 2040 under higher emission scenarios.
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Zubair et al. (2025) Agricultural drought forecasting using remote sensing: A hybrid modeling framework by integrating wavelet transformation and machine learning techniques
This study developed an innovative hybrid modeling framework integrating wavelet transform preprocessing with ensemble machine learning models (XGBoost, AdaBoost, Random Forest) to enhance agricultural drought prediction. The framework significantly improved prediction accuracy, with XGBoost achieving the highest performance (R² = 0.964) in forecasting the Vegetation Health Index (VHI).
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Silver et al. (2025) rOPTRAM: An R package for satellite-derived soil moisture in rangelands using the OPTRAM model
This paper introduces rOPTRAM, an R package that implements the OPtical TRapezoid Model (OPTRAM) for satellite-derived soil moisture estimation in rangelands. The package streamlines image acquisition, automates trapezoid edge delineation with multiple curve fitting options, and is validated across diverse rangeland sites.
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Kim et al. (2025) Hyperspectral Remote Sensing and Artificial Intelligence for High-Resolution Soil Moisture Prediction
This study developed a drone-based hyperspectral approach using visible and near-infrared reflectance to estimate gravimetric soil water content. An artificial neural network model achieved a high coefficient of determination (0.9557), demonstrating accurate and reproducible mapping suitable for operational decision support.
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Mahla et al. (2025) Variability of heatwave and meteorological features associated with exceptionally hot summer 2022 over Rajasthan, India
This study analyzed the spatial and temporal variability of heatwaves (HWs) and associated meteorological features over Rajasthan, India, from 1969 to 2022, revealing a rapid increase in HW frequency and intensity, particularly in western and southeastern regions, with the exceptionally hot summer of 2022 linked to persistent anticyclonic circulation and positive geopotential height anomalies.
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Pérez‐Alarcón et al. (2025) Unveiling the Role of Mediterranean Cyclones in North Africa’s Precipitation
This study quantifies the contribution of Mediterranean cyclones (MCs) to precipitation in North Africa (NA) from 1980 to 2024, revealing that MCs account for approximately 9% of total annual precipitation in the region, with significant seasonal and spatial variability.
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Abidi et al. (2025) Risk-sensitive climate trend detection in the Medjerda High Valley, Tunisia: application of the modified crossing empirical trend analysis (MCETA)
This study applied the Modified Crossing Empirical Trend Analysis (MCETA) to assess temperature and rainfall changes in Tunisia's Medjerda High Valley, revealing a strong warming trend and spatially heterogeneous precipitation patterns with distinct drought and flooding risks at different stations.
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Vidmar et al. (2025) Climate Change and the Escalating Cost of Floods: New Insights from Regional Risk Assessment Perspective
This study evaluates the rising flood risk and damage potential in the lower Vipava River valley under various climate change scenarios, revealing that expected annual damage could more than double under the most extreme scenario, underscoring significant economic consequences.
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Li et al. (2025) Subseasonal prediction of early summer precipitation in the middle and lower reaches of the Yangtze River Basin based on circulation classification
This study develops and evaluates Self-Organizing Map (SOM)-based statistical downscaling models to improve subseasonal precipitation predictions in the middle and lower reaches of the Yangtze River Basin (MLYRB) by establishing nonlinear relationships between circulation patterns and precipitation probability distributions. The models significantly enhance the accuracy and temporal predictability of early summer subseasonal precipitation, showing strong potential for operational regional climate prediction.
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Devitt et al. (2025) Spatiotemporal changes in UK heavy rainfall events not captured by intensity-based methods
This study investigates the evolving spatiotemporal characteristics of heavy rainfall events across the UK using a high-resolution, convection-permitting ensemble. It reveals that future winter events become more localized with increased peak intensities, while summer events expand spatially, leading to greater total precipitation volumes, highlighting changes beyond simple intensity shifts.
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Argañaraz et al. (2025) Building a High‐Resolution Climate Gridded Dataset in Complex Terrain: Validating Different Methods in the Abruzzo Region in Italy
This study compares different interpolation methods to create high-resolution daily gridded maps of precipitation and temperature for Abruzzo, central Italy, and validates the resulting local dataset (ADAMO) against global datasets. It finds that universal kriging performs best, and the locally derived ADAMO dataset significantly outperforms global datasets in regions with high topographic variability.
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Hai et al. (2025) A Novel Feature Engineering Method for Rainfall Prediction
This paper introduces a novel feature engineering methodology designed to extract complex relationships from primary data for rainfall prediction. The proposed method significantly improves the performance of various machine learning models compared to using original features.
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Goelzer et al. (2025) Interactive coupling of a Greenland ice sheet model in NorESM2
This study describes the first interactive coupling of the Greenland Ice Sheet model (CISM) within the Norwegian Earth System Model (NorESM2), detailing the model setup and initialization procedure. Experiments under historical and high-emission future scenarios reveal a limited impact of dynamic ice sheet changes on the global climate response compared to a fixed ice sheet simulation.
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Chakri et al. (2025) Spatial Bias Correction of ERA5_Ag Reanalysis Precipitation Using Machine Learning Models in Semi-Arid Region of Morocco
This study aimed to correct ERA5_Ag reanalysis precipitation data using machine learning models and observational data in the Tensift basin, Morocco. It achieved significant improvements in precipitation accuracy, with R2 values between 0.80 and 0.90, and generated 42-year corrected raster maps for water resource management.
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Lausch et al. (2025) Monitoring Agricultural Land Use Intensity with Remote Sensing and Traits
This review synthesizes existing definitions and methods for monitoring agricultural land use intensification (A-LUI), proposing a novel remote sensing (RS)-based taxonomy of indicators and an integrative framework to enable transparent, standardised, and globally comparable assessments.
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Shaw et al. (2025) Moist Adiabatic Scaling Explains Mean and Fast Upper‐Level Jet Stream Wind Response to Climate Change
This paper derives a moist adiabatic scaling that explains the observed increase in upper-level jet stream strength and shear under climate change, demonstrating that the response is primarily driven by the increase in surface moisture gradient following the Clausius-Clapeyron relation.
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Jiao et al. (2025) Reducing uncertainty in surface solar radiation projections over the Northern Hemisphere using a hierarchical emergent constraint: Insights from CMIP6 and observation reconstruction
This study addresses significant biases in CMIP6 model projections of surface solar radiation (SSR) over the Northern Hemisphere by proposing a Hierarchical Emergent Constraint (HEC) framework, which substantially reduces projection uncertainty and improves reliability for future solar energy assessments.
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Wang et al. (2025) Unveiling the accuracy of global GPP products in data-scarce mountain ecosystems of Southwest China
This study evaluated four global Gross Primary Production (GPP) products (BESS, GOSIF, MOD17, VPM) against eddy covariance observations from 11 flux towers in data-scarce mountain ecosystems of Southwest China. It found that GOSIF had the highest correlation but consistently overestimated GPP, while BESS showed the lowest RMSE and better captured interannual variations, highlighting the need for improved model parameterization in complex regions.
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Han et al. (2025) Connection of Autumn North Asian Snow With Mid‐Winter Tibetan Plateau Snow
This study detects a teleconnection where excessive October-November North Asian snow leads to a weakened stratospheric polar vortex, which subsequently causes an anomalous anticyclone over the Tibetan Plateau, resulting in decreased January snowfall and snow depth.
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Silva et al. (2025) A hydrological model to assess current and future freshwater availability: application to climate change impacts on hydrology in the Amazon River Basin through mid-century
This study developed and applied an enhanced Hydro-BID hydrological model to assess current and future freshwater availability in the Amazon River Basin (ARB) through mid-century under various climate change scenarios. The findings project a predominant decrease in freshwater availability across the ARB, with significant spatial variability and intensified impacts under high-emissions scenarios, particularly during low-water months.
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Singh et al. (2025) Multi-scale assessment and entropy-MCDM framework for evaluating reanalysis precipitation datasets over Indian basins
This study systematically evaluates four reanalysis precipitation datasets (ERA5, IMDAA, MERRA-2, CFSR/CFSv2) across India using a multi-scale, multi-metric, and spatially adaptive Shannon Entropy-TOPSIS framework, finding ERA5 to be the most consistent and IMDAA strong in monsoon/mountainous regions.
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Kumwenda et al. (2025) Evaluation of hydrological response to land use land cover changes of Lufilya catchment
This study conducted the first hydrological modelling assessment of Malawi's Lufilya catchment, revealing that significant forest cover loss (73.3%) between 1994 and 2023 led to substantial increases in runoff (up to 39%) and amplified flood risks.
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Veedu et al. (2025) Flood Forecasting Unveiled: Harnessing the Power of Sentinel-1A Imagery and ESA World Cover Through Multi-data Integration
This study investigates the integration of Sentinel-1A Synthetic Aperture Radar (SAR) imagery and ESA World Cover maps with deep neural networks to improve flood prediction accuracy, achieving 85.79% accuracy by combining these multi-source data.
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Lin et al. (2025) Monitoring surface water in floodplains by satellites: Progress, challenges, and perspectives
This review systematically summarizes advancements in satellite remote sensing for monitoring floodplain hydrological variables, identifies current challenges, and proposes a future multi-source, multi-variable, and automated framework for improved global-scale monitoring.
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Zhang et al. (2025) Comprehensive heatwave magnitude index considering the effect of indicator equilibrium
This paper proposes a Comprehensive Heatwave Magnitude Index (CHMI) that integrates Group Utility and Equilibrium Utility to account for both aggregate impact and indicator equilibrium, revealing that high-magnitude heatwaves exhibit greater disequilibrium with distinct spatial patterns in dry versus wet regions.
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Fu et al. (2025) Response of dry-wet abrupt alternation to precipitation variation in the Hailar River Basin, northern China
This study investigates dry-wet abrupt alternation (DWAA) events in the Hailar River Basin (1980–2019) using a novel Soil Moisture Concentration Index (SMCI) and the VIC hydrological model. It reveals that DWAA driving mechanisms are spatially heterogeneous, shifting from terrestrial factors upstream to meteorological factors downstream, with an overall increasing intensity of dry-wet transitions.
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Han et al. (2025) Impacts of Aerosol Optical Depth on Different Types of Cloud Macrophysical and Microphysical Properties over East Asia
This study investigates the impacts of aerosols on the macro- and microphysical properties of different cloud types over East Asia using nine years of satellite and reanalysis data, revealing pronounced cloud-type dependent effects and significant aerosol influence independent of meteorological conditions.
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Amestoy et al. (2025) Integrated river basin assessment framework combining probabilistic streamflow reconstruction, Bayesian bias correction, and drought storyline analysis
This study develops a generalizable framework for assessing water availability and drought vulnerability in complex river basins. Applied to the Delaware River Basin using a reconstructed 1960s drought, the framework demonstrates significant vulnerabilities for New York City's drinking water supply and highlights tensions with saltwater intrusion risks under modern management.
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Ndlovu et al. (2025) Enhancing the estimation of equivalent water thickness in neglected and underutilized taro crops using UAV acquired multispectral thermal image data and index-based image segmentation
This study evaluated the use of UAV-acquired multispectral and thermal imagery, combined with index-based segmentation, to estimate the equivalent water thickness (EWT) of taro crops in smallholder farmlands. The findings demonstrate that incorporating thermal data significantly improves EWT prediction accuracy, with the Excess Green minus Excess Red (ExGR) technique proving highly effective.
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Zheng et al. (2025) Biases of Sentinel-5P and Suomi-NPP Cloud Top Height Retrievals: A Global Comparison
This study evaluates Sentinel-5P and Suomi-NPP Cloud Top Height (CTH) products against CloudSat/CALIPSO, revealing distinct and complementary systematic biases for different cloud types and layers, suggesting potential for improved CTH accuracy through product combination.
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Achite et al. (2025) Meteorological drought variability and its characterization in sub humid mediterranean region, the Wadi Isser basin of Algeria from 1970 to 2017
This study comprehensively analyzes meteorological drought variability and persistence in the Wadi Isser basin, Algeria, from 1970 to 2017 using the Standardized Precipitation Index (SPI), various trend tests, and Hurst exponent estimation. It reveals decreasing short-term droughts in northern/southern regions, increasing long-term droughts in central/eastern regions, and identifies a significant climate shift around 1997-1998 as a key modulator of drought conditions and persistence.
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Rezaei et al. (2025) Modelling phosphorus and potassium dynamics in drip-irrigated potato systems using coupled agro-hydrological model
This study combined field experiments with coupled DSSAT and HYDRUS-2D models to investigate phosphorus (P) and potassium (K) dynamics in drip-fertigated potato systems under semi-arid conditions, revealing limited vertical mobility for both nutrients but greater lateral movement for K, and demonstrating that frequent, low-concentration fertigation significantly enhances nutrient availability and uptake efficiency.
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Du et al. (2025) Quantile‐Based Fire Weather Index Better Informs Detection and Variability of Wildfire Risks in China
This study developed a quantile-based Fire Weather Index (QFWI) for China, demonstrating its superior accuracy in indicating wildfire occurrences compared to the standard FWI. It revealed that China's wildfire risks exhibit inter-decadal variability, decreasing pre-1990 due to reduced wind speed and increasing post-1990 primarily due to rising temperature and decreasing humidity, signaling heightened future risks.
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Fashoto et al. (2025) Anticipating drought: enhancing prediction models and assessing environmental impact in Eswatini’s Maguga Basin
This study developed and compared drought prediction models for Eswatini's Maguga Basin, finding that a Genetic Algorithm (GA) optimized Long Short-Term Memory (LSTM) model significantly outperformed the Auto-regressive Integrated Moving Average (ARIMA) model in forecasting the Standardized Precipitation Evapotranspiration Index (SPEI) and Maguga Dam water levels. The research provides a robust tool for early drought warning and water resource management in the region.
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Yang et al. (2025) Probabilistic assessment for drought risk: Integrating drought hazard, ecological sensitivity, economic vulnerability, and their coupling coordination
This study developed a robust drought risk assessment framework integrating Eco-DRR capacity and the coupling coordination of drought risk components. Applied to county-level cities in China's three northeastern provinces (2000-2022), the framework revealed generally low/moderate drought hazards, high ecological sensitivity, low economic vulnerability, and low/moderate overall drought risk, with specific spatial patterns and probabilistic classifications for cities.
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Bai et al. (2025) The Shrinkage of Lakes on the Semi-Arid Inner Mongolian Plateau Is Still Serious
This study developed a remote-sensing-based framework to monitor long-term lake water storage (LWS) changes in the semi-arid Inner Mongolian Plateau (IMP), revealing a net decline of 1.21 Gt between 2000 and 2021 with distinct regional and temporal shifts driven by both climatic factors and anthropogenic activities.
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Tripathy et al. (2025) Spatiotemporal dynamics of surface and rootzone soil moisture droughts
This study employed a Complex Network framework and event synchronization to analyze summer surface and root-zone soil moisture droughts across the contiguous United States, identifying the Ohio River Valley as a central drought hub and revealing a west-to-east propagation pattern with stronger spatial coherence in root-zone soil moisture.
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Feng et al. (2025) A novel deep learning approach for high-precision rainfall intensity inversion using urban surveillance audio
This paper introduces MS-TF RainNet, a novel deep learning framework for high-precision rainfall intensity inversion using urban surveillance audio, achieving an RMSE of 0.7708 mm/h and outperforming a Transformer-based baseline by 14.94% in RMSE under denoised conditions.
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Florea et al. (2025) The Impact of Climate Change on Eastern European Viticulture: A Review of Smart Irrigation and Water Management Strategies
This review synthesizes the impacts of climate change on Eastern European viticulture, highlighting increased water stress and phenological shifts. It emphasizes the critical role of integrating climate adaptation measures with smart irrigation and water management strategies, such as Regulated Deficit Irrigation (RDI) and sensor-based systems, to enhance vineyard resilience and sustainability.
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Jiang et al. (2025) Crop water origins and hydroclimate vulnerability of global croplands
This study uses satellite-derived water isotope observations and physical models to trace atmospheric moisture origins for global rain-fed crops, revealing that regions heavily dependent on land-originating moisture (fraction of land-originating rainwater, f ≥ 36%) are significantly more vulnerable to hydroclimate stress and drought, impacting major staple crops.
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Liu et al. (2025) Temporal persistence of postfire flood hazards under present and future climate conditions in southern Arizona, USA
This study investigates the temporal evolution of post-fire hydrologic parameters and quantifies changes in flash flood peak discharges under future climate conditions in a 49.4 km² watershed in southern Arizona. It finds that while soil hydraulic properties recover over three post-fire years, climate change-driven rainfall intensification will significantly increase the magnitude and persistence of post-fire flood hazards, potentially doubling the likelihood of 100-year floods by mid-century under medium emissions scenarios.
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Khadke et al. (2025) Vapor pressure deficit dominates sap flow variability across forest biomes
This study investigates the causal drivers of sap flow (SAPFlow) across 15 global forest sites using information theory-based process networks and wavelet analysis. It finds that vapor pressure deficit (VPD) is the dominant causal driver of SAPFlow variability across all forest types, forming a coupled system with soil water content (SWC) mediated by land-atmosphere feedback.
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Neagoe et al. (2025) Hybrid LSTM-ARIMA Model for Improving Multi-Step Inflow Forecasting in a Reservoir
This study proposes a novel hybrid LSTM-ARIMA model for short-term reservoir inflow prediction, demonstrating significant improvements in accuracy (R² from 0.93 to 0.96, RMSE from 9.74 m³/s to 6.94 m³/s for one-day-ahead forecasts) over standalone LSTM, particularly for multi-step predictions.
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Bindajam et al. (2025) Advanced predictive modelling of urban expansion and land surface temperature dynamics using multi-scale machine learning approaches
This study systematically quantifies urban expansion, assesses its morphological and thermal impacts, and forecasts future land surface temperature (LST) dynamics in Lucknow, India, from 1991 to 2021, revealing a significant LST increase linked to urban morphology.
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Lei et al. (2025) Synergizing machine learning and modified physical models for hydrology modeling: A case study of modified SIMHYD and TANK models
This study investigates the effectiveness of hybrid hydrological models (HMs) that combine machine learning with original and modified physical models (SIMHYD, TANK) across 569 catchments in the United States. It finds that HMs with modified physical layers offer superior runoff predictability and improved reasoning ability for evaporation and baseflow compared to those with original physical models.
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Kapoor et al. (2025) QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification
This paper introduces QDeepGR4J, a quantile regression-based ensemble extension of the DeepGR4J hybrid rainfall-runoff model, to quantify uncertainty in multi-step streamflow predictions and identify extreme flow events. The framework significantly improves predictive accuracy and uncertainty interval quality, demonstrating its suitability as an early warning system for floods.
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D’Ercole et al. (2025) Using daily vegetation and precipitation products to study drought events in the Horn of Africa
This study assesses the capability of high-frequency daily Earth observations (vegetation and precipitation) to detect and monitor meteorological and agricultural drought events in the Horn of Africa, revealing the benefits of daily resolution for capturing short-term wet-dry spells and identifying optimal precipitation products for the region.
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Lin et al. (2025) Characteristics of Extratropical Cyclones and Associated Precipitation Across the Huang‐Huai River Basin, East China
This study analyzed extratropical cyclone (ETC) characteristics and precipitation patterns in the Huang-Huai River Basin (HHRB) from 1979 to 2022, finding that ETCs contribute approximately 60% of the region's total precipitation, with distinct types playing crucial roles in summer floods and droughts.
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Martínez-Castro et al. (2025) Impact of Extreme Droughts on the Water Balance in the Peruvian–Ecuadorian Amazon Basin (2003–2024)
This study assesses the impact of extreme droughts on the surface and atmospheric water balance of the Peruvian Amazon basin from 2003 to 2024, identifying four extreme drought years characterized by major precipitation deficits, reduced runoff and total water storage, and significant imbalances in both surface and atmospheric water balances.
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Lanjie et al. (2025) Efficient urban flood surface reconstruction: integrating deep learning with hydraulic principles for sparse observations
This study proposes a novel deep learning framework, Sparse-Point Learning and Interpolated Surface Reconstruction (SPIR), to efficiently and accurately simulate high-resolution urban flood inundation by integrating a lightweight neural network with hydrodynamic-informed interpolation. The framework significantly reduces computational time while maintaining high prediction accuracy compared to traditional hydrodynamic models.
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Ma et al. (2025) Comprehensive drought detection, spatiotemporal variations, and attribution across different agricultural climate zones in Eastern China using a copula-based drought index
This study developed a novel Copula-based Multivariate Standardized Drought Index (MSDI) to assess drought spatiotemporal variations and attribution across different agricultural climate zones in Eastern China from 2001–2020. Findings reveal a general worsening of drought conditions characterized by a southward shift and increased frequency, intensity, and severity of short-term events, with dominant drivers varying regionally.
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Li et al. (2025) Assessing the Impact of Land‐Use Types on Historical Dryness/Wetness Trends Over Global Land Areas
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Wu et al. (2025) Evaluation of Present and Future Relationships Between Daily Precipitation and Temperature in Eastern China
This study examines historical and future precipitation-temperature relationships over eastern China, assessing the performance of CMIP6 models and ERA5 reanalysis, and projecting increased sensitivity of extreme precipitation to warming, highlighting rising flood risks.
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Moore (2025) Targeted decomposition of tornado records reveals long-term trends in the Great Plains and Southeast United States
This study developed a novel domain-informed decomposition framework to isolate long-term trends in tornado activity in the Great Plains and Southeast United States, revealing a robust decline in the Great Plains and an increase in the Southeast after explicitly filtering out teleconnection-related variability and noise.
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Çıtakoğlu et al. (2025) Multiscale drought forecasting via temporal–spectral decomposition and machine learning integration
This study developed a novel multiscale drought forecasting framework by integrating temporal–spectral decomposition techniques with machine learning models to predict the Multivariate Standardized Drought Index (MSDI) at 1-, 3-, and 6-month time scales for the Sakarya region, Türkiye, finding the TQWT-GPR hybrid model to be the most accurate.
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Tan et al. (2025) Seasonal Prediction of Summer Extreme High Temperature Days in Western North America Based on the Dynamic Origins of the Cross-Pacific Rossby Waves
This study explores the physical processes controlling the three leading modes of summer extreme high-temperature days (EHDs) over western North America (WNA) and establishes a physics-based empirical model (PEM) that effectively predicts their spatial pattern based on cross-Pacific Rossby wave trains.
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Zhang et al. (2025) The effects of increased evapotranspiration on precipitation in the Yellow River Basin
This study quantifies the impact of increased evapotranspiration (ET) on precipitation in the Yellow River Basin (YRB), finding that ET significantly contributes to local and downwind precipitation, partially offsetting the observed ET increase.
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Chivangulula et al. (2025) The Drought Regime in Southern Africa and Recent Climate Change: Long-Term Trends in Climate Elements, Drought Indices and Descriptors
This study assessed long-term climate trends and drought hotspots in Southern Africa (1971-2020) using ERA5 data, revealing widespread increasing temperatures, decreasing precipitation, and expanding drought risk in agriculturally vital regions.
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Teng et al. (2025) A dual-band parametric method for angular normalization of land surface thermal radiation for single-angle thermal infrared image
This study develops a dual-band parametric angular normalization (DPAN) method to correct the thermal radiation directionality (TRD) effect in single-angle thermal infrared remote sensing data, demonstrating its effectiveness in reducing angular effects and enabling operational land surface temperature product normalization without requiring multi-angle or multi-temporal observations.
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Kaman et al. (2025) Prospects of training system and deficit irrigation for increasing yield and water use efficiency of pitaya (Hylocereus spp.) in the Mediterranean region
This study investigated the combined effects of three training systems and four deficit irrigation levels on pitaya yield, water use efficiency, and fruit quality under greenhouse conditions. It found that the DI75 irrigation treatment (75% of full irrigation) combined with Pole or Inverted U training systems optimized yield and water use efficiency.
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Cen et al. (2025) Improving Remote Sensing Ecological Assessment in Arid Regions: Dual-Index Framework for Capturing Heterogeneous Environmental Dynamics in the Tarim Basin
This study introduces ARSEI and CoRSEI to improve ecological assessment in arid regions, demonstrating ARSEI's enhanced sensitivity to desert dynamics and CoRSEI's ability to capture heterogeneous environmental changes and long-term trends in the Tarim Basin from 2000 to 2023. The findings highlight the importance of differentiated ecological modeling for targeted ecosystem management in hyper-arid environments.
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Xue et al. (2025) A study of the dependence between soil moisture and precipitation in different ecoregions of the Northern Hemisphere
This study quantifies the spatiotemporal distribution and driving mechanisms of the nonlinear dependence between soil moisture and precipitation across different ecoregions of the Northern Hemisphere. It reveals that negative dependence is widespread, particularly in surface soil, and is primarily driven by land surface temperature and air temperature–gross primary production interactions, with distinct seasonal and annual patterns influenced by freeze–thaw cycles and long-term climate variability.
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Brigode et al. (2025) Using century-long reanalysis and a rainfall-runoff model to explore multi-decadal variability in catchment hydrology at the European scale
This study evaluates the capacity of century-long global reanalyses (NOAA 20CR, ERA-20C) to simulate multi-decadal catchment hydrology across over 2000 European catchments using a rainfall-runoff model, finding reasonable performance, especially for mean flows, and revealing significant alternating wet and dry periods.
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Modi et al. (2025) Understanding the relationship between streamflow forecast skill and value across the western US
This study investigates the complex relationship between seasonal streamflow forecast skill and economic value in unmanaged snow-dominated basins across the western US, finding that while forecast skill is explained by errors in mean and variability, forecast value is more influenced by irregular error structures impacting categorical measures like hit and false alarm rates, meaning high skill does not always translate to high value.
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Estey et al. (2025) Spaceborne estimates of canopy temperature and soil moisture predict daily and annual subalpine tree growth
This study evaluated the potential of satellite-based canopy temperature (TC) and soil moisture (SM) to predict daily and annual tree growth dynamics in a subalpine forest. The models successfully predicted daily stem radial growth with moderate precision (R² = 0.56) and annual growth performance with high precision (R² = 0.76), demonstrating the efficacy of thermal satellite remote sensing for tracking tree growth.
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Li et al. (2025) Atmospheric circulation regimes modulating Eurasian winter decadal cooling
This study identifies three comparable decadal Eurasian winter cooling episodes since 1901, demonstrating that the Scandinavian pattern (SCAND) and North Atlantic Oscillation (NAO) are intrinsic atmospheric regimes modulating these events, with a projected 30% likelihood of another analogous cooling episode by 2050 due to internal variability.
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Yang et al. (2025) Divergent Drought Paradigms and Their Driving Mechanisms in the Yangtze and Yellow River Basins
This study compares drought patterns and their underlying mechanisms in China's Yangtze and Yellow River Basins (1961-2022), revealing the Yangtze experiences high-frequency, short-duration droughts driven by precipitation deficits, while the Yellow River faces low-frequency, long-duration droughts amplified by evaporative demand.
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Hafeez et al. (2025) Mapping of potential storages and rainwater harvesting sites in arid region of Indus basin using analytical hierarchy technique
This study mapped potential rainwater harvesting and storage sites in the 23,204 square kilometer Pothowar region of the Indus basin using GIS and the Analytical Hierarchy Process (AHP). It found that over 89% of the region is moderately to very highly suitable for rainwater harvesting, proposing specific mini dam locations to address water scarcity.
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Bhattarai et al. (2025) Ensemble learning for enhancing critical infrastructure resilience to urban flooding
This study enhances urban road-network flood prediction in Washington, D.C., using ensemble machine learning models trained on crowd-sourced flood datasets, demonstrating that stacked super-ensemble learning significantly improves prediction accuracy (0.84) and identifies critical infrastructure exposure to high flood likelihood zones.
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Gonzalez-Mora et al. (2025) A climate-informed statistical framework to indirectly estimate trends in future seasonal high flows in snow-dominated watersheds using short-term climate variability indices
This study developed a climate-informed statistical framework to indirectly estimate future seasonal high flow trends in snow-dominated watersheds using short-term climate variability indices (SCIs). It found that future high flow variability can be anticipated using highly correlated SCIs, with a single SCI explaining at least 50% of the variability.
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Szturc et al. (2025) Can we reliably estimate precipitation with high resolution during disastrously large floods?
This study evaluates the reliability of various real-time and offline precipitation estimation techniques during a disastrous flood in the upper and middle Odra River basin in September 2024. It finds that rain gauge measurements, radar data adjusted to rain gauges, and multi-source estimates (RainGRS) provide the most reliable high-resolution precipitation fields for flood protection, especially for extreme events in mountainous areas.
