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Абзалов et al. (2026) Анализ Принципа Измерения Влажности Eмкостным Способом
This paper analyzes the capacitance method for soil moisture measurement, identifies factors affecting accuracy, and proposes an improved sensor system with primary and secondary transducers operating at specific frequencies to enhance sensitivity and address limitations of existing layered soil moisture sensors.
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Wu et al. (2026) Widespread diverse impacts of climate change on the multi-attribute characteristics of flash drought in China: A three-dimensional dynamic assessment based on CMIP6 multimodel ensembles
This study proposes a comprehensive framework to project the spatiotemporal dynamic characteristics of flash drought (FD) in China under future climate change scenarios. It finds that climate change will exacerbate FD severity and intensity across most of China, prolong its duration, and extend the migration distance of long-duration events, while maintaining the overall migration direction.
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Shen et al. (2026) 30-meter Land Surface Temperature from Landsat via Progressive Self-Training Downscaling
## Identification - **Journal:** arXiv (Cornell University) - **Year:** 2026...
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Nurzoda et al. (2026) Effects of Different Colors of Biodegradable Mulch Film on Vegetative Growth, Yield, Fruit Quality, and Soil Properties in Grafted Watermelon
This study investigates the impact of various colored biodegradable mulches (BDM) on watermelon production and quality, comparing them to traditional polyethylene mulch (PM). It finds that silver-black BDM significantly enhances soil moisture and nutrient retention, leading to improved watermelon quality (specifically soluble solids content) and economic viability, advocating for its use in sustainable agriculture.
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Huang et al. (2026) Understanding the stable intensity patterns of extratropical cyclogenesis over East China
This study investigates the stable intensity patterns of extratropical cyclogenesis (EC-genesis) over East China from 1979 to 2022 using ERA5 reanalysis data and moist C-vector diagnosis. It reveals that a counterbalance between decreasing diabatic-driven ascent and increasing adiabatic-driven ascent maintains stable EC-genesis intensity, with a shift in the dominant driving factor from diabatic to adiabatic effects.
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Chen et al. (2026) Interactive Effects of Irrigation Amount and Interval on Cotton Water Use and Productivity: Evidence from Controlled Experiments and AquaCrop Simulations
This study investigated the independent and interactive effects of irrigation amount and interval on soil water dynamics, evapotranspiration partitioning, yield, and water use efficiency in cotton. It found that irrigation interval is a critical management variable, with intermediate intervals improving water use efficiency under moderate irrigation conditions.
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He et al. (2026) Explaining urban flood susceptibility under rainfall uncertainty through probabilistic modeling and interpretable machine learning
This study proposes a comprehensive framework integrating probabilistic hydrodynamic simulations with interpretable machine learning to systematically analyze urban and environmental factors influencing urban flood susceptibility at the grid scale, revealing that terrain features, drainage capacity, and urban form are key predictors with depth- and scale-dependent patterns.
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Li et al. (2026) Improving Ensemble Forecasts of Abnormally Deflecting Tropical Cyclones with Fused Atmosphere-Ocean-Terrain Data
N/A - Paper text unreadable.
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Wang et al. (2026) Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields
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Kira (2026) A scalable framework for monitoring urban vegetation function for planning and management - data
This dataset provides processed results to evaluate the seasonal behavior and cross-sensor consistency of satellite-derived vegetation productivity proxies across six diverse urban environments. It aims to support scalable monitoring of urban vegetation function for planning and management.
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Mondal et al. (2026) Assessing the Potential of Total Lightning for Nowcasting Ground Rainfall in Summer Thunderstorms Using Automatic Density-Dependent Tracking
This study integrates total lightning data and high-resolution radar precipitation data using an automatic storm cell tracking method to improve the prediction accuracy of torrential rainfall, building on prior findings of a strong correlation and time lag between lightning and ground rainfall.
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Zheng et al. (2026) Suitable Lake Water Area for Ecological Security of Terminal Lakes in Xinjiang, China
This study developed a new method based on ecosystem safety demands to determine the suitable water surface area for eight terminal lakes in Xinjiang, China, providing precise recommendations for sustainable ecological security and optimized water resource management.
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Bromand et al. (2026) An Integrated Multi-Criteria and Hydrological Consistency Framework for Evaluating Latest Satellite-Based Winter Precipitation Products in Himalayan Basins
This study develops a new multi-criteria evaluation method for satellite-based winter precipitation products (SPPs) by adding spatial correlation and water balance consistency, finding that IMERG Final V07 and GSMaP NRT V08 are the most suitable for different applications in the Himalayan region.
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An et al. (2026) Physics-Aware Hybrid CNN–Transformer Network for GNSS-R Sea Surface Wind Speed Estimation
This study proposes a Physics-Aware Hybrid CNN–Transformer Network (PA-HCTN) for accurate sea surface wind speed estimation from GNSS-R data, which integrates local feature extraction, global context modeling, and dynamic fusion of physical parameters. The model achieves a global Root Mean Square Error (RMSE) of 1.35 m/s and significantly mitigates high-wind-speed underestimation bias by incorporating a Geophysical Model Function (GMF)-constrained loss function.
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Lin et al. (2026) Persistence of frozen-period soil moisture modulates late-spring surface thermal anomalies in the Tibetan Plateau
This study reveals that the persistence of frozen-period soil moisture (SM) in the Tibetan Plateau (TP) significantly modulates late-spring surface air temperature (SAT) anomalies. It demonstrates that January SM anomalies influence May SAT by altering evapotranspiration and the partitioning of surface sensible and latent heat fluxes, with a particularly strong impact on daily maximum SAT extremes.
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Li et al. (2026) Enhancing flood peak simulation in data-scarce mountain river basins: the CRFMODEL framework
This study develops CRFMODEL, a novel framework for accurate flood peak prediction in data-scarce mountain river basins, demonstrating superior performance over the Xin’anjiang model and meeting high flood forecasting standards in Chinese catchments.
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Rehbein et al. (2026) South American Mesoscale Convective Systems: Present and Future Climates
This study projects future changes in mesoscale convective systems (MCSs) over South America, finding a general intensification with higher maximum precipitation rates and larger precipitation volumes, alongside regional and seasonal shifts in their occurrence and precipitation contribution.
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Wang et al. (2026) Flash flood forecasting in North East England through weak label-guided mixture of experts with multi-scale explainability
This study introduces a Weak Label–Guided Mixture of Experts (WL–MoE) framework for multi-horizon flash flood water-level forecasting in five fast-response catchments in North East England. The framework significantly improves predictive accuracy, particularly for high-water events, by leveraging specialized convolutional experts and a two-stage training scheme, while also providing multi-scale interpretability.
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Zhao et al. (2026) Climatic Effects of Ice Nucleation by Typical Natural and Anthropogenic Aerosols in East Asia Using a Developed Version of RegCM‐Chem
This study integrates an ice-nucleation parameterization into the RegCM-Chem model to investigate the regional climatic impacts of natural (dust) and anthropogenic (carbonaceous, sulfate) aerosols on ice clouds over East Asia. The research finds that aerosol ice nucleation leads to a radiative heating effect, predominantly driven by dust, which significantly alters regional temperature and precipitation patterns.
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Ellison et al. (2026) The Drivers and Impacts of Future Analogs of the 2011–2014 Drought in the Western and Central United States
This study assesses how anthropogenic climate change alters the large-scale drivers and impacts of droughts, focusing on simulated analogs of the 2011-2014 western and central US drought, finding fewer such droughts in the future but with altered hydrological and ecological impacts and potentially increased predictability of their drivers.
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Selkirk et al. (2026) Coherent interdecadal cycles in global rainfall, temperature, and cloud cover with alignment to Jovian orbital periods
This study identifies significant coherent 12.9- and 19.9-year cycles in global rainfall, surface temperature, and cloud cover, suggesting these interdecadal variabilities are externally paced by Jovian orbital dynamics rather than solely internal climate processes.
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Mi et al. (2026) Warming Reshapes Land-Atmosphere Coupling: The LST-SM-ET-GPP Framework
This paper synthesizes an integrated conceptual framework (LST-SM-ET-GPP chain) and a diagnostic roadmap to unify understanding of land-atmosphere coupling, explaining how controlling factors dynamically shift between energy-limited and moisture-limited regimes, particularly highlighting a critical soil moisture threshold during water depletion.
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Boussetta et al. (2026) Long-term NDVI series analysis in a sylvopastoral irrigated area
This study analyzes long-term Normalized Difference Vegetation Index (NDVI) series from 1984 to 2017 in a sylvopastoral irrigated area in northern Tunisia to assess vegetation productivity changes. It reveals a significant increase in crop productivity from 2010, primarily driven by increased irrigation inputs and phenological growth, particularly during rainfall shortage years, where irrigation volumes strongly predict NDVI.
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Singh et al. (2026) Accounting for the bias in the median track of Indian Summer monsoon low-pressure systems in an Earth system model
This study investigates systematic biases in Monsoon Low-Pressure Systems (LPSs) and associated dry biases in the Community Earth System Model (CESM2.1.3) during the Indian Summer Monsoon. It finds a southward shift in LPS activity in CESM2.1.3, linked to biases in the low-level westerly jet and dry air intrusion, which are common across CMIP model generations.
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Ortenzi et al. (2026) Exploring groundwater-surface water interactions and recharge in fractured mountain systems: an integrated approach
This study developed an integrated approach to map groundwater-surface water interactions and quantify aquifer recharge in a fractured Mediterranean mountain catchment. By combining multi-source data, the research revealed significant groundwater contributions to streamflow, identified distinct hydrogeological sources, and quantified snowmelt's substantial role (approximately 18%) in aquifer recharge.
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Sahraei et al. (2026) Numerical simulation of hydrological response to extreme rainfall in Atacama Desert
This study investigates the hydrological response of the hyper-arid Salado River Basin in the Atacama Desert to the extreme March 2015 rainfall event using a fully coupled Atmospheric and Hydrological Modeling System (AHMS), finding that the event produced peak discharges of approximately 1000 m³ s⁻¹ and exceeded the 99.9th percentile of an 8-year modeled streamflow distribution.
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Zelinka et al. (2026) Recent cloud trends and extremes reaffirm established bounds on cloud feedback and aerosol-cloud interactions
This study quantifies the meteorological and aerosol-related factors driving recent cloud-radiative anomalies and increased Earth's energy imbalance, reaffirming established bounds on cloud feedback and aerosol forcing, and supporting an equilibrium climate sensitivity near 3 °C.
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Zhou et al. (2026) Effects of the competing controlling factors of rainfall, landscape position and soil depth on moisture responses in the Mollisol region of China
This study quantified the interactive influence of rainfall patterns and landscape positions on event-scale soil moisture response metrics across various soil depths in the Mollisol region of China. It found that while midslope and downslope positions were critical for explaining response variance, rainfall type was the primary factor driving soil moisture dynamics across all depths, with heavy rainfall leading to greater and faster responses in shallow layers.
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Samalavičius et al. (2026) Sinkhole risk forecasting in the Lithuania–Latvia Karst region using artificial intelligence
This study develops an end-to-end, remote-sensing–informed and data-driven workflow to reconstruct missing daily groundwater-level (GWL) records and to forecast monthly sinkhole formation risk in the Lithuania–Latvia transboundary gypsum karst region. Models combining groundwater level, seasonal encoding, and hydroclimatic features achieved high accuracy (~0.96), high-risk precision (~0.98), and recall (~0.85), highlighting multi-week hydroclimatic preconditioning as the dominant driver.
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Behera et al. (2026) Photosynthetic Recovery Dynamics Reveal Declining Vegetation Functional Resilience in Tropical Ecosystems
This study assesses global vegetation functional resilience from 2000 to 2019 using solar-induced chlorophyll fluorescence (SIF), revealing latitudinal contrasts in recovery rates and widespread resilience decline in tropical and Eurasian high-latitude ecosystems driven by climatic stressors.
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Kim et al. (2026) Diagnostic method of inundation using a dynamical hydrological model
This study develops and validates the Velocity-based Inundation (VI) method, a non-physics-based post-processing algorithm that calculates overbank flow volume from WRF-Hydro streamflow simulations and distributes it onto a high-resolution Digital Elevation Model (DEM) based on river velocity and topography, demonstrating robust diagnostic performance for flash flood assessment in complex terrains.
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Kim et al. (2026) Evaluating rainfall-driven LSTM for statistical analysis of low-flow regimes in the Nakagawa basin, Japan
This study evaluated a rainfall-driven Long Short-Term Memory (LSTM) model for low-flow regime analysis in the Nakagawa Basin, Japan, finding that while the model performed well globally, it exhibited substantial discrepancies and poor reproducibility in extreme low-flow conditions. The research highlights the critical need for regime-specific evaluation of deep-learning runoff predictions for drought and environmental-flow decision support.
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Guo et al. (2026) Divergent Changes in Oceanic and Terrestrial Surface Water Budgets Under Global Warming: Insights From ERA5 Reanalysis
This study develops a process-level decomposition framework to quantify the global surface water budget's sensitivity to climate warming using ERA5 reanalysis. It finds an intensified water cycle with strengthened ocean-to-land moisture redistribution, where circulation and transient eddies primarily shape the spatial response, while thermodynamic effects are secondary due to compensation.
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Zaninelli et al. (2026) AI-Based Anomaly Detection for Extreme Event Attribution: An Analysis of European Heatwaves
This study introduces a lightweight, interpretable AI-based framework combining unsupervised anomaly detection with Bayesian deep learning for near-real-time extreme event attribution. It successfully quantifies the probability of European heatwaves under pre-industrial conditions, demonstrating consistency with traditional methods without relying on computationally intensive climate model ensembles.
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Alitane et al. (2026) Navigating water security in semi-arid regions: SWAT+-based analysis of climate change impacts in Morocco’s R’Dom watershed
This study applied an integrated framework combining the Soil and Water Assessment Tool Plus (SWAT+) and the Statistical Downscaling Model (SDSM) to assess climate change impacts on water balance components in Morocco’s R’Dom watershed under various Representative Concentration Pathways (RCPs). The findings indicate a significant decrease in streamflow (43.00–52.20%) and increased evapotranspiration across all future scenarios, primarily due to heightened water stress.
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Ding et al. (2026) Global Urban‐Rural Differences in Precipitation: Cities See More Light Rain But Milder Extremes
This study investigates how urban areas differentially modulate precipitation intensity, revealing that cities generally increase light precipitation frequency while mitigating extreme precipitation magnitude. These effects, particularly for heavier precipitation, tend to shift to downwind rural areas.
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Bhatla et al. (2026) Fidelity of nested RegCM in representing Indian monsoon droughts through resolution-dependent simulations
This study evaluates the RegCM4.7 regional climate model's performance in simulating two major drought years (2009 and 2015) over India's Core Monsoon Region, comparing non-nested (25 km) and nested (9 km) configurations. The nested high-resolution setup significantly improves the representation of spatial rainfall variability, bias reduction, and mesoscale drought characteristics, highlighting the added value of increased spatial resolution.
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Fernández et al. (2026) Measurement of Soil Moisture Using Capacitance Measurements: Development of a Low-Cost Device for Environmental and Very-Low-Enthalpy Geothermal Energy Applications
This paper presents a novel low-cost capacitive soil moisture sensor with optimized interdigitated electrodes and a protective dielectric coating, demonstrating high sensitivity and precision for applications in very-low-enthalpy geothermal energy, agriculture, and environmental monitoring.
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Rateb et al. (2026) Freshwater Availability in the Mississippi River Basin and Adjacent Texas Aquifers Under Human and Climate Pressures
This study quantifies spatiotemporal water storage trajectories across the Mississippi River Basin and adjacent Texas aquifers (MRB-TX) by integrating diverse long-term datasets and climate projections. It reveals a significant longitudinal gradient in water storage, with substantial declines in arid western aquifers primarily due to irrigation and drought, contrasting with stable or gaining conditions in humid eastern regions.
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Feng et al. (2026) Processes and driving mechanisms of drought propagation in Central Asia: A coupled perspective of meteorological, surface water, agricultural, and groundwater drought
This study investigated the propagation processes and driving mechanisms of meteorological, surface water, agricultural, and groundwater droughts in Central Asia from 2003 to 2023. It found that meteorological, agricultural, and groundwater droughts intensified, with meteorological drought propagating rapidly to surface water (1.43 months) and agricultural drought (3.32 months), but much slower to groundwater drought (14.91–18.63 months), primarily driven by temperature, elevation, and precipitation, with regional variations influenced by aridity and mountain hydrology.
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Xiong et al. (2026) Stage-specific drought impacts and the mitigating role of irrigation on maize yield: evidence from satellite remote sensing
This study investigated the stage-specific impacts of drought on maize yield and the mitigating role of irrigation investment in Henan Province, China, finding that drought significantly reduces yield, especially during tasseling and maturity, while irrigation acts as crucial climate insurance by sustaining vegetation vigor.
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Liu et al. (2026) Cross-regional estimation of leaf chlorophyll and soil moisture content in drip-irrigated citrus orchards using UAV data and transfer learning
This study developed a transfer learning framework using UAV multispectral data to achieve cross-regional estimation of leaf chlorophyll content (LCC) and soil moisture content (SMC) in drip-irrigated citrus orchards. The Fine-tuning strategy, combined with the CNN-LSTM-Attention-XGBoost (CLA-X) model, significantly enhanced estimation accuracy in a new region, demonstrating a viable framework for precision water and fertilizer management.
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Ghazal (2026) Integration of Remote Sensing and Geographic Information Systems for Mapping Water Harvesting Sites in the Shwan Sub-Basin, Kirkuk, NE Iraq
[Information unavailable due to unreadable paper text.]
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Akın (2026) Hybrid Deep Learning for Climate-Driven Atmospheric Irrigation Potential Forecasting: A Case Study for Ankara
This study developed a hybrid LSTM-XGBoost residual model to forecast monthly atmospheric irrigation potential for Ankara, Türkiye, achieving stable forecasts with a Root Mean Square Error of 24.4 mm and a Coefficient of Determination of 0.87.
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Liu et al. (2026) A Graph‐Based Deep Learning Approach for Daily Flash Flood Susceptibility Modeling in China
This study proposes a novel graph-based deep learning model (LTG) for daily-scale spatiotemporal flash flood susceptibility (FFS) simulation in China, addressing limitations of traditional models by integrating temporal dependencies, spatio-temporal interactions, and spatial dependencies between catchments. The LTG model significantly outperforms baseline models, achieving an AUC of 0.911 and CSI of 0.719, and effectively captures seasonal FFS variations and spatial hydrological dependencies.
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Bosc et al. (2026) Predicting thunderstorm risk probability at very short time range using deep learning
This study develops a deep learning methodology to predict thunderstorm risk probability at very short time ranges (every 5 minutes up to 1 hour ahead) for aviation safety. It utilizes an adapted Convolutional Neural Network with attention mechanisms, fed by satellite observations and Numerical Weather Prediction outputs, to generate well-calibrated lightning risk maps.
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Moghairib et al. (2026) A Model‐Agnostic Representation of Prairie Pothole Hydrology: Enhancing Generality and Implementation Across Hydrological Models
This paper introduces HDSv2, an open-source and model-agnostic module designed to improve streamflow modeling in pothole-dominated regions by representing dynamic contributing area and storage-discharge hysteresis. Integrating HDSv2 into various hydrological models significantly enhances numerical stability, process fidelity, and the reproduction of observed hydrographs and depressional storage relationships.
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Wu et al. (2026) Analysis on Multi-Factor Synergistic Hazards Mechanism of Wet Micro-Downburst: a Multi-Source Data Fusion Analysis Based on Passenger Vessel Capsizing Accident in Qianxi, Guizhou on 4 May 2025
This study investigates the multi-factor synergistic mechanism of a wet microburst that caused a passenger vessel capsizing accident in Qianxi, Guizhou, on 4 May 2025, by fusing multi-source data to reconstruct the event and elucidate the dynamic-microphysical coupling. It found that a rapidly descending strong radar echo core triggered an explosive near-surface divergent wind field (34.7 m/s), accompanied by significant temperature and pressure changes, and verified by lightning activity, surveillance videos, and on-site damage.
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Dey et al. (2026) Catchment’s Resilience to Flood Generation Mechanism: The Role of Soil Moisture
This study evaluates the drivers and mechanisms of catchment resilience to dominant flood-generating mechanisms under varying antecedent soil moisture conditions across 191 catchments in peninsular India. It reveals that soil properties, rainfall characteristics, land cover, and meteorological variables during dry spells systematically influence this resilience and the spatial variation of antecedent soil moisture.
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Rahman et al. (2026) Unmasking human-subsidized resilience through hydrological drivers of water use efficiency during compound droughts across the North China Plain
This study quantifies water use efficiency (WUE) in North China Plain (NCP) provinces and analyzes its resilience and vulnerability to multi-type droughts, revealing that human-subsidized resilience temporarily masks meteorological deficits but collapses non-linearly under deep groundwater depletion.
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Su et al. (2026) Prediction of spring agricultural drought in cold and arid regions on the basis of soil freeze thaw processes
This study developed and evaluated a spring agricultural drought prediction method for cold and arid regions, based on soil freeze-thaw processes, finding that the Projection Pursuit Regression (PPR) model significantly outperformed other machine learning models, particularly in shallow soil layers and arid conditions.
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Bostani et al. (2026) Bivariate Copula–Based Stochastic Modeling for Drought Risk Assessment in Southeastern Iran
## Identification - **Journal:** Journal of Hydrologic Engineering - **Year:** 2026...
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Hu et al. (2026) Multi‐Indicator Assessment to Assess the Increasing Impacts of Compound Dry and Hot Events on Global Wheat Yield
This study investigates the evolution of compound dry and hot events (CDHEs) during global wheat growing seasons from 1981 to 2020 and their impact on wheat yield. It finds significant increases in CDHE frequency, duration, and intensity globally, leading to negative yield anomalies, particularly in arid and semiarid regions, with specific multi-indicator combinations proving most effective for detection.
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Lannoy et al. (2026) Advancing crop modeling and data assimilation using AquaCrop v7.2 in NASA's Land Information System Framework v7.5
This paper integrates the AquaCrop v7.2 crop model into NASA's Land Information System Framework (LISF) v7.5, enabling high-performance, scalable crop modeling and satellite data assimilation. Through three showcases, it demonstrates improved canopy cover simulations with satellite-informed crop parameters, quantifies biomass uncertainty in relation to soil moisture, and explores the beneficial but limited impact of fractional vegetation cover (FCOVER) assimilation on yield estimates due to strong model constraints.
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Switanek et al. (2026) Leveraging normalized data to improve point-scale estimates of precipitation–temperature scaling rates
This study proposes and evaluates a methodology using normalized precipitation and dew point temperature data to improve point-scale estimates of precipitation-temperature scaling rates, demonstrating that normalization effectively accounts for spatio-temporal climatological variability and enhances prediction skill in the Upper Colorado River Basin.
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Kumar et al. (2026) A Framework for Selection of a Hydrological Model-SAWT: A Review
This review provides a detailed overview of the Soil and Water Assessment Tool (SWAT) model, including its development, structure, data requirements, and application strategies. It systematically compares SWAT with HEC-HMS, MIKE-SHE, and VIC based on eight key criteria, concluding that SWAT is a versatile and suitable model for watershed-scale hydrological research and management.
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Yu et al. (2026) Flood inundation monitoring with multi-source satellite imagery based on deep learning and explainable frameworks
This study introduces a U-Net deep learning model for flood inundation monitoring by integrating multi-source Sentinel-1 (SAR) and Sentinel-2 (Multispectral) satellite imagery, demonstrating enhanced accuracy and providing insights into model behavior using an explainable framework.
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Shahid (2026) R3GENESI5/biome-specific-forcing-coefficients: v1.0.0 — Initial release
This study develops an analysis pipeline to empirically investigate biome-specific radiative forcing coefficients, demonstrating that greenhouse gas forcing is non-uniformly modulated by ecosystem energy partitioning, leading to a significant reduction in error compared to uniform forcing assumptions.
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Soares et al. (2026) EVAonline: An open-source web platform for global reference evapotranspiration estimation via multi-source data fusion
EVAonline is an open-source web platform designed for high-accuracy global reference evapotranspiration (ETo) estimation through multi-source data fusion, demonstrating significant performance improvements over individual global data sources.
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Paris et al. (2026) Altimetric Rating Curves (ARCs) parameters from "Global Scale River Discharge and Mean Depth from Radar Altimetry and Model"
This study generates a global dataset of calibrated power-law rating curve parameters and associated discharge statistics for over 32,000 river virtual stations by combining satellite altimetry water surface elevation time series with a 30-year global monthly river discharge reanalysis, enabling the first near-global satellite-derived map of mean river depth.
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Pabon-Moreno et al. (2026) Sun Induced Fluroscence Downscaling processor using openEO and Copernicus Data Space Ecosystem Infrastructure
This paper describes a mechanistic model-based workflow for downscaling Sun-induced chlorophyll fluorescence (SIF) observations from the Sentinel-5P TROPOMI sensor to finer spatial resolutions, aiming to improve the spatio-temporal coupling between SIF and gross primary productivity (GPP).
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Sun et al. (2026) envdes/code_CLMU_UK_2000_2014: First release
This release introduces a Python-based software package, `envdes/code_CLMU_UK_2000_2014`, designed for environmental modeling in the UK for the period 2000-2014, which supplements a related journal article.
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Ariyasena (2026) Suranjith19921023/RZSM-Loess-Plateau: Initial release — manuscript submission
This study presents a machine learning framework for estimating multi-layer root zone soil moisture on the Loess Plateau, utilizing bias-corrected ERA5-Land reanalysis data.
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Fikaduway12 et al. (2026) Supplementary Materials for AI Weather Forecasting Systematic Review (Version 1.2)
This record contains supplementary materials for a systematic review on Artificial Intelligence for weather forecasting, providing detailed summaries and quality assessments for 50 included studies to enhance reproducibility and transparency.
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Tooth (2026) NOC-MSM/nemo_cookbook: v2026.03.b1
This release introduces significant enhancements to the `nemo_cookbook` Python library, including a new `NEMODataArray` object and advanced grid-aware methods, to streamline and improve the analysis of NEMO ocean model outputs.
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Gómez et al. (2026) Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill
This study develops a storm-position–conditioned Quantitative Precipitation Forecast (QPF) displacement ensemble using HRRR forecasts to propagate precipitation perturbations through a 2D hydrodynamic model (SFINCS), quantifying their impacts on deterministic and probabilistic flood inundation mapping. The approach improves correlation with observations, reduces biases in predicted flood depths, and enhances the representation of flood impact variability in urban environments.
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Chen et al. (2026) Skillful Kilometer-Scale Regional Weather Forecasting via Global and Regional Coupling
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Antonio et al. (2026) ACE2-NEMO: Coupling an ML atmospheric emulator to a full-depth dynamical ocean model
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Azizi et al. (2026) SVH-BD : Synthetic Vegetation Hyperspectral Benchmark Dataset for Emulation of Remote Sensing Images
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Ballester-Berman et al. (2026) Estimating the Coherency Matrices of Polarised and Depolarised Components of PolSAR Data
This paper introduces a novel two-component decomposition method, extending the model-free three-component (MF3C) decomposition, to separate polarised and depolarised scattering contributions in PolSAR data, demonstrating its consistency with theoretical expectations across various scenarios and sensor frequencies.
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Ren et al. (2026) Evaluating the Collision‐Coalescence Process in Idealized Cloud Convection Using Large‐Eddy Simulations With Lagrangian Microphysics
This paper employs large-eddy simulations with Lagrangian microphysics to evaluate the theoretical non-dimensional drizzle number (Dz) in a simulated convection-cloud chamber, confirming its utility in quantifying the impact of collisional growth on droplet size distribution and estimating collision rates.
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Bochow et al. (2026) Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature
This study introduces a novel physics-constrained generative machine learning framework, based on consistency models, to downscale Greenland's surface mass balance (SMB) and surface temperature (T_s) fields by a factor of up to 32 (from 160 km to 5 km grid spacing). The method ensures physical conservation during inference, enabling robust generalization to extreme climate states and providing realistic, high-resolution climate forcing for ice-sheet simulations with fast computational efficiency.
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Tang et al. (2026) Research on Adaptive Irrigation Decision‐Making Method for the Entire Growth Cycle of Water Spinach Based on Reinforcement Learning
This study proposes an environmentally enhanced proximal policy optimization (EN-PPO) method for precision irrigation control in water spinach production, which addresses challenges from rainfall uncertainty and crop growth stage differences by incorporating a dynamic shearing strategy and a negative incentive mechanism, demonstrating superior performance in water saving, rainfall utilization, and stable policy convergence without affecting crop yield.
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Chen et al. (2026) Ensemble forecast data from the Stevens Flood Advisory System
The Stevens Flood Advisory System (SFAS) provides ensemble total water level forecast data for coastal regions, specifically for research into mid-latitude super-ensemble coastal water level forecasting.
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Moniruzzaman (2026) Explainable Machine Learning for Assessing Urban Expansion and Flood Vulnerability: A Multi-Source Remote Sensing Assessment of Ten Global South Cities
This data publication provides a multi-source remote sensing dataset, comprising 10 CSV files derived from Google Earth Engine, specifically designed to support explainable machine learning assessments of urban expansion and flood vulnerability in ten Global South cities.
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孔 (2026) ERA5-PrecipSR
This paper introduces ERA5-PrecipSR, a regional NetCDF dataset derived from ERA5 hourly data, specifically designed to facilitate precipitation super-resolution and spatial downscaling research. It provides high-resolution meteorological fields and dynamically generated low-resolution precipitation to support physics-informed precipitation reconstruction.
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Groeneveld et al. (2026) Surface Reflectance: An Image Standard to Upgrade Precision Agriculture
This study evaluates atmospheric correction software for converting satellite imagery to surface reflectance for precision agriculture, finding that the CMAC software provides the necessary accuracy and precision for automated, error-free agricultural analytics, unlike established methods.
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Yan et al. (2026) SSF-TransUnet: Fine-Grained Crop Classification via Cross-Source Spatial Spectral Fusion
This paper proposes SSF-TransUnet, a dual-branch deep learning framework, to address the challenge of fine-grained crop classification by effectively fusing high spatial resolution imagery and multi-spectral observations from different satellite sensors. The method achieves an overall accuracy of 81.84% and a mean Intersection over Union of 0.6954, demonstrating superior performance in distinguishing various crop categories.
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zhou (2026) The 2025 Beijing Extreme Event
This Mendeley Data entry provides a dataset detailing an extreme flood event that occurred in Beijing in 2025.
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Kidron et al. (2026) The Effect of Neighboring Objects on Non-Rainfall Water
This study experimentally investigated the effect of neighboring objects, quantified by their obstruction angle, on non-rainfall water (NRW) accumulation. It found a significant decrease in NRW with increasing obstruction angle, following a third-degree polynomial relationship, with implications for both natural ecosystems and urban infrastructure.
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Serna (2026) Cambio Climático en España e Iberoamérica (2000–2024): Temperatura, CO₂ y Eventos Extremos
This dataset compiles comprehensive climate and environmental data for Spain and Iberoamerica from 2000 to 2024, covering key indicators such as temperature anomalies, CO₂ concentrations, greenhouse gas emissions, sea level rise, and extreme meteorological events. It serves as a valuable resource for studying regional climate change trends and impacts.
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Li et al. (2026) A comprehensive China topsoil dataset of high-resolution microstructure, topographical characteristics and physical properties
This study presents CHARM3D, a novel high-resolution 3D topsoil microstructure dataset for China, integrating X-ray computed tomography imaging with comprehensive measurements of topological and physical properties to quantitatively characterize soil morphological parameters across diverse ecosystems. The dataset provides crucial parameters for reactive transport modeling and calibrating soil hydraulic models, addressing the scarcity of such detailed soil microstructural data.
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Wang et al. (2026) Coupling strategies of snowmelt runoff model and machine learning in the Lhasa River Basin
This study developed two coupling strategies between the Snowmelt Runoff Model (SRM) and the Transformer machine learning model to enhance streamflow simulation accuracy and interpretability in the Lhasa River Basin. The residual coupling strategy significantly improved simulation accuracy (NSE: 0.97), and SHAP analysis identified precipitation as the main driving factor, with temperature, solar radiation, relative humidity, and land use types exhibiting complex, threshold-dependent influences on streamflow.
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Shelton et al. (2026) Impacts of subtropical pacific variability on snow precipitation fraction (SPF) in the upper Colorado river basin
This study investigates the spatiotemporal variability of the snowfall-to-total-precipitation fraction (SPF) in the Upper Colorado River Basin (UCRB) during the cold season (1980–2022), revealing a distinct north-south gradient and a strong connection between SPF variability and large-scale ocean-atmosphere interactions, particularly the Pacific Meridional Mode (PMM).
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Fallah-Mehdipour et al. (2026) Evaluation of agro-hydrological subseasonal-to-seasonal forecasts for tactical decision making in crop irrigation
This study investigates the utility of sub-seasonal to seasonal (S2S) forecasts (up to six weeks lead time) for estimating future crop irrigation at the field scale. It found that while forecast accuracy decreases with longer lead times, using a reliability-weighted median of S2S forecasts can potentially reduce irrigation water use and maintain or enhance crop water productivity in irrigated years.
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Montazeri et al. (2026) Spatial and temporal changes in wildfires and their attributes across the western United States
This study analyzed wildfire activity in the Western United States from 1992 to 2020, revealing a 31% decrease in the number of ignitions but a 40% increase in burned area, primarily due to environmental conditions promoting larger fires and earlier human-caused ignitions.
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Zhang et al. (2026) Applying geostatistical electrical resistivity tomography and a water content estimation model for loess spatial mapping
This study developed a novel piecewise model for estimating loess volumetric water content (θ) from electrical resistivity (ρ) data, significantly improving accuracy, especially in low-moisture zones. Coupled with geostatistical electrical resistivity tomography (GERT), this method effectively mapped the spatial distribution of θ in a loess slope, outperforming traditional techniques for geological hazard mitigation.
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Xu et al. (2026) Escalating Extreme River Discharge Events Driven by Precipitation Changes in the Yangtze River Basin
This study investigated the spatiotemporal characteristics and precipitation drivers of 3-hourly extreme river discharge events (ERDEs) across the Yangtze River Basin (YRB) from 2000 to 2019, revealing a significant increasing trend in ERDEs and distinct local/upstream precipitation patterns linked to specific atmospheric processes in different sub-regions.
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Jayawardane et al. (2026) Comparative urban flood hazard mapping using GIS-integrated multi-criteria decision analysis: A remote sensing approach for Colombo, Auckland, and Valencia
This study applies a GIS-integrated hybrid Analytical Hierarchy Process (AHP) framework to map urban flood hazard zones in Colombo (Sri Lanka), Auckland (New Zealand), and Valencia (Spain). It reveals that 13.64% of Colombo, 25.64% of Auckland, and 17.63% of Valencia fall under extremely high flood hazard levels, offering insights for area-specific mitigation strategies.
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Sonon et al. (2026) Artificial intelligence methods for rainy season forecasting: a comprehensive analysis
This critical review comprehensively analyzes artificial intelligence methods for rainfall forecasting, focusing on data validation, prediction across various time horizons, and key rainy season parameters, revealing hybrid models as the most prevalent approach. The study proposes a novel classification framework for AI models based on forecast horizon, target parameters, and algorithmic complexity, while highlighting gaps in methodological standardization and advanced AI model adaptation in tropical regions.
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Gorgij et al. (2026) Metaheuristic-optimized neuro-fuzzy models for meteorological drought prediction
This study develops and evaluates hybrid neuro-fuzzy models, optimized by metaheuristic algorithms, for meteorological drought prediction using the Standardized Precipitation Index (SPI) in Baden-Württemberg, Germany. The ANFIS-MVO model consistently demonstrated superior predictive accuracy, particularly for longer SPI time scales (SPI12), outperforming other hybrid variants and benchmark models.
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Que et al. (2026) Retrieving Soil Water Content in Winter Wheat Fields Using UAV-Based Multi-Source Remote Sensing and Machine Learning
This study proposes a novel hybrid framework integrating an improved water cloud model (IWCM) with machine learning to retrieve farmland soil water content (SWC) in winter wheat with high accuracy and physical interpretability. The framework, using multi-modal UAV data, significantly enhances SWC retrieval performance, achieving an R² of 0.865, MAE of 0.0152, and RMSE of 0.0197 with a Random Forest model driven by spectral reflectance.
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Gu et al. (2026) Nonstationary Runoff Evolution and Structural Regime Shifts in Cold-Region Plateau Rivers Under Climate Change
This study investigates the nonstationary evolution of streamflow and extreme hydrological risks in the Yalong and Dadu River basins (upper Yangtze River) under future climate change (2017–2100) using a SWAT model and advanced time-frequency and spatial analyses. It reveals that precipitation is the primary streamflow driver, but temperature regulates seasonal intensity, with significant hydrological regime shifts projected for the mid-21st century and intensified, clustered extreme events, highlighting an upstream buffering-downstream sensitivity pattern.
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Li et al. (2026) Influence of open-source topographic data on basin-scale flash flood modelling in High Mountain Asia
This study investigates the influence of four widely used open-source topographic datasets on basin-scale flash flood modeling in High Mountain Asia, revealing significant biases in simulated hydrological and hydrodynamic characteristics, as well as delayed early warning times, underscoring the critical need for high-precision topographic data.
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Tan et al. (2026) Holocene Climate Changes: Unraveling Processes, Mechanisms, and Impacts Across Spatiotemporal Scales
This special collection explores Holocene climate changes, focusing on reconstructing variability, elucidating multi-scale mechanisms, and understanding climate-human-environment interactions, revealing that regional climate processes often diverge from global trends, multi-sphere interactions are crucial, and natural and human impacts on regional environments are asynchronous.
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Kyrgyzbay et al. (2026) Spatial Assessment of Flood Susceptibility in the Abai Region, Kazakhstan
This study presents a comprehensive spatial assessment of flood susceptibility in the Abai Region, Kazakhstan, using a multi-criteria Geographic Information System (GIS) approach that integrates twelve flood-conditioning factors with the Analytical Hierarchy Process (AHP). The analysis identified distance to rivers (19.66%) and precipitation (16.42%) as the most influential drivers, classifying 25.0% of the region as high susceptibility, with the model demonstrating strong predictive performance (ROC-AUC = 0.893).