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Alvarenga et al. (2025) Meteorological Droughts in the Paraopeba River Basin: Current Scenarios and Future Projections
This study evaluated the performance of CMIP6 climate models in projecting meteorological droughts in the Paraopeba River Basin using SPI and SPEI indices. The findings indicate a significant intensification of droughts throughout the 21st century, particularly under the pessimistic SSP585 scenario, highlighting the critical role of rising temperatures in exacerbating water deficits.
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Ssembajwe et al. (2025) Assessment and Validation of FAPAR, a Satellite-Based Plant Health and Water Stress Indicator, over Uganda
This study assessed and validated satellite-based Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) as a plant health and water stress indicator over Uganda, finding it to be a robust proxy with strong correlations to established drought and water stress indices. The research revealed increasing photosynthetic activity and FAPAR-centered stress across significant portions of the country, influenced by climatic and land use factors.
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Patel et al. (2025) Advances in remote sensing and GIS applications in watershed hydrology: a systematic review
This systematic review analyzes the advancements in remote sensing (RS) and Geographic Information Systems (GIS) applications in watershed hydrology from 2000 to 2024, with a detailed focus from 2018, highlighting the increasing integration of artificial intelligence (AI) and multi-source modeling for improved hydrological predictions.
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Schepen et al. (2025) Forecasting agricultural drought: the Australian Agricultural Drought Indicators
This study evaluates the skill of the Australian Agricultural Drought Indicators (AADI) system, which forecasts agricultural drought using biophysical and agro-economic models driven by the ACCESS-S2 climate model. It demonstrates that antecedent landscape conditions significantly enhance predictive skill for crop yields, pasture growth, and farm profit, providing earlier and more confident drought warnings than rainfall deficits alone.
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Elbeltagi et al. (2025) An interpretable machine learning approach based on SHAP, Sobol and LIME values for precise estimation of daily soybean crop coefficients
This study developed and evaluated interpretable machine learning models for precise daily soybean crop coefficient (Kc) estimation in Upper Egypt, demonstrating that the Extra Tree model achieved the highest accuracy (r = 0.96, NSE = 0.93, RMSE = 0.05, MAE = 0.02) and consistently identified antecedent Kc and solar radiation as the most influential variables. The research provides a transparent framework for enhancing irrigation scheduling and sustainable water management in arid regions.
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Papastamkou et al. (2025) Assessing impacts on irrigated agriculture using an ecosystem-based approach in a Mediterranean reservoir/lake
This study investigates the impact of ecosystem-based water management and climate change on irrigation water deficits in Lake Kerkini, Greece, using the MIKE HYDRO Basin model, revealing significant increases in deficits under ecological and climate change scenarios.
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Ochege et al. (2025) Enhancing reference crop evapotranspiration prediction in arid regions: A stacking ensemble learning approach for the Amu Darya basin
This study developed a novel stacking ensemble (stkENS) machine learning model, hybridizing Decision Trees, Generalized Linear Models, K-Nearest Neighbours, and Support Vector Regression, to enhance reference crop evapotranspiration (ETo) prediction in the data-limited Amu Darya basin. The stkENS model significantly outperformed individual base learners, achieving high accuracy (R² > 0.96, RMSE: 0.65 mm d⁻¹) with fewer inputs, providing robust ETo estimates crucial for sustainable water management in arid croplands.
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Andréassian et al. (2025) Time shift between precipitation and evaporation has more impact on annual streamflow variability than the elasticity of potential evaporation
Using 4122 catchments from four continents, this study investigates how annual streamflow variability depends on climate variables (rainfall and potential evaporation) and on the synchronicity between precipitation and potential evaporation. The analysis reveals that, across diverse climates, the time shift between precipitation and evaporation is the second most important factor, after precipitation, in explaining annual streamflow anomalies, significantly improving prediction models.
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Wu et al. (2025) Significant sensitivity of global vegetation productivity to terrestrial surface wind speed changes
This study systematically investigates the global impact of terrestrial surface wind speed changes on gross primary production (GPP). It finds a significant negative sensitivity of GPP to wind speed, primarily due to reduced atmospheric dryness and soil drying, making wind speed decline the second most important factor after rising CO2 concentrations in driving GPP increases.
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Li et al. (2025) Enhancing gridded climate products with third party weather data in a rainfall study from Western Australia
This study demonstrates the transformative potential of integrating quality-controlled third-party automatic weather station (TPAWS) data to enhance gridded climate products, specifically daily rainfall estimates in southwestern Western Australia, reducing root mean square error (RMSE) by over 15% and false no-rain rates by 30%.
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Pektaş et al. (2025) Spatio‐Temporal Change of Climate Regions in Türkiye
This study investigates climate zone shifts in Türkiye from 1954 to 2023 using high-resolution reanalysis data and clustering methods. It reveals a significant trend towards aridification, marked by the contraction of humid regions, the emergence of a new arid zone, and a substantial increase in semi-arid areas across the country.
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Lumban-Gaol et al. (2025) Peat subsidence and dynamics in Midden-Delfland, the Netherlands, from time series InSAR analysis and the SPAMS model
This study estimates and analyzes peat subsidence in the Midden-Delfland region, The Netherlands, using Sentinel-1 InSAR data and the SPAMS model. It reveals an average subsidence rate of −5.4 ± 0.7 mm/year, with irreversible subsidence strongly correlated with dry climatic conditions, particularly during drought periods.
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Abbaszadeh et al. (2025) Coupling the ParFlow Integrated Hydrology Model within the NASA Land Information System: a case study over the Upper Colorado River Basin
This study evaluates the newly coupled ParFlow-LIS/Noah-MP model in the Upper Colorado River Basin, demonstrating its enhanced capability to simulate three-dimensional groundwater flow and improve soil moisture representation in complex topography compared to standalone LIS/Noah-MP. The coupled model provides new hydrologic prediction capabilities for both surface and subsurface processes.
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Yang et al. (2025) Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S.
This study evaluated an LSTM-based data integration approach incorporating streamflow (Q) and snow water equivalent (SWE) observations to improve streamflow estimations across various lag times and timescales in the Western U.S. Integrating daily Q observations provided the most significant improvements, boosting the median Kling-Gupta Efficiency (KGE) from 0.80 to 0.96 for 1-day lagged data.
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García et al. (2025) Electrical Resistivity Tomography and 3D Modeling for Groundwater Salinity Assessment in Volcanic Islands: A Case Study in Los Cristianos (Tenerife, Spain)
This study applies Electrical Resistivity Tomography (ERT) and 3D modeling in Los Cristianos, Tenerife, to characterize groundwater salinity and marine intrusion in a volcanic island setting. The methodology effectively delineates saline horizons, providing objective criteria for sustainable borehole siting for desalination purposes.
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Matthews et al. (2025) Dynamic assessment of rainfall erosivity in Europe: evaluation of EURADCLIM ground-radar data
This study evaluates the ground radar-based EURADCLIM dataset for quantifying rainfall erosivity across Europe, finding that it initially overpredicts erosivity due to radar artifacts but significantly improves with an 80 mm h⁻¹ I30 threshold, offering unique spatial detail for soil erosion prediction.
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Maryam et al. (2025) Nonstationarity impacts on the assessment of drought conditions across diverse climate zones of Pakistan
This study quantifies the impacts of nonstationarity on drought assessment across diverse climate zones of Pakistan using the Reconnaissance Drought Index (RDI). It reveals significant zonal and temporal shifts in drought and wet conditions, with nonstationarity generally increasing drought severity in northern and agricultural plains and decreasing it in western and coastal regions during the later period (1986–2021).
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Malakouti (2025) Leveraging SHapley Additive exPlanations (SHAP) and fuzzy logic for efficient rainfall forecasts
This study introduces a hybrid machine learning framework combining a Light Gradient Boosting Machine (LGBM) classifier with a fuzzy logic system to deliver rapid, reliable, and interpretable daily rainfall forecasts using ten years of meteorological data from diverse Australian locations. The framework demonstrates superior accuracy and computational efficiency compared to conventional models, providing valuable insights for decision-makers.
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Wang et al. (2025) Vegetation cover change as a growing driver of global leaf area index dynamics
This study quantifies the contribution of vegetation cover change (VCC) to global leaf area index (LAI) dynamics over the past four decades using a data-driven framework. It finds that VCC explains 18.1 ± 5.9% of the observed global LAI increase, driven by afforestation in the Northern Hemisphere and partially offset by deforestation in the Southern Hemisphere.
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Feldmann et al. (2025) A pan-European analysis of large-scale drivers of severe convective outbreaks
This study systematically analyzes continental-scale atmospheric and land-surface conditions preceding widespread severe convective outbreaks in Europe, revealing distinct regional dynamical and thermodynamic patterns that drive these events.
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Zhang et al. (2025) Innovative trend analysis of annual and seasonal precipitation in the Pearl river basin, Southern China
This study analyzed annual and seasonal precipitation trends in the Pearl River Basin (PRB) from 1959–2018 using Innovative Trend Analysis (ITA) and classical methods, revealing complex regional and seasonal shifts, including increased high precipitation in summer and decreased low precipitation in winter, and highlighting ITA's ability to categorize these trends.
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Zhao et al. (2025) Variability in Meteorological Parameters at the Lenghu Site on the Tibetan Plateau
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Evangelista et al. (2025) Non‐Linear Influence of Reservoir Initial Condition on Flood Reduction
This study investigates the non-linear influence of initial reservoir storage on flood peak attenuation efficiency across approximately 250 large dams in Italy, revealing that dam performance significantly deteriorates with increasing flood return periods, especially when initial storage is high, and that commonly assumed full-reservoir conditions lead to overly conservative flood risk estimates.
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Zhou et al. (2025) Evaluation of the Linkage Between the Variations in Jet Stream Shape and Surface Air Temperature From CMIP6
This study evaluates the capability of CMIP6 models (AMIP and historical runs) to simulate the relationship between jet stream shapes and surface air temperature, finding that AMIP simulations generally outperform historical runs and that models capture key regional temperature anomalies linked to jet patterns and Arctic Oscillation-like circulation.
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Qin et al. (2025) Impact of impervious surface spatial morphologies on urban waterlogging: Insights from a cascade modeling chain at catchment scale
This study investigates how the spatial morphology of impervious surfaces influences urban waterlogging using a cascade modeling chain. It reveals distinct hydrological functions for different road morphologies and static obstruction by buildings, proposing an evidence-based intervention hierarchy that prioritizes road modifications for urban flood resilience.
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Himanshu et al. (2025) AI Crop Analysis and Recommendation System
This study develops an AI-powered Crop Analysis and Recommendation System that integrates machine learning with extensive environmental and historical data to provide data-driven decision support for farming, achieving over 99% prediction accuracy for crop recommendations and yield predictions.
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Troy et al. (2025) Can runoff modeled at coarse resolution simulate floods at finer resolutions? A case study over the Ohio River Basin
This study evaluates a computationally efficient flood modeling framework that couples the coarse-resolution Variable Infiltration Capacity (VIC) land surface model with a newly developed 1 km resolution kinematic wave routing model over the Ohio River Basin. The framework successfully reproduces flood characteristics, demonstrating that while sub-daily temporal resolution has minimal impact, coarser spatial resolutions lead to significant underestimation of flood peaks.
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Zhang et al. (2025) The Response of Alpine Permafrost to Decadal Human Disturbance in the Context of Climate Warming
This study investigated the spatiotemporal response of alpine permafrost in the Muri area of the Tibetan Plateau to decadal mining and climate change from 2000 to 2024, finding that human disturbance primarily caused an increase in active layer thickness without significant spatial expansion, with regional climate and terrain being major controlling factors.
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Ijegwa et al. (2025) Intelligent Web App for Flash Flood Prediction in Nigeria’s Coastal Regions
This study developed an intelligent web application using a Random Forest machine learning model to predict flash flood occurrences in Nigeria's coastal regions, achieving 96% accuracy and real-time latency of less than one second.
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Chaudhary et al. (2025) Comprehensive Analysis of State-of-the-Art Deep Learning-Based Image Fusion Systems in the Context of Remote Sensing Images
This paper provides a comprehensive review and analysis of state-of-the-art deep learning-based image fusion systems specifically applied to remote sensing images, aiming to enhance image quality by combining spatial, spectral, and temporal information. It summarizes reported deep learning techniques used across various remote sensing datasets to improve image detail and quality through fusion.
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Holtanová et al. (2025) Scenarios of Köppen-Trewartha climate types in Europe based on GCM-RCM combined projections
This study assesses projected changes in Köppen-Trewartha climate types across Europe throughout the 21st century using a novel hybrid method that combines global and regional climate model outputs. It finds a significant northward and eastward shift of climate zones, with colder types shrinking and warmer, drier types expanding, particularly in southern Europe, providing an updated CMIP6-based assessment.
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Ganjei et al. (2025) Evaluating climate change impacts on reference evapotranspiration using CMIP6 projections and machine learning in the Aras River Basin
This study evaluated future spatio-temporal trends of reference evapotranspiration (ET₀) in the Aras River Basin, Iran, using CMIP6 projections and machine learning, finding a consistent upward trend in ET₀, especially under the high-emission SSP5-8.5 scenario.
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Kukuntod et al. (2025) Escalating Drought Vulnerability Driven by Land Use Change: Insights from a GIS-Based CA-Markov and Multi-Criteria Assessment of Future Scenarios in the Lam Ta Kong Watershed
This study assesses the impact of land use transitions on drought vulnerability in the Lam Ta Kong watershed, projecting that continued urban expansion and loss of agricultural/forested land will significantly increase high drought vulnerability zones by 10.55% by 2032.
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Şerban et al. (2025) Satellite-based assessment of drought evolution and agricultural stress in Dobrogea, Romania using the Normalized Vegetation Soil Water index (NVSWI)
This study utilized a multi-indicator remote sensing approach to assess agrometeorological drought dynamics in Dobrogea, Romania, from 2001 to 2021, revealing an increased drought frequency and severity, with over 70% of the area experiencing extreme drought in 2020.
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Qadri et al. (2025) Quantifying Flood Impacts on Ecosystem Carbon Dynamics Using Remote Sensing and Machine Learning in the Climate-Stressed Landscape of Emilia-Romagna
This study evaluates flood impacts on ecosystem carbon dynamics (Net Primary Productivity and Above-Ground Biomass) in Emilia-Romagna, Italy, using remote sensing and machine learning, revealing significant short-term localized carbon losses and widespread long-term ecological degradation.
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Fazli et al. (2025) Quantifying single, compound and cascading climate extremes: Implications for agricultural resilience in California
This study quantifies the spatiotemporal dynamics of single, compound, and cascading climate extremes in California's Central Valley from 1951 to 2025, revealing increasing heatwaves and droughts, northward shifting hotspots, and differential vulnerability for almonds and grapes, which informs agricultural resilience strategies.
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Katambo et al. (2025) Understanding ENSO Teleconnections’ Influence on Drought in Southern Africa: A Machine Learning Approach
This study investigates the influence of El Niño-Southern Oscillation (ENSO)-related Sea Surface Temperature (SST) variations on drought patterns across Southern Africa using machine learning. The findings reveal SST's significant and consistent impact across all climate zones, underscoring its value for enhanced drought prediction and adaptation planning.
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Lin et al. (2025) Reanalysis-assisted AI framework for regional pan evaporation estimation in Taiwan without ground-based meteorological observations
This study develops an artificial intelligence-based framework to estimate daily pan evaporation across Taiwan without relying on ground-based meteorological station data. By integrating high-resolution reanalysis inputs with station metadata, the framework enables spatially continuous estimation of evaporation patterns, with XGBoost achieving the best performance (MAE = 0.00092 m/day, CC = 0.72, KGE = 0.58).
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Bista et al. (2025) Local-scale flood hazard projections in historically vulnerable communities
This study developed an integrated hydrologic-hydrodynamic modeling framework to project local-scale fluvial flood hazards in Jackson, Mississippi, finding a significant increase in flood risks under future climate change scenarios, disproportionately impacting vulnerable communities and critical infrastructure.
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Zhou et al. (2025) An efficient flow path traversal algorithm for watershed delineation from raster digital elevation models for multicore architectures
This study proposes a novel and efficient flow path traversal algorithm for watershed delineation from raster flow direction grids, specifically designed for multicore central processing unit (CPU) architectures, demonstrating superior performance compared to existing sequential and parallel methods.
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Zeleke et al. (2025) Using a standardized event-based tool for drought analysis: Application to propagation predictability patterns
This study develops a standardized, Python-based methodology using SWAT+ model outputs to analyze meteorological and hydrological drought propagation, revealing diverse propagation behaviors in the Awash Basin.
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Du et al. (2025) Research on Driving Forces of Spatiotemporal Patterns in Cotton Cultivation Considering Spatial Heterogeneity
This study combined the locally explained stratified heterogeneity (LESH) model with geographically weighted regression (GWR) to investigate the spatiotemporal drivers of cotton-planting patterns in the northern slope of the Tianshan Mountains (NSTM), China, from 2000 to 2020, finding that elevation, sunshine duration, slope, temperature, runoff, and gross domestic product are dominant factors, with significant spatial heterogeneity and factor interactions.
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Yamasaki et al. (2025) SCALE-RM model output used in manuscript 2025JD045671 for JGR Atmospheres
This paper presents a dataset and visualization scripts derived from the Regional Model from Scalable Computing for Advanced Library and Environment (SCALE-RM), designed for analyzing various cloud microphysical processes, radiative properties, and precipitation characteristics, including their sensitivity to model resolution and microphysical parameters.
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Rehamnia et al. (2025) Evaluating the role of dimensionality and complexity structure in time series models for precipitation simulation
This study evaluates various linear and nonlinear time series models for monthly precipitation simulation across 12 stations in Algeria, finding that the optimal model depends on input variables and station-specific precipitation dynamics rather than a universal best fit.
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Amell et al. (2025) Probabilistic Near‐Real‐Time Retrievals of Rain Over Africa Using Deep Learning
This paper introduces Rain over Africa (RoA), a public, near-real-time precipitation retrieval algorithm for the African continent based on Meteosat thermal infrared observations. RoA provides precipitation estimates with low latency and detailed uncertainty descriptions, demonstrating accuracy comparable to slower methods and improved timeliness over established products like IMERG for land regions.
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Ahmed et al. (2025) A Continental-Scale tracking for mobile drought dynamics across Africa using Multivariate drought Index Fusion
This study proposes a novel Multivariate Drought Index Fusion (MDIF) to dynamically track the spatiotemporal trajectory of mobile drought fronts across Africa from 2000 to 2024, revealing persistent drought hotspots and a dominant northeast-to-southwest propagation pathway.
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Lu et al. (2025) A micro–macro coupled approach to assess urban road traffic flood risk at the city scale
This study developed a micro-macro coupled dynamic risk assessment model, integrating hydrodynamic-hydrological, multi-agent, and system dynamics models, to evaluate urban road traffic flood risk and the effectiveness of mitigation strategies at the city scale. The model demonstrated that comprehensive mitigation measures significantly reduce dangerous vehicles and validate preemptive flood prevention strategies.
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Mirabbasi et al. (2025) Investigating the Dependence Structure of Temperature and Precipitation Concentration with Evapotranspiration in Finland
This study performed a first-of-its-kind trivariate analysis using Vine copulas to investigate the dependence structure between temperature concentration (TCI), precipitation concentration (PCI), and potential evapotranspiration (PET) across nine Finnish meteorological stations (1980–2021), revealing significant spatial variations in their joint probabilities and highlighting region-specific flood risks.
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Oware et al. (2025) Evaluating E3SM Global Storm‐Resolving Model Simulations of Deep Convection: Insights From DP‐SCREAM During TRACER
This study evaluates the performance of the Doubly Periodic Simple Cloud-Resolving E3SM Atmosphere Model (DP-SCREAM) in simulating deep convection in the coastal Houston region, finding it reproduces diurnal cycles well but exhibits persistent biases in cloud representation that are partially addressed by sensitivity experiments on mixing length and buoyancy flux.
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Silva et al. (2025) Impact of precipitation variability on erosivity, runoff, and soil erosion in a semiarid basin: a case study from Northeast Brazil
This study investigated the impacts of precipitation variability on rainfall erosivity, runoff, and soil erosion in the Apodi–Mossoró River basin, Northeast Brazil, using climate indices and the SWAT model. Findings indicate a decline in extreme precipitation events and significant spatiotemporal variability in precipitation, which directly influences erosivity, runoff, and soil erosion patterns across the semiarid region.
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Maisonnier et al. (2025) A new biogeochemical modelling framework (FLaMe-v1.0) for lake methane emissions on the regional scale: development and application to the European domain
This study presents a new physical-biogeochemical modelling framework (FLaMe-v1.0) for simulating lake methane (CH4) emissions at regional scales, applying it to the European domain. The model estimates a total annual CH4 emission of 0.97 ± 0.23 Tg CH4 yr−1 from European lakes (0.1–1000 km2), highlighting strong spatio-temporal variability and the importance of carbon biogeochemical dynamics.
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Habash (2025) Su-Enerji̇-Gida-Ekosi̇stem Baği Yaklaşimi Kapsaminda Akarsu Akişlarinin Modellenmesi̇ İçi̇n Yapay Zeka Tabanli Bi̇r Çerçeve: Türki̇ye Deki̇ Su Havzalarinin Kapsamli Bi̇r Değerlendi̇rmesi̇
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Çelik et al. (2025) Flood Risk and Inundation Mapping in a Changing Climate
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Dubey et al. (2025) Entropy theory-based performance appraisal of CMIP6 climate models in regional drought simulation over the Indus River basin: a multifactorial investigation
This study assessed the historical performance of 16 CMIP6 climate models in simulating regional drought and associated meteorological variables over the Indian Indus River basin (1979–2014) using an entropy-based approach, identifying MIROC6, MPI-ESM1-2-HR, and NorESM2-LM as the most suitable models for future climate projections.
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Dayanandan et al. (2025) Systematic Spatio-Temporal Analysis of Long Term Trends and Surface Warming Thresholds Over Oman
This study presents a comprehensive assessment of long-term surface air temperature trends over Oman using ground measurements and ERA5 from 1981 to 2020. Results reveal significant nationwide non-uniform warming, with average, maximum, and minimum temperatures rising by 0.23–0.25 °C per decade, making Oman a vulnerable climate hotspot.
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Lazaar et al. (2025) Rapid Estimation of Soil Electrical Conductivity (ECe) in Arid Regions Using Pedotransfer Functions, FTIR Spectroscopy and Machine Learning
This study develops pedotransfer functions and an innovative FTIR spectroscopy approach coupled with machine learning to rapidly and accurately estimate soil saturated paste extract electrical conductivity (ECe) from diluted soil-to-water extracts. It demonstrates that soil-type-specific conversion factors and integrated soil properties (CEC, CaCO₃) significantly enhance ECe prediction, offering a rapid, cost-effective method for large-scale salinity monitoring.
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Zuecco et al. (2025) Trees use predominantly summer water in a pre-Alpine catchment
This study utilized a 6-year isotopic dataset in a pre-Alpine catchment to investigate the seasonal origin of water sources for soil and plants, revealing that beech and chestnut trees predominantly use summer precipitation with rapid water turnover, even during dry years.
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Deng et al. (2025) Modeling the propagation time from meteorological to root-zone soil moisture drought: a case study in Jiangxi Province, China
This study utilized the Variable Infiltration Capacity (VIC) model to quantify the propagation time from meteorological to root-zone soil moisture drought in Jiangxi Province, China. It demonstrated that climate change, particularly higher temperatures, has increased drought frequency and altered propagation times in recent decades, providing insights for water resource management in humid agricultural regions.
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Shoaib et al. (2025) Hydro-geospatial Modeling for Assessing the Effectiveness of Flood Dikes against Unprecedented Floods: A Case Study of the Chenab River Floodplain in Pakistan
This study developed and validated a hydro-geospatial model to assess flood extents, inundation depths, and the effectiveness of flood dikes in the Chenab River floodplain, Pakistan, against unprecedented floods of various return periods, identifying vulnerable infrastructure and recommending specific improvements.
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Choudhary et al. (2025) Comprehensive Evaluation of Precipitation Reanalysis Products and CMIP6 Models Using Statistical and Machine Learning Techniques With Nature‐Inspired Optimization
This study developed a comprehensive strategy combining reanalysis products, trend analysis, and optimized machine learning models to improve precipitation forecasts and evaluate hydroclimatic variability in the Upper Godavari Sub-basin, India, finding MERRA2 reanalysis and the RF-HHO model to be most accurate for prediction.
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Xiao et al. (2025) Reconstructing historical cloud ice water fraction using machine learning and multi-source satellite data from 1983 to 2009
This study develops and evaluates machine learning schemes to reconstruct global monthly mean ice water fraction (IWF) from 1983 to 2009, leveraging multi-source satellite and reanalysis data to correct biases in earlier observations and provide an improved historical dataset for cloud-climate interaction studies.
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Ofori-Ampofo et al. (2025) On the strategy of exploring spatio-temporal information from Earth observation data for crop yield prediction
This study comprehensively compares multiple strategies for encoding spatial and temporal information from Earth observation data for county-level corn yield prediction in the USA using various machine learning models. It reveals that predicting crop yield effectively using only time series data is possible, with surface reflectance being a critical predictor, and highlights the importance of recent historical data over long-term records for model accuracy.
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Zhou et al. (2025) Global uncertainty assessment of vegetation indices from NASA's Harmonized Landsat and Sentinel-2 Project
This study globally assessed the between-sensor uncertainties of 21 Vegetation Indices (VIs) derived from NASA's Harmonized Landsat and Sentinel-2 (HLS) version 2.0 surface reflectance data. It found high consistency (R² > 0.94) for most VIs, but uncertainties increased with large view azimuth angle differences, high solar zenith angles, terrain shadows, and elevated aerosol levels, particularly at the extreme ends of VI value ranges.
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Nieves et al. (2025) AI-driven insights beneath the surface: deeper ocean layers at play in severe hurricane forecasting
This study developed a convolutional neural network–random forest (CNN-RF) framework that leverages three-dimensional ocean anomaly data down to 500 meters to significantly improve 72-hour lead-time severe hurricane forecasts, demonstrating the critical importance of deeper ocean layers.
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Kim et al. (2025) Simultaneous Multiscale Data Assimilation of GOES‐16 ABI All‐Sky Radiances and Radar Reflectivity on a Dryline Convection Event
This study investigates the impact of simultaneous multiscale data assimilation (MDA) of GOES-16 all-sky radiance and radar reflectivity on a dryline convection event. It demonstrates that MDA significantly improves multiscale analysis of atmospheric features and enhances the predictability of convective systems compared to single-scale data assimilation (SDA).
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Guo et al. (2025) Study on the nonlinear propagation mechanism from meteorological drought to hydrological drought in Jialing river basin
This study investigates the nonlinear propagation mechanism from meteorological drought to hydrological drought in the Jialing River basin, quantifying drought propagation thresholds and revealing spatio-temporal drought characteristics. It found that the probability of propagation decreases with increasing hydrological drought class, with specific duration and intensity thresholds identified for triggering different levels of hydrological drought.
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Amiri et al. (2025) Evaluation of CERES‐Maize Model for Predicting Yield, Evapotranspiration, Water Productivity and Drought Stress Indices Under Center Pivot, Subsurface Drip and Furrow Irrigation Systems With Different Irrigation Levels Simultaneously
This study investigated the impact of various irrigation methods and levels on maize yield, evapotranspiration, water productivity, and drought stress indices using field data and the CERES-Maize model. It found that irrigation management significantly affected productivity variables, and the model showed good agreement with observed yield and evapotranspiration, despite systematically overestimating evapotranspiration.
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Dommo et al. (2025) Assessing the Response of Surface Cloud Radiative Effects to Stratospheric Aerosol Injections Over West and Central Africa
This study investigates the response of surface cloud radiative effects (CREs) to Stratospheric Aerosol Injection (ARISE-SAI-1.5) compared to a climate change scenario (SSP2-4.5) across three African regions. Findings indicate ARISE-SAI-1.5 mitigates shortwave cloud cooling decreases and enhances longwave warming, primarily driven by changes in liquid water path and fractional cloud cover.
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Feizbahr et al. (2025) Flood Susceptibility Mapping Using Machine Learning and Geospatial-Sentinel-1 SAR Integration for Enhanced Early Warning Systems
This study develops a comprehensive framework for flood susceptibility mapping by integrating thirteen geospatial factors with statistical and machine learning models, finding that the XGBoost model achieves superior performance with an Area Under the Curve (AUC) of 0.92.