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Zou et al. (2026) Temporal and Spatial Changes of Extreme Precipitation Indices in Jilin Province During 1960–2019 and Future Projections Under CMIP6 Scenarios
This study investigated the spatiotemporal characteristics and future trends of extreme precipitation in Jilin Province, China, finding that from 1960-2019, it exhibited increased frequency and total amount but decreased intensity, with future projections (2025-2100) indicating scenario-dependent intensification, particularly under SSP5-8.5, reversing the past intensity trend.
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Santiago-Ayala et al. (2026) Estimation of Water Balance and Nitrate Load in the Upper Basin of Aguascalientes, Mexico, Using SWAT
This study evaluates the hydrological response and nitrate leaching dynamics in the Upper Aguascalientes watershed using the SWAT model, revealing a critical disconnect between high fertilization rates and crop absorption capacity that leads to rapid nitrogen transport during runoff events.
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Zhu et al. (2026) Observed global increases in tropical cyclone rapid acceleration and deceleration events
This study empirically analyzes global tropical cyclone (TC) observations from 1988 to 2024 to identify temporal and spatial trends in TC translation speed changes. It finds that both rapid acceleration and strong deceleration of TCs have become increasingly prevalent globally, indicating a more variable pattern of storm motion.
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Achite et al. (2026) Bivariate Characterization of Long-Term Hydrological Drought Risks Using SRI and Archimedean Copulas
This study develops a bivariate probabilistic framework to characterize long-term hydrological drought risk in the Wadi Sahouat basin, northwestern Algeria, using the 12-month Standardized Runoff Index (SRI-12). It demonstrates that multivariate return periods significantly differ from univariate estimates, especially for extreme events, underscoring the compounded risk of prolonged and severe droughts.
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Wu et al. (2026) Phenology-Aware Collaborative Decision-Making and AG-PSTC Algorithm for Precision Irrigation in Smart Tea Gardens
This study develops a precision irrigation system for smart tea gardens, integrating a Phenology-Aware Collaborative Decision-Making (PACD) model and an Adaptive Gain Predictive Super-Twisting Sliding Mode Control (AG-PSTC) algorithm. The system effectively mitigates time delays and nonlinear interference while dynamically adjusting irrigation based on actual plant needs, significantly improving precision and suppressing false irrigation commands caused by biomass fluctuations from plucking.
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Akhunboboyev et al. (2026) Impact of Soil Salinization and Groundwater Levelson Vegetation Cover in Irrigated Agroecosystems: An Analysis Based on GIS and Vegetation Indices
This study analyzes how soil salinization and groundwater levels impact vegetation cover in irrigated agroecosystems, finding that high groundwater levels increase soil salinity and deteriorate vegetation with a 3–4 month delay.
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Yoloğlu et al. (2026) Groundwater sustainability for irrigation in the semi-arid Konya Closed Basin, Türkiye, under climate change scenarios
This study assesses groundwater sustainability in the semi-arid Konya Closed Basin, Türkiye, under climate change and agricultural scenarios using a coupled groundwater flow model and the net inflow concept. It concludes that combining enhanced irrigation efficiency with a shift to traditional rainfed crops is crucial for mitigating groundwater overexploitation and achieving sustainable use.
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Gutiérrez-Cabrera et al. (2026) Climate-Smart Framework for Olive Yield Estimation: Integrating Soil Properties, Thermal Time, and Remote Sensing NDVI Time Series
This study developed a climate-smart framework integrating soil properties, thermal time, and remote sensing NDVI to improve olive yield estimation at the parcel scale in southern Spain, revealing key relationships between early-season vegetation, rainfall during specific thermal windows, and subsequent year's yield.
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Deng et al. (2026) Global stalled tropical cyclones in a changing climate
This study globally analyzes tropical cyclone (TC) stalling behavior and its response to climate warming, finding a distinct hemispheric asymmetry where Southern Hemisphere basins are more prone to stalling. While a warming climate reduces the global probability of TC stalling occurrence, it significantly increases the daily rainfall associated with these storms, particularly over land and nearshore regions.
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Haagmans et al. (2026) How montane forests shape snow cover dynamics across the central European Alps
This study comprehensively analyzed how montane forests influence snow cover dynamics across the central European Alps using a process-based snow model over eight hydrological years. It found that forests store 20–30% of midwinter snow, generally reducing peak snow water equivalent (SWE) but often delaying snow disappearance, with these effects varying significantly by elevation, aspect, region, and interannual weather variability.
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Zhang et al. (2026) Atmospheric Rivers Linking Tropical Forcing to Winter Precipitation over Southern China
This study identifies a specific tropical sea surface temperature pattern involving ENSO, IOD, and WNP SST anomalies that enhances atmospheric river intrusion into southern China, leading to significant winter precipitation anomalies that are reasonably predicted by seasonal forecast models.
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Tsypin et al. (2026) Influence of groundwater recharge projections on climate-driven subsurface warming: insights from numerical modeling
This study numerically models the coupled effects of rising surface temperatures and changing groundwater recharge on subsurface warming in Brandenburg, Germany, until 2100. It finds that while surface temperature is the primary driver of groundwater warming (up to 2.5 °C), groundwater flow dictates its spatial variability, and even increased winter recharge cannot counteract the overall warming trend.
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Tanhapour et al. (2026) Potential of Sentinel-3 snow cover fraction data for improving hydrological simulations at the regional scale
This study evaluates the accuracy and potential of a new Sentinel-3 snow cover fraction (SCF) product for improving hydrological simulations at a regional scale in Austria, demonstrating that its use in multiple-objective model calibration significantly enhances snow and runoff simulations, particularly in lowland catchments.
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Lou et al. (2026) A highly generalizable data-driven model for spatiotemporal urban flood dynamics real-time forecasting based on coupled CNN and ConvLSTM
This study proposes a novel data-driven model, coupling CNN and ConvLSTM, for real-time spatiotemporal urban flood inundation depth forecasting. The model effectively captures inundation dynamics and demonstrates robust spatial generalization with significantly higher computational efficiency compared to physics-based models.
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Deng et al. (2026) Legacy effects of extreme precipitation sustain carbon sink of a subtropical forest
This study investigates the seasonal to interannual effects of extreme precipitation on net ecosystem productivity (NEP) in subtropical forests using a land model, revealing that while extreme events immediately reduce NEP, legacy effects from deep soil water recharge sustain the carbon sink in subsequent years.
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Du et al. (2026) Large-scale water depth reconstruction of Tibetan Plateau lakes using ICESat-2 photon data: Performance, limitations, and environmental drivers
This study reconstructs water depths for 350 Tibetan Plateau lakes using ICESat-2 photon data, achieving a root mean square error of 0.45 m, and identifies lake area, longitude span, and water clarity as key influencing factors.
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Mkhonta et al. (2026) Towards improved flood prediction: a review of deterministic hydrologic-hydraulic model coupling
This scoping review synthesizes 94 peer-reviewed studies from 1994 to 2024 to trace the evolution of deterministic hydrologic–hydraulic model coupling for flood forecasting, highlighting model prevalence, performance, and regional disparities, particularly in data-scarce regions like Africa. It finds that HEC-HMS and HEC-RAS are the most widely used models, consistently achieving high predictive accuracy, and emphasizes the need for context-specific solutions and improved data infrastructure in underrepresented areas.
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Zi et al. (2026) East Asian spring precipitation and its dry trend revealed by CMIP6 high-resolution coupled models
This study evaluates CMIP6 HighResMIP coupled models' ability to simulate East Asian spring precipitation climatology and its drying trend (1980–2014), finding that high-resolution models generally improve mean precipitation patterns but show varied and often limited success in reproducing the observed drying trend, with only a few models demonstrating significant improvement due to better representation of regional circulation drivers.
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Tilahun et al. (2026) Topographic modulation of drought propagation establishes low-elevation hotspots and mid-elevation climatic refugia in Southern Africa
This study quantifies the topographic modulation of drought propagation in Southern Africa, revealing that mid-elevation zones act as climatic refugia with slow drought propagation, while low and high elevations are vulnerable hotspots experiencing accelerated aridification, a pattern projected to intensify.
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Ellahi et al. (2026) A Novel Integrated Standardized Index for Drought Assessment of Homogeneous Regions
This study introduces the Regionally Integrated Standardized Drought Index (RISDI), a novel framework integrating Model-Based Clustering, Principal Component Analysis, and K-Component Gaussian Mixture Distribution, to efficiently and accurately assess multi-regional drought severity by reducing data complexity while remaining sensitive to local changes.
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Bharadiya (2026) Hydro-Resource-Hub v1.0.0: The Complete HEC-RAS Master Guide
This paper introduces Hydro-Resource-Hub v1.0.0, an open-source repository providing a comprehensive master guide for HEC-RAS, interactive Python templates for hydrological data, and curated directories of models and datasets, designed to support advanced water management and civil engineering research.
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haticeturk (2026) haticeturk/Forward_Backward_Trasit_Time: Tracking Event‐Scale Precipitation Partitioning Reveals Comparable Roles of Event Characteristics and Seasonality in Shaping Precipitation Fate in a Forested Landscape
This study investigates event-scale precipitation partitioning in a forested landscape, revealing that both event characteristics and seasonality comparably influence the fate of precipitation.
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drchavas et al. (2026) drchavas/tcpyVPI: New version for zenodo
This software release provides version 1.0.1 of tcpyVPI, a tool designed for calculating the ventilated potential intensity of tropical cyclones.
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Ren (2026) ECARM: Energy-Consistent Advanced Regional Model (Version 1.0.0)
This paper introduces ECARM, an energy-consistent dynamical core integrated into the WRF v4.2 modeling framework, designed to reduce spurious energy sources and sinks, thereby minimizing numerical dissipation and dispersion.
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Key (2026) kesondrakey/diurnal-divergence-factor: Code and data for Towards Biophysically-informed Drought Indicators Across US Ecosystems (V1.0.0)
This release provides processed datasets and analytical code to support the development and analysis of biophysically-informed drought indicators across various US ecosystems, enhancing reproducibility and transparency for the associated manuscript.
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Nikrou et al. (2026) Intercomparison of flood inundation models across land use types and hydrological flood stages
This study provides a context-stratified intercomparison of five flood inundation models across multiple hydrograph phases, land-use types, and benchmark datasets, revealing that model performance rankings systematically shift depending on the evaluation context. The findings offer guidance for model selection and the design of future intercomparison studies.
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Fu et al. (2026) Evapotranspiration-partitioned generalized complementary relationship model for heterogeneous vegetation
This study proposes an evapotranspiration-partitioned generalized complementary relationship (EPGCR) model to estimate total evapotranspiration (ET) and its components (transpiration, soil evaporation, and interception evaporation) using only meteorological and leaf area index data. Applied to heterogeneous vegetation, the EPGCR demonstrated high accuracy and consistent parameters, significantly expanding the applicability of the complementary relationship.
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Bah et al. (2026) A new comprehensive monitoring framework for global drought assessment
This study introduces a novel Comprehensive Drought Index (CoDI) for global drought assessment, integrating meteorological, agricultural, and hydrological drought dimensions using Principal Component Analysis. CoDI was validated across 60 major basins and 22 historical drought events, demonstrating superior capability in capturing diverse drought conditions and identifying global drought hotspots from 1982 to 2018.
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Lu et al. (2026) Transferring Physical Priors into Remote Sensing Segmentation via Large Language Models
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Simm et al. (2026) Calibrated Conformal Prediction Intervals for Microphysical Process Rates
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Kui et al. (2026) Detection of nine plateau lakes water level changes in Yunnan, China from ICESat-2 data
This study developed an outlier-detection framework for ICESat-2 ATL13 data to monitor water level changes in nine Yunnan plateau lakes from 2018 to 2024, revealing consistent declining trends for most lakes driven by seasonal natural factors modulated by anthropogenic influences.
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Ye et al. (2026) Agent-based intelligent real-time control for pluvial flood mitigation at urban scale
This study develops an Urban Flooding Control Model (UFCM) by integrating hydrological-hydrodynamic models with deep reinforcement learning (DRL) for real-time pluvial flood mitigation at the urban scale. Applied to Jinan, China, UFCM significantly reduced inundated areas compared to traditional methods, demonstrating efficient and accurate real-time decision-making for complex urban flood systems.
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Yaseen (2026) The legacy and implications of cyberinfrastructure systems in hydrological process modeling
This communication discusses the critical components, challenges, and solutions for effective cyberinfrastructure (CI) systems in hydrological process modeling, emphasizing data quality, integration, accessibility, sustainability, ethics, and human capacity. It advocates for strategic guides to enhance CI utility and trustworthiness in hydrological engineering.
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Capecchi et al. (2026) Can Hot Water Discharged from Industrial Processes Enhance the Likelihood of Waterspouts?
This study reconstructs the mesoscale meteorological conditions of four intense waterspouts near Rosignano Solvay, Italy, using a high-resolution weather model to assess the potential influence of heated industrial wastewater discharge, concluding that synoptic and mesoscale conditions are the primary drivers, with the industrial plume having only a minor direct impact.
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Chen et al. (2026) MDE-UNet: A Physically Guided Asymmetric Fusion Network for Multi-Source Meteorological Data Lightning Identification
This paper proposes a novel lightning identification network that integrates multi-source meteorological data by addressing challenges like modal competition, data sparsity, and class imbalance, resulting in improved hit rates and reduced false alarms for lightning strike area identification.
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Yao et al. (2026) First Triple‐Frequency Retrievals of Binned Raindrop Size Distributions
This study develops a two-step optimal estimation technique for retrieving binned raindrop size distributions (DSDs) aloft using a newly developed vertically-pointing triple-frequency radar system, demonstrating good agreement with ground-based disdrometer observations and providing the first radar-based view of DSD variations in Meiyu front rainfall.
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Wang et al. (2026) Unraveling uncertainty in compound flood modeling: sensitivity of simulations to forcings and model parameters
This study evaluates the sensitivity of the SFINCS model to forcing and parameter uncertainties during the Beryl compound flood event in Houston. It reveals strong spatial variability in forcing sensitivity and consistent parameter sensitivity, concluding that parameter-induced uncertainty generally outweighs forcing-induced uncertainty.
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Zhu et al. (2026) Integrated Analysis and Prediction of Drought in the Endorheic Basins of China
This study systematically analyzes historical (1960–2024) and future (2025–2100) drought trends and characteristics, including compound and extreme events, across the diverse Endorheic Basins of China, revealing a pronounced drying trend influenced by radiative forcing and oceanic teleconnections.
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Yau (2026) mwyau/PyStormTracker: v0.5.0.dev0
This paper presents PyStormTracker, a high-performance Python-based software designed for tracking cyclones, leveraging parallel computing libraries like Dask and MPI.
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Akarma et al. (2026) Multi-Agent Reinforcement Learning for Cloudburst Prediction and Disaster Response
This paper introduces a multi-modal reinforcement learning framework designed for autonomous emergency response to extreme weather events, leveraging both radar and satellite data for decision-making.
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Xie et al. (2026) Deep reinforcement learning for long-horizon reservoir operation: Temporal horizon, state representation, and hydrological data synthesis
This study develops a Deep Reinforcement Learning (DRL) framework for long-horizon reservoir operation, systematically evaluating the impact of episode length, state representation, and synthetic hydrological data. It finds that a 4-year episode length, two-dimensional periodic date encoding, and extreme-enhanced synthetic inflows significantly improve policy performance, stability, and robustness for the Three Gorges Reservoir.
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Zhang (2026) Spectral-Aware Text-to-Time Series Generation with Billion-Scale Multimodal Meteorological Data
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Feng (2026) StretchCast: Global-Regional AI Weather Forecasting on Stretched Cubed-Sphere Mesh
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Muhawenayo et al. (2026) PRUE: A Practical Recipe for Field Boundary Segmentation at Scale
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Mittermeier et al. (2026) Omega-blocks with spatially compounding extremes over Europe are highly sensitive to remote atmospheric drivers
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Andalib et al. (2026) An intelligent dual-stage fusion framework of optical and radar data for land cover classification
This study introduces a novel dual-stage fusion framework that integrates optical and radar remote sensing data at both feature and knowledge levels to improve land cover classification accuracy. The proposed method achieves an overall accuracy of 94.7% and a Kappa coefficient of 0.93 in urban environments.
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Yang et al. (2026) ReDF-net: a feature extraction and dynamic fusion framework based on residual networks for runoff forecasting
This paper introduces ReDF-Net, a residual network-based dynamic fusion framework for multimodal runoff forecasting that adaptively couples spatial feature extraction with temporal modeling and quantifies input contributions. It demonstrates significantly enhanced accuracy (NSE > 0.97) and improved interpretability across two Chinese basins, outperforming various conventional and state-of-the-art models.
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Subhadarsini et al. (2026) EXtreFormer: a general deep learning framework for forecasting compound extreme events: experience with dry-hot extremes and vegetation response
This study introduces EXtreFormer, a novel deep learning framework for long-horizon forecasting of compound dry-hot extreme events and vegetation response, demonstrating superior performance and interpretability in the Godavari River Basin. The framework effectively captures the complex interplay between temperature, soil moisture, and vegetation across diverse land use types, achieving high predictive accuracy for extreme events.
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Bartczak et al. (2026) Dry and wet periods in north-central Poland in the years 1952–2020
This study quantifies dry and wet periods in north-central Poland from 1952–2020, investigating their correlation with atmospheric circulation and climatic variables. It reveals statistically significant trends of increasing potential evaporation (+27.8 mm per decade) and decreasing Climatic Water Balance (-29.6 mm per decade), with dry periods linked to anticyclonic circulation and wet periods to cyclonic circulation.
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López-Teloxa et al. (2026) Ecosystems in Mexico Are Experiencing an Increase in Trend and Intensity in Aridity
This study investigates aridity dynamics in Mexico (1999-2024) in relation to El Niño–Southern Oscillation (ENSO) phases, identifying ecosystems exposed to emerging aridification, and finding that significant areas, particularly in central and southern Mexico, experience increased aridity under both El Niño and La Niña conditions, suggesting persistent aridification.
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Ding et al. (2026) HBiM2: A multi-angle hyperspectral soil radiative transfer model for simulating the reflectance of dry and wet soils
This paper develops HBiM2, a multi-angle hyperspectral soil radiative transfer model, to accurately simulate the spectral and directional reflectance of both dry and wet soils, demonstrating robust performance across diverse soil conditions and observational geometries.
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Yang et al. (2026) Forewarning extreme precipitation events using scaling behaviors
This study investigates the temporal clustering of hourly extreme precipitation in Eastern China using scaling properties to improve forecasting capabilities. It finds that hourly extreme precipitation events tend to occur in clusters, unlike daily extremes, and proposes a scaling-behavior-based forewarning method that shows comparable performance to dynamical forecast models.
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Tiwari et al. (2026) Enhanced precipitation estimation in a Himalayan river basin through the fusion of multi-source datasets using various machine learning techniques
This study developed a Spatially Weighted Grid-Wise Ensemble Learning framework to enhance precipitation estimation in the Budhi Gandaki basin by fusing nine gridded precipitation products and six rain gauge observations with four machine learning algorithms. The integrated framework significantly improved precipitation estimates, achieving a correlation coefficient of 0.68 compared to 0.18-0.36 for individual products.
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Shaddy et al. (2026) Probabilistic Forecasting of Localized Wildfire Spread Based on Conditional Flow Matching
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Ordaz et al. (2026) A climatology of hail in Europe (2014–2024) based on GPM-DPR sensor data
This study developed a continental-scale hail climatology for Europe (2014–2024) using GPM-DPR satellite data and four detection algorithms to overcome surface observation limitations. It identified the Alpine range as the primary hail epicenter, with activity extending over the Balkan peninsula and Apennines, and revealed a clear seasonal migration of hail hotspots across the continent.
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Qiu et al. (2026) Attribution of extreme precipitation on the Loess Plateau, China: Roles of internal variability and external forcing
This study investigated precipitation and extreme precipitation trends on the Loess Plateau (1980-2018), attributing changes to internal climate variability and external forcings. It found an overall increase in precipitation, particularly after 2000, influenced positively by natural forcing and greenhouse gases, with aerosols having a negative effect.
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Lagos-Castro et al. (2026) Towards a balancing performance, uncertainty coverage, and spatial consistency in climate model sub-selection
This study introduces a standardized multi-criteria framework to evaluate climate model sub-selection methods, integrating performance, uncertainty coverage, and spatial consistency across basins with contrasting hydrological regimes. It finds that methods based on future change diversity and adaptive consensus best balance accuracy and uncertainty representation, consistently outperforming mono-model strategies, and highlights the critical role of spatial variability in method evaluation.
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Lee et al. (2026) Enhancement of the Operational GK2A Fog Detection Product over South Korea Through Integrated Surface–Satellite Post-Processing (2021–2023, Part II)
This study developed and optimized a post-processing algorithm for the Geo-KOMPSAT-2A fog detection algorithm (GK2A_FDA) by integrating high-resolution gridded surface observations, significantly reducing false alarms and bias with minimal impact on detection probability.
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Gu et al. (2026) Integrating Sentinel-2 and MODIS BRDF Imagery to Invert Canopy Fractional Vegetation Cover for Forests and Analyze the Corresponding Spatio-Temporal Evolution
This study integrates Sentinel-2 and MODIS data to develop a multi-angle retrieval method for canopy fractional vegetation cover (FVCc), demonstrating improved accuracy over single-angle approaches and revealing an overall upward trend in FVCc in Changting County.
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He et al. (2026) Enhancing runoff simulation in data-scarce mountainous regions: a coupled SWAT and transferable transformer approach
This study develops the SWAT-HydroTransformer, a physics–data hybrid framework that integrates SWAT-simulated hydrological processes as physical constraints into a multi-scale Transformer architecture. The framework aims to enhance runoff prediction in data-scarce mountainous regions, demonstrating superior predictive skill and robust transferability.
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Liu (2026) data for “Projected impacts of extreme heat stress on China’s economy...”
This study develops a multi-model integrated assessment framework to project future heat stress and its cascading economic impacts in China under a high-emission scenario, finding that national industrial value-added loss could reach 4.7% of GDP by 2050, with the transport and warehousing sector acting as a critical loss transmission node.
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Hadjij (2026) Rainfall data Mostaganem,Algeria
This dataset provides annual maximum daily precipitation and recorded urban flooding events for Debdaba meteorological station in Mostaganem, Algeria, from 1989 to 2024, intended to support research on the impact of urbanization on stormwater runoff and flooding.
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Huang et al. (2026) A Dual-Branch Typhoon-Gated Axial Transformer for Accurate Tropical Cyclone Path Forecasting
This paper proposes a dual-path, multi-modal deep learning model, incorporating a gated axial Transformer, to enhance typhoon track prediction accuracy by improving the fusion of meteorological features and modeling of spatio-temporal evolution. The model demonstrates superior prediction accuracy across multiple time scales compared to existing methods.
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song et al. (2026) Ground-based solar irradiace measurements from 164 radiometric stations of CMA and CERN.
This paper presents a dataset comprising hourly ground-based global horizontal irradiance (GHI) measurements collected during 2019 from 164 radiometric stations jointly operated by CMA and CERN.
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Siqueira et al. (2026) Do our models capture the range of ENSO events?
This study employs a novel event-based space-time analog approach to evaluate whether state-of-the-art coupled climate models (CESM1 and E3SM1) capture the full range of observed ENSO variability. It reveals that low-resolution models generally outperform their high-resolution counterparts in reproducing observed ENSO events, and increasing resolution does not significantly reduce mean-state biases.
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Topete-Pozas et al. (2026) Land Surface Phenology Reveals Region-Specific Hurricane Impacts Across the North Atlantic Basin (2001–2022)
This study analyzed pre- and post-hurricane land surface phenology using the Enhanced Vegetation Index (EVI) for 44 hurricanes over 22 years across the North Atlantic Basin to characterize subregional forest impacts. It found significant subregional variability in forest responses, influenced by climate, land cover, and ecological factors like drought sensitivity and rapid refoliation, which complicate optical remote sensing assessments.
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Kuchinski et al. (2026) Causes, seasonality and climate variability of floods in southern Brazil
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BAYDAR et al. (2026) Evaluating soybean yield responses to future climate change and irrigation regimes: a DSSAT multi-model assessment
This study evaluated soybean yield responses to future climate change and irrigation regimes in the Mediterranean region of Türkiye using the DSSAT-CROPGRO-Soybean model with multi-GCM projections. It found that while elevated CO2 could increase yields, especially under the high emission scenario (RCP 8.5), seasonal soil water availability remained the primary constraint, highlighting the critical role of irrigation for stabilizing production.
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Zhao et al. (2026) Global Warming Potential Induced by Albedo and Greenhouse Gases Across Different Land Uses of the Saline-Alkaline Agropastoral Ecotone in the Songnen Plain
This study investigates the impact of land-use conversion from undisturbed meadow to clipped meadow, saline-alkaline meadow, and paddy rice on global warming potential (GWP) in the saline-alkaline agropastoral transition zone of Northeast China. It finds that conversion to paddy rice results in a net warming effect primarily due to high methane emissions and lower albedo, while other conversions show warming effects offset by CO2 exchange.
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Ames (2026) Mapping Water: A Brief History of GIS in Hydrology and a Path Toward AI-Native Modeling
This review traces the seven-decade evolution of Geographic Information Systems (GISs) in hydrologic science, from manual methods to AI-native spatial water intelligence, and articulates a future vision where AI blurs the lines between GIS and hydrologic modeling.
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Wang et al. (2026) Future Climate Projections of Hazardous Convective Weather Using an Ensemble of Environment‐Informed, Convection‐Permitting Dynamical Downscaling Simulations
This study introduces "environment-informed" convection-permitting dynamical downscaling (CPDD) to efficiently generate ensembles of hazardous convective weather (HCW) projections driven by multiple global climate models (GCMs). It demonstrates that future supercell occurrences, a key HCW indicator, vary significantly across GCMs, projecting an increased frequency over the Missouri Bootheel and an earlier start to the annual HCW risk cycle.
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Yan et al. (2026) Assessing the warming biases in CMIP6 models: the roles of fast response and cumulative effects to external forcings
This study introduces a novel method to assess warming biases in CMIP6 climate models using two indices, 𝑎 (fast response sensitivity) and 𝐻 (cumulative effects/long-term memory), derived from the climate system's scaling behavior. It finds that overestimated cumulative effects are a primary driver of warming biases in these models, offering an efficient framework for model evaluation and improvement.
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Gopalan et al. (2026) The water available for industrial sector (WAIS) framework for assessing the industrial water deficit risk while securing downstream availability
This study introduces the Water Available for Industrial Sector (WAIS) framework to quantify the double materiality of industrial water withdrawal (IWW) by jointly assessing its impacts on downstream water availability and operational risks. Applied to Thailand's Chao Phraya River Basin, the framework revealed localized water conflicts and substantial impacts, emphasizing the need for context-sensitive allocation strategies.
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Guan et al. (2026) Increasing Population and Cropland Exposure to Human‐Induced Sequential Heatwave‐Downpour Events
This study quantifies the anthropogenic influence on the increasing trend of compound sequential heatwave-downpour (SHD) events across the Northern Hemisphere and projects future population and cropland exposure. It finds that anthropogenic influences, primarily greenhouse gas emissions, account for approximately 82.2% of the increase in affected areas, with future exposure projected to increase nearly 8-fold under a high-emission scenario, predominantly driven by climate change.
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Wang et al. (2026) Water-balance health in China (2011–2018): UWBI assessment and elasticity-based driver attribution
This study developed an integrated natural-social water balance assessment framework to analyze the spatiotemporal patterns and drivers of water balance health in China from 2011 to 2018, revealing significant regional heterogeneity and identifying terrestrial water storage and total water use as key influencing factors.
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Liu et al. (2026) Multidimensional aspects of drought event evolution drive the spatiotemporal heterogeneity of vegetation photosynthetic responses
This study employs a three-dimensional clustering approach with daily soil moisture data (2000–2024) to identify and track 32 typical drought events across China, linking their evolutionary characteristics to vegetation photosynthetic response thresholds. It reveals that distinct drought types (High-Disturbance Migratory, Quasi-Stationary, Localized Outbreak) drive heterogeneous vegetation sensitivities, with varying environmental factors governing these responses.
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Erdem et al. (2026) Evapotranspiration and crop coefficient of drip irrigated walnut trees in semi-arid climatic conditions, Türkiye
This study determined the seasonal evapotranspiration (ETc) and crop coefficients (Kc) for drip-irrigated walnut trees (ages one to nine) in Türkiye's semi-arid climate. The research provides specific monthly Kc values, ranging from 0.55 in April to 1.07 in July, which are essential for optimizing irrigation water management.
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Dito et al. (2026) Establishing hazelnut stem water potential baseline to improve water management
This study established the first species-specific stem water potential (SWP) baseline for hazelnut (Corylus avellana L.) under non-limiting soil moisture conditions, revealing a stable, linear relationship between SWP and vapor pressure deficit (VPD) to improve irrigation management.
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Fang et al. (2026) Effects of Fenlong-Ridging Deep Tillage on Soil Water and Salt Transport Under Brackish Water Irrigation
This study investigated the effects of fenlong-ridging deep tillage (FL) on soil water and salt dynamics under brackish water irrigation, finding that FL significantly promotes water infiltration and reduces soil electrical conductivity, especially with 3 g·L−1 brackish water and 60 cm tillage depth.
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Pereira et al. (2026) Innovations in the Revised FAO56 Guidelines for Computing Crop Water Requirements: Data, Calculation Methods, Irrigation, and Climate Change Challenges
This article summarizes the main features and innovations of the recently released FAO Irrigation and Drainage Paper 56 Revision 1 (FAO56 Rev.1), an updated guidebook for estimating crop evapotranspiration and calculating crop water requirements, incorporating modern scientific and technological advancements and addressing current challenges like climate change and water scarcity.
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Wang et al. (2026) Interpretable hierarchical Bayesian modeling of monthly streamflow for heterogeneous basins: A comparative study of two basins
This study develops and compares two interpretable Bayesian hierarchical models (BHMs) for simulating monthly streamflow in two heterogeneous basins, demonstrating their ability to leverage shared information while capturing basin-specific storage dynamics and seasonal variations. The models provide an interpretable framework for understanding storage-driven responses and offer a robust pathway for operational streamflow forecasting in multi-basin settings.
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Sinha et al. (2026) ENSO-modulated heat source and moisture sink of Asian monsoon and its impact on rice production
This study quantifies the influence of ENSO-modulated Asian monsoon dynamics and associated high-pressure systems on rice yield variability across Asia. It reveals a strong seasonal asymmetry in climate-yield coupling and demonstrates that only La Niña conditions provide a robust, technology-independent positive climatic influence on Asian rice production.
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Pan et al. (2026) Ocean meridional heat transport estimated from energy budget constraint
This study quantifies global oceanic meridional heat transport (MHT) from 1985 to 2023 using an energy budget approach, revealing distinct MHT patterns and controls in the Atlantic and Indo-Pacific oceans, and providing improved estimates for climate research.
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Vishnu (2026) Contribution of Low Pressure Systems Rainfall to Interannual Variability of the Indian Summer Monsoon
This study re-examines the relationship between Monsoon Low-Pressure Systems (LPS) and Indian Summer Monsoon Rainfall (ISMR) variations, finding a strong correlation where increased LPS-generated rainfall, driven by higher rainfall rates from elevated humidity and stronger vertical motions, contributes substantially to excess ISMR, and reveals a coupling between LPS dynamical intensity and rainfall rates.
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Burns et al. (2026) Influence of Weather Fronts on Design Storm Profiles: Applied Event Partitioning and Comparative Analysis
This study objectively partitioned over 700 historic rainfall events by frontal storm signatures to develop synthetic storm profiles, revealing that frontal storms exhibit distinct temporal characteristics and shapes that significantly depart from standard distributions, which is crucial for hydrologic designs sensitive to temporal loading.
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Maruf et al. (2026) Soil moisture decorrelation timescales are sensitive to precipitation variability and land-atmosphere coupling
This study investigates the sensitivity of soil moisture decorrelation timescales to meteorological forcing autocorrelation and land-atmosphere coupling using the Community Land Model version 5 (CLM5). It finds that precipitation autocorrelation and soil moisture-precipitation feedback significantly enhance decorrelation timescales, with randomized forcing substantially reducing them, highlighting the influence of climate variability.
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Yong et al. (2026) Revealing the influence of topography and vegetation on hydrological processes using a stepwise modelling approach in cold alpine basins of the Mongolian Plateau
This study employed a stepwise modelling approach to systematically assess the influence of topography and vegetation on hydrological processes in cold alpine basins of the Mongolian Plateau, revealing that distributed and landscape-based models significantly improve runoff and snow water equivalent simulations, with high elevations driving sustained snowmelt runoff and low elevations generating rapid rainfall-driven runoff.
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Yuan et al. (2026) Baseflow in karst regions is significantly higher than the global average and exhibits spatial variability
This study quantifies baseflow characteristics in 1375 global karst basins, revealing that baseflow constitutes approximately 78 % of streamflow, significantly higher than the global average of 60 %. It also identifies significant spatial variability and an increasing temporal trend in karst baseflow, primarily influenced by vegetation factors.
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Ferreiro-Lera et al. (2026) Projected Reorganization of Euro-Mediterranean Bioclimates under Climate Change: Evidence from CMIP6 Multi-model Ensemble
This study projects future bioclimatic shifts in the Euro-Mediterranean region using a multi-model ensemble of 25 CMIP6 GCMs and two classification systems (Köppen–Geiger and WBCS), revealing a consistent trend towards warmer, more xeric, and continental conditions, with the WBCS providing more nuanced insights into transitional changes.
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Li et al. (2026) Global Spring–Autumn Phenology Coupling Inferred from Satellite Observations and Reanalysis-Based Climate Limitations
This study globally assesses the coupling between spring and autumn phenology and its modulation by growing season climate limitations using satellite observations and reanalysis data. It reveals that spring onset primarily influences autumn senescence through a direct phenological pathway, with climate-mediated effects being smaller and spatially heterogeneous.
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Zhang et al. (2026) Deceleration of water cycle for global semi-arid regions driven by climate warming
This study reveals a systematic deceleration of the water cycle in global semi-arid regions from 1982 to 2022, driven by climate warming, which contradicts the expected global intensification and is attributed to water-limited evapotranspiration responses and a positive feedback loop among water cycle components.
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Nunno et al. (2026) Teleconnection-informed clustering of temporally compound hydroclimatic extremes in Europe: post-drought extreme rainfall probability and long-term trends
This study analyzed compound drought-rainfall extremes across Europe (1976–2023) using teleconnection-informed clustering, revealing three distinct hydroclimatic regimes where post-drought extreme rainfall probabilities are heterogeneous and strongly modulated by large-scale atmospheric circulation. The findings indicate that extreme rainfall can occur during or after drought without necessarily ending the drought, emphasizing the critical role of atmospheric teleconnections.
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Wu et al. (2026) Decoding the drivers of global desertification sensitivity from 2005 to 2020
This study assessed global desertification sensitivity from 2005 to 2020 using the GEE-MEDALUS model, identifying climate and soil quality as dominant drivers and highlighting their co-limitation for targeted interventions.
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Zabaleta et al. (2026) Changing rivers: Hydrological shifts in the Pyrenees revealed by daily streamflow indicators (1950–2019)
This study provides the first region-wide assessment of streamflow trends across the Pyrenees using daily flow records from 93 gauging stations (1950–2019). Results reveal a robust long-term decline in Pyrenean river flows, with a seasonal redistribution showing declines in spring, summer, and autumn, and emerging increases in winter high flows, reflecting a transition from snow- to rain-dominated regimes.
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Sorando et al. (2026) Simulating mediterranean rice paddies’ water balance under climate change scenarios
This study applies the SWAT+ paddy rice module to simulate the water balance of a Mediterranean rice irrigation district in Albufera de Val`encia, Spain, under climate change scenarios. Projections indicate significant precipitation reductions (9–31%) and potential evapotranspiration increases (8–18%) by mid- and late-century, leading to higher irrigation requirements (4–10%) and modest rice yield declines (up to 8%).
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Gonçalves et al. (2026) Irrigated agriculture in the United States: Current status and future frontiers
This review assesses the current status and future frontiers of irrigated agriculture in the United States, analyzing regional trends, water sources, crop diversity, and management practices from 2003-2023, and identifies key challenges like groundwater depletion and an eastward shift in irrigation.
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Schotte et al. (2026) Comparing absolute and standardized drought indices for modelling tree mortality of spruce, beech, pine, and oak based on the Crown Condition Survey in Germany
This study evaluated the association of absolute aridity and standardized drought indices with tree mortality for four major tree species in Germany (1990–2022). It found that standardized drought indices, particularly the Standardized Precipitation Evapotranspiration Index (SPEI), better explained mortality for Norway spruce, European beech, and Scots pine, with effects increasing over longer aggregation periods of up to five years.
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Et-Takaouy et al. (2026) Performance evaluation of four satellite-based precipitation products (SPPs) in hydrological modelling across five northern moroccan catchments
This study evaluates the performance of four satellite-based precipitation products (GPM, TRMM, CHIRPS, PERSIANN-CDR) in hydrological modeling using HEC-HMS across five Northern Moroccan catchments. It reveals significant variability in SPP performance based on elevation and modeling phase, with CHIRPS demonstrating superior performance in high-altitude, semi-humid regions during validation.
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Wang et al. (2026) Spatial Downscaling of Satellite-Based Precipitation Data over the Qaidam Basin, China
This study downscaled Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 km to 1 km resolution for the data-scarce Qaidam Basin using four machine learning methods, finding the Cubist model to be the most accurate for generating high-resolution annual and monthly precipitation products.
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Cheng et al. (2026) Direct and Indirect Effects of Aerosols During the 2023 Canadian Wildfires
This modeling study found that incorporating real-time 2023 Canadian wildfire aerosol forcing significantly improved the representation of atmospheric conditions and aerosol optical depth compared to using climatology, though the positive impact on overall medium-range weather forecasting performance (5–10 days) was modest.
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Nam et al. (2026) Everything Comes Down to Timing: Optimal Green Infrastructure Placement and the Effect of Within-Storm Variability
This paper develops a timescale-based framework to understand how green infrastructure (GI) placement affects urban flood peak mitigation, revealing that optimal placement depends on the alignment of storm temporal structure, network response, and GI filling dynamics, quantifiable by two nondimensional ratios and storm descriptors.