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Chauke et al. (2025) Monthly Scale Validation of Climate Models’ Outputs Against Gridded Data over South Africa
This study evaluates the performance of multiple global and regional climate models (CMIP5, CMIP6, CORDEX) against observational data for monthly temperature and precipitation over South Africa, revealing improved simulation outputs in CMIP6 and CORDEX compared to CMIP5.
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Chepfer et al. (2025) Variability and Trends in Cloud Properties Over 17 Years From CALIPSO Space Lidar Observations
This study investigates fingerprints of cloud changes using 11 years of CALIPSO space lidar data, finding statistically insignificant decreases in opaque cloud cover and increases in altitude, suggesting the record may be too short or imprecise for confident detection of climate change signals.
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Singha et al. (2025) Machine learning-based mapping of fog water harvesting potential in Pithoragarh, Uttarakhand: Evaluating climate scenarios and geospatial influences
This study maps current and future fog water harvesting (FWH) potential in Pithoragarh, Uttarakhand, using five machine learning models and 23 geo-environmental variables under CMIP6 climate scenarios, identifying significant areas with very high potential (up to 44.8% currently, and approximately 22% in future scenarios) to address water scarcity. The research provides a foundation for mitigating water scarcity and contributing to water security in the eastern Himalayas, aligning with Sustainable Development Goal 6 (SDG 6).
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Bagtasa (2025) Influence of Philippine topography on the pre-landfall intensification of typhoon Rai (2021)
This study investigates the rapid intensification (RI) of Typhoon Rai (2021) prior to its Philippine landfall, revealing that terrain-induced boundary layer wind convergence from the rugged Mindanao topography significantly enhanced its maximum wind speeds by up to 16%.
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Hamma et al. (2025) Hydrogeochemical assessment of groundwater for agricultural suitability in the Ksour Mountains, Algeria
This study characterized the hydrogeochemical composition and evaluated the suitability of groundwater for agricultural irrigation in the arid Ain-Sefra region, Algeria, finding it generally suitable but requiring salinity control measures, particularly in downstream areas.
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Qiu et al. (2025) Spatiotemporal pattern of terrestrial ecological drought based on ecological water deficit in the Yellow River Basin
This study developed a novel ‘Vegetation-Evapotranspiration-Water Balance’ framework to comprehensively assess the spatiotemporal patterns of terrestrial ecological drought (ED) in the Yellow River Basin (YRB) from 1982 to 2020, revealing a predominantly alleviating drought trend despite increasing ecological water requirements and consumption.
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Strydom (2025) Climate-related drivers of fire danger and activity in the Drakensberg mountains, South Africa
This study quantifies the geospatial characteristics and trends of fire activity and danger in the uKhahlamba-Drakensberg Park (UDP), South Africa, linking observed changes to climate variability. It found significant increases in fire activity and fire danger in specific areas of the park, driven by changes in atmospheric conditions such as temperature, relative humidity, and wind speed.
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Guo et al. (2025) Towards regional drought monitoring with Sentinel-3 vegetation temperature condition index in the Sichuan Basin, PR China
This study evaluates the potential of the Vegetation Temperature Condition Index (VTCI) derived from Sentinel-3 satellite data for regional drought monitoring in the Sichuan Basin, PR China, demonstrating its capacity to accurately reflect spatiotemporal drought variations and its consistency with MODIS VTCI and meteorological data.
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Chapagain et al. (2025) Evaluating the U.S. National Water Model Retrospective Evapotranspiration Simulation using Eddy-Covariance Flux Tower Measurements
This study provides the first national-scale evaluation of the U.S. National Water Model's (NWM) evapotranspiration (ET) simulations using eddy-covariance flux tower measurements across 72 sites in the contiguous United States. It found moderate overall performance, with better agreement in humid, forested regions and under water-limited conditions, but lower performance in arid, agricultural, and wetland areas, with temperature forcings not being a major source of error.
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Lü et al. (2025) Spatiotemporal Variations and Seasonal Climatic Driving Factors of Stable Vegetation Phenology Across China over the Past Two Decades
This study evaluated remote sensing methods for stable vegetation phenology (SVP) in China over two decades, finding solar-induced chlorophyll fluorescence (SIF) superior to traditional vegetation indices, and revealed distinct spatiotemporal patterns and seasonal climatic drivers, particularly the influence of spring temperature on the Start of Season (SOS) and summer/autumn vapor pressure deficit on the End of Season (EOS).
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Chen et al. (2025) An Interpretable Attention Decision Forest Model for Surface Soil Moisture Retrieval
This study developed an Attention Decision Forest (ADF) model to integrate interpretability and generalization for surface soil moisture (SSM) retrieval. ADF demonstrated superior performance compared to traditional models and produced high-quality large-scale SSM maps while maintaining interpretability comparable to tree-based ensemble methods.
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Rizk et al. (2025) Environmental hazards of wastewater disposal on groundwater at the West Sohag site, Egypt
This study assessed the environmental impact of wastewater disposal at the West Sohag site, Egypt, using remote sensing and geochemical techniques, confirming significant leakage of sewage water and heavy metals (Zn, Cu, Pb, Cd) into the groundwater aquifer due to insufficient land and high soil permeability. The findings highlight severe contamination risks for local water supplies and public health.
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Liu et al. (2025) The spatiotemporal characteristics of extreme drought events in China from 1961 to 2022 via a copula function
This study systematically analyzed extreme drought events in China from 1961 to 2022 using the Standardized Precipitation Index (SPI) and copula functions, revealing significant spatiotemporal variations in drought trends and severity across different regions. It highlights increased drought severity in Northeast and South China, while Northwest China and the Qinghai–Tibet Plateau experienced increased humidity.
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Shourabi et al. (2025) Enhancing Flood Frequency Predictions Under Climate Change and Uncertainty Using Machine Learning Model Fusion and Wavelet Transform
This study developed an integrated framework to enhance flood frequency predictions under climate change and uncertainty in the Haraz River Basin, Iran. It combined machine learning model fusion, wavelet transform, and uncertainty quantification, projecting a general decrease in future flood frequency, particularly under high-emission scenarios.
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Ellahi et al. (2025) A framework for spatiotemporal drought analysis using proposed multi-regional weighted aggregative SPI and Bayesian inference
This study develops a novel framework for spatiotemporal drought analysis using a proposed multi-regional weighted aggregative standardized precipitation index (MRWASPI) and Bayesian inference, demonstrating its effectiveness in providing insights into drought severity and patterns for homogeneous regions.
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Azhar et al. (2025) Comprehensive portfolio of adaptation measures to safeguard against evolving flood risks in a changing climate
This review compiles and critically analyzes a comprehensive portfolio of 39 flood adaptation measures, classifying them into four groups and evaluating their advantages, disadvantages, co-benefits, and tradeoffs to inform successful, socially just, practically feasible, and technically sound adaptation strategies against evolving flood risks in a changing climate.
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Vicente‐Serrano et al. (2025) Developing science-informed maps and climate service for extreme rainfall in Spain
This study develops the first high-resolution hazard probability maps of extreme precipitation for Spain, integrating them into a national climate service. Using a stationary Generalized Pareto Distribution and universal kriging on long-term daily precipitation data, the maps provide reliable estimates of extreme precipitation quantiles, revealing distinct spatial patterns and supporting decision-making through an interactive online platform.
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Mamić et al. (2025) Solar-induced fluorescence as a robust proxy for vegetation productivity across climate zones and vegetation types in the United States
This study provides the first continental-scale assessment of Solar-induced fluorescence (SIF) across diverse vegetation types and climate zones in the contiguous United States, demonstrating SIF's consistently stronger and more reliable correlation with Gross Primary Productivity (GPP) than Normalized Difference Vegetation Index (NDVI), even under high vapor pressure deficit (VPD) conditions.
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Granata et al. (2025) The anatomy of drought in Italy: statistical signatures, spatiotemporal persistence, and forecasting potential
This study comprehensively analyzes six-month Standardized Precipitation–Evapotranspiration Index (SPEI-6) time series across Italy using advanced statistical, persistence, clustering, and deep learning methods to characterize drought patterns and improve forecasting, revealing a tripartite drought structure and regional forecasting skill.
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Sahithi et al. (2025) Rising compound heatwave exposure in India: insights from CMIP6 climate model projections
This study analyzes the variability of daytime-only, nighttime-only, and compound heat waves (HWs) and their impact on population exposure across India under various Shared Socioeconomic Pathways (SSPs) scenarios. The findings project significant increases in compound and nighttime-only HWs and associated population exposure, particularly in Northwest and Central Northeast India, while daytime-only HWs may decline in some regions.
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Aathithyan et al. (2025) Response of indigenous low cost smart fertigation system on growth, physiology, root characters and yield of groundnut (Arachis hypogaea L.)
This study evaluated an indigenous low-cost smart fertigation system for groundnut, finding that sensor-based automated drip irrigation combined with sensor-based fertigation at 100% NPK significantly enhanced growth, physiological parameters, root characteristics, and yield while reducing water and fertilizer use.
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Zhang et al. (2025) Climate warming shortens the propagation time from meteorological drought to groundwater drought over 1960–2100
This study investigates how climate warming influences the propagation time from meteorological drought to groundwater drought in the Ganjiang River Basin from 1960 to 2100, revealing that warming shortens the propagation time for drought onset and center, while prolonging it for drought end, primarily due to increased evapotranspiration and groundwater storage anomalies.
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Tran et al. (2025) A Machine Learning Approach for Improving the Accuracy of Gridded Precipitation With Uncertainty Quantification
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Park et al. (2025) Unicorn: U-Net for sea ice forecasting with convolutional neural ordinary differential equations
This paper introduces Unicorn, a novel deep learning architecture integrating U-Net with convolutional neural ordinary differential equations (ConvNODE) and time series decomposition, to forecast weekly sea ice concentration and extent in the Arctic. Through real data analysis from 1998 to 2021, Unicorn significantly outperforms state-of-the-art models, achieving a 12% average MAE improvement for sea ice concentration and an 18% improvement in IIEE for sea ice extent forecasting.
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Fazeldehkordi et al. (2025) The impact of window size on the performance and accuracy of time series forecasting models for meteorological drought prediction
This study investigates the impact of input window size on the predictive performance of SARIMAX, MLP, Seq2Seq-LSTM, and BiLSTM models for meteorological drought forecasting. It finds that optimal window sizes significantly improve forecasting accuracy, with a 12-month window enabling all models to accurately predict conditions for the first six months of 2024.
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Örçen (2025) Soil salinization in the era of climate change
This chapter provides a foundational description of soil, detailing its composition, formation processes, and critical importance as a natural resource essential for life and human survival.
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Vallés et al. (2025) SERGHEI v2.1: a Lagrangian model for passive particle transport using a two-dimensional shallow water model (SERGHEI-LPT)
This paper introduces SERGHEI v2.1, a new Lagrangian particle transport (LPT) model coupled with a 2D shallow water model, designed to simulate passive particle advection and turbulent diffusion. The study evaluates the accuracy and computational efficiency of various numerical schemes, concluding that the online Euler method offers the best compromise for large-scale applications.
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Sharbaf et al. (2025) Rain gauge network design via implementation of global sensitivity analysis coupled with geostatistics and principal component analysis
This study introduces a novel rain gauge network design methodology that integrates global sensitivity analysis (variance decomposition) with geostatistics (Block Ordinary Kriging) and Principal Component Analysis. The proposed approach prioritizes stations based on their contribution to the uncertainty of mean annual areal rainfall estimates, demonstrating comparable performance to existing variance-minimization methods while offering computational efficiency.
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Ribes et al. (2025) Towards annual updating of forced warming to date and constrained climate projections
This paper demonstrates that annually updating estimates of current human-induced warming and observationally constrained climate projections significantly improves their accuracy. It shows that a 1-year estimate of forced warming is more accurate and stable than a 10-year average for characterizing the current state of the climate system.
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Mubeen et al. (2025) A Geospatial Assessment Toolbox for Spatial Allocation of Large-Scale Nature-Based Solutions for Hydrometeorological Risk Reduction
This study developed and improved a GIS-based multi-criteria analysis toolbox for spatially allocating large-scale Nature-Based Solutions (NBSs) to reduce hydrometeorological risks, demonstrating its application across six European river basins and identifying suitable areas for floodplain restoration, retention/detention, afforestation, and forest buffer strips. The improved models, incorporating higher-resolution land use data, showed a decrease in suitable areas compared to previous versions, highlighting enhanced accuracy in avoiding built-up zones.
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Lu et al. (2025) Atmospheric rivers emerge as future freshwater reserves and heat stocks
This study assesses the evolving roles of global atmospheric rivers (ARs) in moisture and energy transport under climate change, projecting that by 2100, 70% of mid-latitude ARs will exceed Amazon River-scale moisture transport, intensifying flood risks and shifting poleward. It also reveals their crucial role in redistributing heat and energy on subseasonal scales, with distinct heat archetypes influencing regional precipitation and temperature patterns.
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Liu et al. (2025) Opportunities and challenges in the application of Digital Twins for orchard management
This review article systematically summarizes the current state, opportunities, and challenges of Digital Twin (DT) applications in orchard management, aiming to optimize resource allocation and decision-making despite the inherent complexities of perennial fruit tree production. It concludes that DTs are in an exploratory stage, with a dominant focus on harvest operations, and suggests potential for standardized models and broader applications like natural disaster response.
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Poudel et al. (2025) Uncertainty in estimating the relative change of design floods under climate change: a stylized experiment with process-based, deep learning, and hybrid models
This study conducts a stylized model-as-truth experiment across 30 Massachusetts basins to evaluate uncertainty in estimating relative changes of design floods under climate change using process-based, deep learning, and hybrid hydrological models. Findings reveal that structural limitations and equifinality dominate uncertainty in change estimates, which are significantly reduced in variance through regional pooling.
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Nunes et al. (2025) Climate and soil: a complex relationship and implications of climate change
This chapter aims to analyze the complex relationship between climate and soil and the implications of climate change on this interaction. The provided text serves as an introduction, defining key atmospheric concepts relevant to climate.
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Elmotawakkil et al. (2025) Machine Learning and Remote Sensing for Soil Moisture Prediction
This study introduces an AI-driven framework to forecast soil moisture across five sites in Morocco's Draa Valley, demonstrating that tree-based models (Random Forest, XGBoost, CatBoost) significantly outperformed deep learning models with high accuracy.
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Tandon et al. (2025) Rainfall Variability and Rising Extremes in Urbanizing Himalayan Foothills: A Machine Learning and data-driven Exploration of Hydroclimatic Shifts in Uttarakhand, India
This study investigates rainfall variability and hydroclimatic extremes in the urbanizing Himalayan foothills of Uttarakhand, India, using integrated statistical and machine learning approaches, revealing that urbanization significantly increases vulnerability to extreme rainfall events.
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Vischer et al. (2025) Spatially resolved rainfall streamflow modeling in central Europe
This study develops an end-to-end, spatially resolved neural network pipeline for rainfall-streamflow modeling in central Europe, addressing the limitations of previous aggregated approaches in large and human-impacted catchments. The pipeline demonstrates improved accuracy, data efficiency, and interpretability, particularly for larger river basins.
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Mathias et al. (2025) A theoretical appraisal of the GR4J rainfall-runoff modelling framework
This study theoretically appraises the GR4J rainfall-runoff model, linking its heuristic components to physically-based processes and proposing modifications to eliminate operator splitting. The revised model structure maintains calibration and validation performance across 671 UK catchments while offering improved physical interpretability and computational efficiency.
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Pan et al. (2025) A semi-infinite type-2 fuzzy multi-objective programming model for irrigation water resource management under uncertainties
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Gang et al. (2025) Plant-specific crop evapotranspiration estimation system for greenhouse tomatoes using convolutional neural network and rail-based monitoring device
This study developed a plant-specific crop evapotranspiration (ET) estimation system for greenhouse tomatoes, integrating a rail-based monitoring device with a convolutional neural network (CNN) for leaf area index (LAI) estimation and a simplified Penman–Monteith model, achieving high accuracy in both LAI and ET predictions.
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Kelley et al. (2025) State of Wildfires 2024–2025
This second annual report from The State of Wildfires project tracks global and regional fire activity for the 2024–2025 season, revealing that global carbon emissions from fires totalled 2.2 Pg C (9% above average) despite below-average burned area, driven by extreme events in South America and Canada, with climate change significantly increasing the likelihood and impact of these extreme wildfires.
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Roy et al. (2025) The next Landsat: Mission turning point?
This correspondence raises awareness about the pending decision on the Landsat Next (LNext) mission architecture, outlining the originally envisioned advanced observation requirements derived from extensive user needs assessments and contrasting them with a recently proposed descoped mission that would only meet existing Landsat-9 capabilities. The paper advocates for the original LNext design to ensure future scientific and operational capacity for Earth observation.
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Ge et al. (2025) Freeze–Thaw-Driven Dynamics of Soil Water–Salt and Nitrogen: Effects and Implications for Irrigation Management in the Hetao Irrigation District
This study investigated the synergistic transport mechanisms of soil water, salt, and nitrogen under freeze-thaw cycles in salinized farmlands. It found that freeze-thaw cycles drive upward salt accumulation and enhance nitrogen transformation, identifying optimal low-salinity and moderate-nitrogen irrigation strategies to mitigate salinization and improve nitrogen utilization.
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Qian et al. (2025) Extratropical Cyclones Act as a “Bridge” to the Concurrent Impact of ENSO on the Arctic Oscillation During Boreal Winter
This study reveals that extratropical cyclones (ECs) over the North Atlantic act as a crucial "bridge" for the concurrent impact of the El Niño-Southern Oscillation (ENSO) on the Arctic Oscillation (AO) during boreal winter, demonstrating this concurrent influence is more significant than the one-year-lagged effect. The mechanism involves ENSO-induced shifts in the westerly jet stream, modulating atmospheric baroclinicity and EC activity, which ultimately drives the AO pattern.
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Navale et al. (2025) Investigating Land‐Atmosphere Interactions in the North West Himalaya Through Recycled Precipitation: Seasonal Dynamics, Trends, and Topographic Impacts
This study investigates land-atmosphere interactions and their trends in the North West Himalaya (NWH) over two decades (2001–2020) using the WRF model, revealing a high summer recycling ratio and varying links between terrestrial and atmospheric segments influenced by topography and seasonality.
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Patro et al. (2025) Collaborative Station Learning for Rainfall Forecasting
This study proposes a novel framework combining geometry-based weather station selection with deep learning to enhance extreme rainfall predictions. The Bi-GRU model, utilizing a linear station topology, achieved the highest predictive accuracy (R2 = 0.9548, RMSE = 2.2120 mm) for real-time, location-specific early warning systems.
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Stuivenvolt‐Allen et al. (2025) Atmospheric Nonlinearity Controls ENSO Asymmetry in a Hybrid Statistical‐Dynamical Climate Model
This study investigates the causes of El Niño Southern Oscillation (ENSO) asymmetry, finding that while oceanic nonlinearities would lead to stronger La Niñas, the observed asymmetry (stronger El Niños) is critically dependent on nonlinear atmospheric wind-stress responses to sea surface temperature anomalies.
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Bansal et al. (2025) Lake bathymetry and GLOF modelling of Drang Drung glacial lake, Ladakh, India, using machine learning techniques
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Sodhi et al. (2025) Limits to the Practical Predictability of Convective‐Scale Forecast Systems With World‐Class Data Assimilation
This study investigates the uncertainty and error growth in two advanced radar data assimilation systems for thunderstorm forecasting, revealing significant remaining uncertainty in unobserved atmospheric properties and concluding that substantial new measurement technologies or radical ideas are necessary for accurate forecasts.
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Liu et al. (2025) Feasibility Study of Microwave Radiometer Neural Network Modeling Method Based on Reanalysis Data
This study proposes and validates a neural network retrieval method, based on high-resolution FNL reanalysis data, to derive atmospheric profiles from microwave radiometer brightness temperatures, effectively addressing the challenge of limited radiosonde data availability in certain regions.
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Reale et al. (2025) Response of Early Winter Precipitation and Storm Activity in the North Atlantic–European–Mediterranean Region to Indian Ocean SST Variability
This study investigates the influence of Indian Ocean Dipole (IOD) variability on early winter precipitation and storm activity in the North Atlantic–European–Mediterranean (NAEM) region, revealing a significant December-specific teleconnection characterized by a positive NAO-like pattern, altered precipitation, and reduced cyclone activity driven by changes in baroclinicity.
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Yu et al. (2025) Research on the reciprocal feedback relationship and influencing factors between meteorological and agricultural drought in Northeast China
This study quantifies meteorological and agricultural drought dynamics and their reciprocal feedback mechanisms in Northeast China (2000–2023) using advanced statistical and machine learning methods, revealing significant spatiotemporal variations and key influencing factors like precipitation and temperature.
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Gallois (2025) Projet Aqui-FR - Phase 4 - Plateforme nationale de modélisation AQUI-FR : Calibration de l’application hydrogéologique "Eaudyssée-Loire" sous forçage SURFEX V8F
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Stuecker et al. (2025) Global climate mode resonance due to rapidly intensifying El Niño-Southern Oscillation
A high-resolution climate model projects that greenhouse warming will cause the El Niño-Southern Oscillation (ENSO) to rapidly transition to a highly regular, intensifying oscillation, leading to global climate mode resonance where ENSO synchronizes with other major climate modes. This synchronization would imprint ENSO's predictable variability onto these modes, potentially causing widespread "whiplash impacts" on regional hydroclimates.
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Rajulapati et al. (2025) Prevailing Climate Patterns for Concurrent High Temperature and Low Precipitation Days in Canada
This study evaluated the changing frequency of concurrent daily High Temperature and Low Precipitation (HTLP) across Canada and its relationship with large-scale climate patterns. It found a significant increase in HTLP in the Canadian Arctic and southern British Columbia, with four specific climate indices identified as significant influencers.
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Singh et al. (2025) Physics‐Aware Probabilistic Modeling of Subsurface Soil Moisture Using Diffusion Processes Across Different Climate Settings
This study developed a physics-aware probabilistic denoising diffusion model to estimate subsurface soil moisture (10–40 cm) solely from surface observations, demonstrating robust and accurate performance across 24 globally distributed sites with diverse climate settings and varying temporal resolutions. The model eliminates the need for site-specific physical parameters by integrating Fickian diffusion principles as weak physical constraints within a data-driven framework.
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Xu et al. (2025) Monitoring monthly dynamics of Nile River Basin surface water by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery
This study developed a collaborative multi-source data integration framework (CMDIF) combining Sentinel-1 SAR and Sentinel-2 optical imagery to generate monthly surface water maps of the Nile River Basin (NRB) at 10-meter resolution. The approach achieved mapping accuracy exceeding 94.5%, offering improved spatiotemporal consistency and revealing distinct spatial and temporal patterns of surface water distribution across the NRB.
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Vert (2025) Ice Melt as a Means to Assess Earth's Heat Budget Imbalance and Climate Changes from the Last Glaciation to the Inevitable Next One
This paper proposes an alternative mechanism for climate change, positing that heat, managed by water and its phase changes, is the primary driver rather than carbon dioxide's radiative forcing, and demonstrates that Earth's heat balance has historically been, and continues to be, imbalanced based on ice melt data.
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Guillaume‐Castel et al. (2025) ENSO Diversity Explains Interannual Variability of the Pattern Effect
This study systematically assesses the dominant modes of sea surface temperature (SST) variability influencing the top of atmosphere energy budget. It identifies Eastern Pacific and Modoki El Niño–Southern Oscillation (ENSO) as the two leading interannual modes most relevant to the pattern effect, exhibiting distinct radiative signatures due to subtle shifts in SST anomaly locations.
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Allwayin et al. (2025) Investigating Characteristic Droplet Size Distributions in Large Eddy Simulations of Stratocumulus Clouds
This study investigates the existence and properties of characteristic cloud droplet size distributions within Large-Eddy Simulations (LES) of stratocumulus clouds using both Lagrangian and bin microphysics schemes. It finds localized characteristic distributions in bin microphysics simulations, but notes that simulated clouds are significantly more uniform than observed, potentially due to poorly resolved entrainment interfaces or uniform large-scale forcing in LES.
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Takaya et al. (2025) Influence of Soil Moisture on Surface Air Temperature in Monsoons
This study quantifies the influence of soil moisture on surface air temperature across monsoon regions using information-theoretic causal analysis, revealing a significant, seasonally linked influence, even in wet tropical conditions, and a strong connection between land desiccation and pre-monsoon hot days.
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Dong et al. (2025) Evolution and Characteristics of Mesoscale Convective Systems over the Congo Basin
This study constructs a 20-year climatology of Mesoscale Convective System (MCS) characteristics and rainfall over the Congo basin to understand their life cycle, structure, and environmental drivers. It reveals distinct seasonal patterns, with September–November MCSs producing more rainfall despite being less intense, and identifies trends of increasing MCS intensity but declining precipitation feature rain rates and longer upscale growth times from 2001 to 2020.
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Rautiainen et al. (2025) An operational SMOS soil freeze–thaw product
This paper introduces an updated operational SMOS Level-3 Soil Freeze–Thaw (FT) product, detailing its L-band passive microwave-based classification algorithm with enhanced noise reduction. Validation against in situ measurements and reanalysis data demonstrates improved accuracy in detecting the day of first freezing, providing crucial data for carbon cycle studies in high-latitude environments.
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mohamed et al. (2025) A hybrid deep learning and rule-based model for smart weather forecasting and crop recommendation using satellite imagery
This study develops a hybrid deep learning and rule-based framework for smart weather forecasting and crop recommendation in Egypt's Al-Sharkia region, focusing on rice and wheat. The framework integrates CNN-based land suitability classification (training loss reduced from 0.2362 to 6.87e-4) with RNN-LSTM-based weather prediction (Root Mean Squared Error of 0.19) and rule-based crop advisories to provide precise, localized agricultural guidance.
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Ji et al. (2025) Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning
This study introduces a high-resolution, physics-embedded, big-data-trained hydrologic model to accurately capture global hydrologic response patterns and their shifts. The model reveals widespread and significant shifts in green-blue-water partitioning and baseflow ratios worldwide over the past two decades, with critical implications for flood risks, water supply, and aquatic ecosystems.
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Khare et al. (2025) Estimates of global surface water dynamics harnessing near real-time land cover observations and open science geospatial capabilities
This study developed the first 10 m resolution Global Surface Water Extents (GSWE) dataset for 2015–2023 using Sentinel-2 Dynamic World products, estimating 2.5 million km² of permanent and 8 million km² of seasonal waters globally, and providing an operational framework for actionable water information.
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Navarro et al. (2025) Seamless seasonal to multi-annual predictions of temperature and Standardized Precipitation Index by constraining transient climate model simulations
This study develops a computationally inexpensive analog-based method to generate seamless seasonal to multi-annual predictions of surface air temperature (TAS) and Standardized Precipitation Index (SPI) by constraining CMIP6 simulations, demonstrating competitive skill compared to state-of-the-art initialized systems and offering continuous monthly initializations.
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Liu et al. (2025) Global burned area dynamics and time-lagged relationships with climate teleconnections from 1982 to 2018
This study investigated global burned area dynamics from 1982 to 2018 and its time-lagged relationships with 11 Climate Teleconnection Indices (CTIs), revealing distinct temporal trends in burned area and varying lag correlations across different climate zones.
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Ding et al. (2025) Dual Synoptic Pathways of Winter Extreme Precipitation over the Tibetan Plateau: Classification, Moisture Origin, and Rossby Wave Activity
This study identified two distinct types of winter extreme precipitation events over the Tibetan Plateau (TP) between 1980 and 2020, driven by different hemispheric-scale Rossby wave trains, with Type 2 events showing a significant increase in frequency while Type 1 events declined.
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Jena et al. (2025) Effects of Deficit Irrigation and Growth Regulators on Physio‐Biochemical Changes, Yield, Fruit Quality and Water Productivity in Pomegranate ( Punica granatum ) Growing in Shallow Basaltic Soils
This field study evaluated the impact of deficit irrigation strategies and plant growth regulators on pomegranate yield, water use efficiency, and physiological responses in shallow basaltic soils. It found that irrigation at 80% of crop evapotranspiration combined with salicylic acid and naphthalene acetic acid significantly increased fruit yield and water use efficiency, particularly under partial root-zone drying.
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Zeng et al. (2025) Emerging remote sensing techniques for hydrological applications
This editorial provides a systematic overview of 31 publications within a special issue, highlighting advancements in remote sensing techniques, including multi-sensor platforms and machine learning, for monitoring and modeling hydrological variables and their operational applications.
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Dai et al. (2025) Human Influence on Changes in Seasonal Extreme Precipitation Across Different Land Regions
This study conducted a detection and attribution analysis of extreme precipitation changes across the Northern and Southern Hemispheres from 1950 to 2018, finding significant intensification in the Northern Hemisphere driven over 70% by anthropogenic greenhouse gases, while changes in the Southern Hemisphere were more regionally and seasonally complex.
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Li et al. (2025) Vegetation greening decrease water yield during the growing season over the Qinling-Daba Mountains in Central China
This study quantifies the impact of vegetation greening on water yield in the Qinling-Daba Mountains (QB) using a regional climate model with a water vapor tracer, revealing that greening significantly decreases water yield during the growing season due to a larger increase in evapotranspiration than precipitation.
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Ding et al. (2025) Effects of irrigation and fertilization management on kiwifruit yield, water use efficiency and quality in China: A meta-analysis
This meta-analysis synthesized 1038 observations to evaluate the impact of irrigation and fertilization management on kiwifruit yield, water use efficiency (WUE), and quality in China. It found that optimizing water and nutrient inputs, particularly reducing super-optimal nitrogen and utilizing drip irrigation, significantly improves kiwifruit productivity and quality, highlighting regional and age-dependent responses.
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He et al. (2025) A multi-band remote sensing method for small-and-medium river discharge estimation based on fused Sentinel-2/Landsat images
This study developed a framework for estimating discharge in small and medium rivers using fused Sentinel-2 and Landsat 8 satellite images. The framework, which integrates a modified image fusion model and a novel multi-band remote sensing (MBRS) method, demonstrated enhanced discharge inversion capabilities, particularly with a Random Forest-based MBRS model.
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Lee et al. (2025) A comparative assessment of a hybrid approach against conventional and machine-learning daily streamflow prediction in ungauged basins
This study compared a hybrid model (differentiable Parameter Learning with HBV) against traditional HBV and a standalone LSTM for daily streamflow prediction in 671 ungauged basins across the contiguous United States. The LSTM achieved the highest predictive accuracy, but the hybrid model offered valuable diagnostic insights into model failure modes, revealing systematic low-flow truncation caused by specific parameter biases.
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Rahman et al. (2025) Water level forecasting in coastal cities using a hybrid deep learning approach
This study introduces a novel hybrid deep learning model, CNN-Transformer-SKANs, for accurate and real-time hourly water level forecasting in coastal cities. The model achieved superior accuracy (NSE ≈0.99, RMSE < 0.03 m) and robustness in Venice, Italy, even under data-scarce and extreme event scenarios, providing an effective early warning tool.
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Alexander et al. (2025) Less Intense Daily Precipitation Maxima in Regional Compared to Global Gridded Products
This study consistently evaluates the annual wettest day (Rx1day) across global and regional gridded observational precipitation datasets, revealing that regional products are consistently drier than their global counterparts, yet the long-term trend in Rx1day aligns with the expected 7%/°C increase per global mean temperature rise.
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Karkınlı (2025) Multi-scale remote sensing of desertification trends and climate–vegetation interactions in the Konya basin, Türkiye (2000–2025)
This study investigates desertification trends and climate-vegetation interactions in the Konya Basin, Türkiye, from 2000 to 2025, revealing a "greening-degradation paradox" where subtle basin-wide greening coexists with significant localized degradation hotspots driven by intensive agricultural practices and warming temperatures.
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McMichael et al. (2025) Investigation of Ship‐Induced Mesoscale Circulation Mechanics and Aerosol Plume Spreading Rates
This study employs large-eddy simulations to investigate the physical mechanisms controlling the spreading rate of ship-emitted aerosol plumes in precipitating stratocumulus clouds. It finds that cloud droplet sedimentation and collision-coalescence are the primary drivers of plume buoyancy and horizontal spreading, while the effective radius has a negligible influence.
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Rakshit et al. (2025) Unseasonal lightning over the Indian Region: Case study integrating observations and model simulations
This study investigates an unseasonal extreme lightning event in Gujarat, India, on November 26, 2023, by integrating observations and WRF-ELEC model simulations to characterize the synoptic and microphysical drivers. It reveals that the interaction of a cyclonic circulation with Western Disturbances created an atypically moisture-rich environment, leading to enhanced ice and snow concentrations and inverted dipolar charge centers in clouds.
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Bałdysz et al. (2025) Detecting Localized Floods in Tropical Regions With CYGNSS SmallSat Constellation: A Proof of Concept From the Maritime Continent
This study demonstrates a novel methodology utilizing CYGNSS SmallSat data to detect and monitor small- to regional-scale flooding events in Sumatra, identifying key parameters for flood detection to improve risk assessment and forecasting in tropical regions.
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Studart et al. (2025) A Bivariate Return Period Copula Application of Flood Peaks and Volumes for Climate Adaptation in Semi-Arid Regions
This study investigates the limitations of univariate flood frequency analysis in semi-arid regions by proposing a bivariate approach using copula functions to jointly model flood peak and a newly introduced "average flood intensity." It demonstrates that this bivariate method reveals significantly higher return periods for complex flood events, improving compound flood risk assessment and dam safety planning.
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Han et al. (2025) Spatial series approach to estimate soil moisture over wheat fields from a single SAR image
This study introduces a spatial series approach (SSA) to estimate soil moisture (SM) over wheat fields using a single Synthetic Aperture Radar (SAR) image, leveraging backscattering intensity ratios (BIRs) to implicitly account for vegetation and roughness effects. The method achieves stable SM retrieval with a root mean square error (RMSE) of approximately 10% and a correlation coefficient (R) exceeding 0.7, offering a robust solution for SM monitoring in scenarios with limited time-series SAR data.
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Nayak et al. (2025) Regional and vertical scaling of water vapor with temperature over Japan during extreme precipitation in a changing climate
This study investigates the regional and vertical scaling of atmospheric water vapor with temperature over Japan during extreme precipitation events in present and future climates. It finds a strong positive relationship between specific humidity and temperature in the lower atmosphere, with a rate of change of 8.3 ± 2.4% per degree Celsius, indicating increased intensity of extreme precipitation in a warming climate.
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Lan et al. (2025) Biogeophysical warming effects of vegetation growth in the temperate water-limited region
This study quantifies the biogeophysical effects of vegetation growth on land surface temperature (LST) in temperate water-limited regions, revealing distinct diurnal and seasonal LST trends and identifying key drivers (ET, Albedo, SM, LAI) across different land use types from 2000 to 2020.
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Unknown (2025) Mountain glaciers will lose their cooling capacity as they shrink
This paper highlights that the protective microclimates currently found on mountain glaciers, which slow melting, will decay as glaciers shrink, leading to increased sensitivity of glacier temperatures to atmospheric warming by the latter half of the 21st century.
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Gong et al. (2025) Spatiotemporal Dynamics of Forest Fire Risk in Southeastern China Under Climate Change: Hydrothermal Drivers and Future Projections
This study developed a meteorology-driven machine learning model to assess and project forest fire risk in Southeastern China under climate change scenarios, revealing a significant northward and inland migration and aggregation of high-risk zones by the end of the 21st century, despite a historical decline in fire frequency.
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Akbari et al. (2025) Enhancement of border irrigation systems: Leveraging simulation–optimization techniques
This study proposes a novel simulation–optimization model for designing open-end border irrigation systems to enhance hydraulic performance under field constraints. Integrating a modified hydro-empirical SCS simulation framework with the Grey Wolf Optimizer, the model significantly improves distribution uniformity and requirement efficiency, substantially reduces total applied water, and identifies shortening the advance phase as the most effective strategy for performance enhancement.
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Chano et al. (2025) Comparison of Pluvial Flooding Modeling Software Applied in Highly Urbanized Settlements Using the Case of Lake Ganzirri
This study compares pluvial flooding modeling software (EPA-SWMM 5.2, HEC-RAS 6.2, InfoWorks ICM 2021.9) in highly urbanized areas, focusing on the representation of buildings and Low-Impact Development/Sustainable Urban Drainage Systems (LID/SUDS) using the Lake Ganzirri area as a case study. It finds that a proposed building representation method in HEC-RAS 6.2 is comparable to the building void method in InfoWorks ICM 2021.9, and 2D LID/SUDS adaptations significantly increase infiltration volume despite minimal changes in water depth maps.
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Zheng et al. (2025) GeoAI for Land Use Observations, Analysis, and Forecasting
This paper explores how Geographic Artificial Intelligence (GeoAI) is fundamentally transforming the observation, understanding, and governance of land systems.
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Gu et al. (2025) Diurnal variation features and dry times impact based on the latest hourly satellite-based precipitation data across China
This study comprehensively evaluated the spatiotemporal and diurnal performance of three latest hourly satellite-based precipitation products (IMERG V07, GSMaP_Gauge_V8, CMORPH_V1.0) across China. It found that IMERG and GSMaP generally outperformed CMORPH, and highlighted the significant impact of dry times on evaluation results, proposing a bias evaluation method based solely on wet times.
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Zhuang et al. (2025) Integrating social media data and machine learning methods for flash flood susceptibility mapping in China
This study integrates social media data and five machine learning algorithms to map flash flood susceptibility across China, revealing spatiotemporal patterns and key influencing factors, while demonstrating the utility of social media for risk assessment.
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Zhang et al. (2025) Intensified aridification of the Tarim Basin since the beginning of the Pliocene: Implications for the interaction between tectonics and climate
This study investigates the timing and forcing mechanisms of aridification in the Tarim Basin since the Pliocene using environmental magnetism, revealing that extreme aridification began around 5.3 Ma due to the interplay between regional tectonics and climate, amplified by dust feedback.
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Hendrickx (2025) Bodemvocht schatten met SWIM²: Inverse modellering met bodemvochtsensoren voor verbeterde voorspellingen van irrigatiebehoefte
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Boisier et al. (2025) Increasing water stress in Chile revealed by novel datasets of water availability, land use and water use
This study evaluates past, present, and future water stress in Chile using novel, national-scale datasets of water availability, land use, and water use. It reveals a steady increase in water stress in central Chile, primarily driven by rising water consumption and reduced availability, projecting permanent megadrought-like conditions and extreme water stress in many basins under adverse climate scenarios.
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Zhou et al. (2025) Glacier induced different changing patterns of temperature and precipitation on glacial regions compared with nonglacial regions over Tibetan Plateau
This study compares temperature and precipitation trends between glacial and nonglacial regions of the Tibetan Plateau from 1979 to 2022, revealing that glaciers induce distinct regional climate change patterns, including slower warming and greater precipitation increases in glacial areas due to altered surface energy fluxes and water vapor dynamics.
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Jia et al. (2025) Non-Invasive Inversion and Characteristic Analysis of Soil Moisture in 0–300 cm Agricultural Soil Layers
This study systematically benchmarks eight regression algorithms to non-invasively infer deep soil moisture (20–300 cm) using surface soil moisture and meteorological variables. It finds that non-linear models, particularly Multi-Layer Perceptron (MLP), consistently outperform linear models for deep layers, and proposes a depth-adaptive modeling strategy for practical application.
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Fang (2025) Global Standardized Precipitation Evapotranspiration Index (SPEI) for the Common Era, calculated using monthly climate variables from MPI-ESM model simulations from past2k experiment in CMIP6
This study describes the generation of a global gridded Standardized Precipitation Evapotranspiration Index (SPEI) dataset for the past 2000 years (1 CE to 2014 CE) at monthly, seasonal, and annual time scales, utilizing climate model outputs and established drought index methodologies.
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Sun et al. (2025) Multi-source precipitation product fusion strategy based on a novel ensemble validation framework
This study develops a novel Ensemble Validation Precipitation Framework (EVPF) using a CNN-LSTM deep learning architecture to address significant validation randomness in multi-source precipitation data fusion. The EVPF robustly fuses six precipitation products, eliminating the "validation set gambling" phenomenon and achieving high accuracy for precipitation estimation in the Yujiang River Basin, China.
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Han et al. (2025) Baseflow Separation for Improving Dam Inflow Prediction Using Data-Driven Models: A Case Study of Four Dams in South Korea
This study developed and evaluated data-driven models (Deep Neural Network and Random Forest) coupled with a baseflow separation process to improve dam inflow prediction accuracy in four South Korean dams, demonstrating that baseflow separation significantly enhances predictive performance.
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Cho et al. (2025) Thermodynamic Differences Between Moderately and Extremely Long Heat Waves in South Korea
This study categorizes South Korean summer heat waves over 52 years into moderately and extremely long events, revealing that extremely long events are sustained by distinct thermodynamic processes involving persistent high-pressure systems, subsidence, land-atmosphere interactions, and delayed surface cooling, which differ from shorter events.
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Liu et al. (2025) Effects of green infrastructure composition and configuration on runoff regulation in watersheds: A global meta-analysis
This meta-analysis quantified the global effects of green infrastructure (GI) composition and configuration on watershed runoff characteristics, revealing that shrubland and forest land have strong regulatory effects, while higher GI patch density increases runoff. The study also identified critical forest land area thresholds for watershed ecological security.
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Bulgin et al. (2025) Spatial Sampling Uncertainty for MODIS Terra Land Surface Temperature Retrievals
This study develops a spatial sampling uncertainty model for MODIS Terra Land Surface Temperature (LST) products when coarsening from 0.01° to 0.05° and 0.1° resolutions, revealing that uncertainty is dependent on land cover and solar zenith angle.
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Acar et al. (2025) Correction: Median-Linear model for solar radiation estimation by comparison with ANFIS and Angström-Prescott methods
The original article developed and evaluated a novel Median-Linear model for estimating solar radiation, comparing its performance against established ANFIS and Angström-Prescott methods.
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Weißenborn et al. (2025) Neural networks in catchment hydrology: a comparative study of different algorithms in an ensemble of ungauged basins in Germany
This study comparatively evaluates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) for daily discharge prediction in 35 ungauged basins in Hesse, Germany. It finds that all models show significant predictive capabilities, with CNN exhibiting slightly superior accuracy, while GRU offers the best computational efficiency, and the inclusion of static catchment features consistently improves performance.
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Nakao et al. (2025) Reconstruction of thermally-driven flows using Lagrangian particle data assimilation
This study develops a four-dimensional variational (4DVar) Marker-in-Cell method to reconstruct time-dependent temperature fields and the Rayleigh number in thermally driven flows by assimilating sparse Lagrangian particle trajectories from laboratory experiments. The method successfully reconstructs hidden thermal and flow structures and predicts future evolution beyond the assimilation window.
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Rivera et al. (2025) Hydrological response to the summer 2024 high-elevation heatwave in Central-Western Argentina
This study characterizes the hydrological response to the summer 2024 high-elevation heatwave in Central-Western Argentina's Mendoza River Basin, revealing an unprecedented snowmelt and glacier melt pulse that significantly increased streamflow, forcing extraordinary dam releases and refilling downstream wetlands for the first time in nearly two decades.
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Chen et al. (2025) Evaluating the effects of biodegradable film and plastic film mulching on soil water and nitrogen cycles and maize yield in different regions of rainfall and soil texture: An experimental and modeling study
This study quantifies the differences in soil water and nitrogen cycling, maize yield, and water/nitrogen use efficiency under biodegradable film, plastic film, and no film mulching across varying rainfall and soil texture regions, concluding that biodegradable film mulching is most effective in high rainfall and loam soil areas.
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Li et al. (2025) A Study on Flood Susceptibility Mapping in the Poyang Lake Basin Based on Machine Learning Model Comparison and SHapley Additive exPlanations Interpretation
This study addresses bottlenecks in machine learning-based flood susceptibility mapping (FSM) in complex basins by establishing a high-precision sample database, comparing PSO-optimized hybrid models, and employing SHAP for interpretation, finding that ensemble learning models (especially RF with AUC 0.9536) exhibit superior performance and reveal complex, spatially heterogeneous driving mechanisms.
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Yılmaz et al. (2025) Evaluation of NEX-GDDP-CMIP6 to simulate precipitation using multi-criteria decision-making analysis over Türkiye
This study evaluates the performance of 27 NEX-GDDP-CMIP6 models in simulating precipitation over 28 river basins in Türkiye using a novel multi-criteria decision-making approach, finding that a Best Multi-Model Ensemble (BMME) consistently outperforms individual models and simple Multi-Model Ensembles.
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XinHao (2025) A novel hybrid DOA-PSO-enhanced LSSVM model for monthly runoff forecasting in the upper Heihe river basin
This study proposes a novel hybrid DOA-PSO-LSSVM model for monthly runoff forecasting, integrating the Dream Optimization Algorithm (DOA) for global exploration, Particle Swarm Optimization (PSO) for local refinement, and Least Squares Support Vector Machine (LSSVM) for nonlinear learning. Applied to the upper Heihe River Basin, the model demonstrates superior predictive accuracy and robustness compared to conventional and single-optimizer models, effectively addressing the challenges of nonlinear and non-stationary hydrological processes.
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Baiamonte et al. (2025) Exploring the impact of average water content on wetting bulb expansion from a buried point source
This study investigates the influence of the average volumetric water content (θavg) on the expansion of the wetting bulb from a buried point source in a subsurface drip irrigation (SDI) system. By calibrating a k parameter for θavg within Philip's analytical model using six years of experimental data, the study significantly improved the accuracy of wetting front travel time (tt) estimations, reducing the standard error from 15012 seconds to 1560 seconds.
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Pang et al. (2025) Comprehensive evaluation of the spatiotemporal distribution characteristics of multi-source precipitation products: a case study of an extreme climate event in Henan, Central China
This study comprehensively evaluates the spatiotemporal characteristics of multi-source precipitation products (radar, satellite, model, and merged) during an extreme rainstorm in Henan, Central China, revealing that the merged product (CMPAS) significantly outperforms single-source products in accurately capturing fine precipitation features.
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Lin et al. (2025) A Theory for the Spurious Pressure Waves in Compressible Large Eddy Simulations of Shallow Cumulus Clouds
This paper investigates the omnipresent spurious pressure waves in large-eddy simulations (LES) of shallow cumulus clouds, proposing an analytic theory that links their generation to latent heating and suggesting more frequent microphysics updates for stabilization.
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Tau et al. (2025) Thermal Response of Lakes to Cyclic Environmental Forcing
This study develops and tests precise mathematical expressions to predict the thermal response of lakes to cyclic environmental forcing and global changes, demonstrating that lake surface temperature departure from equilibrium depends on the forcing period, thermal response time, lake/thermocline depth, and wind speed.
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S. et al. (2025) Soil infiltration variability across diverse soil reference groups, textures, and landuse types
This study evaluates the variability of soil infiltration parameters, such as saturated hydraulic conductivity (Ks) and final infiltration rate (ic), across diverse soil reference groups, textures, and land-use types using a global database. It finds that World Reference Base (WRB) soil groups, especially when combined with land-use and texture, are significantly more effective in explaining infiltration parameter variability than soil texture or land-use alone, thereby improving upscaling for hydrological modeling.
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Chen et al. (2025) Do Tropical Cyclone Outer Size Forecasts Improve Simultaneously With Intensity Forecasts?
This study evaluates the tropical cyclone (TC) intensity and outer size forecast performance of five NOAA numerical models for 15 North Atlantic hurricanes from 2020 to 2022, revealing little correlation between intensity and size forecast accuracy, with higher resolution improving intensity but not size forecasts, and initial storm size influencing size prediction challenges.
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Crenganiș et al. (2025) Flood Risk Prediction and Management by Integrating GIS and HEC-RAS 2D Hydraulic Modelling: A Case Study of Ungheni, Iasi County, Romania
This study applies a high-resolution two-dimensional (2D) hydraulic modeling framework using HEC-RAS and GIS to assess urban flood risk in a data-scarce Romanian setting, quantifying potential impacts on buildings, land parcels, and infrastructure for various flood scenarios.
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Qin et al. (2025) Remote Influence of Andean Convection on Amazonian Rainfall and Its Mechanisms
This study investigates the Wet Andes-Dry Amazon (WADA) precipitation bias in climate models, demonstrating that Andean convection significantly reduces Amazonian rainfall during austral summer through rapid, weather-timescale atmospheric teleconnections involving moisture budget changes and Kelvin waves.
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Zeynoddin et al. (2025) Overcoming hydrological forecasting challenges through augmented adaptive deep algorithms: a case study of the great lakes across Canada and the U.S
This study proposes a novel framework integrating grid search-optimized Seasonal Autoregressive Integrated Moving Average (GS-SARIMA), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGB) models, optimized using the Augmented Weighted Mean Vector Optimizer (AWMVO), for accurate lake level forecasting in the Great Lakes. The GS-SARIMA model achieved the highest accuracy for site-specific predictions, while AWMVO-LSTM demonstrated superior generalizability across lakes despite higher computational cost, highlighting trade-offs between accuracy, efficiency, and transferability.
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Oerlemans et al. (2025) Flood exposure in Rotterdam's unembanked areas from 1970 to 2150: sensitivities to urban development, sea level rise, and adaptation
This study quantifies historical (1970-2020) and projected (to 2150) residential flood exposure in Rotterdam's unembanked areas, attributing changes to urban development, sea level rise, and the Maeslant storm surge barrier. It finds that without further adaptation, flood exposure is projected to increase significantly by 2150, primarily driven by sea level rise, despite the historical mitigating effect of the Maeslant barrier.
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Schiller et al. (2025) Optimising Long-Range Agricultural Land Use Under Climate Uncertainty
This paper introduces the spatio-temporal agricultural land use sequencer (STALS) model to determine climate-aware annual crop land uses for the Murrumbidgee Irrigation Area, finding that higher-value crops like horticulture can maximize regional economic benefit with minimal water usage under climate change.
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Cao et al. (2025) Brief communication: Reanalyses underperform in cold regions, raising concerns for climate services and research
This study quantifies the relative quality of five state-of-the-art reanalyses in cold regions, revealing that the ensemble spread for mean annual air temperature and maximum snow water equivalent is significantly higher in these areas compared to other regions, raising concerns for climate services and research.
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Tu et al. (2025) Simulation and Parameter Law of HEC-HMS for Multi-Source Flood in Arid Region Based on Three-Dimensional Classification Criteria: A Case Study of Manas River Basin
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Cheshmberah et al. (2025) Ensemble machine learning for predicting soil hydraulic properties in semi-arid regions
This study developed an ensemble machine learning approach combining Random Forest (RF) and Cubist models to predict and map soil hydraulic properties in a semi-arid region of Iran. The RF–Cubist ensemble consistently outperformed individual models, achieving higher accuracy and improved spatial reliability for field capacity (FC), permanent wilting point (PWP), and available water capacity (AWC).
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Tang et al. (2025) Multi-indicator comparison in characterizing spatiotemporal patterns of water disasters and corresponding agricultural applications in the Middle-and-lower Yangtze River
This study systematically compared five hydrometeorological indicators to characterize spatiotemporal drought and flooding patterns in the Middle-and-lower Yangtze River Region, revealing increasing trends in both disasters and identifying high-risk zones for cotton and rapeseed, with rapeseed facing significantly higher risks.
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Datta et al. (2025) Assessment of climate change impacts on runoff and hydrological drought risk using the VIC-3L model and four-variate D-vine copulas in the Upper Bhima basin, India
This study developed a novel D-vine copula-based framework, integrated with the VIC-3L hydrological model, to assess climate change impacts on runoff and four-variate hydrological drought risk in the Upper Bhima basin, India. It projects significant seasonal shifts in runoff and an increased frequency of both mild and extreme droughts under future climate scenarios.
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Kalin et al. (2025) Sub-daily rainfall extremes in Croatia: a basis for improved warning thresholds
This study provides the first comprehensive climatology of sub-daily rainfall extremes across Croatia (1961–2020) to establish an improved basis for rainfall warning thresholds. It reveals distinct regional and seasonal patterns of extreme rainfall and proposes new warning thresholds based on stationary Generalized Extreme Value (GEV) return levels.
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Macfee et al. (2025) Rising snowline altitudes across Southern Hemisphere glaciers from 2000 to 2023
This study determined the spatial patterns and temporal trends of end-of-summer snowline altitudes (SLAEOS) for 6364 glaciers across five Southern Hemisphere mountain regions from 2000 to 2023. It revealed a pervasive rise in SLAEOS on the majority of glaciers, with rates up to 7.18 meters per year, indicating widespread glacier mass loss, though some western regions experienced declines.
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Zhang et al. (2025) Synoptic Origins of Extreme Riverine Floods Over the North China Plain With Focus on the Tail Behaviors
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Ashraf et al. (2025) Assessing hazardous flash flood susceptibility using multivariate zonation mapping techniques in Pishin District, Balochistan province of Pakistan
This study utilized an integrated Analytical Hierarchy Process (AHP), Geographic Information Systems (GIS), and Remote Sensing (RS) approach to map flash flood susceptibility in Pishin District, Pakistan, identifying that 29.7% of the area is highly to very highly susceptible with a model accuracy of 0.993 Area Under the Curve (AUC).
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Zhang et al. (2025) Comparative Analysis of Deep Learning and Traditional Methods for High-Resolution Cropland Extraction with Different Training Data Characteristics
This study comparatively analyzes deep learning (UNet, DeepLabv3+) and traditional (OBIA-RF) methods for high-resolution cropland extraction, evaluating the impact of classifier choice, band combinations, crop growth stages, and training data mislabeling. Deep learning models consistently outperformed traditional methods, demonstrating higher robustness to varying data characteristics and complex landscapes.
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Zhang et al. (2025) Asymmetry of Tropical Pacific Precipitation Responses to El Niño and La Niña in a Changing CO 2 Pathway
This study constructs an idealised scenario of symmetrical CO2 ramp-up and ramp-down phases to analyse changes in the asymmetric precipitation response to El Niño (EN) and La Niña (LN). It finds that during the CO2 ramp-down phase, both EN and LN precipitation anomalies shift eastward and southward compared to the ramp-up phase, with EN shifts being more pronounced, primarily driven by circulation changes and an EN-like climatological sea surface temperature warming pattern.
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Liu et al. (2025) Precession Affects the Timing and Duration of Summer and Rainy Season in East Asia
This study uses an Earth System Model to reconstruct a full precession cycle, revealing how precession significantly impacts the timing and duration of the East Asian summer and rainy season due to changes in perihelion timing, surface temperature, and subtropical high system shifts.
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Li et al. (2025) Impacts of drought on the spring phenology of temperate vegetation along a climate gradient: a case study in Inner Mongolia
This study investigated the differential impacts of various drought types (atmospheric, shallow-soil, deep-soil, comprehensive) on the start of greening season (SOS) of temperate vegetation along a climate aridity gradient in Inner Mongolia, revealing varied lag effects, relative importance, and sensitivities across different vegetation types and aridity levels.
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Li et al. (2025) Weather-Regime-Based Heatwave Risk Typing and Urban Climate Resilience Assessment in New Delhi (1997–2016)
This paper develops a physically interpretable and computationally efficient typology of heatwave risk in Delhi using aggregated station observations, identifying three distinct weather regimes with varying heatwave incidences to support early warning and management.
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Ji et al. (2025) Distinct Hadley circulation attributable to rapid and slow El Niño decay and its regional impacts
This study reveals that rapid decay (RD) El Niño events induce an equatorially asymmetric global Hadley Circulation (HC), contrasting with the quasi-symmetric structure during slow decay (SD) events. These distinct HC configurations, driven by anomalous sea surface temperatures in the central-eastern Pacific, lead to opposing regional precipitation impacts across the Indo-Pacific Warm Pool coastal countries.
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Liu et al. (2025) Quantifying winter wheat phenology patterns in the North China Plain using Solar-Induced Chlorophyll Fluorescence
This study compared winter wheat phenology derived from Solar-Induced Chlorophyll Fluorescence (SIF) with other remote sensing indicators across the North China Plain from 2000 to 2023, finding that SIF-derived metrics offered superior stability and clearer delineation of growth stages, with phenological shifts primarily driven by rising temperatures.
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Sazzad et al. (2025) IoT based soil moisture measurement and type prediction using advanced regression and machine learning models
This study developed an Internet of Things (IoT)-based system utilizing capacitance sensors and machine learning (polynomial regression, Random Forest) for real-time soil moisture measurement and soil type prediction. The system achieved high accuracies of 96.49% for moisture content prediction and 97.77% for soil type classification across various sand types.
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Song et al. (2025) Indirect assimilation of radar reflectivity data with an adaptive hydrometer retrieval scheme for severe short-term weather forecasts
This study develops an adaptive blending scheme for hydrometeor retrieval in indirect radar reflectivity assimilation, combining temperature-based and background hydrometeor-dependent methods. It demonstrates that this new scheme improves the accuracy of short-term severe weather forecasts by enhancing hydrometeor distributions and thermodynamic/dynamic structures.