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Liu et al. (2026) Impact Pathways of Environmental Factors on the Spatiotemporal Variations in Surface Soil Moisture in Tianshan Mountains, China
This study investigates the complex impacts of climate, topography, soil, and vegetation factors on surface soil moisture (SM) spatiotemporal dynamics in the Tianshan Mountains from 2000 to 2022. It reveals that vegetation greenness, precipitation, and relative humidity are the primary drivers of SM variations, with strong interactive effects of climate factors shaping its spatial distribution.
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Li et al. (2026) Uncovering the Spatiotemporal Evolution and Driving Factors of Flash Flood in the Qinghai–Tibet Plateau
This study investigates the spatiotemporal evolution and driving factors of flash floods in the Qinghai–Tibet Plateau from 1950 to 2015, revealing an exponential increase in flood frequency primarily driven by soil moisture and intensified human activities.
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Xie et al. (2026) Interactions Between Multiple Drought Types Across Temporal Scales
This study analyzed the complex interactions between meteorological, soil, vegetation, and groundwater droughts in the Heihe River Basin using an extended convergent cross mapping algorithm, revealing that temporal scale significantly influences interaction intensity and lag, with groundwater drought playing a crucial role in propagation.
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Saha et al. (2026) Plant acoustic emission as early stress signals: Towards remote integrated monitoring for sustainable agriculture
This review synthesizes current understanding of plant acoustic emissions (AEs) as early, non-invasive indicators of abiotic stress in crops, particularly hydraulic dysfunction. It proposes an integrated monitoring framework combining ground-based AE sensors with remote sensing data and machine learning to enable proactive precision agriculture.
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Huning et al. (2026) Cascading impacts of natural disasters in a connected world
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
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Wu et al. (2026) Prediction of Forest Fire Occurrence Risk in Heilongjiang Province Under Future Climate Change
This study quantified the multi-source drivers of forest fire occurrence in Heilongjiang Province and developed a long-term fire risk forecast using a Deep Neural Network with Residual Connections (ResDNN), which achieved 85.6% accuracy and was applied with CMIP6 projections to map future fire probability from 2030 to 2070.
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Shakeel et al. (2026) Optimizing GCM ensemble selection and weighted MME development for improved drought projection under global climate models simulations
This study proposes a novel framework for selecting optimal Global Climate Model (GCM) subsets and developing weighted Multi-Model Ensembles (MMEs) to improve drought projection accuracy. It introduces the Multi-Location Multimodel Standardized Drought Index (MLMSDI), demonstrating its effectiveness in assessing future drought across various Shared Socioeconomic Pathways (SSPs) and timescales in Punjab Province, Pakistan.
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Li et al. (2026) A three stage ‘Matching-Retrieval-Optimization’ method for radar–rainfall retrieval: a case study in the Yiluo River Basin, China
## Identification - **Journal:** Geomatics Natural Hazards and Risk - **Year:** 2026...
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Pengxin et al. (2026) Multi-model integrated error correction for extreme precipitation: method and application
This study developed a novel multi-model integrated error correction framework for CMIP6 extreme precipitation projections, significantly improving simulation accuracy in the Hanjiang River Basin (HRB). The corrected data reveal pronounced upward trends in extreme precipitation in the HRB, particularly in its southwestern and downstream areas, under future moderate to high radiative forcing scenarios.
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Sujono et al. (2026) Remote sensing-based early warning for agricultural flood damage mitigation in recurrent flood-prone areas
This study develops a remote sensing-based early warning system for agricultural flood damage mitigation in recurrent flood-prone areas, using Sentinel-1 SAR data and a localized change detection approach in Demak Regency, Indonesia, to identify high-risk zones and estimate potential crop losses.
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Barahona et al. (2026) Deep learning representation of the aerosol size distribution
This study develops MAMnet, a deep learning model, to predict the aerosol size distribution (ASD) and mixing state for seven lognormal modes based on bulk aerosol mass and meteorological conditions. MAMnet accurately reproduces the output of a two-moment modal aerosol scheme and shows good agreement with field measurements when driven by reanalysis data, offering an efficient way to improve aerosol representation in atmospheric models.
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Adhikari et al. (2026) Identifying ENSO events and their nexus with precipitation and flood dynamics in the Karnali River Basin, Nepal
This study investigates the influence of El Niño Southern Oscillation (ENSO) events on precipitation and flood dynamics in Nepal's transboundary Karnali River Basin (KRB) from 1964 to 2020, revealing a strong positive correlation between basin mean precipitation and discharge, and significant river channel shifts during strong ENSO events.
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Faliagka et al. (2026) Adaptation of the VegSyst model to predict crop nutrient uptake and water needs for precise soilless crop fertigation in greenhouses
This study adapted and validated the VegSyst model for precise soilless crop fertigation in greenhouses, integrating climate forecasts to predict water and macronutrient needs for cucumber and tomato, demonstrating reduced nutrient leaching and increased agronomic efficiency without compromising yield.
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Wang et al. (2026) Cumulative and lagged effects of climate factors on vegetation end of the growing season in the Yangtze River Basin
This study quantifies the cumulative and lagged effects of temperature, solar radiation, and precipitation on the end of the growing season (EOS) in the Yangtze River Basin from 2001-2023. It reveals that incorporating these temporal effects significantly improves the explanation of EOS variability and prediction accuracy, highlighting their critical role in phenology modeling.
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Singh et al. (2026) START: A Hybrid Spatio-Temporal Attention ResNet Transformer for Explainable Multivariable Meteorological Bias-correction
This study introduces START, a hybrid deep learning framework for multivariable meteorological bias correction over the contiguous United States, integrating heterogeneous data streams to achieve substantial improvements in forecast accuracy and provide explainable, calibrated uncertainty estimates.
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Mascaut et al. (2026) Ground-based atmospheric measurements at the Onsala Space Observatory (Sweden): data & trends (2009–2025)
This study presents and analyzes a comprehensive, long-term (2009-2025) dataset of ground-based atmospheric measurements from the Onsala Space Observatory, Sweden, revealing a statistically significant warming trend of approximately 0.15 kelvin per year, most pronounced in winter, and a significant decrease in rain rate intensity.
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Zeng et al. (2026) Synergistic Effects of Multi‐Timescale Atmospheric Teleconnections on Spring Monthly Droughts in Central‐Eastern China
This study investigates spring monthly drought variations in central-eastern China (CEC) and the synergistic effects of multi-timescale atmospheric teleconnections. It finds that in-phase alignments of high-frequency and low-frequency teleconnections (SCA, WP, NAO) amplify specific atmospheric circulation anomalies, leading to decreased precipitation and increased potential evapotranspiration, thus causing pronounced droughts in the CEC.
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Guo et al. (2026) Monitoring glacier-fed river width dynamics in High Mountain Asia from Sentinel-2 time series using a deformable UNet and skeleton evolution framework
This study developed a novel framework integrating a deformable UNet (DUNet) deep learning model and a discrete, shape-preserving skeleton evolution algorithm to accurately monitor glacier-fed river width dynamics in High Mountain Asia using Sentinel-2 time series. The proposed method demonstrated superior performance over conventional deep learning models and existing global datasets, revealing significant seasonal variations in river width.
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Campomanes et al. (2026) Assessment of Flood-Prone Areas in the Lacramarca River Basin in the Santa Clemencia and Pampadura Region, Peru, Under Climate Change Effects
This study assesses flood-prone areas in the Lacramarca River basin, Peru, under historical and 2050 climate change scenarios, revealing a significant increase in flood extent due to projected climate variability and highlighting the inadequacy of current protection infrastructure.
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Xu et al. (2026) Divergent latitude-specific urban humid heat risks are regulated by local climate types
This study systematically investigates the spatiotemporal evolution and drivers of urban wet-bulb temperature across 56 global cities from 2005-2024, revealing significant increases since 2020 with responses regulated by local climate types.
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Yang et al. (2026) DANRA: the kilometer-scale Danish regional atmospheric reanalysis
This paper introduces DANRA, a novel 2.5-kilometer resolution regional atmospheric reanalysis dataset covering Denmark and its surrounding regions from 1990 to 2023. DANRA demonstrates superior performance compared to global reanalyses like ERA5 in representing essential climate variables and extreme weather events, providing unprecedented detail for climate adaptation and impact modeling.
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Gensini (2026) Extreme events and the insurance industry in a changing climate
This letter argues for the formal integration of high-resolution downscaling, ensemble modeling, and catastrophe risk analysis to bridge the gap between coarse global climate models and the insurance industry's need for local, probabilistic risk assessment in a changing climate.
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Zhang et al. (2026) Mountain front recharge of a karst aquifer in the Denver Basin, southeastern Wyoming (USA): Recharge mechanism and multiyear drought impacts
This study investigated mountain-front stream recharge mechanisms to a karst aquifer in the Denver Basin, southeastern Wyoming, and the impacts of a multiyear drought (2017–2022) on aquifer water levels. It found that snowmelt-driven streamflow is the primary recharge source via fractures and conduits, and drought significantly reduced aquifer recharge, highlighting the critical reliance on mountain snowmelt.
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Agbesi et al. (2026) Development and performance evaluation of a low-cost sensor-based automated drip irrigation system for small-scale farming
This study developed and evaluated a low-cost, sensor-based automated drip irrigation system for small-scale farming, demonstrating reliable performance in fine-textured soils (clay) compared to a commercial sensor, while identifying limitations in coarse-textured soils due to compaction and probe-soil contact issues.
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SINGH et al. (2026) Subsurface fertigation modifies soil–plant–water interactions to improve productivity of cotton–wheat systems under reduced tillage
This study investigated how subsurface drip fertigation (SDF) influences soil physical properties, plant physiological functioning, and system productivity in a low-tilled cotton-wheat rotation over two growing seasons. It found that SDF significantly improves soil-plant interactions and resource-use efficiency, enhancing productivity while mitigating pressure on groundwater resources compared to conventional surface flood irrigation.
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Makhambetov et al. (2026) Assessment of the Spatial Structure and Condition of Urban Green Infrastructure in Aktau (Kazakhstan) Under Arid Climate Conditions Using NDVI and SAVI
This study assesses the spatial structure and condition of urban green infrastructure in Aktau, Kazakhstan, under arid climate conditions from 2015 to 2025 using satellite imagery and inventory data. It found a moderate overall increase in vegetation but with persistent spatial fragmentation and center-periphery asymmetry, emphasizing the critical role of irrigation and targeted greening strategies.
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Xiao et al. (2026) Rainfall Regionalization for Mainland China Based on Storm Characteristics
This study develops a high-resolution rainfall regionalization for mainland China using sub-daily storm characteristics from hourly station data. It identifies five event types and clusters stations into six groups, aggregated into eight rainfall zones, revealing distinct spatial patterns of rainfall depth, duration, and intensity, and their correlation with topography and weather systems.
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Düzenli et al. (2026) Assessing the utility of statistical downscaling for subseasonal temperature forecasts
This study benchmarks 27 statistical downscaling methods for subseasonal temperature forecasts, demonstrating that while most methods successfully transfer skill from coarse (~100 km) to local (~5 km) resolution, method choice is critical, with some enhancing and others degrading skill, and incorporating atmospheric patterns or using weekly predictors showing benefits.
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Xu et al. (2026) A global all-weather PWV retrieval model integrating multi-band satellite observations considering land cover types and NDVI
This study developed an all-weather, high-resolution global model for retrieving precipitable water vapor (PWV) by integrating multi-band satellite observations (NIR, TIR, MW) with GNSS data, achieving a substantial reduction in global average RMSE from 15.19 mm to 5.37 mm compared to the MYD05 product.
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Baker et al. (2026) Reduced future North Atlantic eddy-driven jet variability in high-resolution, fully coupled global climate models
This study evaluates the impact of model resolution on North Atlantic winter jet streams under historical and future climate conditions, finding that higher resolution improves zonal wind representation and projects a strengthening and poleward shift of the mid-latitude jet by 2050, contrasting with low-resolution models.
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Millin et al. (2026) Analyzing Stratospheric and Tropical Contributions to the Subseasonal Forecasts of the December 2017 and January 2004 North American Cold Air Outbreaks
This study investigates the individual roles of stratospheric and tropical variability in driving subseasonal cold air outbreak (CAO) forecast skill in the central United States using targeted nudging experiments. It finds that the impact of these modes on CAO prediction skill is event-dependent, with stratospheric nudging significantly improving forecasts for one event while both modes had limited surface impact for another.
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Zhang et al. (2026) Lake bathymetric reconstruction and water storage estimation method based on terrain feature similarity
This study proposes a novel method for lake bathymetric reconstruction and water storage estimation by extrapolating surrounding topographic parameters, demonstrating its applicability and accuracy for lakes on the Qinghai–Tibet Plateau, particularly for those lacking measured underwater data.
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Irfan et al. (2026) Forecasting of global water usage in agriculture and total global consumption by using the Bi-GRU model
This study develops and applies a Bidirectional Gated Recurrent Unit (Bi-GRU) model to forecast global Total Water Consumption (TWC) and Agricultural Water Use (AWU), demonstrating its superior accuracy compared to other deep learning models for effective water resource management.
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Levin et al. (2026) Influence of sea surface temperature patterns and mean warming on past and future Atlantic tropical cyclone activity
This study investigates the relative contributions of large-scale thermodynamic and dynamic processes to decadal and multidecadal changes in Atlantic tropical cyclone (TC) activity from the late 19th century to 2100. It finds that TC frequency changes are primarily governed by potential intensity and moist entropy deficit, with regional sea surface temperature (SST) patterns, rather than global-mean warming, controlling both past variability and future changes.
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Pérez-Campo et al. (2026) Streamflow Simulation Based on a Hybrid Morphometric–Satellite Methodological Framework
This study investigated the relationships between GR4J hydrological model parameters and watershed characteristics in the Caquetá River Basin, finding strong correlations (R² 0.80-0.98) that support parameter regionalization based on physiographic and environmental descriptors.
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Naresh et al. (2026) Deep Learning-Based Agricultural Drought Monitoring and Prediction Using Vegetation Health Index in the Papagni River Basin, India
This study developed and validated a deep learning-based framework using the Vegetation Health Index (VHI) to monitor historical agricultural drought (2001–2022) and predict future conditions (2025–2040) in India's semi-arid Papagni River Basin, revealing chronic mild drought and achieving high predictive accuracy with an LSTM model.
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Liu et al. (2026) Sensing Vegetation Resistance and Recovery Along Urban–Rural Gradients
This study investigates how vegetation resistance and recovery to extreme heat events vary along urban-rural gradients in the North Tianshan Slope Urban Agglomeration, China. It finds that rural vegetation provides a strong cooling effect and exhibits higher resistance and recovery than urban vegetation, with driving factors varying by spatial scale.
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Sonny et al. (2026) Hydrological drought projections across Europe under climate change
This study comprehensively assesses future hydrological drought dynamics across Europe using the Standardized Runoff Index (SRI) under two climate change scenarios, revealing an intensification of drought conditions, particularly in southern Europe, with spring identified as the most drought-prone season. The findings project significant increases in drought frequency, severity, and spatial extent, necessitating urgent, seasonally adaptive water management strategies.
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Zhang et al. (2026) Divergent impacts of flash drought drivers on alpine ecosystem resilience
This study reveals divergent impacts of flash drought drivers on alpine ecosystem resilience in the Yarlung Tsangpo River Basin, finding that while soil moisture deficits consistently weaken resilience, temperature-driven events can temporarily enhance it due to meltwater subsidies, masking underlying vulnerabilities.
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Liang et al. (2026) Vegetation in Central Asia is more sensitive to soil moisture stress than to precipitation and vapor pressure deficit stresses
This study investigated vegetation sensitivity to precipitation, soil moisture, and vapor pressure deficit stresses across Central Asia from 1982 to 2020, revealing that vegetation is most sensitive to soil moisture stress, a trend projected to continue increasing in the future.
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Bounajra et al. (2026) Reference evapotranspiration variability and trends in relation to irrigation demand in a semi-arid agricultural region of Morocco
This study investigates the spatio-temporal variability and long-term trends of reference evapotranspiration (ET₀) in the semi-arid Chichaoua agricultural province of Morocco over 45 years, explicitly linking ET₀ dynamics to operational drip irrigation design practices. It reveals a significant upward trend in ET₀ driven by thermo-radiative factors, which directly translates into increased irrigation water requirements based on current engineering assumptions.
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Çırağ et al. (2026) Multi-scale drought analysis and machine learning-based completion of missing streamflow data in the Aras Basin
This study conducted multi-scale meteorological and hydrological drought analyses in the Aras Basin, Türkiye, and developed a machine learning approach (XGBoost) to complete missing streamflow data, demonstrating that data imputation significantly enhances the reliability of early hydrological drought detection.
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Banerjee et al. (2026) Sub-seasonal Modulation and Predictability of Indian monsoon hourly Rainfall Extremes
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Proistosescu et al. (2026) The forgotten role of wave dynamics in modulating the low cloud response to warm pool warming
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Gurau et al. (2026) Validation of SMAP Surface Soil Moisture Using In Situ Measurements in Diverse Agroecosystems Across Texas, US
This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product against in situ measurements across diverse agroecosystems in Texas. It found that SMAP effectively captures seasonal dynamics but exhibits spatially variable accuracy, with quantile mapping significantly improving performance at some stations.
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Wu et al. (2026) Basin-wide and regional low-frequency variability of Yangtze precipitation revealed by clustering and slow feature analysis
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Chrysanti et al. (2026) Large-scale drivers and ocean-land feedbacks contributing to extreme precipitation during the January 2021 South Kalimantan flood, Indonesia
This study investigated the meteorological drivers and ocean-land feedbacks contributing to the January 2021 South Kalimantan flood, finding that active cold surges and cross-equatorial northerly surges, modulated by the Madden-Julian Oscillation and Kelvin waves, were primary drivers, with land-ocean feedbacks playing a secondary, amplifying role. The research utilized both standalone and coupled atmospheric-hydrological models to delineate synoptic and mesoscale interactions.
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Zou (2026) training_samples_zarr_v32_final
## Identification - **Journal:** ScienceDB - **Year:** 2026...
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Zhou et al. (2026) Simulation of Extreme Weather Events in the Wanzhou Region of the Three Gorges Reservoir Area Using the WRF Model Coupled With Machine Learning Techniques
This study systematically evaluates the performance of the Weather Research and Forecasting (WRF) model with various physical parameterisation schemes for extreme precipitation and high-temperature events in the Wanzhou District of the Three Gorges Reservoir region. It identifies optimal WRF configurations and demonstrates that coupling these with machine learning models, particularly Random Forest, significantly enhances prediction accuracy and reliability.
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Duan (2026) Dataset for hybrid streamflow simulation in a semi-arid grassland catchment
This dataset supports a study on hybrid streamflow simulation in a semi-arid grassland catchment, utilizing an enhanced distributed hydrological model and deep learning for residual correction. It provides processed hydro-meteorological forcing data, model input files, parameter-related variables, and simulation outputs for model calibration and validation.
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Daliakopoulos (2026) Monitoring reservoir storage using remote sensing and large language models
This study presents an innovative framework for monitoring reservoir storage using Sentinel-1 Synthetic-aperture radar (SAR) imagery, validated against quantitative storage values extracted from online media with the assistance of large language models (LLMs), demonstrating a scalable solution for data-scarce regions.