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Chu et al. (2025) Increasing ecological drought risks with warming climate over Northwestern China
This study characterizes ecological drought in Northwestern China using a novel standardized ecological water shortage index (SEWDI) under CMIP6 SSP2-4.5 and SSP5-8.5 scenarios, revealing a significant increase in ecological drought risk with warming climate, particularly in western and central regions.
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Janzing et al. (2025) Data supplement to Hyper-resolution large-scale hydrological modelling benefits from improved process representation in mountain regions
This study investigates how improved process representation enhances hyper-resolution large-scale hydrological modeling in mountain regions, providing PCR-GLOBWB 2.0 model output for the Alpine region at 30 arcsec resolution for evaluation.
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Silva et al. (2025) A methodological approach to flood dynamics based on satellite-derived spectral indices and altimetric forecast models: a case study in Southern Brazil
This study developed an integrated remote sensing methodology to monitor flood-prone areas in the Porto Alegre Metropolitan Region, Brazil, combining topographic modeling (HAND) with spectral indices (NDWI, WNDWI) and validating results against hydrodynamic simulations (HEC-RAS). The approach demonstrated high accuracy, with WNDWI outperforming NDWI in detecting flood extent, particularly in turbid urban environments.
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Zhang et al. (2025) Multi-scale assessment of ERA5 hourly pressure-level data on a global scale with in- situ observations
This study globally evaluates the performance of ERA5 hourly pressure-level data (2020–2023) for temperature, pressure, specific humidity, and relative humidity against 9,957 in-situ observations, revealing variable accuracy influenced by seasonal, latitudinal, and altitudinal factors.
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Zhang et al. (2025) A Dual-Branch Framework Integrating the Segment Anything Model and Semantic-Aware Network for High-Resolution Cropland Extraction
This study proposes SAM-SANet, a novel dual-branch framework integrating the Segment Anything Model (SAM) with a semantically aware network, to overcome challenges in precise boundary localization and cross-domain adaptability for high-resolution cropland extraction, demonstrating superior performance on custom datasets.
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Saleem et al. (2025) Projecting future climate extremes in the glacier-fed upper indus basin using machine learning based downscaling of CMIP6 GCMs
This study downscaled CMIP6 Global Circulation Model (GCM) data for the Upper Indus Basin (UIB) using Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) to project future climate extremes. The research found that CNNs outperformed ANNs, revealing robust warming trends in temperature and uncertain precipitation trends across the UIB under future Shared Socioeconomic Pathways (SSP) scenarios.
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Gonçalves et al. (2025) Assessing Riparian Evapotranspiration Dynamics in a Water Conflict Region in Nebraska, USA
This study applied a remote sensing-based two-source energy balance model (SETMI) to estimate actual evapotranspiration (ETa) for riparian vegetation in Nebraska, demonstrating its accuracy against eddy covariance field data and its utility for water management. The model successfully quantified ETa, highlighting significant water usage by irrigated crops like corn and soybeans, which directly impacts local water resources.
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Eslami et al. (2025) Climatic, topographic, and groundwater controls on runoff response to precipitation: evidence from a large-sample data set
This study utilized ensemble rainfall-runoff analysis (ERRA) across 189 Iranian catchments to quantify how climatic, topographic, and groundwater factors control peak runoff response to precipitation, revealing that steeper slopes, smaller catchment areas, shallower groundwater, and more humid climates lead to higher peak runoff.
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Bărbulescu (2025) Climate Change and Hydrological Processes, 2nd Edition
This editorial introduces and synthesizes eight diverse research papers within a special issue on climate change and hydrological processes, highlighting the dynamic nature of hydrological vulnerability, advancements in diagnostic tools, and persistent challenges in understanding and predicting water resource changes.
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Montero‐Martínez et al. (2025) How does the influence of wind on the fall speed of raindrops change with altitude?
This study investigates how horizontal wind intensity affects raindrop fall speed and its variability at two distinct altitudes in Mexico. It finds that while mean fall speed changes are not statistically significant, wind significantly increases the dispersion of fall speeds, with a more pronounced effect at lower-altitude coastal sites due to higher air density.
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Ehrenfeucht et al. (2025) Impacts of bed topography resolution on sea-level rise projections from coupled subglacial hydrology and ice dynamics for Thwaites Glacier, Antarctica
This study investigates the impact of bed topography resolution on sea-level rise projections from coupled subglacial hydrology and ice dynamics for Thwaites Glacier, West Antarctica. It finds that specific bed topography is a first-order control on accumulated mass loss, but final sea-level rise does not scale with bed resolution, and coupling between hydrology and ice dynamics accelerates mass loss.
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Qi et al. (2025) Monitoring River–Lake Dynamics in the Mid-Lower Reaches of the Yangtze River Using Sentinel-2 Imagery and X-Means Clustering
This study developed a robust Sentinel-2 and X-means clustering-based method to monitor river-lake dynamics in the Mid-Lower Reaches of the Yangtze River (MLRYR) from 2018-2023, finding overall surface water area (SWA) stability but significant declines in major lakes (Poyang, Dongting, Shijiu) and an increase in Danjiangkou Reservoir, with river networks buffering climatic impacts.
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Ali et al. (2025) Cascading Risks and Adaptive Deficits: A Review of Climate Change Impacts on Pakistan’s Water Security
This review synthesizes climate vulnerability and cascading risks to Pakistan’s water security (2003-2023), revealing that climate change acts as a risk multiplier, intensifying physical changes and exacerbating institutional weaknesses. It proposes an integrated, systems-based research agenda to transition from reactive disaster response to proactive, evidence-based adaptation planning.
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Aslan‐Sungur et al. (2025) Artificial neural networks estimate evapotranspiration for Miscanthus × giganteus as effectively as empirical model but with fewer inputs
This study compares artificial neural networks (FFN and NARX) with an empirical model (Granger and Gray) for estimating actual evapotranspiration (ET) of *Miscanthus × giganteus*. It demonstrates that ANNs can predict ET as effectively as empirical models but with fewer input variables, and the NARX model exhibits superior generalization across different sites.
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Xie et al. (2025) Vegetation Transpiration Drives Root-Zone Soil Moisture Depletion in Subtropical Humid Regions: Evidence from GLDAS Catchment Simulations in Fujian Province
This study investigates the spatiotemporal patterns and interactions between vegetation transpiration and root-zone soil moisture in Fujian Province using GLDAS Catchment data from 2004 to 2023. It reveals a strong negative correlation where transpiration primarily drives soil moisture depletion with a 1- to 2-year lead time.
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Quansah et al. (2025) Evaluating the Performance of the National Water Model: A Spatiotemporal Analysis of Streamflow Forecasting
This study evaluates the performance of the National Water Model (NWM) v2.1 in simulating streamflow across the Alabama Black Belt Region, finding that its accuracy significantly improves with longer forecast terms, achieving good performance at the monthly scale despite a consistent negative bias across all time scales.
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Cho et al. (2025) Remote Sensing of Live Fuel Moisture for Wildfires Using SMAP Satellite Observations
This study evaluates the relationship between Live Fuel Moisture (LFM) and remotely sensed Vegetation Water Content (VWC) and Soil Moisture (SM) derived from SMAP L-band brightness temperature, demonstrating that MEP-retrieved VWC serves as a strong, scalable proxy for LFM in the Western U.S. for wildfire risk assessment.
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Wang et al. (2025) Corrigendum to “Variability and uncertainty in net ecosystem carbon exchange modeling: Systematic estimates at global flux sites via ensemble machine learning” [Agricultural and Forest Meteorology, 374 (2025), 110784]
This corrigendum addresses and corrects errors in citation information and acknowledgments within an earlier published article titled "Variability and uncertainty in net ecosystem carbon exchange modeling: Systematic estimates at global flux sites via ensemble machine learning."
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Wang et al. (2025) Loss and recovery of terrestrial carbon sinks induced by 2020 extreme precipitation in the Yangtze River Valley
This study investigated the impact of extreme precipitation in the Yangtze River Valley in June-July 2020 on terrestrial carbon sinks, finding a significant decline in net biome productivity primarily due to reduced gross primary production, followed by a rapid recovery in August.
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Yu et al. (2025) Optimal Water Distribution Model for Channels With Complex Supply and Demand Relationships
This study developed a novel optimized water distribution model for channel systems, incorporating water transmission time and a rotation irrigation mode, to achieve synchronized water distribution completion and reduce flow adjustment frequency in complex irrigation networks.
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Li et al. (2025) Different Effects of Two ENSO Types on the Northern Mid‐to‐High Latitude Surface Air Temperature Distribution in Late Winter
This study investigates the distinct late winter Northern Hemisphere surface air temperature responses to Eastern Pacific (EP) and Central Pacific (CP) ENSO types. It finds that EP El Niño induces a "north cold, south warm" Eurasian temperature dipole, contrasting with a "north warm, south cold" pattern during CP El Niño, driven by differences in tropical Pacific convection and subsequent atmospheric wave trains.
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Nikolov et al. (2025) Contemporary Tendencies in Snow Cover, Winter Precipitation, and Winter Air Temperatures in the Mountain Regions of Bulgaria
This study addresses a national knowledge gap by systematically investigating snow conditions in Bulgarian mountains from 1961 to 2020, revealing mixed trends for snow depth and precipitation but a predominant and significant increase in mean winter air temperature, particularly between 1000 meters and 1700 meters altitude.
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Han et al. (2025) Estimation of canopy fAPAR using optical reflectance and airborne LiDAR data
This study developed a physically-based model (fAPARRL) that integrates optical reflectance and airborne LiDAR data to improve the estimation of the fraction of absorbed photosynthetically active radiation (fAPAR), demonstrating superior accuracy and robustness compared to traditional vegetation index- and LiDAR-based methods.
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Pérez et al. (2025) Beyond Deterministic Forecasts: A Scoping Review of Probabilistic Uncertainty Quantification in Short-to-Seasonal Hydrological Prediction
This scoping review synthesizes methodological trends in predictive uncertainty (PU) quantification for short-to-seasonal hydrological forecasting, identifying an exponential growth in machine learning applications, geographic concentration of studies, and persistent operational barriers.
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Lin et al. (2025) MJO Energy Budget Residuals in CMIP6: A Focus on Vertical Resolution
This study investigates how the vertical resolution of model output influences the Madden-Julian Oscillation (MJO)-related moist static energy (MSE) budget, finding that limited vertical levels in daily output lead to significant budget residuals due to overestimated vertical MSE advection caused by unrealistic dry static energy (DSE) gradients.
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Han et al. (2025) Assessing the effects of controlled drainage on regional hydrological cycle and crop waterlogging and drought based on a coupled agro-hydrological model
This study developed a coupled agro-hydrological model to assess the regional impacts of controlled drainage (CD) on hydrological cycles and crop water stress in the Huaihe River Basin, China. The research quantified how CD regulates groundwater-soil water interactions and crop yields across different precipitation regimes, proposing precipitation-specific management schemes to mitigate waterlogging and drought.
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Stephens et al. (2025) Corrigendum to “Substantial increases in burned area in circumboreal forests from 1983 to 2020 captured by the AVHRR record and a new autoregressive burned area detection algorithm” [Remote Sensing of Environment 325(2025) 114789]
This corrigendum addresses a data processing error in a previous study, which led to a slight overestimation of burned area in circumboreal forests from 1983 to 2020, and provides revised quantitative results while affirming the original conclusion of substantial increases in burned area.
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Li et al. (2025) Investigating the effect of urban form on land surface temperature at block and grid scales based on XGBoost-SHAP
This study integrates XGBoost-SHAP to investigate the effects of urban factor indexes (UFIs) on land surface temperature (LST) at block and grid scales, and across local climate zones (LCZs). It reveals seasonal and scale-dependent LST patterns, identifying key UFI contributors and specific thresholds for warming or cooling effects.
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Kim et al. (2025) Uncertainty quantification and optimization of precipitating hydrometeor parameters for winter precipitation in a cloud microphysics scheme
The study quantifies uncertainties and optimizes 13 precipitating hydrometeor parameters in the WRF Double-Moment 6-class (WDM6) microphysics scheme using ICE-POP 2018 observations, achieving up to a 30.2% reduction in precipitation forecast root mean square error through Bayesian optimization.
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Chai et al. (2025) A novel index for evaluating the salinity control effectiveness of winter and spring irrigation during soil freezing−thawing process
This study developed and validated a coupled water-heat-salt model to quantitatively evaluate the salinity control effectiveness of winter and spring irrigation during soil freezing-thawing cycles. It introduced a novel Salt-Time Index (STI) and found that winter and spring irrigation have complementary effects, with winter irrigation providing longer-lasting suppression and spring irrigation achieving deeper desalination.
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Alharfouch et al. (2025) Ecohydrological and isotopic insight into Mediterranean montane Scots pines water use dynamics under different wetness conditions
This study investigated Scots pine water use dynamics in a Mediterranean montane environment by integrating high-resolution hydrological monitoring with stable water isotope data. It found that Scots pines predominantly sourced water from winter precipitation tightly bound in small soil pores, even after large convective summer precipitation events, highlighting the critical importance of winter precipitation for sustaining hydraulic functioning in drought-prone ecosystems.
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Sharifi et al. (2025) Editorial for Special Issue “Remote Sensing of Precipitation Extremes”
This editorial introduces a special issue on "Remote Sensing of Precipitation Extremes," synthesizing nine research papers to highlight advancements, persistent challenges in monitoring and understanding extreme rainfall and snowfall events globally, and outlining future research directions.
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Najmaddini et al. (2025) Modeling groundwater responses to the Persian Gulf water transfer and deficit irrigation in the Sirjan Plain
This study utilized the MODFLOW model to evaluate groundwater responses to the Persian Gulf water transfer and deficit irrigation in the Sirjan Plain, Iran, finding that while both interventions mitigate depletion, integrated management is essential for long-term aquifer sustainability.
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Wang et al. (2025) Hybrid Gaussian process regression-based harmony assessment in a water–land–energy–food–carbon-emission coupled system
This study developed an improved Gaussian process regression model (AOA-L-BFGS-GPR) to assess the dynamic harmony of water–land–energy–food–carbon-emission (WLEFC) coupled systems. Applied to Heilongjiang Province, China, the research identified key obstacles and projected future harmony under different SSP pathways, demonstrating the framework's utility for sustainable agricultural development.
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Pei et al. (2025) Spatiotemporal variability in surface radiation and energy budget in the Earth's three poles over the past 20 years
This study systematically investigates the spatiotemporal variability of surface radiation and energy budgets in the Earth's three pole regions (Arctic, Antarctic, Tibetan Plateau) from 2001 to 2023, utilizing satellite, reanalysis, and model data. It reveals contrasting long-term trends in radiation components across these regions and attributes these changes to variations in surface albedo, water vapor, clouds, and air/surface temperatures using the radiative kernel method.
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Akkala et al. (2025) Improved Streamflow Forecasting Through SWE-Augmented Spatio-Temporal Graph Neural Networks
This study comparatively evaluates statistical, machine learning, and deep learning models for streamflow forecasting in snowmelt-dominated basins, demonstrating that Spatio-Temporal Graph Neural Networks (STGNNs) with integrated Snow Water Equivalent (SWE) data achieve superior accuracy and provide a scalable forecasting approach.
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Oliveira et al. (2025) High-Throughput Identification and Prediction of Early Stress Markers in Soybean Under Progressive Water Regimes via Hyperspectral Spectroscopy and Machine Learning
This study developed a high-throughput, nondestructive method using hyperspectral spectroscopy and machine learning to identify and predict early stress markers in soybean under progressive water regimes. It demonstrated that a minimal set of 12 spectral bands can accurately classify drought severity and predict biochemical changes, offering a rapid solution for precision irrigation.
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Dai et al. (2025) Divergent impacts of Tibetan Plateau lakes on local and downstream water availability
This study quantifies how Tibetan Plateau lake clusters regulate moisture transport and water availability through competing retention and compensation mechanisms. It finds that strong lake-effect years enhance local precipitation (retention) while weak lake-effect years promote downstream moisture export (compensation), significantly impacting dry-season water security in major Asian river basins.
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Zhang et al. (2025) Research on Optimal Water Resource Allocation in Inland River Basins Based on Spatiotemporal Evolution Characteristics of Blue and Green Water—Taking the Taolai River Basin of the Heihezi Water System as an Example
This study investigated the spatiotemporal distribution and supply-demand balance of blue and green water resources in the Taolai River Basin using the SWAT model, finding green water resources significantly more abundant and identifying substantial potential for blue water transfer to improve regional water allocation.
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Kašpar et al. (2025) Improving design precipitation estimates by combining estimates from high-resolution adjusted radar data and long-term ombrographic measurements
This study introduces an innovative geostatistical merging method for design precipitation estimates, combining high-resolution adjusted radar data and long-term ombrographic measurements. Applied to Czechia, the approach yields enhanced spatial accuracy and reliability, crucial for robust flood risk management and infrastructure planning.
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Perez et al. (2025) Advancing Water Resources Management Through Reservoir Release Optimization: A Study Case in Piracicaba River Basin in Brazil
This study presents a methodology to optimize reservoir water release in the Piracicaba River Basin, São Paulo, Brazil, using rainfall-runoff and hydrological routing models with meteorological forecasts, demonstrating significant water savings of up to 66% compared to actual operations over a ten-year period.
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Zhang et al. (2025) Synergistic Regulation of Vegetation Greening and Climate Change on the Changes in Evapotranspiration and Its Components in the Karst Area of China
This study quantified the synergistic and competing effects of vegetation greening and climate change on evapotranspiration (ET) and its components in Southwest China's karst region (2000-2018) using a dual-scenario PT-JPL model. It found that vegetation restoration had a net positive effect on total ET, primarily driven by radiation and temperature, while precipitation had minimal direct influence.
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Temel et al. (2025) Examining the Probabilistic Characteristics of Maximum Rainfall in Türkiye
This study evaluated the performance of four goodness-of-fit tests to select the most appropriate probability distribution for maximum rainfall in Türkiye, identifying the Wakeby distribution as the best fit across various durations and regions.
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Song et al. (2025) Modelling water and land resources synergy and trade-off in a major grain-producing area, China
This study develops a novel water-carbon-food-ecology (WCFE) nexus framework to spatially optimize water and land resource allocation in China's Sanjiang Plain. It demonstrates significant water savings, enhanced carbon sequestration, and improved ecological connectivity under both conventional and water-saving irrigation scenarios, albeit with a slight reduction in economic benefits.
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Hani et al. (2025) Compound hydrological and thermal extremes: A nonstationary risk modeling approach for riverine ecosystems
This study developed a nonstationary multivariate risk modeling framework using dynamic additive copulas to assess the joint behavior of extreme summer river water temperature (Tw) and concurrent low flow (Q) in six Atlantic salmon rivers in eastern Canada. The proposed joint nonstationary model (JNS) systematically outperformed alternative models, revealing that temporal trends significantly increased Tw extremes and teleconnections (SOI, NAO) were dominant drivers of variability, leading to elevated compound risks.
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Huang et al. (2025) Drought propagation in china: Uncertainties originate more from dataset choice than drought index selection
This study quantifies the uncertainties in drought propagation time and probability from meteorological to soil moisture drought across China, revealing that dataset choice contributes more to uncertainty than drought index selection, and identifies SPEI as optimal for minimizing these uncertainties.
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Kim et al. (2025) Development of the machine learning and deep learning models with SHAP strategy for predicting groundwater levels in South Korea
This study developed and compared machine learning and deep learning models to predict groundwater levels (GWLs) in Jeju Island, South Korea, under three input data scenarios. The Random Forest model, utilizing lagged GWL data (Scenario 03), achieved the highest predictive accuracy, with its interpretability enhanced by SHAP analysis and statistical validation via ANOVA.
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Abulikemu et al. (2025) A case study on convection initiation mechanisms of an extreme rainstorm in Hotan, Xinjiang, NW China
This study investigates the convection initiation mechanisms of an extreme rainstorm in Hotan, Xinjiang, in June 2021, revealing that a boundary layer convergence line, formed by a cold pool and a low-level easterly jet, was crucial for continuous convective initiation and rapid development.
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Nguyen et al. (2025) Toward real-time high-resolution fluvial flood forecasting: A robust surrogate approach based on overland flow models
This study presents a hybrid framework integrating machine learning with physics-based hydrodynamic models to enable efficient real-time high-resolution fluvial flood forecasting. It demonstrates that ML-based surrogate models, trained on TELEMAC outputs, achieve substantial computational efficiency while preserving accuracy for flood inundation prediction in the Cambodia floodplain.
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Kiranmai et al. (2025) Moisture stress assessment in rabi maize through UAV-mounted multispectral sensor
This study evaluated the effectiveness of UAV-mounted multispectral vegetation indices and the Crop Water Stress Index (CWSI) for detecting moisture stress in rabi maize under nine irrigation regimes. It found that reproductive stages are highly vulnerable to water deficit, leading to significant yield reductions, and that these indices strongly correlate with both stress levels and kernel yield.
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Cannon et al. (2025) CanCPLD: Convective Parameters and Lightning Data to Support Future Thunderstorm Projections in North America
This paper introduces CanCPLD, a comprehensive multi-decade dataset of lightning flash totals and 201 convective parameters for North America, compiled from reanalysis and high-resolution climate model simulations. The dataset aims to support research on future thunderstorm projections by providing precomputed environmental conditions, thereby reducing computational barriers for researchers.
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Chenevat (2025) Commande optimale de l'irrigation : Double modélisation mathématique et agronomique vers une application au modèle Optirrig
N/A (Paper text is unreadable)
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Kwon et al. (2025) Synoptic systems influence the effectiveness of spectral nudging in high-resolution simulations of extreme precipitation
This study investigates the effectiveness of spectral nudging (SN) in convection-permitting model simulations of warm-season extreme precipitation in South Korea, finding that SN improves simulations by maintaining synoptic circulations consistent with observations, with its effectiveness depending on synoptic conditions, domain size, and the dominant wave scales of the event.
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Ersoy et al. (2025) Exploring the Potential of Multi-Hydrological Model Weighting Schemes to Reduce Uncertainty in Runoff Projections
This study evaluates hydrological model weighting strategies to reduce runoff projection uncertainty under future climate scenarios, introducing the Uncertainty Optimizing Multi-Model Ensemble (UO-MME) framework which dynamically balances calibration performance and projection uncertainty, achieving an average 30% reduction in uncertainty compared to standard methods.
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Mirtabatabaeipour et al. (2025) Distance Transform-Based Spatiotemporal Model for Approximating Missing NDVI from Satellite Data
This paper proposes a novel spatiotemporal model to accurately approximate missing or contaminated Normalized Difference Vegetation Index (NDVI) data in satellite imagery due to clouds and shadows, demonstrating significant accuracy improvements over existing methods.
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Ghosh et al. (2025) Optimal Configuration of a Convection-Permitting Regional Climate Model in Simulating Precipitation Extremes: The Saguenay Flood
This study evaluates the added value of convection-permitting (2.5 km) simulations and the impact of spectral nudging and initial soil moisture conditions on extreme precipitation characteristics using the CRCM6/GEM5 regional climate model for the July 1996 Saguenay flood. It finds significant improvements in reproducing precipitation extremes with the 2.5 km model, with spectral nudging enhancing spatial and temporal patterns, and initial soil moisture conditions improving rainfall intensity capture.
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Hirko et al. (2025) Using machine learning and satellite data to analyse climate change in the Upper Awash Sub-basin, Ethiopia
This study integrates satellite and observational datasets with future projections using both machine learning (ML) and climate modeling to assess long-term climate trends in the Upper Awash Sub-basin, Ethiopia. It reveals significant disparities between ML-based projections, which indicate a slight precipitation rise and temperature decrease, and CMIP6 SSP5-8.5 projections, which anticipate a substantial precipitation decline and temperature increase, underscoring the need for multi-model and region-specific analyses.
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Lima et al. (2025) STABLE: An open-source atmospheric blocking and subtropical ridge detection system
This paper introduces STABLE, an open-source Python algorithm for detecting and tracking atmospheric blockings (ABs) and subtropical ridges (SRs), demonstrating improved accuracy and adaptability over previous methods through validation with reanalysis data.
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Wu et al. (2025) Hotspots of summer heatwaves in East Asia and their associated radiative and dynamical forcing
This study classifies daily heatwave circulation over East Asia from 1979 to 2022, identifying three distinct hotspots, and quantitatively attributes their formation to specific atmospheric circulation patterns, cloud changes, water vapor, and atmospheric dynamics.
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Zhang et al. (2025) Evaluation of CMIP6 models in simulating the impact of rapid decay El Niño events on the Hadley Circulation
This study evaluates 27 CMIP6 models' ability to simulate Hadley Circulation anomalies during rapid decay El Niño events, finding that models with cold sea surface temperature biases in the Indo-Pacific Warm Pool underestimate these anomalies and exhibit structural deviations.
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Pappaccogli et al. (2025) MLUCM BEP + BEM: an offline one-dimensional multi-layer urban canopy model based on the BEP + BEM scheme
This study develops and validates MLUCM BEP + BEM, an offline one-dimensional multi-layer urban canopy model, demonstrating its reliable reproduction of urban surface-atmosphere fluxes with significantly reduced computational demands compared to coupled mesoscale models.
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Tran et al. (2025) Evaluating Drought Evolution in Vietnam Using Cmip6-Vn Climate Projections
This study evaluates drought evolution across Vietnam's seven climatic sub-regions from 1985 to 2099 using the one-month Standardized Precipitation Evapotranspiration Index (SPEI) derived from the 10-km resolution CMIP6-VN dataset under three Shared Socioeconomic Pathways (SSPs). It projects increasing drought intensity and severity, particularly in central and northern regions, driven by amplified evapotranspiration outpacing precipitation gains, highlighting the need for adaptive water management.
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Luo et al. (2025) Runoff components’ changes and their driving mechanism in a typical cryosphere basin, northeast Tibetan Plateau
This study partitioned runoff components (glacier, snow, rainfall, baseflow) in the Upper Shule River basin (Tibetan Plateau) from 1975 to 2022 using the SPHY model, identifying abrupt changes and their driving mechanisms. It found a significant increase in total runoff, glacier runoff, rainfall runoff, and baseflow, with a declining contribution from snow runoff, primarily driven by combinations of temperature, precipitation, evapotranspiration, vegetation, and snow cover extent.
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Huang et al. (2025) Spatiotemporal Evolution, Transition, and Ecological Impacts of Flash and Slowly Evolving Droughts in the Dongjiang River Basin, China
This study systematically investigated the spatiotemporal evolution, transition, and ecological responses of flash and slowly evolving droughts in the Dongjiang River Basin, China, from 1950 to 2024. It found that flash droughts frequently precede longer-lasting droughts, particularly in winter, and exhibit low ecosystem resilience despite causing relatively mild initial vegetation suppression, contrasting with cross-seasonal droughts.
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Alexander (2025) Cyclones, hurricanes, and typhoons
This chapter provides a foundational overview of cyclones, hurricanes, and typhoons, detailing their definition, formation via the Coriolis effect, classification based on wind speed, and regional naming conventions, while highlighting their destructive impact.
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Liu et al. (2025) Next-Generation Drought Forecasting: Hybrid AI Models for Climate Resilience
This study developed a hybrid machine learning and deep learning framework for drought forecasting in Inner Mongolia, finding that a Long Short-Term Memory (LSTM) network accurately predicts increased drought severity and variability under high-emission climate scenarios.
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Siqueira et al. (2025) Estimation of Kcb for Irrigated Melon Using NDVI Obtained Through UAV Imaging in the Brazilian Semiarid Region
This study estimated the basal crop coefficient (Kcb) for irrigated melon using Normalized Difference Vegetation Index (NDVI) derived from Unmanned Aerial Vehicle (UAV) imagery in the Brazilian semiarid region. It demonstrated that UAV-derived NDVI provides reliable Kcb estimates across warm and cool seasons, supporting precision irrigation and efficient water management.
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Legarth et al. (2025) Characteristics of Extreme Precipitation and Flood Producing Atmospheric Rivers in the Alouette Watershed of British Columbia and the Development of a Modified Severity Scale
This study investigated the influence of various Atmospheric River (AR) characteristics on extreme precipitation and streamflow in the Alouette watershed, British Columbia, Canada, identifying key AR features like approach angle, Integrated Water Vapor Transport (IVT), rain-on-snow, and antecedent soil moisture as strong predictors of extreme streamflow, and recommending an enhanced AR severity scale for the region.
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Mishra et al. (2025) Assessment of EOS-07 MHS satellite observations and retrieval of specific humidity profiles using a random forest-based algorithm
This study presents a preliminary performance assessment of the EOS-07 MHS satellite instrument, validating its brightness temperature observations and developing a random forest-based algorithm for retrieving specific humidity profiles, which demonstrated good agreement with reanalysis and radiosonde data and improved atmospheric forecasts when assimilated into a numerical weather prediction model.
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Efrat et al. (2025) Modeling seasonal water status and predicting yield in almond orchards using UAV multi-sensor and meteorological data
This study developed a scalable framework integrating UAV multi-sensor, meteorological, and irrigation data with Random Forest models to accurately predict seasonal plant water status (Stem Water Potential, SWP; Trunk Growth Rate, TGR) and yield in almond orchards. The research revealed that distinct water stress profiles, identified by fuzzy clustering, are strongly linked to significant yield reductions, offering actionable insights for precision irrigation.
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Li et al. (2025) Ubiquity and Causes of Soil Water Preferential Flow Across 17 Ecoregions
This study quantifies the ubiquity and drivers of soil water preferential flow (PF) across 17 diverse ecoregions in the USA using high-frequency soil moisture data. It reveals that PF is widespread, occurring in up to 60% of rainfall events, and is primarily driven by peak rainfall intensity, soil texture, antecedent soil moisture variability, humid climate, and net primary productivity.
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Polychroni et al. (2025) Compound extremes of air temperature and precipitation over the Mediterranean region. Understanding the influence of atmospheric circulation
This study quantifies the spatiotemporal variability and trends of four compound extreme climate indices (Cold/Dry, Cold/Wet, Warm/Dry, Warm/Wet) across the Mediterranean region from 1950 to 2018, revealing a significant shift towards warmer and drier conditions modulated by the North Atlantic Oscillation (NAO) and North Sea–Caspian Pattern (NCP).
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Lolli et al. (2025) Evaluating the NASA MPLNET Rain Masking Algorithm at Goddard Space Flight Center and Barcelona sites: Relevance to EarthCARE Cloud Profiling Radar Validation
This study evaluates the NASA MPLNET Rain Masking Algorithm (RMA) for detecting rainfall and distinguishing it from non-rain events over multiple years at two distinct sites. The RMA is found to be highly effective, outperforming IMERG in sensitivity and accuracy, and uniquely capable of detecting virga, which is crucial for validating the upcoming EarthCARE Cloud Profiling Radar.
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Wang et al. (2025) Beyond the Surface: Understanding Salt Crusts’ Impact on Water Loss in Arid Regions
This study experimentally investigated how salt crust morphology and coverage, regulated by sand mulching thickness, influence soil moisture evaporation in arid regions. It found that while high-coverage crusty salt crusts inhibit evaporation, medium-coverage patchy crusts and internal "salt tree" structures can promote it, challenging the conventional understanding that salt always suppresses evaporation.
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Graff et al. (2025) Sensitivity of winter Arctic amplification in NorESM2
This study investigates the drivers of uncertainty in Arctic climate change projections by performing sensitivity experiments with NorESM2, modifying five key processes. The results show that these modifications consistently enhance future Arctic warming, with the amplitude of additional winter warming varying by approximately 9 K, primarily driven by an enhanced greenhouse effect.
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Xie et al. (2025) Near-Surface Temperature Prediction Based on Dual-Attention-BiLSTM
This study developed a Dual-Attention-BiLSTM model, integrating random forest-based feature selection and two novel attention mechanisms, to improve hourly short-term near-surface temperature prediction. The model significantly enhanced prediction accuracy compared to a standalone BiLSTM network, demonstrating superior practical application value for short-term forecasts in inland areas.
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Luo et al. (2025) A framework for assessing the impact trends of neglecting water surface evaporation and substituting streamflow on water budget closure
This study introduces a framework to quantify the impact of neglecting water surface evaporation (WSE) and substituting runoff (R) for observed streamflow (Q) on water budget closure across 62 global river basins. It reveals that these practices introduce significant, context-dependent uncertainties, particularly when the water surface area ratio is high or in humid basins.
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Filippucci et al. (2025) Development of HYPER-P: HYdroclimatic PERformance-enhanced Precipitation at 1 km/daily over the Europe-Mediterranean region from 2007 to 2022
This study introduces HYPER-P, a high-resolution (1 km, daily) precipitation product for Europe and the Mediterranean basin (2007-2022), developed by downscaling and merging remote sensing, reanalysis, and in situ observations. The merged product, particularly the configuration including ERA5-Land, consistently outperforms individual parent datasets and reference products, especially in regions with sparse gauge coverage and complex topography.
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Zhao et al. (2025) The potential of AI global weather models for reference evapotranspiration forecasting: a comparison with numerical weather prediction models
This study evaluates the potential of AI global weather models (GraphCast) for daily reference evapotranspiration (ET0) forecasting in mainland China, comparing its performance against numerical weather prediction models (ECMWF, JMA) and demonstrating significant improvements through XGBoost post-processing. The GraphCast-XGBoost model is recommended for 1–7 day lead times.
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Colleoni et al. (2025) smash v1.0: a differentiable and regionalizable high-resolution hydrological modeling and data assimilation framework
This paper introduces smash v1.0, an open-source, differentiable, and regionalizable framework for high-resolution hydrological modeling and data assimilation. It demonstrates the framework's capabilities in local calibration (median Kling–Gupta efficiency > 0.8 at 3 km resolution) and regionalization (Kling–Gupta efficiency > 0.6 and Nash–Sutcliffe efficiency > 0.6 at 500 m resolution) across various scales and model structures.
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Shaw et al. (2025) Mountain glaciers recouple to atmospheric warming over the twenty-first century
This study assesses how temperature decoupling over mountain glaciers changes under warming, finding that local cooling is maximized in the 2020s-2030s before widespread glacier retreat leads to a recoupling of glacier air temperatures with their surroundings, increasing their sensitivity to warming.
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Pan et al. (2025) Capabilities of microwave-based soil moisture products in capturing extreme hydrological conditions
This study evaluates the capabilities of microwave-based soil moisture products in capturing both long-term average errors and short-term extreme hydrological events (droughts and floods). It finds that retrieval algorithms govern long-term accuracy, while L-band products are superior for detecting short-term extremes.
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Delhasse et al. (2025) Exploring the Greenland Ice Sheet’s response to future atmospheric warming-threshold scenarios over 200 years
This study uses a coupled regional atmospheric-ice sheet model (MAR-PISM) to project the Greenland Ice Sheet's response to various atmospheric warming scenarios and a climate reversal over 200 years, finding that warming beyond +1.4 °C triggers non-linear mass loss, but a rapid climate reversal can stabilize the ice sheet at a reduced state.
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2025 et al. (2025) CLLMate: A Multimodal Benchmark for Weather and Climate Events Forecasting
This paper introduces Weather and Climate Event Forecasting (WCEF), a new task that leverages numerical meteorological raster data and textual event data to predict weather and climate events. To facilitate this, the authors present CLLMate, the first multimodal dataset for WCEF, and benchmark 32 existing models, revealing their advantages and limitations for this task.
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Qiao et al. (2025) Extreme dry-hot in North America and Europe: the amplified role of warming-enhanced land-air coupling
This study investigates the role of land-atmosphere interactions in future warming hotspots, projecting that North America and Europe will experience the highest warming by the late 21st century, with approximately one-quarter of this warming linked to hot-dry feedback mechanisms.
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Senapati et al. (2025) High-resolution agricultural drought hazard mapping using the potential of geospatial data and machine learning approaches
This study developed a machine learning-geospatial framework to map high-resolution agricultural drought hazard (ADH) zones in semi-arid, rainfed basins, demonstrating that the Random Forest model achieved superior performance (AUC-ROC of 97.8%) and identified 31.77% of the Upper Dwarakeshwar River Basin as very high hazard zones.
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Shi et al. (2025) Meter-level resolution surface soil moisture estimation over agricultural fields from time-series quad-pol SAR with constraints of coarse resolution CCI data products
This study developed a novel time-series L-band quad-polarimetric SAR algorithm, constrained by coarse-resolution soil moisture products, to estimate meter-level surface soil moisture over agricultural fields. The method achieved high accuracy, with root mean square errors ranging from 0.03 to 0.08 cm³/cm³ and correlation coefficients between 0.5 and 0.86 across various crop types.
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Du et al. (2025) Joint influences of ENSO and East Asian winter monsoon on the Hadley circulation in the Asian monsoon domain
This study investigates the combined influences of the El Niño-Southern Oscillation (ENSO) and the East Asian Winter Monsoon (EAWM) on the Hadley Circulation in the Asian monsoon domain (AMHC), finding a synergistic weakening of the AMHC by 15.5 % during co-occurring El Niño and weak EAWM events.
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Gómez-Gómez et al. (2025) Impact of climate change on droughts and their propagation in an alpine-semiarid basin in Granada, Spain. Does the snow component help to anticipate adaptation strategies?
This study assesses past and projected climate change impacts on various drought types and their propagation in the Alto Genil Basin, an alpine Mediterranean region in southern Spain, with a focus on the role of snow in early adaptation strategies. Results indicate a significant temperature rise and precipitation decrease, leading to more frequent, severe, and prolonged droughts, particularly in snow-reliant areas, and a reduced lead time for operational drought response.
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Mabrouk et al. (2025) Artificial intelligence evaluation of nature based flood resilience in hilly terrain
This study evaluates the effectiveness of nature-based solutions (NBS), specifically flexible and rigid vegetation, in mitigating flash floods in hilly terrain by using artificial intelligence (AI) models to predict peak discharge. It found that flexible vegetation reduced peak discharge by 8% more than rigid vegetation, with the Random Forest model demonstrating superior predictive accuracy (R² of 0.9809 for flexible and 0.9906 for rigid vegetation).
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Darnell et al. (2025) The interplay of future emissions and geophysical uncertainties for projections of sea-level rise
This study disentangles the relative contributions of future CO2 emissions and geophysical uncertainties to sea-level rise projections, finding that emissions trajectories become the primary driver of variability by mid-century, while accelerated Antarctic Ice Sheet melting and equilibrium climate sensitivity are key geophysical uncertainties.
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Karimi et al. (2025) Remote sensing-based bathymetry mapping in shallow lakes: comparative analysis of Sentinel-2 and Landsat-8 imagery integrated with machine learning techniques
This study evaluates the efficacy of Sentinel-2 and Landsat-8 satellite imagery combined with machine learning (MLP, RF, SVR, XGBoost) for bathymetric mapping in shallow inland waters. It demonstrates that the Multi-Layer Perceptron (MLP) model, particularly when integrated with Landsat-8's optical and thermal infrared bands, can produce sub-metre accuracy bathymetric maps, highlighting the significant contribution of thermal data.
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Huerta et al. (2025) Enhancing daily precipitation reconstruction: An improved version of the reddPrec R package
This paper introduces an improved version of the reddPrec R package for daily precipitation reconstruction, featuring enhanced quality control, homogenization, and flexible machine learning models with dynamic covariates. Case studies in Switzerland and Spain demonstrate its superior accuracy in gap-filling and grid creation, and its effectiveness in detecting and adjusting data inhomogeneities.
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Béland (2025) Mapping wood area in forests from ground lidar and estimating their light interception using radiative transfer modeling
This study quantifies the partitioning of photosynthetically active radiation (PAR) and near-infrared radiation (NIR) absorption between leaves and woody structures in two broadleaf forests using ground lidar and radiative transfer modeling. It reveals that woody structures absorb 30–35 % of incoming NIR within the canopy space, while leaves account for approximately 90 % of absorbed PAR.
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Kalu et al. (2025) Basin-scale evaluation of current and future climate influences on groundwater variations using satellite and model observations
This study evaluates current and future climate influences on groundwater variations in the Murray Darling Basin (MDB) using an integrated approach of satellite, model, and in-situ observations. It reveals that hydrological and climatic factors drive groundwater trends and predicts a slight upward trend of 0.32 mm/month in groundwater storage for 2024–2029.
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Su et al. (2025) Unravelling the hidden drivers of crop sensitivity to precipitation in the arid and semi-arid regions of Northwest China
This study investigates the spatiotemporal sensitivity of maize and wheat to precipitation across the Loess Plateau from 2001 to 2023, revealing how aridity, soil texture, and elevated atmospheric CO₂ modulate crop responses and providing insights for adaptive agricultural management.
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Babaei et al. (2025) Geospatial analysis of Lake Urmia’s drying: predicting land surface temperature changes using remote sensing and machine learning
This study investigates the impact of Lake Urmia's desiccation on regional cooling effects and predicts future land surface temperature changes from 1986 to 2035 using remote sensing and machine learning. It found a drastic reduction in Lake Urmia's area, leading to a significant increase in regional land surface temperature and a diminished cooling effect, with projections indicating further warming.
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Wang et al. (2025) Discriminating the impact of soil moisture and vapor pressure deficit on vegetation greening over multiple time scales
This study investigated the latent time scales and nonlinear characteristics of global vegetation greening from 1982 to 2020, examining the sensitivity and response mechanisms of Leaf Area Index (LAI) to soil moisture (SM), vapor pressure deficit (VPD), and their interactions across multiple time scales. The findings indicate that LAI and its multi-temporal components are more sensitive to SM than to VPD or their interaction, with these influences exhibiting scale dependence.
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Amiranipour et al. (2025) Meteorological and agricultural drought assessments using satellite imagery and machine learning models
This study comprehensively assessed meteorological and agricultural droughts and their interrelationship across Iran using satellite imagery and machine learning, demonstrating robust predictive accuracy for drought early warning and management.
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Scheuerer et al. (2025) Multi-decadal streamflow projections for catchments in Brazil based on CMIP6 multi-model simulations and neural network embeddings for linear regression models
This study develops an interpretable linear regression model, enhanced with neural network embeddings, to link monthly streamflow anomalies to precipitation and temperature anomalies. The model is used to generate multi-decadal streamflow projections for 157 Brazilian catchments based on CMIP6 multi-model simulations, predicting reduced streamflow in northern/central/southeastern Brazil and increased streamflow in southern Brazil.
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Gou et al. (2025) Predicting land-surface specific humidity from radiative temperature and ambient weather for evapotranspiration modelling: Lessons from South Australian field sites
This study develops an empirical "Tr-Weather" method to estimate land-surface specific humidity using surface radiative temperature and ambient micrometeorological variables, demonstrating its superior performance for evapotranspiration (ET) modeling, especially under sunny conditions, compared to using ambient specific humidity.
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Sharghi et al. (2025) Practical investigation of climate extremes and IDF curves under climate change with applications of SSP scenarios (case study: Silakhor Plain, Iran)
This study projects future climate extremes and Intensity-Duration-Frequency (IDF) curves for the Silakhor Plain, Iran, using downscaled CMIP6 GCM data under SSP2-4.5 and SSP5-8.5 scenarios, revealing increased intensity of heatwaves and sub-daily extreme precipitation, alongside a decline in frost waves and overall wet spell frequency.
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Mielke et al. (2025) GRACE‐FO and Future Satellite Gravity Missions Will Need to Account for Global Cloud Water Convergence
This study identifies and quantifies over 50,000 extreme cloud water mass change events globally from 2002 to 2023, demonstrating that these events, comparable in magnitude to water vapor variations, are detectable by GRACE-FO and show increasing frequency and intensity, necessitating their integration into gravity field processing for improved hydrological and climate research.
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Shang et al. (2025) A rainfall similarity-based dataset construction framework for enhanced urban inundation prediction using machine learning
This study proposes a rainfall similarity-based framework to construct high-quality datasets for machine learning models, significantly enhancing urban inundation prediction accuracy by incorporating process-oriented rainfall features. The framework improves predictive performance, with Random Forest models showing particular synergy and achieving inundation-extent accuracy exceeding 85 %.
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Lamichhane et al. (2025) Dynamical prediction of sub-seasonal tropical cyclones: IAP-CAS model advances
This study evaluates the sub-seasonal tropical cyclone (TC) forecasts from the IAP-CAS dynamic prediction system at one to eight weeks lead. The model demonstrates skill in capturing TC climatology and annual cycles, identifies areas for improved anomaly prediction, and highlights the influence of MJO and ENSO-MJO interactions on forecast proficiency.
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Ali et al. (2025) Development and application of a novel drought index for regional drought assessment: a case study from Pakistan
This study introduces the Inter-Het Regional Drought Index (IHRDI), a novel drought assessment tool for Pakistan that integrates precipitation data interdependence and heterogeneity using Bayesian networks and square deviation. The IHRDI demonstrates superior performance over existing indices in capturing regional drought conditions and reveals increasing drought trends across most regions of Pakistan.
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Li et al. (2025) Identification of Driving Factors of Long-Term Terrestrial Water Storage Anomaly Trend Changes in the Yangtze River Basin Based on Multisource Data and Geographical Detector Method
This study quantifies the individual and interactive effects of natural and anthropogenic factors on long-term terrestrial water storage anomaly (TWSA) trends across the Yangtze River Basin (YRB) using multisource data and the Geographical Detector method. It reveals spatially heterogeneous and synergistic drivers, highlighting the critical role of both climate variability and human activities in regional water storage dynamics.
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Wang et al. (2025) Runoff Prediction in the Songhua River Basin Based on WEP Model
This paper investigates historical and projected hydrological changes in the Songhua River Basin, revealing that future climate change, particularly temperature increases, will significantly alter runoff patterns, elevate summer flood risk, and lead to system instability at a 2.5 °C warming threshold.
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Regmi et al. (2025) Enhancing hydropower resilience through dynamic rule curve modifications under climate change in the Sunkoshi multipurpose scheme, Nepal
This study assesses climate change impacts on the Sunkoshi River Basin and proposes dynamic rule curve modifications for four hydropower projects to optimize reservoir operations. The adaptive strategy significantly increases average yearly energy generation, with up to 71.26% for Sunkoshi No.1, enhancing hydropower resilience under future climate scenarios.
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Lab (2025) Inundation2Depth Dataset
This paper introduces Inundation2Depth, a novel dataset designed to overcome the scarcity of georeferenced flood depth data for deep learning applications in urban areas, providing paired inundation extent-depth labels derived from aerial imagery and LiDAR-based Digital Terrain Models.
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Sutanto et al. (2025) Future intensification of compound and consecutive drought and heatwave risks in Europe
This study projects the future intensification of single and compound/consecutive drought and heatwave events across Europe and their impacts in Germany under climate change scenarios, finding significant increases in event characteristics and impacts across multiple regions.
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Pyarali et al. (2025) Assessment of Selected Climate Indicators Across the Elbe River Basin to Analyse Changes in Climate Extremes and their Effects
This study assesses the future impacts of climate change on extreme temperature, precipitation, and agrometeorological events across the Elbe River basin using high-resolution climate model ensembles under RCP2.6 and RCP8.5 scenarios, finding increased heatwaves, regional floods, and complex, often hindering, effects on agriculture by the end of the century.
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Yang et al. (2025) Retrieval of land surface temperature in mountainous areas considering terrain shadows
This study proposes a fast and efficient mountain shadow correction method based on the Normalized Difference Mountain Vegetation Index (NDMVI) to improve land surface temperature (LST) retrieval accuracy in mountainous areas. The method significantly enhances computational efficiency and reduces LST errors, particularly in valleys where deviations can reach 1.17 K due to neglected shadow effects.
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Neupane et al. (2025) “QuantumIrrigation” – a new quantum computing python package for irrigation demand assessment
This study introduces QuantumIrrigation, a Python package integrating quantum computing with deep learning for irrigation demand assessment by predicting crop reference evapotranspiration (ETo) and soil water tension. It demonstrates that while classical and quantum models perform similarly for ETo, hybrid classical-quantum models significantly enhance soil water tension prediction accuracy and computational efficiency.
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Villegas-Vega et al. (2025) Optimization of LSTM networks through neuroevolution for drought forecasting in Mexico
This study proposes DeepGA-LSTM, a neuroevolution-based method using genetic algorithms to optimize Long Short-Term Memory (LSTM) networks for drought forecasting in Mexico. The DeepGA-LSTM consistently outperformed baseline LSTM and CNN-LSTM models in two Mexican regions (Chihuahua and Zacatecas) using SPEI and SPI indices, demonstrating its effectiveness in finding optimal network architectures.
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Pradhan et al. (2025) Atmospheric Rivers intensify extreme precipitation and flooding across Australia
This study quantifies the impact of Atmospheric Rivers (ARs) on extreme precipitation and flooding across Australia, revealing that ARs significantly increase the magnitude (20–70% higher) and frequency (2 to 12 times shorter return periods) of these events, particularly in southeast Australia.
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Zheng et al. (2025) Attention mechanism-based multi-scale spatiotemporal fusion for precipitation nowcasting
This study proposes STAt-Former, a novel deep learning model integrating multi-scale spatiotemporal channel attention and Transformer architecture, to enhance precipitation nowcasting accuracy by effectively capturing both local and long-range spatial dependencies from radar echo images. The model demonstrates superior performance over baseline methods for forecasts up to 120 minutes using a Netherlands radar dataset.
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Douglas et al. (2025) Effects of temperature overshoot amplitude on regional climate
This study investigates regional climate responses to global temperature overshoot scenarios, finding that while global temperature may be reversible, regional impacts on temperature and precipitation vary significantly and some extreme temperature changes persist for centuries, with the magnitude of overshoot being a critical factor.
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Meng et al. (2025) Soil salinity patterns reveal changes in the water cycle of inland river basins in arid zones
This study quantitatively analyzed soil salinization changes in the Shiyang River basin of Northwest China from 2002 to 2022 using remote sensing, revealing that human activities, particularly water conservancy projects and agricultural irrigation, are the primary drivers of intensifying salinization patterns.
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Saberian et al. (2025) HydroQuantum: A new quantum-driven Python package for hydrological simulation
This research introduces "HydroQuantum," a new Python package leveraging quantum computing for hydrological simulations. It demonstrates the potential of quantum algorithms, specifically QLSTM, for daily streamflow and stream water temperature simulations, showing promising results for streamflow but underperformance for stream water temperature compared to classical LSTM.
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Liu et al. (2025) Validation of Multi-Scale LAI Products in Heterogeneous Terrain-Based UAV Images
This study comprehensively validated multi-scale Leaf Area Index (LAI) products (Sentinel-2, Landsat-8/9, MCD15A3H) against fine-resolution LAI maps derived from UAV imagery and field measurements, revealing a consistent systematic underestimation across all products, with MCD15A3H demonstrating the highest accuracy.
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Magnini et al. (2025) Informativeness of teleconnections in frequency analysis of rainfall extremes
This study proposes a reproducible framework to assess the informative content of teleconnections for regional frequency analysis of rainfall extremes in North-Central Italy. It identifies significant spatial patterns of correlation between specific climate indices (WeMOI, EA-WR) and rainfall extreme statistics, demonstrating that climate-informed regional models can improve the goodness-of-fit compared to stationary approaches.
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González-Márquez et al. (2025) Estimation of dam water volume in northern Sinaloa, Mexico using Landsat imagery and artificial intelligence models
This study developed and evaluated a methodology to estimate dam water volumes in northern Sinaloa, Mexico, addressing data gaps where traditional area-volume curves were unavailable. By integrating Landsat imagery, spectral indices, and artificial intelligence models, the Deep Neural Network achieved the best performance, accurately estimating volumes with high coefficients of determination.
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Hung et al. (2025) Downscaled global 60-meter resolution estimates of irrigation water sources (2000–2015)
This study generates high-resolution (60 meters) global maps of irrigation water sources (rainfed, groundwater, surface water) for 2000-2015 by downscaling existing datasets using spatial patterns and crop water requirements, demonstrating significantly improved accuracy in distinguishing groundwater use in the U.S. compared to previous global maps.
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Zhao et al. (2025) Eco-evolutionary modelling of global vegetation dynamics and the impact of CO 2 during the late Quaternary: insights from contrasting periods
This study uses an eco-evolutionary optimality (EEO)-based modeling approach to quantify the relative impacts of climate fluctuations and CO2 levels on global vegetation dynamics and gross primary production (GPP) during the Last Glacial Maximum (LGM) and mid-Holocene (MH) compared to pre-industrial (PI) conditions. It reveals that low CO2 was nearly as important as climate in reducing GPP and increasing C4 plant abundance at the LGM, and it significantly offset positive climate impacts on GPP during the MH.
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Phillips et al. (2025) Remotely sensed tree mortality rates in mesic forests of the US Southwest during an extended drought
This study quantified recent tree mortality in isolated mesic forests of the US Southwest from 1997 to 2023 using high-resolution aerial imagery and Landsat time series, finding overall low mortality rates comparable to background levels but highlighting challenges in remotely sensing low-severity disturbances.
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Suna et al. (2025) Stacked hybridization of deep learning model with grey wolf optimization for accurate and explainable reference evapotranspiration
This study developed and evaluated a novel hybrid Deep Neural Network-Grey Wolf Optimization (DNN-GWO) model for accurate and explainable monthly reference evapotranspiration (ET0) forecasting in data-scarce regions. The DNN-GWO model significantly improved predictive accuracy, reducing RMSE by nearly 60% compared to the best-performing standalone deep learning model, offering a robust and interpretable solution for agricultural water management.
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Jomaa (2025) Other Drivers of Climate Change, Groundwater Water Depletion
This paper synthesizes evidence demonstrating that groundwater depletion is a critical, often overlooked, driver of climate change through its impact on heat exchange and soil moisture, and it also compromises the accuracy of climate monitoring data.
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Kesselring (2025) Remote Sensing of 3D Gas Exchange Variables Across Forests
This thesis quantifies the uncertainty in remote sensing estimates of forest evapotranspiration (ET) and gross primary production (GPP) caused by the top-of-canopy (TOC) perspective, revealing that canopy structure and light distribution are primary drivers of this divergence. It highlights the need for multi-sensor integration to improve large-scale gas exchange models.
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Cao et al. (2025) Research on the Characteristics of the Convective Initiation During the Warm Season Over the Tibetan Plateau
This study investigates the temporal and spatial characteristics of convective initiation (CI) over the Tibetan Plateau during the warm season using FengYun-4A satellite data. It reveals that CI predominantly occurs on windward slopes, linked to monsoon activity, and exhibits distinct diurnal and day-night spatial patterns.
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Liu et al. (2025) Risk assessment of rainstorm flood disasters in the China–Pakistan economic corridor
This study developed a comprehensive flood risk assessment model for the China–Pakistan Economic Corridor (CPEC) from 1979 to 2024, revealing a significant expansion of middle- to high-risk zones in central plains and southern lowlands, primarily driven by extreme rainfall.
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Alavi et al. (2025) High-Resolution Crop Evapotranspiration Estimation Using the Automated OPTRAM-ETc Method
This study proposes an automated Optical Trapezoid Model for crop evapotranspiration (OPTRAM-ETc) using high-resolution Sentinel-2 data to overcome limitations of thermal-optical models. It demonstrates robust field-scale ETc estimation in sugarcane cultivation through novel wet/dry edge parameterization and multi-year temporal data fusion, providing actionable insights for water management in water-scarce regions.
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Staudinger et al. (2025) How well do process-based and data-driven hydrological models learn from limited discharge data?
This study systematically compares the learning behavior of process-based and data-driven hydrological models under varying discharge data availability, selection strategies, and spatial input resolutions. It finds that while process-based models initially outperform data-driven ones with limited data, Long Short-Term Memory (LSTM) networks achieve superior and continuously improving performance with sufficient training data, demonstrating the critical role of data quantity, memory, and spatial input in model learning.
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Wang et al. (2025) Saudi Rainfall (SaRa): hourly 0.1° gridded rainfall (1979–present) for Saudi Arabia via machine learning fusion of satellite and model data
This paper introduces Saudi Rainfall (SaRa), a high-resolution, hourly, gridded precipitation product for the Arabian Peninsula developed using machine learning fusion of satellite and model data. SaRa significantly outperforms 19 other state-of-the-art precipitation products in the region across various evaluation metrics.
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Badawy (2025) Assessment of Vapour Pressure Deficit Trends and Their Connections to Climate Variability in the Nile Delta
This study analyzed three decades (1990–2020) of vapour pressure deficit (VPD) variability in Egypt's Nile Delta, revealing a statistically significant increase in atmospheric aridity driven primarily by rising air temperatures and decreasing relative humidity, with implications for agricultural resilience and water management.
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Teng et al. (2025) Can alternating irrigation technology drive water saving in agricultural irrigation? Evidence from China
This study empirically investigates the macro-level impact of Alternating Irrigation Technology (AIT) on agricultural irrigation water consumption (AIWC) across 31 provinces in China from 2004 to 2023. It finds that AIT significantly reduces AIWC, primarily by expanding effective irrigation areas and improving the effective irrigation rate of farmland, with the water-saving effect being more pronounced in major grain-producing areas and regions with higher R&D investment.