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Corbetta et al. (2026) Modeling Spatio-Temporal Surface Elevation Changes in Argentino and Viedma Lakes, Patagonia, Employing ICESat-2
This study develops and validates operational models for spatio-temporal surface elevation changes in Argentino and Viedma Lakes, Patagonia, using ICESat-2 laser altimetry data. The models accurately separate water volume changes, atmospheric forcing effects (wind and air pressure), and geoid contributions, significantly reducing elevation variability and demonstrating ICESat-2's capability for high-precision water resource monitoring in data-sparse regions.
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Salvi et al. (2026) Discrepancy in the sign of temperature trends in reanalysis datasets
This study evaluates the alignment of annual mean daily maximum and minimum temperature trends from three reanalysis datasets (ERA5, MERRA-2, NLDAS-2) against observed trends from 7,059 stations across the continental United States, revealing substantial trend misalignment (21-31%) that persists across various regions and record lengths.
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Li et al. (2026) Fine‐Scale Characterization of Groundwater Recharge Efficacy Under Ecological Water Replenishment: An AI‐Enhanced Learning Framework Benchmarked Against Traditional Geostatistics
This study reconstructs high-resolution (250 m) groundwater level dynamics in the Yongding River basin using LightGBM and multi-source data, demonstrating that Ecological Water Replenishment (EWR) drives groundwater recovery but with diminishing marginal returns, while outperforming traditional interpolation methods.
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Jellis et al. (2026) Simulation of Water Vapor Transport to the Stratosphere by Overshooting Convection
## Identification - **Journal:** Journal of Geophysical Research Atmospheres - **Year:** 2026...
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Chen et al. (2026) Different Responses of the Madden‐Julian Oscillation to the Fast and Slow Decaying El Niño in Spring
This study investigates how El Niño's decay rate modulates the Madden-Julian Oscillation (MJO) during decaying springs, finding that fast-decaying El Niño weakens MJO activity over the central Pacific, while slow-decaying El Niño significantly enhances it through stronger moisture advection and lower-tropospheric moistening.
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Peng et al. (2026) A Multi-Source Radar Data Complementary Enhancement Generation Method Based on Diffusion Model
This paper proposes the Multi-source Radar Reflectivity Complementary Enhancement (MSR-CE) method, utilizing a conditional diffusion model and a Radar-Physics-Aware Loss, to fuse S-band Doppler radar and X-band phased-array radar data, generating high-resolution pseudo X-band reflectivity fields that overcome the individual limitations of each radar type.
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Feng et al. (2026) Spatiotemporal response of depth‑to‑water table (ZWT) to the Three Gorges Reservoir impoundment across hydrological year types
This study quantifies the spatiotemporal response of depth-to-water table (ZWT) to the Three Gorges Reservoir (TGR) impoundment across hydrological year types in the Jianghan Plain. It finds that TGR impoundment leads to a general deepening of ZWT, with the strongest and most coherent response observed in wet years, concentrating in a narrow corridor near the Yangtze River.
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Li et al. (2026) An accurate 10 m annual crop map product of maize and soybean across the United States
This study developed an openly available, annual, 10 m spatial resolution maize and soybean map product for the Contiguous United States (CONUS) from 2019 to 2022, achieving consistent overall accuracies greater than 95%. The research demonstrates that these higher-resolution maps significantly reduce mixed pixels compared to existing 30 m products, enhancing agricultural monitoring capabilities.
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Mahamadou et al. (2026) Statistical Assessment of the Extreme Rainfall Event of 2024 in Maradi and Zinder of South-Eastern Niger
This study analyzed the 2024 rainy season in south-eastern Niger, determining it to be an exceptional climatic anomaly with record rainfall, surpassing previous extremes and indicating a profound climate mutation in the region.
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Yang et al. (2026) Multi-Channel Super-Resolution Reconstruction Model Based on Dual-Band Weather Radar Fusion
This study proposes a deep neural network-based super-resolution method for S-band reflectivity, fusing dual-frequency (S-band and X-band) radar observations to address resolution mismatch and enhance the spatial resolution of S-band data, demonstrating improved detail recovery and structural reconstruction under severe weather conditions.
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Khan et al. (2026) Physics-informed Bayesian Neural Network for groundwater recharge estimation in data-scarce arid regions
This study developed a Physics-Informed Bayesian Neural Network (PI-BNN) to estimate groundwater recharge and its uncertainty in the data-scarce South Al Batinah (SAB) Basin, northern Oman. The PI-BNN significantly reduced uncertainty bounds by approximately 50% compared to Latin Hypercube Sampling (LHS) while maintaining physically consistent and realistic recharge estimates.
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Wan et al. (2026) Stable Isotope Record of Precipitation Dynamics in the Semi‐Arid Subtropics
This study investigated rainfall stable isotope variability in subtropical northwest Australia over 10 years to understand precipitation mechanisms and moisture sources, identifying a significant "amount effect," substantial sub-cloud evaporation, and significant land evapotranspiration recycling.
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Keefe et al. (2026) Projections of Temperature-Driven Changes in Seasonal Ice Coverage Around Prince Edward Island, Canada
This study assesses the influence of climate change on seasonal ice coverage along Prince Edward Island's coast, projecting a substantial decline in freezing degree days, seasonal ice indices, and ice season length by the 2090s under various emission scenarios.
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Abebe et al. (2026) Assimilating leaf area index and soil moisture into the WOFOST model for improved maize (Zea mays L.) yield estimation in Ethiopia
This study developed a data assimilation framework using the Ensemble Kalman Filter to jointly assimilate satellite-derived Leaf Area Index (LAI) and Soil Moisture (SM) into the WOFOST crop model, significantly improving maize yield estimation accuracy in Ethiopia compared to univariate assimilation or open-loop simulations.
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Teng et al. (2026) Coupling GSFLOW with a river hydrodynamic model for flow simulation in a mountain river basin
This study developed a numerical algorithm to dynamically couple the GSFLOW hydrological model with a river hydrodynamic model (RHM-SG) to improve streamflow and flood simulation accuracy in mountain rivers. The integrated GSFLOW–RHM-SG model significantly enhanced predictions of extreme flows during flood events and better represented stream–groundwater interactions in the Zamask–Yingluoxia subbasin of the Heihe River Basin, China.
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Wang et al. (2026) Projected Earlier Australian Summer Monsoon Onset Associated With Faster Eastward MJO Propagation
This study projects a robust earlier onset of the Australian Summer Monsoon (AUSM) by approximately 5 days by the late 21st century under global warming, attributing this shift to the earlier arrival and accelerated eastward propagation of the first austral-spring Madden–Julian Oscillation (MJO) event.
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Seidov (2026) Self-Organization of Ocean Circulation: A Synergetic Perspective on Ocean and Climate Dynamics
This study reinterprets large-scale ocean circulation, particularly the Atlantic Meridional Overturning Circulation (AMOC), using self-organization theory and synergetics, demonstrating how a simplified nonlinear model (Brusselator) can capture key bifurcation behaviors relevant to AMOC instability and regime transitions.
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Yu et al. (2026) Identification of the Global Cloud‐Clear Sky Transition Zone and Its Shortwave Radiation Effects
This study developed a globally consistent method to detect the Cloud-Clear Sky Transition Zone (CCTZ) over both land and ocean using MODIS data and radiative transfer modeling. It found that the CCTZ has a cloud-type-dependent spatial scale (approximately 5 km from clouds) and significantly enhances global mean diffuse shortwave radiation by 16.3% while reducing direct shortwave radiation by 0.8% compared to pure clear-sky conditions.
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Li et al. (2026) Interpretable Cotton Mapping Across Phenological Stages: Receptive-Field Enhancement and Cross-Domain Stability
This study developed an interpretable semantic segmentation framework for cotton mapping in arid irrigated agroecosystems using multi-source remote sensing data, achieving high classification accuracy and robust generalization while explicitly quantifying the importance of different predictors across phenological stages.
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Gowera et al. (2026) Spatial prediction and mapping of soil salinity using machine learning and remote sensing covariates
This study evaluated remote sensing models for mapping soil salinity in irrigated agroecosystems with predominantly low electrical conductivity values, finding that Support Vector Machine models outperformed Random Forest using Landsat and LiDAR data.
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Wang et al. (2026) Drivers of Shrinkage in Daihai Lake Based on Influence of Climate Change, Vegetation Variation and Agricultural Water Saving on ET
This study investigated the impact of vegetation dynamics on Daihai Lake shrinkage, finding that forest expansion and its associated evapotranspiration, alongside climate change, are significant drivers, and recommends shrub-grass combined restoration for sustainability.
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Hussain et al. (2026) Development of high-resolution land surface temperature and paddy area estimation technique using multi-source satellite image-based downscaling
This research developed a technique to downscale Land Surface Temperature (LST) from 30 meters to 3 meters using multi-source satellite imagery and multispectral indices, enabling accurate estimation and monitoring of paddy field areas and their temporal changes for precision agriculture. The study successfully revealed fine-scale LST variations crucial for understanding agricultural heterogeneity and resource management.
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Eleftheratos et al. (2026) Evaluation of variability in GOME-2 total water vapor and nitrogen dioxide columns associated with natural oscillations
This study investigates the variability of total water vapor (H₂O) and nitrogen dioxide (NO₂) columns in association with natural oscillations (QBO, ENSO, NAO) using GOME-2 satellite data. It finds that GOME-2 data effectively captures these variabilities, with NO₂ showing QBO-type periodicity in the tropics and H₂O responding to ENSO in the tropics and NAO in northern mid-latitudes, demonstrating good agreement with reanalysis and model results.
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Kim et al. (2026) ENSO phase transition enables prediction of winter North Atlantic Oscillation one year ahead
This study demonstrates that the predictability of the winter North Atlantic Oscillation (NAO) one year ahead significantly improves during El Niño–Southern Oscillation (ENSO) phase transition years, a phenomenon linked to the northward propagation of atmospheric angular momentum anomalies.
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Wang et al. (2026) DAR-type model based on “long memory-threshold” structure: a competitor for daily streamflow prediction under changing environment
This study proposes a novel Fractional-differenced Dual-Threshold Double Autoregressive (FDTDAR) model to improve daily streamflow prediction accuracy under changing environments by capturing non-stationarity, non-linearity, and long-term memory. Applied to the Yellow River basin, the FDTDAR model, particularly with a Student's t-distribution for residuals, demonstrates superior predictive ability compared to AR-GARCH and LSTM models.
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Sylvestre et al. (2026) Decadal-scale droughts disrupted the African Humid Period in the Sahara
This study reconstructs the hydrological history of Lake Yoa, Chad, over the past 10.25 thousand years, revealing that the African Humid Period was interrupted by decadal-scale droughts, particularly a prominent 8.2 kyr bp event linked to Atlantic Meridional Overturning Circulation weakening.
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Warren (2026) Extreme climate outcomes could still occur with just 2 °C of global warming
This News & Views article highlights that even if global warming is limited to 2 °C above pre-industrial levels, extreme climate impacts, such as drought and flooding, could still occur at levels often predicted for much higher warming, emphasizing the need for policy to consider worst-case scenarios.
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Matus et al. (2026) The sub-seasonal connection between the land surface and Great Plains low-level jet
This study investigates the modulation of Great Plains low-level jet (GPLLJ) intensity by sub-seasonal dry soil moisture anomalies. It finds that neglecting this land-surface interaction leads to significant errors in reconstructed GPLLJ wind speeds, highlighting the land surface's crucial role in GPLLJ variability.
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Zhou et al. (2026) Synergistic retrievals of leaf area index and leaf chlorophyll content in deciduous broadleaf forests from Sentinel-2 and Landsat
This study systematically evaluates synergistic Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) retrievals for deciduous broadleaf forests from Sentinel-2 and Landsat data. It identifies limitations in canopy structural representation as a primary driver of mutual error compensation and demonstrates that integrated parameterization strategies significantly improve retrieval accuracy and seasonal dynamics.
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Su et al. (2026) Precipitation observing network gaps limit climate change impact assessment
This study evaluates the global distribution of 221,483 precipitation gauges and identifies priority regions for network expansion under historical and future climate/socioeconomic scenarios. It finds that only 13.4% of the global land surface meets WMO monitoring requirements, with 25% currently needing urgent expansion, increasing to 32.1% under a high-emission scenario when socioeconomic vulnerabilities are considered.
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Ji et al. (2026) Robust hyperspectral reconstruction from satellite and airborne observations via a deep hierarchical fusion network across heterogeneous scenarios
This study develops a deep learning framework for robust high spatial resolution hyperspectral imagery (HR-HSI) reconstruction by fusing low-resolution hyperspectral (EMIT) and high-resolution multispectral (PlanetScope) satellite data. The framework consistently outperforms state-of-the-art models, demonstrating high spectral fidelity and reconstruction accuracy across diverse landscapes.
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Bevacqua et al. (2026) Moderate global warming does not rule out extreme global climate outcomes
This study reveals that extreme global climate outcomes for several sectors (e.g., droughts, precipitation, fire weather) may occur even under a moderate 2 °C global warming, often exceeding model-averaged projections for 3 °C or 4 °C warming, primarily due to large uncertainties in climate model projections.
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Pellicone et al. (2026) Understanding Trends in Near-Surface Air Temperature Lapse Rates in a Southern Mediterranean Region
This study investigated the spatiotemporal variability of near-surface air temperature lapse rates in Calabria, identifying altitude as the dominant driver of temperature distribution and revealing a significant long-term decline in lapse rates, indicating accelerated warming at higher elevations.
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Azizi et al. (2026) Comparative machine learning and deep learning approaches for agricultural drought monitoring: Dual-index modeling in Iran
This study develops a dual-index machine learning framework for agricultural drought monitoring in Iran, integrating the Soil Moisture Deficit Index (SMDI) and the 3-month Standardized Precipitation–Evapotranspiration Index (SPEI-3) using multi-source predictors. It demonstrates that SMDI is estimated more reliably (best RMSE = 0.80, R² = 0.82) than SPEI-3 (best RMSE = 0.96, R² = 0.55) and proposes an operational classification system with uncertainty quantification.
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Bulut et al. (2026) Toward early warning of drought impacts: a framework for predicting drought impacts in the UK
This study presents a data-driven framework to predict real-world drought impacts. Different modelling approaches were tested and evaluated in the United Kingdom using predictions at the time of occurrence, with the best-performing method selected for forecasting impacts months ahead. Both predictions and forecasts were validated using independent UK data and applied to Germany to test transferability, supporting early warning systems and improved drought risk planning.
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Abeysingha et al. (2026) Drought pattern under climate change in Harris County, Texas, USA based on CMIP6 projections
This study assessed future drought conditions in Harris County, Texas, using CMIP6 GCMs under various SSP scenarios for 2026–2085, revealing a substantial projected increase in drought frequency, intensity, and severity, especially in the far-future (2056–2085).
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Keoagile et al. (2026) Assessing Crop Yield Variability Using Meteorological Drought Indices for Agricultural Drought Monitoring in Botswana
This study assesses drought impact on Botswana's agricultural sector by evaluating the predictive power of various drought indices on crop yields and integrating local knowledge, revealing the sector's high vulnerability and the need for integrated early warning systems.
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Ouassanouan et al. (2026) Crop and irrigation types ground-truth dataset for Moroccan agricultural regions
This paper presents a comprehensive, open-access ground-truth dataset comprising 10,000 geolocated agricultural parcels in Morocco, detailing 45 crop types and 6 irrigation systems, to serve as a high-quality reference for calibrating and validating Earth Observation-based agricultural monitoring products.
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Liu et al. (2026) Global assessment of drought risk to expanded urban land from 2020 to 2100
This study assesses global drought risk for expanded urban land from 2020 to 2100 by integrating future urban land cover projections with climate, socioeconomic, and demographic data, finding a continuous increase in drought risk across all climate change scenarios, particularly in developing countries.
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Wu et al. (2026) MA-UQNet: A multi-modal uncertainty quantification neural network for remote sensing-based wheat aboveground biomass estimation
This study introduces MA-UQNet, a multi-modal deep learning framework for wheat aboveground biomass estimation that achieves superior prediction accuracy (R2 = 0.856) with well-calibrated uncertainty (97.18% coverage) by integrating adaptive multi-modal attention, growth stage-specific processing, and epistemic–aleatoric uncertainty decomposition.
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Jiang et al. (2026) Deep-learning full-waveform inversion of snowpack GPR: joint permittivity–resistivity imaging for snow–soil hydrological mapping
This study introduces a hybrid deep learning framework (ViT-BiLSTM) for dual-parameter full-waveform inversion of GPR data, enabling fast and accurate joint imaging of snowpack permittivity and resistivity for hydrological mapping. The framework demonstrates robust performance on synthetic and real-world data, providing spatially coherent snow liquid water content and soil moisture estimates.
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Martini et al. (2026) From near surface to root zone soil water losses: a new model validated with field TDR and remotely sensed data
This study introduces muSEC, a new multilayer soil moisture model based on the surface evaporative capacitor (SEC), designed to link surface and root zone soil moisture drydown dynamics. Validated against three other models using field TDR and satellite SMAP data across contrasting soils, muSEC demonstrates superior transferability from surface to root zone, supporting its potential for agricultural water management.
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Willaredt et al. (2026) The effects of extreme climatic events on urban trees in Mediterranean regions: A review
This review synthesizes existing literature on the effects of climatic and weather-related extremes on urban trees in Mediterranean cities, revealing consistent impairment of urban tree functioning across multiple scales and methods, and highlighting significant research gaps.
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Dong (2026) A Comparative Study of Machine Learning Models for Hourly Forecasting of Air Temperature and Relative Humidity
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Ku et al. (2026) Enhanced persistence of Ural blocking under strong positive AO: the role of North Atlantic storm tracks and potential vorticity dynamics
This study investigates how the magnitude of a positive Arctic Oscillation (AO) influences the persistence of Ural Blocking (UB) events. It finds that strong positive AO paradoxically enhances UB longevity by organizing North Atlantic storm tracks, leading to Arctic warming, sea ice loss, and a weakened meridional potential vorticity gradient that anchors the blocking system.
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MICHALKE et al. (2026) Field Trial of a Low-Cost Sensor Network for Hydrometeorological Monitoring of Water Pans and Small Dams in Kenya
This paper describes the development and field testing of a low-cost monitoring station network designed to measure water level, precipitation, and air temperature/humidity for small, decentralized water pans in rural areas. The system, costing approximately 93 USD per station, demonstrated potential for addressing data scarcity, with water level measurements proving accurate, despite inaccuracies in precipitation data and biases in air temperature.
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Wang et al. (2026) Anomaly Detection and Correction for High-Spatiotemporal-Resolution Land Surface Temperature Data: Integrating Spatiotemporal Physical Constraints and Consistency Verification
This paper proposes a two-stage anomaly detection and correction framework for high-spatiotemporal-resolution Land Surface Temperature (LST) data, integrating temporal physical constraints and spatial consistency verification. The method significantly enhances LST data quality by effectively distinguishing physically plausible weather changes from data errors, outperforming conventional statistical methods with substantial improvements in accuracy and correlation.