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Chen et al. (2025) A comprehensive assessment of the FireCCILT11 global burned area product
This study comprehensively assessed the FireCCILT11 global burned area product (1982–2018) using national fire history datasets and independent satellite products, revealing significant limitations in its accuracy and consistency, especially before 2001, which renders it generally unsuitable for robust long-term fire regime analyses.
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Braz et al. (2025) Development of Automatic Labels for Cold Front Detection in South America: A 2009 Case Study for Deep Learning Applications
This study introduces an automatic cold front detection method using ERA5 reanalysis for South America, generating spatially consistent labels for machine learning applications, and demonstrating high spatial concordance with manual charts despite pixel-level differences.
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Cohen et al. (2025) Reply to: Observed warming of cold extremes is not captured with a fixed threshold definition
This paper replies to a critique regarding the definition of cold extremes, re-examining trends in Arctic and mid-latitude cold extremes using a moving threshold definition, and concludes that the original findings of inconsistent warming in mid-latitude cold extremes remain robust.
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Wang et al. (2025) A flood susceptibility prediction method for climate change scenarios driven by coupled land simulation and spatiotemporal dual convolution synergy
This study develops a novel comprehensive framework coupling the PLUS model and a spatiotemporal dual attention network (STDAN) to dynamically predict flood susceptibility and land use changes under future CMIP6 climate scenarios. Applied to Shenzhen, the method projects an increase in flood susceptibility by 2030 across all scenarios compared to 2020, with the SSP585 scenario showing the highest increase.
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Luo et al. (2025) Impact of Assimilating Doppler Radar Data on Short-Term Numerical Weather Forecasting at Different Spatial Scales
This study investigates the impact of assimilating Doppler radar data on short-term numerical weather forecasts for heavy rainfall in Southern China, demonstrating significant improvements in hourly precipitation forecasts, particularly for mesoscale systems within the first two hours.
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Chung et al. (2025) Statistical characteristics of storm cells and centroid-based probability nowcasts by tracking error
This study statistically characterized summer storm cells over Taiwan using 8 years of radar data and the Storm Cell Identification and Tracking (SCIT) algorithm, focusing on tracking error to develop and verify two storm-based nowcasting techniques, Potential Track Area for Storm (PTAS) and Probability of Storm Tracking (PoST), which enhance early warning capabilities by quantifying forecast uncertainty.
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Ismail et al. (2025) Unravelling the spatiotemporal causality chain between meteorological and agricultural drought propagation in the China–Pakistan Economic Corridor
This study investigates the nonlinear spatiotemporal causality of meteorological to agricultural drought propagation in the China–Pakistan Economic Corridor (CPEC) from 1981–2022, revealing regional variations in propagation times and rates, with maximum temperature and soil moisture identified as key predictors.
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Yin et al. (2025) Distinct Eurasian Teleconnection Wave Trains Contributing to the Intraseasonal Variations of East Asian Jet Streams
This study reveals that intraseasonal variations of East Asian polar-front and subtropical jets are preceded by Eurasian wave trains originating from the Iceland and Norwegian Sea, with Atlantic Sea Surface Temperature (SST) anomalies playing a crucial role in stimulating these upstream Rossby wave sources.
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Rooij (2025) Fitting the junction model and other parameterizations for the unsaturated soil hydraulic conductivity curve: KRIAfitter version 1.0
This paper introduces and evaluates a novel "junction model" for unsaturated soil hydraulic conductivity (UHCC) that simplifies the conceptualization of water domains (films or capillaries) and documents the associated Fortran fitting code, KRIAfitter. The junction model often provides good fits to observed data with fewer parameters compared to existing multi-domain models, offering a more parsimonious alternative, though some complex soil behaviors may still require models with more parameters.
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Wang et al. (2025) Multi-scale evaluation of six fused evapotranspiration products over mainland China: Accuracy, consistency and uncertainty
This study comprehensively evaluates the accuracy, consistency, and uncertainty of six fused evapotranspiration (ET) products over mainland China from 1982 to 2017, revealing that AutoML performs best at the site scale while REA exhibits the lowest uncertainty.
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Suresh et al. (2025) U-Net++ and ConvLSTM based Semantic Event Stream Processing for Real-Time Flood Monitoring
This paper introduces UConvFloodNet, a deep learning framework that integrates image enhancement, U-Net++, and ConvLSTM to achieve highly accurate real-time flood monitoring by segmenting flood zones and tracking their temporal evolution.
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Jasim et al. (2025) DDMSA-U-Net: A Lightweight Deep Learning Framework for Multi-Spectral Change Detection for Agricultural Land Use Monitoring
This research proposes a novel, light deep learning architecture, Depthwise Dilated Multi-Spatial Attention U-Net (DDMSA-U-Net), to enhance the accuracy and efficiency of agricultural change detection using multi-temporal satellite imagery, achieving 91.6-96.6% overall accuracy and Kappa values above 0.85.
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Lakatos et al. (2025) Extreme weather risks for European agriculture (1981–2020): A quantitative review using the E3CI
This study provides the first pan-European, multi-hazard assessment of agricultural climate risks during the growing season (1981–2020) using the European Extreme Events Climate Index (E3CI), revealing significant increases in extreme warm events, drought, and wildfire risk, with distinct zonal and meridional spatial patterns across Europe.
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King et al. (2025) ESD Ideas: Extended net zero simulations are critical for informed decision making
This paper argues that extended, millennial-length Earth System Model simulations of net zero emissions are crucial for understanding long-term committed climate changes, particularly regional extremes and the impact of delayed emissions cessation, to inform robust policymaking.
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Sun et al. (2025) Causal pathways underlying global soil moisture–precipitation coupling
This study employs an information flow technique on satellite observations and reanalysis data to map global surface soil moisture-precipitation (SSM-P) coupling hotspots and identify the dominant sensible heat or evapotranspiration-mediated causal pathways, revealing that most CMIP6 models fail to reproduce these patterns.
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Sun et al. (2025) Mechanisms of Heavy Rainfall over the Southern Anhui Mountains: Assessment for Disaster Risk
This study investigates the spatiotemporal characteristics and atmospheric circulation mechanisms of heavy rainfall in southern Anhui (2022–2024), identifying key large-scale circulation systems and multi-channel water vapor sources, and developing an optimal power index model for rainstorm disaster risk assessment.
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Vaccaro et al. (2025) Assessing microtensiometers for monitoring stem water potential in mandarin (Citrus reticulata Blanco) orchard under different irrigation regimes
This study assessed the reliability of microtensiometers (MTs) for continuous stem water potential (Ψstem) monitoring in a mandarin orchard under two irrigation regimes. It found MTs promising for real-time monitoring, but their first-order response dynamics (characterized by significant time lags and signal attenuation) necessitate the development of compensation protocols for direct application of readings to existing crop-specific Ψstem thresholds.
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Li et al. (2025) Transformer-based detection of abnormal rice growth using drone-based multispectral imaging
This study proposes ARG-TR, a lightweight transformer-based semantic segmentation model, to accurately detect various abnormal rice growth patterns using drone-based multispectral imaging, demonstrating superior performance and computational efficiency compared to existing state-of-the-art methods.
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An et al. (2025) Future projections of wet and dry spells in southern Sweden: The impact of climate model resolution
This study evaluates the performance of five Regional Climate Models (RCMs) with resolutions from 44 km to 3 km in reproducing historical wet and dry spells in southern Sweden and projects future changes under RCP8.5. It finds that the convection-permitting model (CPM) AROME (3 km) outperforms coarser RCMs, projecting increases in wet spells and extreme precipitation intensities, while dry spells show more modest, non-monotonic changes in duration.
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Yuan et al. (2025) Enhancing runoff prediction with causal lag-aware attention and multi-scale fusion in transformer models
This study addresses the non-causal issue in Transformer models for runoff prediction by proposing a novel Causal Lag-Aware Attention Mechanism, a multi-scale fusion module, and a frequency-domain-based loss function, achieving significant improvements in prediction accuracy over existing state-of-the-art Transformer models.
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Li et al. (2025) Assessing the Annual-Scale Insolation–Temperature Relationship over Northern Hemisphere in CMIP6 Models and Its Implication for Orbital-Scale Simulation
This study evaluates Northern Hemisphere land surface air temperature responses to the annual insolation cycle in CMIP6 models, finding that while models capture thermal inertia, they consistently overestimate temperature sensitivities to insolation, impacting orbital-scale paleoclimate simulations.
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Yue et al. (2025) The Unevenness of Warm‐Season Precipitation Over the Steep Terrain in North China and Its Related Environmental Conditions
This study investigates the underlying mechanisms governing the spatial unevenness of precipitation over complex terrains in North China, identifying distinct environmental conditions and fine-scale characteristics for strongly uneven (terrain-influenced, convective) versus weakly uneven (synoptic-driven, widespread) precipitation events.
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Xu et al. (2025) Three decades evolution of land subsidence driven by anthropogenic activities in the Yellow River Delta (YRD) from continuous SAR interferometry
This study developed a novel Trend-Adaptive Functional Modeling and Connection method (TAFMC) to integrate multi-sensor InSAR time series, enabling the first continuous, three-decade (1992-2024) monitoring of land subsidence in the Yellow River Delta (YRD) and revealing its evolution driven by anthropogenic activities. It found subsidence up to 220 cm, with rates decreasing after 2021, and identified new subsidence funnels linked to the brine industry and shrimp aquaculture.
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Peng et al. (2025) Typhoon Prediction Analysis of Pangu Weather Model
This paper evaluates the Pangu Weather Model for typhoon prediction, demonstrating its suitability and high accuracy with prediction errors not exceeding 1%, and proposes its potential for artificial typhoon modification through engineering cybernetics.
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Blackport et al. (2025) Observed warming of cold extremes is not captured with a fixed threshold definition
This paper demonstrates that the previously reported lack of warming trends in mid-latitude cold extremes by Cohen et al. (2023) is an artifact of using a fixed threshold definition. By employing a moving threshold, this study reveals clear and statistically significant warming trends in cold extremes, consistent with overall climate warming.
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Kandasamy et al. (2025) Hierarchical attention-enhanced multihead CNN and level sets segmentation: A proposed approach to enhance the cyclone intensity estimation
This study proposes a novel deep learning approach combining a Hierarchical Attention-Enhanced Multihead Convolutional Neural Network (CNN) with level sets segmentation and Bidirectional Long Short-Term Memory (Bi-LSTM) Networks to improve real-time tropical cyclone intensity estimation and time-series forecasting, demonstrating superior performance over existing methods in the North Indian Ocean region.
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Pickens et al. (2025) Rapid monitoring of global land change
This paper introduces DIST-ALERT, a global land change monitoring system that rapidly tracks vegetation loss and generic land cover anomalies at 30 m resolution using Harmonized Landsat Sentinel-2 imagery. In 2023, it identified 28.6 ± 7.6 Mha of anthropogenic land use conversions and 14.9 ± 4.3 Mha of conversion fires, demonstrating high accuracy and low latency for detecting diverse land changes across all ecosystems.
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Prathyusha et al. (2025) Geospatial assessment of cropping intensity: Advances, challenges and future directions
This review synthesizes the current state, advances, and challenges in geospatial assessment of cropping intensity, highlighting the evolution of methodologies from traditional time-series analysis to modern machine and deep learning algorithms. It identifies critical gaps, such as the need for better integration of socio-economic data and standardized methodologies, and proposes future directions for more accurate and integrated monitoring to address global food security and sustainable agriculture.
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Huang et al. (2025) Modulation of ENSO on the MJO Forecast
This study systematically evaluates the influence of El Niño-Southern Oscillation (ENSO) on Madden-Julian Oscillation (MJO) forecast skill across seasons, initial phases, and amplitudes using multiple dynamical models, revealing distinct seasonal dependencies where La Niña enhances MJO predictability in boreal winter and El Niño improves it in boreal summer.
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Tang et al. (2025) Seamless Reconstruction of MODIS Land Surface Temperature via Multi-Source Data Fusion and Multi-Stage Optimization
This study develops a seamless MODIS Land Surface Temperature (LST) reconstruction framework by integrating multi-source data fusion and a multi-stage optimization strategy, achieving high spatiotemporal fidelity and outperforming conventional methods.
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Ning et al. (2025) Atmospheric Freezing Level Height Changes
This study updates global freezing level height (FLH) changes to 2023 using observations and models, revealing significant FLH increases, particularly in extra-tropical and high northern latitudes, which are linked to accelerated glacier mass loss driven by tropical sea surface temperature variability.
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Wang et al. (2025) Harnessing TabTransformer Model and Particle Swarm Optimization Algorithm for Remote Sensing-Based Heatwave Susceptibility Mapping in Central Asia
This study introduces a fully remote sensing-based framework for mapping heatwave susceptibility using a Particle Swarm Optimization (PSO)-optimized TabTransformer deep learning model. The framework successfully achieves accurate, scalable, and spatially detailed heatwave susceptibility mapping in data-scarce Central Asian regions, outperforming a baseline model and identifying key environmental predictors.
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Nasslahsen et al. (2025) AI and IoT in Precision Agriculture: Image Classification in Action
This paper reviews the recent advancements and applications of AI and IoT in precision agriculture, specifically focusing on image classification for tasks such as disease and pest detection, crop health monitoring, phenotyping, and weed detection. It highlights how the integration of deep learning models with IoT devices enables real-time, data-informed decision-making in smart farming.
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Cufí et al. (2025) A Farm-Scale Water Balance Assessment of Various Rice Irrigation Strategies Using a Bucket-Model Approach in Spain
This study developed and validated a farm-scale bucket-type water mass balance model to assess the water use efficiency of various rice irrigation strategies in a 121-hectare farm in Spain. The model demonstrated that 10-day fixed-turn irrigation achieved the highest water savings (30%), followed by early cut-off (17%) and dry seeding with delayed flooding (15%), primarily by reducing the flooded period.
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Zhang et al. (2025) Characterizing snow droughts and deluges in the Sacramento River Basin, California using GNSS-derived snow depth (2009–2023)
This study retrieves daily snow depth using GNSS Interferometric Reflectometry (GNSS-IR) at four stations in the Sacramento River Basin (2009–2023) to characterize snow droughts and deluges, revealing that 57.1 % of snow drought events exhibited compound characteristics driven by both precipitation and temperature anomalies.
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Gao et al. (2025) Climate change and human activities amplify runoff variability risks in lower reaches of large rivers
This study developed a three-tiered attribution framework to analyze spatiotemporal runoff variations and their drivers in the Yellow River Basin (1952–2021), revealing that cumulative climatic and, predominantly, human activities amplify runoff variability risks in downstream regions with significant seasonal fluctuations.
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Mezin et al. (2025) A Comparative Study of Traditional Models and AI-Based Techniques for Hydrological Modeling
This review paper comparatively examines AI-based techniques and traditional models for hydrological modeling, identifying their strengths, limitations, and predictive accuracy across diverse geographic regions to inform future water management strategies.
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Ahmed et al. (2025) Flood Mapping from Satellite Imagery: A Response-Based Framework for Quantifying Flood Hazard and Uncertainty
This study develops a response-based framework for probabilistic flood hazard mapping using time series of satellite-derived flood maps and multiple digital elevation models (DEMs). The framework provides a rapid and computationally inexpensive alternative to numerical hydrodynamic models, particularly for data-scarce regions, while quantifying flood hazard and associated uncertainties.
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Farchouni et al. (2025) Mapping groundwater recharge potential zones in a semi-arid, anthropogenically modified mountainous basin
This study maps groundwater recharge potential zones (GRPZ) in the semi-arid Tensift basin, Morocco, using a multi-factorial approach combining remote sensing, GIS, Analytical Hierarchy Process (AHP), and stable isotopes for validation. It identifies mountains and piedmonts as primary recharge areas, with moderate potential in irrigated plains and low potential in urbanized zones.
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Kumar et al. (2025) Sustainability and resiliency of regional groundwater through enhanced conveyance and application efficiencies of irrigation water
This study utilized groundwater modeling to assess the impact of various irrigation efficiencies on groundwater levels in an intensively cultivated Indian village, revealing that only high-efficiency drip irrigation can effectively reverse the alarming groundwater depletion trend. The research provides quantitative insights into how improved water management technologies can contribute to sustainable groundwater use in water-stressed agricultural regions.
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Li et al. (2025) Damage intensity increases ice mass loss from Thwaites Glacier, Antarctica
This study incorporates ice damage processes into an ice-sheet model for the Thwaites Glacier basin, demonstrating that explicitly accounting for ice damage significantly increases projected ice mass loss, more than doubling it by 2300 compared to simulations that neglect damage.
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Hong et al. (2025) The Effects of Ice Habit Models on Passive Microwave Snowfall Rate Retrievals
This study investigates the significant impact of ice habit models on snowfall rate (SFR) derived from space-borne passive microwave observations, revealing that an optimal ice habit choice is environmentally dependent. It proposes a machine learning model that integrates multiple ice habits, improving SFR retrieval accuracy by approximately 10% overall.
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Li et al. (2025) Revisiting the Role of Ocean Circulation Changes in Polar Ocean Heat Transport Anomalies under Global Warming
This study introduces a novel passive-active decomposition to re-evaluate the contributions of ocean circulation and passive ocean temperature changes to polar ocean heat transport anomalies, demonstrating that circulation changes have a significantly weaker effect than inferred from the standard decomposition.
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Liang et al. (2025) Reducing Uncertainty in Climate Projections for the Mid and High Latitudes of the Northern Hemisphere
This study applies emergent constraints derived from observed global warming and Arctic sea ice loss to significantly reduce uncertainty in projected air temperature and precipitation changes over Northern Hemisphere mid-to-high latitude land areas.
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Du et al. (2025) Regional Variability in Summer Extreme Precipitation over Northern China and Associated Atmospheric Teleconnections from 1961 to 2022
This study comprehensively analyzes summer extreme precipitation in northern China from 1961 to 2022, revealing distinct regional trends and underlying atmospheric and oceanic drivers. It finds a significant increase in extreme precipitation in western northern China, while eastern northern China shows no significant changes, with different large-scale circulation patterns and North Atlantic sea surface temperature anomalies modulating each region.
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Kongo et al. (2025) The 2023 drought in West Africa and associated vulnerability to food insecurity
This study analyzes the changes in local climate variables during the 2023 planting season in West Africa and their potential consequences for agricultural productivity. It found significant shifts in rainfall patterns, increased temperatures, and vegetation loss, leading to reduced crop yields, while attributing a weak direct influence to El Niño, suggesting other factors like global mean temperature are more dominant drivers.
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Shen et al. (2025) Spatiotemporal characteristics of meteorological and hydrological droughts across Europe
This study analyzed the spatiotemporal dynamics of meteorological and hydrological droughts across Europe from 1970 to 2024, revealing stronger intensification and persistence of hydrological droughts, particularly in Western, Eastern, and Southern Europe, and a divergence between drought types at shorter timescales.
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Yeo et al. (2025) Trends and Climate Sensitivity of Precipitation Correlation Distances Across the Contiguous U.S.
This study quantifies changes in the length scale of seasonal precipitation correlations across the contiguous United States from 1950 to 2019, revealing significant decreases in summer precipitation correlation distance in several regions, which indicates increased spatial variability.
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Xu et al. (2025) Evaluating and Improving Light Absorption Retrievals of Black Carbon Using In Situ Polar Nephelometry
This study addresses the inaccuracy of black carbon (BC) remote sensing retrievals due to the common assumption of spherical morphology. It demonstrates that using the Multi-Sphere T-Matrix (MSTM) method, which accounts for BC's fractal-like morphology, significantly improves the accuracy of retrieved BC properties like absorption coefficient and volume concentration, compared to standard Lorenz-Mie theory.
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Koster et al. (2025) Investigations into the Subseasonal Predictability of Soil Moisture and Streamflow
This study investigates the subseasonal predictability of soil moisture and streamflow using an offline land modeling system forced by S2S forecasts. It finds that the offline system's hydrological forecasts are comparable in skill to fully coupled S2S systems, with skill influenced by soil column depth and improved streamflow forecasts achievable through climatological precipitation forcing.
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Potsiou et al. (2025) Impact of Climate Change and Other Disasters on Coastal Cultural Heritage: An Example from Greece
This study proposes and validates an integrated geospatial framework, including Digital Twin technology, land administration systems, and open geospatial data, for proactive risk assessment and management of coastal cultural heritage sites. The approach, demonstrated at Aegina Kolona, provides a practical pathway to enhance resilience against climate change threats through multi-scale hazard simulation and scenario testing.
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Zarei et al. (2025) A disaggregated system dynamics and agent-based modeling of the water-energy-food nexus for optimizing water allocation
This study develops an integrated System Dynamics and Agent-Based (SD-AB) model with multi-objective optimization (NSGA-II) and post-optimization analysis (AHP) to optimize water allocation in the Zayandehrud Basin, Iran. The model demonstrates that optimal allocation significantly reduces water and energy consumption while increasing groundwater levels and environmental water allocation, albeit with a trade-off in net agricultural benefit.
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Sardeshmukh et al. (2025) Learning ENSO Dynamics from Data
This study estimates the relative roles of dominant positive and negative feedbacks on El Niño–Southern Oscillation (ENSO) directly from observational data, revealing a critical competition between destabilizing positive feedbacks (zonal wind, subsurface ocean) and a stabilizing negative surface shortwave flux feedback due to cloud shielding, which primarily renders ENSO asymptotically stable.
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Chen et al. (2025) Impact of El Niño‐Southern Oscillation and Madden‐Julian Oscillation on the US Puget Sound Regional Hydroclimate
This study investigates the connections of El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO) to hydroclimate conditions and extremes in the Puget Sound basin. It finds that ENSO significantly modulates cold season temperature and runoff, leading to snow drought, while MJO phases 6–7 trigger extreme precipitation, temperature, snowmelt, and runoff at short lags, offering potential for improved regional water resource prediction.
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Brivoal et al. (2025) Implementation of an intermediate-complexity snow-physics scheme (ISBA-Explicit Snow) into a sea ice model (SI 3 ): 1D thermodynamic coupling and validation
This study implements an intermediate-complexity snow-physics scheme (ISBA-Explicit Snow) into the SI3 sea ice model, validating its 1D thermodynamic coupling against observations and another advanced snow model. The coupled model realistically simulates dynamic snow properties, leading to more accurate snow-ice interface temperatures and heat transfer compared to simpler schemes.
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Pang et al. (2025) Amplified contrasts in evapotranspiration between wet and dry regions caused by compound drought-hot events
This study investigates the response of evapotranspiration (ET) to concurrent drought and hot extremes (CDHEs) compared to individual droughts, revealing an amplified contrast where CDHEs lead to stronger negative ET anomalies in arid regions and stronger positive anomalies in humid regions. This "less for less and more for more" paradigm is driven by more severe meteorological conditions during CDHEs.
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Miles et al. (2025) High-Resolution Interoperable Human-Friendly Naming System for Hydrographic Features and Model Elements (HRI-HydroName)
This paper introduces HRI-HydroName, a high-resolution, interoperable, and human-friendly naming system for hydrographic features and model elements, which assigns hierarchical codes to enhance model interoperability, clarity, and reproducibility in regional flood modeling, as demonstrated in the Amite River Basin.
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Wang et al. (2025) Satellite observations reveal ecosystem resistance and resilience to short-term water stress driven by dominant vegetation along a rainfall gradient in Australia
This study quantified ecosystem resistance and resilience to short-term water stress (2000-2018) along Australia's North Australian Tropical Transect using satellite-derived GPP and flux tower data. It found that resistance and resilience patterns are primarily driven by dominant vegetation types along the rainfall gradient, with semi-arid grasslands exhibiting low resistance but high resilience, while mesic woody savannas and arid shrublands show higher resistance but lower resilience.
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Oh et al. (2025) Scalable, adaptive and risk-informed design of hydrological sensor networks
This study introduces a data-driven framework for designing streamflow monitoring networks that enhances hydrological predictions and integrates socio-environmental constraints. The framework, utilizing rank-revealing QR decomposition, demonstrates superior streamflow reconstruction at ungauged locations compared to existing methods, while also being scalable and adaptable to flood risk.
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Zhang et al. (2025) A seamless global daily 5 km soil moisture product from 1982 to 2021 using AVHRR satellite data and an attention-based deep learning model
This study generated a consistent and seamless global daily 5 km surface soil moisture (0–5 cm) product spanning 1982–2021 using an attention-based deep learning model (AtLSTM) trained with AVHRR satellite data and other multi-source inputs. The resulting GLASS-AVHRR SM product demonstrates superior accuracy, spatiotemporal completeness, and richer spatial details compared to existing long-term global soil moisture datasets.
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Mohammadiigder et al. (2025) Evaluation of differences between gridded precipitation products in the Southern Prairies of Manitoba
This study evaluates the discrepancies among seven daily gridded precipitation datasets against 103 independent gauge observations in Southern Manitoba from 2018 to 2023. It finds that MRMS and CaPA are the top-performing products with the lowest errors and highest correlations across seasons, while other products exhibit varying seasonal biases and accuracy.
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Pena (2025) Dataset of Extreme Rainfall Quantiles over Italy from Six Satellite and Reanalysis Products Using GEV and MEVD
This dataset provides spatial maps of extreme daily precipitation quantiles over Italy, derived from six Remote Sensing and Reanalysis products using GEV and MEVD statistical approaches, including a novel downscaling method to estimate point-scale extremes.
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Ezzini et al. (2025) Optimization of Solar Irradiation Prediction and Recalibration of Weather Stations Using a Hybrid GRU-ANN Model
This study develops a hybrid GRU-ANN model for accurate solar irradiance prediction and proposes an innovative method for indirect recalibration of weather stations, demonstrating high reliability and potential for optimizing photovoltaic systems.
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Sengupta et al. (2025) How seas whisper to snow: teleconnections drive spatio–temporal variability of snow cover in Western Himalayas
This study investigates the spatio-temporal variability of snow cover (SC) in six Western Himalayan watersheds, identifying key local meteorological and large-scale oceanic-atmospheric teleconnection drivers influencing these variations across different seasons and timescales, providing crucial insights for season-ahead SC predictions.
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Yan et al. (2025) Intensifying impacts of compound drought and heatwave events on water use efficiency in U.S. corn and soybean
This study investigates the spatiotemporal impacts of compound drought and heatwave events on U.S. corn and soybean water use efficiency (WUE) from 1960 to 2018, finding significant reductions in WUE (corn: 14.7%, soybean: 11.3%) and intensifying adverse effects in substantial production areas over time.
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Kamat et al. (2025) Dynamics of convective clouds near and below the lifting condensation level over a semi-arid Western-Indian region
This study investigates the occurrence of convective clouds forming below the lifting condensation level (LCL) over a semi-arid region in Western India. It finds that such anomalous clouds occur predominantly during post-monsoon and winter months, associated with thermal inversions and specific surface sensible and latent heat flux conditions.
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Chere et al. (2025) Earth observation-based drought studies in Ethiopia: a review on current state and future research directions
This review synthesizes Earth observation (EO)-based drought studies in Ethiopia from 2011 to 2022, revealing an increasing trend in publications, a prevalence of agricultural drought studies, and the widespread use of CHIRPS rainfall, MODIS vegetation products, and SPI/VCI indices, with most studies including validation.
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Pareja-Quispe et al. (2025) Meteorological Droughts in the Peruvian Andes: Characteristics and Relationships with Climate Variability
This study analyzed the characteristics and likely causes of meteorological droughts in the Peruvian Andes over 83 years using SPI and SPEI, revealing that western regions experience more frequent and intense droughts, primarily linked to positive phases of the Atlantic Multidecadal Oscillation (AMO), positive El Niño Southern Oscillation (Niño 4 region), and negative Tropical South Atlantic (TSA) sea surface temperature anomalies.
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Martel et al. (2025) Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis
This study investigates six methods to improve the peak streamflow simulation skill of Long Short-Term Memory (LSTM) models for flood frequency analysis (FFA) in ungauged catchments. It demonstrates that hybrid LSTM-hydrological model implementations can simulate peak streamflow as well as, or better than, traditional distributed hydrological models.
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Syahputra et al. (2025) A Predictive Model for Crop Irrigation Schedulling Using Machine Learning and IoT-Generated Environmental Data
This study develops and evaluates a machine learning model for predicting optimal irrigation schedules using real-time environmental data collected from an Internet of Things (IoT) system. The Long Short-Term Memory (LSTM) neural network model achieved high predictive accuracy (R² = 0.95), enabling a shift from reactive monitoring to proactive, data-driven precision agriculture.
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Shao et al. (2025) Varying flood exposure due to uncertain data of flood hazard and population distribution
This study comparatively assessed flood exposure in China using 25 combinations of five population and five flood hazard datasets, revealing substantial variations in exposure estimates primarily driven by flood hazard data differences, while consistently showing a disproportionately high population share in floodplains.
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Liu et al. (2025) Understanding copula-based multivariate standardized drought indices for characterizing meteorological, hydrological and agricultural droughts across global land areas
This study systematically evaluates the Copula-based Multivariate Standardized Drought Index (CMSDI) for characterizing meteorological, hydrological, and agricultural droughts across global land areas, demonstrating its applicability and reliability in monitoring diverse composite drought conditions using bivariate and vine copulas.
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Santana et al. (2025) Analysis of Sprinkler Irrigation Uniformity via Multispectral Data from RPAs
This study assessed the spatial variability of water distribution and soil dynamics under conventional sprinkler irrigation using Remotely Piloted Aircraft (RPA)-mounted multispectral sensors and in-situ measurements, demonstrating the approach's effectiveness for irrigation system characterization and precision water management.
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Alsamarray et al. (2025) Compatibility of Crop Patterns with Climate Change for Irrigation Projects in Semi-Arid Regions: The Case Study of the Abu Ghraib Project in Iraq
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Yuan et al. (2025) Impacts of rising atmospheric dryness on terrestrial ecosystem carbon cycle
This review synthesizes historical and projected trends in atmospheric vapour pressure deficit (VPD) and its mechanisms affecting the terrestrial carbon cycle, revealing a global increase in VPD since the late 1990s and observed reductions in key carbon cycle components, while highlighting challenges in attribution and model limitations.
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Ke et al. (2025) Influence of boundary layer-cloud coupling on cloud microphysics based on aircraft observations in the North China plain
This study utilized 35 aircraft observations over the North China Plain from 2019 to 2021 to investigate the vertical and spectral distributions of aerosols and cloud microphysical variables, revealing how boundary layer-cloud coupling influences cloud microphysics and aerosol characteristics.
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Lu et al. (2025) Deficit irrigation alleviates the increase in soil salinity content in saline-alkali regions of China and improves irrigation water productivity: A meta-analysis
This meta-analysis investigated the effects of deficit irrigation (DEI) on soil salinity content, crop yield, and irrigation water productivity (IWP) in saline-alkali regions of China, finding that DEI significantly increased IWP by 29.77% while slightly increasing soil salinity by 2.52% and decreasing crop yield by 6.77%. The study identified optimal DEI strategies based on irrigation practices, climatic conditions, and soil properties using machine learning.
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Farman et al. (2025) Activation function impact on rainfall prediction: comparative insights across ML and DL architectures
This study systematically compares the impact of various activation functions on deep learning (LSTM, BiLSTM, Transformer) and machine learning models for next-day rainfall prediction. It finds that BiLSTM with Leaky ReLU/ELU and Transformer with ELU/ReLU/Swish consistently achieve the highest accuracy (up to 99%) and stability, significantly outperforming traditional ML models.
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Khanam et al. (2025) Predictive understanding of socioeconomic flood impact in data-scarce regions based on channel properties and storm characteristics: application in High Mountain Asia (HMA)
This study introduces a novel geomorphologically guided machine learning method to predict socioeconomic flood impacts in data-scarce regions. Applied to High Mountain Asia (HMA), the model effectively identifies flood susceptibility hotspots and their evolution from 1980 to 2020, demonstrating its versatility for ungauged areas.
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Matt et al. (2025) HYD-RESPONSES: daily hydro-meteorological catchment-level time series to analyse HYDrological drought dynamics in RESPONSE to (cumulative) water deficits in Swiss catchments
This paper introduces the HYD-RESPONSES dataset, providing new daily hydro-meteorological time series and drought indicators for 184 Swiss catchments to facilitate the analysis of hydrological drought dynamics and their responses to cumulative water deficits. The dataset supports process studies, statistical analyses, and the training of machine learning models for improved drought understanding and warning applications.
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Katata et al. (2025) Reconstruction of the Background Air Temperature Record in Japan (1916–2023): Implications for Climate Change and Urbanisation Bias in the 20th Century
This study reconstructed long-term annual mean temperature trends in rural Japan (1916-2023) using newly digitized data, revealing a lower warming rate (+0.11 °C per decade) compared to JMA stations (+0.15 °C per decade) due to urbanization bias, and uncovering a significant climatic jump (decreasing trend) in the 1960s after bias removal.
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Dey et al. (2025) Assessment of extreme climate trends using temperature, rainfall, and cyclones in the West Bengal coastal region
This study analyzed 42 years (1982–2023) of rainfall, temperature, and extreme climate events in the West Bengal coastal region, revealing statistically significant increasing trends in annual and post-monsoon rainfall (surpassing monsoon levels), rising minimum temperatures, and an increasing frequency of cyclonic events, which collectively heighten flood risk and impact agricultural planning.
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Zhang et al. (2025) Quantifying the relative importance of factors influencing rainfall partitioning in a rainfed apple orchard
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Kamali et al. (2025) Evaluating the environmental and economic effects of groundwater quotas and markets using multi-agent system simulation and centralized optimal model
This study evaluates the environmental and economic impacts of groundwater quotas and markets in the Ardabil Plain, Iran, using a multi-agent system simulation (MASS) model and a centralized optimal model (COM). It finds that decentralized, self-interested agents under uniform quotas perform poorly compared to a centralized optimal approach, while permit trading can enhance economic benefits and, in some scenarios, reduce environmental violations.
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Park et al. (2025) Arctic stratospheric ozone as a precursor of ENSO events since 2000s
This study reveals a significant shift in the relationship between Arctic stratospheric ozone (ASO) and El Niño–Southern Oscillation (ENSO) since the 2000s, demonstrating that springtime ASO variations now serve as a precursor to subsequent winter ENSO events via a Eurasian teleconnection pathway.
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Bonacci et al. (2025) Understanding flash floods in a changing urban landscape: the Zagreb 1989 and 2020 events
This study analyzes two major urban flash flood events in Zagreb (1989 and 2020) to characterize their drivers in a changing urban landscape, finding that retention basins are effective, and floods primarily occur during summer nights due to localized high-intensity rainfall and urbanization, rather than a long-term increase in daily precipitation totals.
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Dhote et al. (2025) Unveiling the First Impressions of the Wide‐Swath Altimetry SWOT Mission Over the Ganga River, India
This study evaluates the performance of Surface Water and Ocean Topography (SWOT) mission's node and raster products for river hydrodynamics over a 210 km stretch of the Ganga River, finding varying accuracies for water surface elevation and slope, with node products generally more accurate for WSE but raster products providing better spatial detail.
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Sharma et al. (2025) IoT-Enhanced Machine Learning for Remote Sensing and Image Processing in Agriculture
This paper presents an IoT-enhanced machine learning system for precision agriculture, integrating sensor data and remote sensing imagery to improve crop health monitoring, yield estimation, and resource utilization. The system achieved a 92% accuracy in crop health classification, demonstrating its potential for sustainable smart farming.
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Yan et al. (2025) Multi-scale partitioning of evapotranspiration using the three-temperature model in an oasis–desert landscape of northwest China
This study applied the three-temperature (3T) model to partition evapotranspiration (ET) into soil evaporation (LE) and plant transpiration (LT) across an oasis–desert landscape in Northwest China using multi-scale remote sensing data, demonstrating the model's consistent performance and scale resilience for areal mean ET partitioning from 3 meters to 1000 meters resolution.
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Hossan et al. (2025) Retrieval and validation of total seasonal liquid water amounts in the percolation zone of the Greenland ice sheet using L-band radiometry
This study presents a microwave retrieval algorithm for quantifying total seasonal liquid water amounts (LWA) in the Greenland Ice Sheet's percolation zone using NASA SMAP L-band radiometry. The retrieved LWA shows good agreement with two independent energy and mass balance models, demonstrating the potential for advancing understanding of ice sheet melt processes and improving sea level rise projections.
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Hojabri et al. (2025) Spatio-temporal Dynamics of Land Surface Temperature and Land Use and Land Cover Changes in the Urmia Lake Basin: Exploring Land-Atmosphere Interactions Through Satellite Data and Ground Observations (2000–2023)
This study investigated the spatiotemporal dynamics of Land Surface Temperature (LST) and Land Use/Land Cover (LULC) changes in the Urmia Lake Basin (ULB), Iran, from 2000 to 2023. It revealed a significant 9.63 °C increase in Urmia Lake Surface Temperature (ULST) due to lake drying, while Basin Surface Temperature (BST) showed no consistent trend, partially mitigated by expanded irrigated agriculture.
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Ma et al. (2025) Interdecadal shift of the dipole pattern in summer persistent extreme precipitation over Southwest China around 2003 and its possible causes
This study investigates the interdecadal shift around 2003 of the leading mode of summer persistent extreme precipitation (PEP) over Southwest China (SWC), which exhibits a north-south dipole pattern, and attributes this shift to interactions between atmospheric circulation anomalies, southern European soil moisture, Barents-Kara Sea sea ice concentration, and Tibetan Plateau thermal feedback.
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Cai et al. (2025) Boreal forest cover change since 2000 contributes to cold winters in Eurasia
This study investigates the impact of observed boreal forest cover change (2000-2020) on winter climate using an Earth System Model, revealing that afforestation in western Europe drives Eurasian winter cooling by enhancing snow-albedo feedback, stimulating planetary waves, and weakening the stratospheric polar vortex.
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Bianchi et al. (2025) Synchrony of Wind, Solar and Hydroelectric Resources Over Argentina and Its Climatic Drivers
This study comprehensively analyzes the interannual complementarity of wind, solar, and hydroelectric resources in Argentina over 38 years, assessing the impact of low-frequency ocean-atmosphere variations. It finds limited complementarity with the current spatial distribution of renewable capacity, emphasizing the significant control of climatic drivers and the need for alternative spatial schemes for future energy development.
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Wang et al. (2025) Polarised Changes in Sub‐Daily Precipitation Extremes and Underlying Mechanisms Over Southwest China in a Warmer Climate
This study statistically analyzed sub-daily precipitation extremes (SPEs) over Southwest China from 1971–2024, revealing simultaneous increases in both wet and dry SPEs driven by atmospheric circulation changes and thermodynamic factors in a warming climate.
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Mazhar et al. (2025) Enhancing aridity index assessment in Pakistan's dryland ecosystems: A machine learning approach integrating remote sensing and seasonal lag effects
This study evaluated the aridity index (AI) and Standardized Precipitation Index (SPI-3) in Pakistan's dryland ecosystems from 1990 to 2023 using machine learning and remote sensing, revealing that Gradient Boosting Regression with a three-month lag accurately predicted AI and highlighted the significance of seasonal effects and biophysical indicators for regional water management.
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Nguyen‐Duy et al. (2025) Increasing compound heat and precipitation extremes and population exposure in a warming Vietnam
This study projects future changes in compound heat and precipitation extremes (CHPEs) and associated population exposure across Vietnam using high-resolution climate data and population scenarios. It finds a significant increase in CHPEs and population exposure under warmer scenarios, primarily driven by climate change.
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Cruz‐Pérez et al. (2025) Climate Projections and Temperature Evolution in the Canary Islands: High Resolution Analysis at Island Scale
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Guo et al. (2025) The inclusion of time-lag response reveals contrasting effects of meteorological and agricultural drought on agroecosystem water use efficiency in Africa
This study disentangles the contrasting time-lag effects of meteorological and agricultural droughts on agroecosystem water use efficiency (WUE) in Africa, revealing agricultural drought as the predominant contributor with a longer time-lag and distinct thresholds for abrupt shifts in WUE.
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Mu et al. (2025) Waveguide Teleconnection Mechanisms Driving Summer Compound Heat‐Humidity Extremes in China Land Monsoon Region
This study reveals consistently increasing trends of summer compound heat-humidity extremes (CHHEs) across the China Land Monsoon (CLM) region from 1961–2022, attributing this rise to a weakening British Okhotsk Corridor (BOC)-Silk Road Pattern (SRP) nexus since 2000, which is modulated by North Atlantic sea surface temperature anomalies.
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Nawale et al. (2025) Lake recharge projections with autoencoders and hydro-climatic indicators
This study models groundwater recharge dynamics for Nakane Lake, India, from 2024–2030 using an autoencoder integrated with hydro-climatic and remote sensing data, demonstrating its superior accuracy in forecasting a cumulative groundwater depth increase of approximately 0.92 m by December 2030.
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Li et al. (2025) Multi-objective optimization of seasonal emergency storage in cascade reservoirs for enhanced drought resilience and hydropower synergy
This study presents a multi-objective optimization framework for enhancing seasonal emergency storage capacity (SESC) in cascade reservoirs to simultaneously improve hydropower generation (HG) and drought resilience. Applied to a five-reservoir cascade on the Yangtze River, the model outperforms the standard operation policy (SOP), yielding an 8.7% increase in hydropower output, a 45.5% reduction in river drought duration, and a 4.5% improvement in low-flow conditions.
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Deng et al. (2025) Economic consequences of cascading drought-flood events: evidence from central Europe
This study empirically compares cascading drought-flood events (CDFEs) with flood-only events (FEs) in Central Europe, revealing that CDFEs are associated with significantly higher streamflow, deeper water depths, and greater economic losses.
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Filipiak et al. (2025) Assessing Dynamic and Thermodynamic Variability in Initial and Boundary Conditions for Snowstorm Prediction in the Northeast United States
This study investigated the impact of different initial condition sources on winter precipitation prediction in the Northeast United States, finding that relative humidity variability in initial and boundary conditions was the primary source of uncertainty in model simulations, heavily influencing precipitation accumulation discrepancies.
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He et al. (2025) A novel index for directly indicating fractional vegetation cover based on spectral differences between vegetation and soil
This study introduces the Vegetation Coverage Index (VCI), a novel remote sensing index designed for directly estimating fractional vegetation cover (FVC) by leveraging spectral differences between vegetation and soil. VCI demonstrates comparable or superior accuracy to existing methods, particularly in the presence of non-photosynthetic vegetation, and exhibits broad applicability across various satellite sensors.
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Laly (2025) Lidars mobiles pour la mesure de la vapeur d'eau et les applications météo-climatiques
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Abulaiti et al. (2025) Ecological sensitivity assessment and driving force analysis of the Tarim river basin
This study assesses the ecological sensitivity and its driving forces in the Tarim River Basin using a 15-indicator system and the Optimal Parameter Geographic Detector model. It reveals that heat and temperature are the dominant drivers, with their synergistic interaction with land use significantly amplifying ecological sensitivity, particularly in the arid central and eastern regions.
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Pradhan et al. (2025) Diurnal variability of global precipitation: insights from hourly satellite and reanalysis datasets
This study comprehensively intercompared the diurnal variability of global precipitation using five state-of-the-art hourly satellite and reanalysis datasets, revealing consistent broad spatial patterns but significant regional uncertainties in precipitation amount, frequency, and intensity, particularly at higher latitudes.
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Shakeel et al. (2025) Advancing climate modeling: a multi-framework methodology for evaluating GCMs and predicting drought trends
This study develops a multi-framework methodology to evaluate 22 Global Climate Models (GCMs) for historical precipitation simulation in Sindh, Pakistan, and proposes a novel Hybrid Framework-Gaussian Climate Drought Index (HF-GCDI) for future drought prediction under Shared Socioeconomic Pathways (SSPs), finding increased severe drought likelihood under higher emission scenarios.
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Song et al. (2025) TIF: A time-series-based image fusion algorithm
This study developed the Time-series-based Image Fusion (TIF) algorithm to generate 10-meter surface reflectance time series by synthesizing Landsat 8/9 and Sentinel-2 A/B data. TIF consistently outperformed state-of-the-art methods in accuracy and efficiency, offering a practical pathway for creating 10-meter Harmonized Landsat and Sentinel-2 (HLS) products for fine-scale Earth observations.
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Perkins‐Kirkpatrick et al. (2025) Heatwaves in a net zero World
This study examines heatwave projections after anthropogenic greenhouse gas emissions reach net zero, finding that heatwaves remain systematically hotter, longer, and more frequent for 1000 years, with no decline, especially if net zero is delayed. It challenges the belief that conditions will improve for near-future generations post-net zero, highlighting the need for permanent adaptation measures.
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Keshta et al. (2025) Enhancing Climate Modeling over the Upper Blue Nile Basin Using RegCM5-MOLOCH
This study enhances the RegCM5 climate model with the MOLOCH dynamical core to improve precipitation and temperature simulations over the Upper Blue Nile Basin, finding that the MOLOCH-UW configuration is the most reliable for reproducing regional climate variability.
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Unknown (2025) Natural land disturbances are worsening but humans are not off the hook
High-resolution satellite maps of the contiguous USA over 35 years reveal a fundamental shift in land disturbances, showing a decrease in human-directed disturbances while 'wild' disturbances (e.g., fire, vegetation stress, wind, geohazards) are surging, alongside evolving patterns in their frequency, size, and severity.
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Chen et al. (2025) Global patterns and trends of vegetation water use efficiency inferred from solar-induced chlorophyll fluorescence from 2001 to 2020
This study developed a novel satellite-based framework integrating solar-induced chlorophyll fluorescence (SIF) and plant physiological theory to globally assess vegetation (WUET) and ecosystem (WUE) water use efficiency from 2001 to 2020. It revealed distinct global trends for WUET and WUE, attributing them to CO2 fertilization, vapor pressure deficit, and vegetation structure, and projected future changes under climate scenarios.
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Dommo et al. (2025) Assessment of Anticipated Changes in Extreme Temperature and Precipitation Under 1.5°C and 2°C Warming Over the Mississippi River Basin
This study analyzes projected changes in extreme precipitation and temperature indices over the Mississippi River Basin under 1.5°C and 2°C global warming scenarios and two Shared Socio-economic Pathways, finding an exacerbation of most extreme events, significant sensitivity to warming levels and emission scenarios, and a predominant contribution of internal climate variability to total uncertainty.
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Zhou et al. (2025) Unraveling nonlinear urban waterlogging responses to rainfall structure: A data-driven analysis in a highly urbanized megacity
This study investigates the nonlinear relationships between rainfall structure and urban waterlogging in Shanghai using a long-term dataset and data-driven models. It reveals that short-term rainfall intensities, temporal asymmetry, and peak-mean ratios are more critical drivers of waterlogging than total rainfall volume, exhibiting complex non-monotonic and threshold effects.
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Wang et al. (2025) Climate drives observational changes in hydrological extremes across most global regions
This study analyzes changes in drought and flood flows and their dominant drivers across 9,531 global hydrological stations from 1980 to 2014, revealing that most regions experience simultaneous increases (28.14%) or decreases (33.36%) in both extremes, with climate primarily influencing the Southern Hemisphere and human activities dominating specific Northern Hemisphere regions.
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Brunner et al. (2025) Spatially Compounding Drought‐Flood Events Are Favored by Atmospheric Blocking Over Europe
This study investigates the occurrence, seasonality, and large-scale atmospheric drivers of spatially compounding drought-flood events across Europe using streamflow and precipitation observations. It reveals that these events exhibit strong seasonality, occurring most frequently in winter, spring, and June, and are primarily favored by specific blocking and Zonal weather regimes.
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Wang et al. (2025) Estimation and mechanism analysis of global evapotranspiration based on a physics-informed deep-learning model
This paper introduces a physics-informed deep-learning model, Self-attention Influence (SAI), for global evapotranspiration (ET) estimation, demonstrating superior spatial extrapolation and robustness, especially in data-poor regions, and providing explainable insights into ET mechanisms and climate impacts.
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Liu et al. (2025) The CO 2 Balancing Act: Why Global Warming and Greening Don't Dry Earth as Much as We Thought
This study develops a novel physical model to quantify the complex interplay between evapotranspiration, atmospheric CO₂ concentration, and climate/vegetation changes, revealing that CO₂-induced stomatal closure significantly offsets global terrestrial drying effects from warming and greening, thereby exposing systematic biases in traditional drought indicators.
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Xiao et al. (2025) Modeling Dynamic Flood Population Exposure in Coupled Human‐Water Systems: The Role of Reservoir Regulation and Population Movement
This study calculates dynamic flood population exposure by integrating hydrological modeling with human mobility data, revealing that reservoir operations reduce hazard levels for a portion of the exposed population and that nighttime flood exposure significantly surpasses daytime levels due to diurnal population fluctuations.
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Yu et al. (2025) Widespread but Divergent Drought Legacy Effects on Gross Primary Productivity Across Biomes
This study investigates the drivers of drought legacy effects on Gross Primary Productivity (GPP) across a diverse range of global ecosystems, identifying key climatic, ecosystem, and plant hydraulic traits that modulate the post-drought recovery of carbon uptake.
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Hissan et al. (2025) Predicting long-term meteorological drought using random forest and multi-scale drought indices
This study assessed meteorological drought patterns in Pakistan from 1960–2023 using SPI and SPEI at multiple timescales and applied a Random Forest model to predict long-term drought, finding that longer-term indices (SPI-12 and SPEI-12) are the most informative for forecasting.
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Wondzell et al. (2025) Rethinking Paired‐Catchment Studies: Should We Be Replicating Our Controls?
This paper addresses the statistical uncertainty in paired-catchment studies by developing a bootstrapped sampling method using reference-by-reference comparisons to determine minimum detectable effect sizes (MDES) for changes in streamflow.
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Shi et al. (2025) Improved soil moisture mapping using an integrated cyclic modeling and bias correction approach
This study developed an integrated cyclic modeling and bias correction approach using an XGBoost model to downscale soil moisture, producing a 500 m resolution product with significantly improved accuracy compared to single-shot modeling.
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Feng et al. (2025) Linkages of Multiple Types of Compound Droughts and Hot Events at the Global Scale
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He et al. (2025) APPLE-GO: Modeling high-spatial resolution forest canopy reflectance with effect of Adjacent Pixels using Path Length Extended Geometric Optical theory
This study introduces APPLE-GO, a novel high-spatial resolution forest canopy reflectance model that comprehensively accounts for shading and cross-radiation effects from adjacent pixels. The model demonstrates high accuracy in calculating the bi-directional reflectance factor (BRF), validated against a 3D radiative transfer model and satellite observations.
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Yin et al. (2025) Addressing Data Imbalance in Hydrological Machine Learning: Impact of Advanced Sampling Methods on Performance and Interpretability
This study evaluates advanced sampling methods, particularly feature space coverage sampling (FSCS), in hydrological machine learning applications to address data imbalance. It demonstrates that FSCS significantly enhances model accuracy, feature importance estimation, and interpretability for predicting forest cover types and saturated hydraulic conductivity, even with smaller training sets.
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Liu et al. (2025) Study on the characteristics and formation mechanism of extreme heat and drought in central Hexi Corridor, China
This study analyzed the characteristics and formation mechanisms of the extreme heat and drought in the central Hexi Corridor from May to September 2023, revealing record-high temperatures and record-low precipitation driven by specific atmospheric circulation and sea surface temperature anomalies.
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Xu et al. (2025) A One‐Dimensional Variational Precipitation Retrieval Algorithm Considering Cloud Types for Western North Pacific Tropical Cyclones Using FengYun‐3E Microwave Sounders
This study develops an enhanced one-dimensional variational (1DVAR) precipitation retrieval algorithm for tropical cyclones using Chinese FengYun-3E (FY-3E) passive microwave observations. The algorithm significantly improves retrieval accuracy and reduces systematic bias by incorporating precipitation-type differentiation and utilizing the Advanced Radiative Transfer Modeling System (ARMS) as the forward operator.
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D’Odorico et al. (2025) Deciphering tree drought responses across species: linking leaf water potentials with remote sensing greenness and photoprotection dynamics
This study investigated the drought responses of seven common European tree species in a temperate forest, integrating drone-based multispectral imagery with leaf water potential and pigment measurements. It found that the Photochemical Reflectance Index (PRI) strongly correlated with leaf water potentials, capturing both drought-induced declines and post-rainfall recovery, while the Normalized Difference Vegetation Index (NDVI) primarily detected greening losses in some species but failed to reflect recovery.
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Boers et al. (2025) Destabilization of Earth system tipping elements
This review demonstrates how interactions between Earth system tipping elements can generate spurious signals and mask genuine signs of destabilization. It presents observation-based evidence that the stability of the Greenland Ice Sheet, Atlantic Meridional Overturning Circulation, South American monsoon system, and Amazon rainforest has declined in recent decades, suggesting they are nearing critical thresholds.
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Tong et al. (2025) Evolution and prediction of drought-flood abrupt alternation in mainland China using an improved index
This study develops and validates a daily Drought-Flood Abrupt Alternation Index (DFAI) to overcome limitations of monthly indices, revealing increased DFAA frequency and intensity across mainland China from 1961–2022 and projecting intensified events under future climate change scenarios despite stable or decreasing frequency.
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Xue et al. (2025) Unraveling the seasonal variability of meteorological droughts: multiscale insights into the coupling with atmospheric circulation factors
This study investigated the seasonal characteristics and atmospheric circulation factor (ACF) coupling of meteorological droughts in the North China Plain (NCP) from 1961-2019, revealing distinct seasonal drought patterns and specific multi-factor combinations driving them.
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Brennan et al. (2025) Insights from hailstorm track analysis in European climate change simulations
This study investigates the impact of a 3 K global warming scenario on European hailstorms using kilometer-scale climate simulations and an object-based tracking algorithm. It reveals significant shifts towards more intense hailstorms with larger hail diameters, expanded swath areas, and increased associated hazards (precipitation, wind), driven by enhanced convective available potential energy and specific humidity.
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Ge et al. (2025) Nonlinear behavior of urban flood peaks in the U.S. Mid-Atlantic region
This study analyzes observed flood peaks in 262 U.S. Mid-Atlantic watersheds, revealing a V-shaped nonlinear relationship where flood peaks initially decrease and then increase with urban development, with a shift around 10% developed area, driven by complex interactions of climate and landscape properties.
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Peng et al. (2025) Spatiotemporal characteristics of drought in the Tarim River Basin from 2000 to 2022 based on the SPEI
This study analyzed the spatiotemporal characteristics and abrupt changes of meteorological drought in the Tarim River Basin from 2000 to 2022 using the Standardized Precipitation Evapotranspiration Index (SPEI). It found complex trends in temperature, precipitation, and SPEI, with an overall decrease in annual drought severity but an increase in seasonal drought severity, and identified light drought as the predominant type.
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Aminzadeh et al. (2025) Water storage paradox of reservoir expansion and evaporative losses in the MENA region
This study quantifies the storage capacity and evaporative losses of over 133,700 small agricultural reservoirs (< 0.1 km²) in the Middle East and North Africa (MENA) region from 2016 to 2023, revealing a paradox where high evaporation rates, potentially exceeding 2.4 x 10⁹ cubic meters annually, significantly undermine their storage efficiency despite their crucial role in water supply.
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Engdahl (2025) Impacts of Uncertain Permeability Fields on the Transient Hydrologic Response in Coupled Surface‐Subsurface Simulations of a Headwaters Catchment
This study assesses the confidence of an Integrated Hydrologic Model (IHM) simulation of a first-order basin under transient conditions, using an ensemble of 250 permeability realizations and varying recharge signals. It finds high confidence in surface water simulations but lower confidence in groundwater, with groundwater uncertainty decreasing over time after a recharge increase, and determines the ensemble sizes needed for convergence.
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Cao et al. (2025) Runoff changes and influencing factors in the Nyang River Basin in Xizang
This study developed and evaluated a large-scale Variable Infiltration Capacity (VIC) hydrological model for the high-altitude Nyang River Basin, demonstrating its applicability for runoff simulation and identifying key meteorological and geographical factors influencing runoff volume.
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Rondeau‐Genesse et al. (2025) Storyline analytical framework for understanding future severe low-water episodes and their consequences
This study develops a storyline analytical framework to project the impacts of future severe low-water episodes in Quebec, based on the 2021 drought, under +2 °C and +3 °C global warming scenarios, revealing significant deterioration in river conditions and extended low-water durations.
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Soundharajan et al. (2025) A Meta‐Analysis to Disentangle the Impacts of Climate and Land Use Changes on Streamflow
This meta-analysis quantitatively assesses the individual and combined impacts of precipitation, temperature, and land use/land cover (LULC) changes on streamflow. It finds that precipitation is the dominant driver, explaining nearly half of streamflow variance, followed by nuanced LULC effects (agriculture increases, forests decrease), while temperature has a minimal and inconsistent influence.
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Izzaddin et al. (2025) How well do RCMs simulate Portugal’s climate?
This study evaluates the performance of EURO-CORDEX regional climate models (RCMs) in simulating daily temperature and precipitation over Portugal (1971-2004), finding that RCMs reliably capture mean temperature indices but struggle with extreme precipitation events.