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Peng et al. (2026) Forecasting a Hailstorm in Western China Plateau by Assimilating XPAR Radar Network Data with WRF-FDDA-HLHN
This study evaluates the assimilation of high spatiotemporal resolution X-band phased-array radar (XPAR) data into the WRF model, combined with a humidity adjustment scheme, to improve hailstorm prediction over the Yun-Gui Plateau. It demonstrates that XPAR data assimilation significantly reduces model error and enhances the representation of rapid hail cloud evolution, especially when coupled with humidity adjustments.
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Liu et al. (2026) Deciphering divergent atmospheric river environments in extreme and non-extreme precipitation over the lower reach of Yangtze River Basin
This study investigates the distinct atmospheric environments of atmospheric rivers (ARs) that lead to extreme precipitation (EP) versus those that do not, focusing on the lower Yangtze River Basin (LYRB) over 32 summer seasons. It reveals that AR&EP events are characterized by enhanced moisture from mid-to-high latitudes, specific large-scale circulation anomalies, and the crucial role of the Mei-yu front, providing key insights for improved forecasting.
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Bo (2026) High Resolution Agricultural Irrigation Water Use Dataset In China
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Yu et al. (2026) Investigating the Dry–Wet Differentiation of the Yellow River Basin Driven by Climate Change and Anthropogenic Activities
This study investigates the long-term evolution and driving mechanisms of dry-wet patterns in the Yellow River Basin, constructing a TWSA-DSI for historical analysis (1995–2014) and projecting future changes (2026–2100) under SSP scenarios, finding a historical shift from aridification to humidification and projecting continued humidification driven primarily by precipitation.
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Zhang et al. (2026) Daily Snow Depth Fusion Products for Arid Regions of Central Asia
This study developed a high-precision daily snow depth fusion product for Central Asia (1980–2023) by integrating multiple existing snow depth products and in-situ observations using an XGBoost machine learning model, achieving significantly improved accuracy.
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Gogineni et al. (2026) An integrated machine learning and decomposition framework for enhanced drought prediction
This study introduces a novel integration-prediction framework combining multiple signal decomposition algorithms with machine learning models for enhanced drought prediction. It found that hybrid decomposition models significantly improved accuracy over standalone models, with the VMD-SVR model consistently demonstrating superior performance across the studied drought-prone regions.
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Horzum et al. (2026) Investigation of base flow changes in Konya Closed Basin
This study analyzed daily flow data from 1987 to 2022 at six sites in the Konya Closed Basin to determine base flow trends. It found a statistically significant decreasing trend in base flow, indicating increasing water scarcity and drought risk due to climate change.
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Demir (2026) Multi-Depth Soil Moisture Prediction Using Machine Learning Across Türkiye's Diverse Environments
This study developed a machine learning framework to predict soil moisture at multiple depths using environmental variables in Türkiye. The Extreme Gradient Boosting (XGBoost) model achieved strong accuracy (R² up to 0.74) and revealed depth-dependent and spatially varying controls on soil moisture dynamics.
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Mohajer et al. (2026) Key natural influences on groundwater storage changes in Central and Southern Arizona
This study quantifies the natural hydroclimatic controls on groundwater storage variability in Central and Southern Arizona using GRACE/FO data, revealing that natural factors account for approximately 16% of spatial variance, primarily driven by evapotranspiration, precipitation, and subsurface runoff. The research identifies distinct subbasin clusters based on their hydroclimatic responses, offering a transferable framework for groundwater sustainability assessments.
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Taylor et al. (2026) Assessing the Ability of Tree-Ring Derived Aridity Records to Detect Compound Drought and Heatwave Events
This study investigates whether extremes in the Palmer Drought Severity Index (PDSI), particularly those derived from tree-ring reconstructions, can identify past compound drought and heatwave events (CDHWs) in North America and Europe. It finds that in regions with strong land-atmosphere coupling, such as Central North America and Eastern Europe, negative summer PDSI values co-occur with precipitation deficits and high temperatures, allowing tree-ring PDSI reconstructions to identify CDHWs predating instrumental records.
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Boussalim et al. (2026) Integrated RUSLE-machine learning modeling for water erosion risk assessment under climate change in a Mediterranean semi-arid region: a comparison of LR, SVM, and RF models
This study integrates the RUSLE model with machine learning (LR, SVM, RF) to predict future water erosion risk in the Ksob watershed, Morocco, under climate change scenarios (SSP2-4.5, SSP5-8.5), demonstrating that Random Forest best models rainfall erosivity (R) and vegetation cover (C) factors, leading to a projected dominant downward trend in erosion risk by the 2030s and 2050s due to decreased rainfall erosivity and improved vegetation.
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Castelli et al. (2026) Editorial: Sociohydrology in drylands
This editorial synthesizes the contributions of a Research Topic on "Sociohydrology in drylands," highlighting the critical need for interdisciplinary approaches, integration of local knowledge, and consideration of socio-political dynamics to address water scarcity and foster sustainable human-water co-evolution in these vulnerable regions. It advocates for broadening sociohydrological research beyond flood-centric studies to encompass long-term water scarcity and justice issues prevalent in drylands.
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Xu et al. (2026) Semi-empirical model of soil organic matter and soil moisture content with bayesian joint inversion
This study developed a novel semi-empirical radiative transfer model (SW-ETM) and a Bayesian joint inversion framework to simultaneously estimate soil organic matter (SOM) and soil moisture content (SMC) from spectral reflectance, effectively addressing their mutual interference and significantly improving prediction accuracy.
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Cao et al. (2026) Impact of boundary layer schemes in RegCM-Chem on East Asian climate and its response to the aerosol-radiation interaction
This study evaluates the Holtslag and University of Washington planetary boundary layer schemes in RegCM-Chem over East Asia to understand their impact on aerosol-radiation interactions and regional climate. It finds that the UW scheme better reproduces observations and yields a stronger surface effective radiative forcing compared to the Holtslag scheme, highlighting the importance of PBL parameterization.
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Nuriddinov et al. (2026) High Resolution Flood Extent Detection Using Deep Learning with Random Forest Derived Training Labels
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Kim et al. (2026) SIGMAformer: a spatiotemporal Gaussian mixture correlation transformer for global weather forecasting
This paper introduces SIGMAformer, a spatiotemporal Gaussian mixture correlation transformer for global multi-station weather forecasting, which integrates a dynamic spatiotemporal correlation (DSTC) mechanism with a Gaussian mixture pattern extractor (GMPE) to adaptively model nonlinear dependencies. The model consistently outperforms state-of-the-art forecasting models in global wind speed and temperature prediction, especially for extreme events, while providing interpretable insights into spatiotemporal patterns.
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Li et al. (2026) Improving seasonal prediction of global mean surface temperature by incorporating dynamic ENSO realistic forecasts
This study identifies an underrepresented ENSO-driven pantropical coupling mechanism as a major source of error in autumn-initialized global mean surface temperature (GMST) predictions. By incorporating skillful ENSO realistic forecasts into a new dynamic-statistical framework, the reliable GMST prediction lead-time is extended from two to four months, reducing hindcast errors by an average of 41% during 1980–2024.
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Liu (2026) Optimizing Multi-Agent Weather Captioning via Text Gradient Descent: A Training-Free Approach with Consensus-Aware Gradient Fusion
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Qin et al. (2026) Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors
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Stavrou et al. (2026) SmaAT-QMix-UNet: A Parameter-Efficient Vector-Quantized UNet for Precipitation Nowcasting
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Masquil et al. (2026) Deep S2P: Integrating Learning Based Stereo Matching Into the Satellite Stereo Pipeline
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He et al. (2026) Unraveling future hydrological and sediment dynamics through an integrated GCMs-PLUS-SWAT coupling framework
This study developed an integrated GCMs-PLUS-SWAT framework to project future hydrological and sediment dynamics in the Yangtze River Basin under SSP245 and SSP585 scenarios, revealing significant increases in precipitation and temperature, distinct intra-annual streamflow redistribution, and spatiotemporal divergence in sediment transport.
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Al-Yaqoubi et al. (2026) Modeling the fresh–saline water interface dynamics in coastal aquifers under managed aquifer recharge (MAR)
This study utilized the SEAWAT numerical model, calibrated against sand-tank experiments, to simulate and analyze saline water dynamics in a coastal unconfined aquifer under Managed Aquifer Recharge (MAR) conditions, demonstrating that MAR effectiveness is highly dependent on aquifer hydraulic conductivity, saline water density, and injection rate.
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Cai et al. (2026) Fusion-Based Regional ZTD Modeling Using ERA5 and GNSS via Residual Correction Kriging
This study proposes a Residual Correction Kriging (RK ZTD) method to fuse sparse Global Navigation Satellite System (GNSS) Zenith Tropospheric Delay (ZTD) data with continuous but biased ERA5 ZTD grids, significantly improving the precision and mitigating systematic biases of regional ZTD products in the Netherlands.
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Lu et al. (2026) Application and Comparison of Two Transformer-Based Deep Learning Models in Short-Term Precipitation Nowcasting
This study systematically compares Earthformer and LLMDiff, two Transformer-based deep learning models, for short-term extreme precipitation nowcasting using the SEVIR dataset, finding Earthformer excels for rapid early warning of light precipitation at shorter lead times (0-30 minutes) while LLMDiff is better for high-accuracy nowcasting of heavy precipitation at longer lead times (up to 60 minutes).
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Zhou et al. (2026) Slope-Controlled Partitioning of Vertical and Lateral Solute Transport Pathways Revealed by Inclined Leaching Experiments
This study investigated how slope influences the partitioning of vertical and lateral transport pathways for a highly mobile solute (PFOA) using laboratory-scale experiments. It found that solute transport shifts from vertical-dominated under flat conditions to lateral-dominated at moderate slopes, a shift well described by an exponential partitioning model with a critical crossover at approximately 4° slope.
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Ma et al. (2026) SGCAD: A SAR-Guided Confidence-Gated Distillation Framework of Optical and SAR Images for Water-Enhanced Land-Cover Semantic Segmentation
This paper introduces SAR-guided class-aware knowledge distillation (SGCAD) to resolve fusion conflicts in multimodal SAR and optical semantic segmentation, particularly for critical categories like water bodies, by leveraging SAR as a water-expert teacher and enhancing boundary continuity.
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Jurgens et al. (2026) Decadal Shifts in Groundwater Age Detected by Environmental Tracers Across California, USA
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Fu et al. (2026) Land-atmosphere feedbacks and anthropogenic greenhouse gas forcing intensify subseasonal drought-to-pluvial abrupt transitions
This study investigates subseasonal drought-to-pluvial abrupt transitions, revealing their global occurrence with an average probability of 45% and identifying land-atmosphere feedbacks as a key intensifying mechanism. Under high-emissions scenarios, both the frequency and probability of these transitions are projected to increase across over 75% of global land, primarily driven by anthropogenic greenhouse gas forcing.
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Pepelnik et al. (2026) Isotopic Composition of Precipitation and Its Role in Forest Hydrology Under Climate Change: Insights from Slovenian Lowland Forests
This study systematically analyzed 65 years of air temperature and precipitation changes in two Slovenian lowland forests, combining it with throughfall isotopic composition. It found that rising temperatures and altered precipitation patterns are reflected in throughfall isotopes, confirming extreme events and aiding in estimating groundwater residence time and tree water origin.
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Belarbi et al. (2026) Machine learning estimation of reference evapotranspiration using MODIS-Derived and limited ground variables across Moroccan agro-climatic zones
This study evaluates machine learning models for estimating daily reference evapotranspiration (ETo) in data-scarce Moroccan agro-climatic zones, demonstrating that MODIS remote sensing and limited ground variables can achieve high accuracy and support water management, despite challenges in inter-regional transferability.
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Nguyen et al. (2026) PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data
This study introduces PERSIANN-Unet (PUnet), a new quasi-global, high-resolution, near-real-time precipitation algorithm leveraging infrared (IR) data and a UNet architecture. PUnet provides half-hourly, 0.04° precipitation estimates, closely matching its training target (IMERG V07 Final) globally and demonstrating good performance against Stage IV over the Continental United States (CONUS).
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St-Pierre et al. (2026) Emergence time of CO2-forced European summer climate trends
This study quantifies the Time of Emergence (ToE) for European summer climate trends, including near-surface temperature, soil moisture, and the hydrological cycle, using a large ensemble climate model. It reveals rapid emergence for near-surface temperature (20-70 years) but delayed or absent emergence for precipitation, while demonstrating that extreme summer climate distributions are significantly altered even when mean trends do not formally emerge from natural variability.
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Dong et al. (2026) A fully automated OPTRAM (aOPTRAM) for soil moisture retrieval: Evaluating multiple fitting functions, vegetation indices, land-cover types, and scales
This study introduces a fully automated Optical Trapezoid Model (aOPTRAM) for high-resolution soil moisture retrieval, systematically evaluating its performance across diverse ecosystems using Sentinel-2 imagery and in-situ data. It demonstrates that aOPTRAM, without manual calibration, achieves performance comparable to optimal OPTRAM, providing a fast and robust framework for monitoring soil moisture in heterogeneous landscapes.
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Rooyen et al. (2026) Anthropogenic tritium as a continental-scale tracer in river-derived recharge
This study evaluates anthropogenic tritium (³H) and natural stable isotopes (δ¹⁸O, δ²H, deuterium excess) as tracers to quantify groundwater flow dynamics and travel times in an alluvial Managed Aquifer Recharge (MAR) system along the Rhine River in Switzerland, demonstrating their effectiveness for sustainable groundwater management.
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Shin et al. (2026) Negative CO2 emissions for long-term mitigation of extremes in land hydrological cycle
This study investigates terrestrial precipitation and vegetation feedbacks under idealized zero and negative CO2 emissions scenarios, finding that sustained negative emissions are crucial for long-term mitigation of hydrological extremes and enhanced water availability, primarily due to amplified transpiration.
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Ukkola et al. (2026) Future changes in seasonal drought in Australia
This study assesses future seasonal drought changes across Australia using an ensemble of 32 hydrological simulations, revealing widespread increases in meteorological, hydrological, and agricultural droughts, particularly in populated and agricultural regions, with Global Climate Models (GCMs) being the dominant source of uncertainty.
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Thompson et al. (2026) Simulation of the Hydro-ecological Impacts of Climate Change on an Upland Peatland in the Massif Central
This study assesses the hydro-ecological impacts of 60 climate change scenarios on peat ecosystems in the Dauges National Nature Reserve using high-resolution hydrological modeling. Results project increased hydrological seasonality, with wetter winters and drier summers, leading to declining summer peat groundwater levels and a reduction in the area suitable for mire vegetation, particularly at peatland margins.
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Irifi et al. (2026) Landscape Dynamics of Marly Slopes in the Lower Valley of Wadi Tamri (Morocco): An Integrated Approach Using Geomorphometry, Toposequences, and Remote Sensing
This study investigates the landscape dynamics of marly slopes in the lower Wadi Tamri valley, Morocco, using an integrated approach of geomorphometry, remote sensing, and field observations. It reveals significant landscape degradation, characterized by a substantial loss of Argan forest cover and an increase in exposed marly substrate, driven by recurrent droughts and human activities, leading to accelerated gully erosion and slope instability.
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Akbary et al. (2026) Projected changes in sub-daily extreme precipitation: comparing temperature-scaling approaches and convection-permitting models across an Alpine gradient
This study evaluates the reliability of temperature-scaling approaches for projecting sub-daily extreme precipitation changes by comparing them against convection-permitting model (CPM) outputs across a complex Alpine region. It finds that optimal scaling rates vary with duration and return period, and their reliability is modulated by local variability, seasonality, and elevation.
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Margarita et al. (2026) RusWeather-GF: A gap-filled daily weather dataset for Russia (1980–2023) with integrated topographic data
This study presents RusWeather-GF, a novel gap-filled daily temperature and precipitation dataset for 593 Russian weather stations from 1980 to 2023, achieving 100% temporal completeness through a validated multi-method approach and integrating high-resolution topographic data. The dataset addresses critical limitations of existing Russian climate data, extending coverage and enhancing utility for diverse climate research applications.
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Cai et al. (2026) Interactive effects of severity and duration of compound dry–hot events on vegetation resistance time and recovery time in China
This study developed a novel framework to systematically detect compound dry-hot events (CDHEs) and quantified their impacts on vegetation resistance and recovery times across China from 1982 to 2022. It found increasing trends in CDHE occurrences, severity, and duration, with severe and prolonged events significantly shortening forest resistance time while extending recovery time, and identified precipitation as the dominant driver of these temporal responses.
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Chen et al. (2026) An integrated framework for mapping agricultural water impoundments using Sentinel −2 and GEE in Northwest China
This study developed an integrated framework using Sentinel-2 and Google Earth Engine to accurately map and monitor small-scale agricultural water impoundments (AWIs) in arid regions, providing the first high-resolution, multi-year inventory for the Hexi Corridor.
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Ye et al. (2026) Anthropogenic climate change amplifies autumn heatwave risks for children during school reopening
This study attributes and projects the risk of autumn heatwaves for children during school reopening in China, revealing that anthropogenic climate change has significantly amplified the frequency and intensity of such heatwaves, increasing children's exposure risk by approximately 55% under the 2024 climate. Projections indicate continued increases in heatwave intensity, which will eventually outweigh declining child populations, leading to rising exposure risks by the end of the century under high emission scenarios.
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Wang et al. (2026) Interpretable WTConv1D-BiLSTM monthly-scale precipitation prediction model based on novel multilevel and multi-scale decomposition
This study proposes an interpretable deep-learning framework, WTConv1D-BiLSTM, for accurate monthly precipitation prediction by integrating novel multilevel and multi-scale decomposition techniques to address nonstationarity and scale mixing. The model demonstrates superior performance and interpretability in predicting monthly precipitation across 30 provinces in mainland China.
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Akiner et al. (2026) Is runoff the key input of evapotranspiration?: AI-based hydro-climatic assessment in Southeastern Türkiye’s Dams Region
This study assesses hydro-climatic variability and evapotranspiration dynamics in Southeastern Türkiye's Dams Region using AI models and long-term reanalysis data. It identifies runoff as the most critical, non-linearly influential factor in evapotranspiration, highlighting its importance for water resource management in semi-arid, dam-regulated environments.
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Westfall (2026) Shifts in Transport Pathways Before, During, and After Drought
This thesis investigates the cause of a long-term shift in Victoria's water balance, finding that changes in stream salinity suggest reduced water and salt transport to streams due to long-term shifts in the vertical groundwater gradient.
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Xv et al. (2026) Biophysical regulation mechanisms of land surface temperature driven by the spatiotemporal evolution of cropland
This study systematically investigated the biophysical mechanisms linking cropland evolution to land surface temperature (LST) variations across China from 2000 to 2020, revealing spatially heterogeneous effects where cropland expansion caused warming in arid northwestern regions and cooling in northeastern regions, primarily driven by ground heat flux, surface emissivity, and albedo.
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Lv et al. (2026) An adaptive decomposition-denoising and temporal context fusion framework for multi-station water-level forecasting
This study constructs a high-resolution hydrological dataset for the Mengjiang River Basin and proposes a hybrid deep learning framework, CEEMDANVF-WD-TCF-LSTM, for multi-station water-level forecasting. The framework demonstrates superior accuracy and stability, particularly in mitigating multi-step error accumulation for both short-term and medium-term predictions.
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Fathi et al. (2026) Toward accelerating fluvial morphodynamic simulations through a speed accuracy trade-off assessment
This study evaluates the combined application of morphological acceleration factor (morfac) and condensed hydrograph inputs to accelerate physics-based fluvial morphodynamic simulations. The integration of these two techniques achieved a theoretical computational efficiency exceeding a 98.8% reduction in total runtime, enabling more feasible long-term simulations.
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Wang et al. (2026) Seasonal variation patterns and drivers of baseflow recession dynamics across Australia
This study quantifies the event-scale seasonal variation of the baseflow recession parameter 'a' across 596 Australian catchments and identifies vegetation, temperature, and evaporative demand as key drivers using machine learning.
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Choukri et al. (2026) Comparative analysis of CHIRPS and ERA5-Land for precipitation and drought assessment in Morocco (1987–2016)
This study comprehensively compared CHIRPS and ERA5-Land precipitation products against 114 ground stations in Morocco (1987–2016) to evaluate their performance in precipitation estimation and drought detection across various temporal scales and altitudes. It found ERA5-Land generally more accurate for precipitation variability and long-term drought, while CHIRPS showed limitations, especially in mountainous regions and for drought severity.
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El-Haddad et al. (2026) Flood and heavy metal risks from wastewater site in Sohag Governorate, Egypt: integrating hydrological modeling and mapping
This study evaluates flood inundation hazards and heavy metal contamination from untreated wastewater disposal sites in the Al-Kola Basins, Sohag, Egypt, by integrating hydrological models and geochemical analysis. It found that increased rainfall significantly exacerbates flood hazards and can mobilize high concentrations of anthropogenic heavy metals into critical surface water systems like irrigation canals and the River Nile.
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Hamed et al. (2026) How the carbon emission reduction scenarios affect drought patterns in the Middle East and North Africa region
This study assesses how carbon emission reduction scenarios, aligned with the Paris Agreement's 1.5 °C and 2.0 °C warming targets, affect drought patterns in the Middle East and North Africa (MENA) region. The findings project a significant increase in drought frequency, intensity, and duration across MENA, with extreme droughts becoming dominant, primarily driven by increased potential evapotranspiration variability, even under these mitigation efforts.
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Wang et al. (2026) Spatiotemporal dynamics of global surface and rootzone soil moisture: a comprehensive assessment from dominant factors, impact pathways, and deficit probability
This study comprehensively assessed global surface and root-zone soil moisture dynamics from 2001 to 2021, identifying atmospheric water demand as the primary driver of aridity and revealing vegetation's mediating role in climate-soil moisture interactions, with precipitation, SPEI, and vegetation dynamics as key deficit risk factors.
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Laassilia et al. (2026) Integrated Multi-scale Assessment of CHIRPS and PERSIANN-CDR for Meteorological, Agricultural, and Hydrological Drought Monitoring in Semi-arid Environments
This paper evaluates two satellite precipitation products (CHIRPS and PERSIANN-CDR) for multi-scale meteorological, agricultural, and hydrological drought monitoring in the semi-arid Moulouya Basin, Morocco. The study found that CHIRPS generally outperforms PERSIANN in accuracy and event-scale detection, while both products effectively capture observed drought patterns and reveal a progressive aridification trend in the region.
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Niu et al. (2026) Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction
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Roundy (2026) Zonal Propagation of the Indian Basin MJO Across Varying Background Wind and Seasonal Background Wind States
This paper investigates the seasonal variability of the Madden-Julian Oscillation's (MJO) eastward propagation and its relationship with equatorial upper tropospheric background wind patterns, finding that propagation speed is strongly modulated by the strength of these background winds and that upper tropospheric signals are often stronger than lower tropospheric ones.
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Fiorillo et al. (2026) Magnitude of hydrological events and extremes using the Z value
This study introduces a statistical method using the dimensionless Z value, derived from standardizing hydrological time series, to quantify event magnitude and define extremes. It demonstrates that the Z value offers a more stable and context-invariant measure of hydrological event magnitude than the traditional return period, particularly for extreme events.
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Ahmed et al. (2026) Application of Precision Agriculture and IoT-Based Smart Irrigation in Greenhouse Vegetable Production
This review synthesizes the application of Precision Agriculture and IoT-based smart irrigation systems for optimizing greenhouse vegetable production, demonstrating significant reductions in water use and energy consumption while enhancing crop yield and nutrient efficiency.
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Yin et al. (2026) Exploring a process-aware spatiotemporal graph-based surrogate for integrated urban drainage simulation
This study proposes PAST, a Process-Aware Spatio-Temporal graph-neural-networks-based surrogate model, to efficiently simulate integrated urban drainage processes by holistically representing rainfall–runoff-routing and incorporating regulation effects. PAST achieves high performance and physical explainability, significantly outperforming baseline models, especially under regulated and extreme rainfall conditions.
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Belghit et al. (2026) Applying AdaBoost algorithm on multiclass OvA-SVM for the delineation of rainy clouds using multispectral MSG-SEVIRI data
This study implements and evaluates an AdaBoost-enhanced multiclass One-versus-All Support Vector Machine (AdaOvA-SVM) model for classifying and delineating precipitating clouds in northern Algeria using satellite and radar data, demonstrating its superior performance compared to existing techniques.
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Khattaoui et al. (2026) Runoff–Based Streamflow Modeling at a Catchment Outlet Using HEC-HMS
The study evaluated the HEC-HMS model for ungauged basins, revealing significant variability in initial abstraction (IA) across three catchments, thus emphasizing the need for site-specific IA assessment for accurate hydrological modeling.
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Fronzi et al. (2026) Toward quantitative DNA tracer tests: development and validation of a novel capture device for groundwater flow characterization in karst and carbonate aquifers
This study develops and validates a novel passive device for selectively capturing biotinylated synthetic DNA tracers, enabling time-integrated, semi-quantitative assessment of groundwater flow. Laboratory and field tests demonstrate that the device produces breakthrough curves consistent with conventional tracers, facilitating broader application of DNA tracers in complex hydrogeological systems.
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Prayag et al. (2026) Assessing infiltration dynamics using integrated hydrogeophysical monitoring in a managed aquifer recharge pond
This study investigated infiltration dynamics in a Managed Aquifer Recharge (MAR) pond over eight months using an integrated hydrogeophysical monitoring system (automated Direct Current Resistivity and Induced Polarisation (DCIP), Ground Penetrating Radar (GPR), and hydrological data). It revealed subsurface heterogeneity and time-dependent infiltration pathways, including a high-permeability westward-dipping layer, and identified signs of clogging affecting lateral water spreading.
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Yang et al. (2026) Fusing dynamic physical constraints with PINN-xLSTM to enhance accuracy and physical consistency in runoff prediction under extreme hydrological events
This study introduces a novel PINN-xLSTM model with a dynamic physical constraint weighting mechanism to enhance runoff prediction accuracy and physical consistency, particularly during extreme hydrological events. The model demonstrates superior performance in accuracy, flood peak characterization, and adherence to hydrological principles compared to existing models.
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Jaeger et al. (2026) Lost in translation: Reconciling different streamflow permanence data products
This study develops a framework to reconcile and evaluate two streamflow permanence datasets (NHDPlus HR and PROSPER model output) for the Pacific Northwest, finding 68% agreement regionally and identifying reliability patterns to inform land and water management decisions.
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Feldman et al. (2026) Widespread Co‐Location of Less Frequent and More Intense Daily Precipitation Over Land
This study investigates the global co-location of trends towards more intense and less frequent daily precipitation events, finding that fewer, larger events are common and distributed across terrestrial ecosystems, often counteracting increases in annual precipitation totals due to simultaneous decreases in small-to-moderate events.
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Huang et al. (2026) Impacts of timescales on the relationship between compound drought-hot extremes based on precipitation and groundwater
This study investigates the spatial distribution and differences between compound groundwater droughts and hot extremes (CGDHEs) and compound meteorological droughts and hot extremes (CMDHEs), attributing these differences to hydrological lags. It identifies optimal precipitation timescales to reduce these discrepancies, thereby improving the potential for near-real-time monitoring of CGDHEs.
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Li et al. (2026) Global Agricultural Drought Crisis: Synergistic Impacts of Climate Change and Human Activities and Their Feedback Mechanisms
This review synthesizes the synergistic impacts of climate change and human activities on global agricultural drought, revealing how their interactions form amplifying feedback loops that intensify drought frequency, intensity, duration, and spatial extent, leading to ecological degradation, crop yield loss, and socioeconomic inequality. It proposes a three-dimensional framework integrating mitigation, adaptation, and collaborative governance to address this escalating crisis.
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Dastjerdi et al. (2026) Comparing novel backward hydrological models for watershed-scale precipitation estimation: an evaluation of inverted PDM and Kirchner-hybrid structures
This study developed and evaluated two novel backward hydrological models, an inverted Probability Distributed Model (PDM) and a hybrid Soil Moisture to Rain (SM2RAIN)-Kirchner model, for daily watershed-scale precipitation estimation. The locally calibrated backward models significantly outperformed established Global Gridded Precipitation Products (GGPPs), with the Kirchner model achieving the highest performance (KGE = 0.62) and the inverted PDM proving robust (KGE = 0.55).
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Chen et al. (2026) A novel soil moisture retrieval method via combining radiative transfer model and machine learning
This study introduces a novel, interpretable soil moisture retrieval framework by integrating a radiative transfer model (RTM) with a Kolmogorov–Arnold Network (KAN) to derive explicit mathematical expressions from satellite observations, achieving global soil moisture estimates comparable in accuracy to the SMAP Level-3 product.
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Zarei (2026) Medium -term monitoring and machine learning-based forecasting of drought dynamics in Iran
This study comprehensively assesses historical drought conditions in Iran from 1967 to 2024 and forecasts decadal drought dynamics for 2025–2036 using climate observations and machine learning, revealing a projected significant increase in drier conditions and the disappearance of extreme wet periods.
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Kuntiyawichai et al. (2026) Combined drought index for drought monitoring and severity assessment under future climate and land use changes
This study developed a Combined Drought Index (CDI) for the Prom-Choen-Upper Phong River Basin using Principal Component Analysis (PCA) of SPEI, SSFI, and SSDI to assess future drought risk under climate and land use changes. It found that while increased future rainfall may reduce overall drought risk, the SSP585 scenario significantly expands very high-risk drought areas, underscoring the need for the CDI in mitigation strategies.
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Khorami et al. (2026) Estimation of root zone water storage capacity (S) in natural ecosystems subject to high interannual climate variability
This study evaluates two cumulative water deficit (CWD) methods for estimating root zone water storage capacity (Sr) in 105 Australian forested catchments, finding that a multi-year CWD approach yields significantly higher Sr estimates (median 25% higher) in regions with high interannual climate variability compared to the conventional single-year method.
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Zhang et al. (2026) Dynamic conversion coefficients improve alpine lake daily evaporation estimation based on multi-evaporator observations
This study developed a refined pan conversion method with dynamic coefficients, based on multi-evaporator observations, to estimate daily lake surface evaporation (LSE) for alpine lakes, including ice-covered periods, and quantified the meteorological controls on LSE variability.
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Wu et al. (2026) EcoTWIN 1.0: a fully distributed tracer-aided ecohydrological model tracking water, isotopes, and nutrients
This paper introduces EcoTWIN 1.0, a fully distributed tracer-aided ecohydrological model that simultaneously tracks water, isotope, and nutrient fluxes. The model demonstrated good performance in reproducing calibrated in-stream targets and uncalibrated internal fluxes across 17 large European catchments, proving its flexibility and transferability for prediction and process inference in diverse terrestrial ecosystems.
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Zowam et al. (2026) Climate Variability and Groundwater Levels: A Correlation and Causation Analysis
This study investigated the dynamic relationship between terrestrial water cycle intensity (WCI) and groundwater level (GWL) anomalies in arid Arizona, USA, using statistical correlation and causation analyses. It found a dominantly negative, lagged relationship where GWL changes typically precede WCI responses by 1–2 months, implying that an intensified water cycle may signal already depleting groundwater resources.
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Simantiris et al. (2026) AIFloodSense: A Global Aerial Imagery Dataset for Semantic Segmentation and Understanding of Flooded Environments
This paper introduces AIFloodSense, a comprehensive and globally diverse evaluation benchmark designed to advance domain-generalized Artificial Intelligence for climate resilience and flood detection. It demonstrates that rigorous dataset diversity, rather than sheer scale, is more effective for training robust flood detection models, leading to superior generalization capabilities.
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Jasechko (2026) Global cases of groundwater recovery after interventions
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Szanyi et al. (2026) Assessment of Changes in Groundwater Resources Due to Climate Change for the Purpose of Sustainable Water Management in Hungary
This study assessed climate and pumping impacts on the Nyírség groundwater system using monitoring and modeling, finding that climate-driven recharge reductions will dominate basin-scale declines by 2050, with managed aquifer recharge offering localized benefits.
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Maragkaki et al. (2026) Hydrological and Geochemical Modeling of Water Availability and Quality in the Jordan Valley Under Climate Change
This study applied an integrated hydrological and hydrogeochemical modeling framework to quantify water availability and quality and assess climate change impacts in the Jordan Valley, revealing it is evapotranspiration-dominated, highly dependent on imported irrigation, and faces exacerbated water scarcity under future climate change.
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Li et al. (2026) Assessing the global performance of a parsimonious soil temperature model for frozen ground prediction
This study globally evaluates a simplified soil temperature model for predicting frozen/unfrozen ground states using only air temperature and snow cover data. The model demonstrates robust global performance (average true frozen rate of 0.90, false frozen rate of 0.06), offering a computationally efficient solution for hydrological models, though it shows limitations in mountainous regions.
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Liu et al. (2026) A snow-fire bridge mechanism for the 2025 Southern California winter wildfire
This study identifies a "snow-fire bridge" mechanism where reduced western Eurasian snow cover triggers an atmospheric teleconnection, leading to weather conditions favorable for winter wildfires in Southern California, as observed during the January 2025 event.
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Wang et al. (2026) A novel adaptive soil moisture retrieval method via stacked ensemble learning and a local Bayesian dynamic algorithm
This study introduces a novel local Bayesian dynamically weighted stacking ensemble learning model (Stacking-BO) and a high-resolution spatiotemporal multilayer soil moisture simulation framework to enhance the accuracy and stability of soil moisture retrieval, demonstrating superior performance over existing methods.
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Mesallam et al. (2026) Strategic dam site selection and hazard mapping using remote sensing: insights from Wadi Araba, Egypt
This study developed an integrated remote sensing, GIS, and Analytical Hierarchy Process (AHP) framework to map flash flood susceptibility and identify optimal dam sites for flood mitigation and groundwater recharge in Wadi Araba, Egypt. The framework successfully delineated flood-prone areas and prioritized dam locations, demonstrating its utility for water resource management in hyper-arid regions.
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Jin et al. (2026) Spatiotemporal evolution and hyetograph changes of global extreme precipitation
This study systematically analyzes the global spatiotemporal evolution of annual maximum 3-hour precipitation events and their hyetograph changes, revealing a global decline in peak intensity but an increase in total event precipitation due to more temporally distributed rainfall, which is likely to exacerbate flood risk.
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Redaelli et al. (2026) Changes in irrigation practices may deplete aquifers faster and more severely than meteorological droughts: A numerical modeling approach
This study quantitatively assessed the drivers of aquifer depletion in an intensively irrigated system, finding that changes to more efficient irrigation practices, which reduce irrigation return flow, have a more severe impact on groundwater resources than meteorological droughts.
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Alhathloul (2026) Spatiotemporal Trends and Abrupt Changes in Annual Potential Evapotranspiration and Water Balance over Saudi Arabia
This study investigates the interannual variability, long-term trends, and abrupt regime shifts in potential evapotranspiration (PET) and water balance (WB) across Saudi Arabia from 1985 to 2022. It reveals a widespread increase in atmospheric evaporative demand and declining WB, indicating an intensifying water deficit, with a significant hydroclimatic regime shift identified in the late 1990s.
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Garrido et al. (2026) A Scalable Method to Delineate Active River Channels and Quantify Cross-Sectional Morphology from Multi-Sensor Imagery in Google Earth Engine Using the Photo Intensive System for Channel Observation (PISCOb)
This study developed and validated an automated Google Earth Engine workflow using multispectral indices from Landsat and Sentinel-2 to delineate active channel width, finding Sentinel-2 with MNDWI-EVI provided the highest accuracy and highlighting the importance of local geomorphic and ecological conditions for threshold selection.
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Nie et al. (2026) Soil Moisture Retrieval Using Multi-Satellite Dual-Frequency GNSS-IR Considering Environmental Factors
This study developed a dual-frequency GNSS-IR framework for soil moisture retrieval, integrating multi-satellite observations and environmental factors. It found that retrieval performance converges with 5-6 satellites per constellation, and that dual-frequency fusion and environmentally informed nonlinear models significantly enhance accuracy and stability.
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Zhang et al. (2026) Integrating GLASS LAI into the SWAT Model for Improved Hydrological Simulation in Semi-Arid Regions
This study modified the SWAT model by integrating high-resolution Global Land Surface Satellite (GLASS) Leaf Area Index (LAI) data to improve hydrological simulations in the semi-arid Wuding River Basin, significantly enhancing runoff and evapotranspiration accuracy by correcting unrealistic vegetation dynamics.
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Kumar et al. (2026) Estimation of location-specific precipitation using Deep Neural Networks
This study introduces two Deep Neural Network (DNN) architectures for location-specific precipitation estimation, demonstrating their superior accuracy and computational efficiency compared to traditional Kriging methods across various meteorological conditions in India. The DNN models, especially one incorporating additional meteorological variables, consistently outperform Kriging in capturing spatial precipitation patterns and extreme events.
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Mo et al. (2026) Self-Supervised Reservoir Water Area Detection Across Multi-Source Optical Imagery
This paper develops a geo-spectral feature-guided Self-Supervised Water Detection (SWD) framework for automated, multi-source optical imagery to monitor reservoir water extent. The SWD framework outperforms supervised methods, demonstrating high consistency and stable generalization across scales and regions, and accurately captures water-level fluctuations without manual labels or model training.
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Mondal et al. (2026) Advancements in Spatio-temporal agricultural drought monitoring and modeling: a comprehensive review on multi-source remote sensing and machine learning techniques
This comprehensive review synthesizes advancements in spatio-temporal agricultural drought monitoring and modeling, focusing on the integration of multi-source remote sensing data with machine learning (ML) and deep learning (DL) techniques. It highlights the effectiveness, cost-efficiency, and transferability of these advanced geospatial methods for assessing and predicting agricultural drought conditions across various scales.
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Johnston et al. (2026) The snow meteorology and phenology classification (SnowMAP): global snow cover observations enhance snow’s representation
This study introduces SnowMAP, a novel global snow classification system that integrates meteorological controls (snowfall, temperature, wind) with snow phenology (seasonal presence, melt timing), providing a more complete and decision-relevant view of global snow conditions. The system identifies 18 distinct snow classes that reflect variations in snow depth, geography, land cover, and infrastructure, enhancing the understanding of snowpack formation and evolution.
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Fengour et al. (2026) A taxonomy-based benchmark of parametric and non-parametric machine learning models for data-driven precipitation prediction in Morocco
This study introduces a taxonomy-based benchmark of parametric and non-parametric machine learning models for data-driven precipitation prediction in Morocco. It demonstrates that non-parametric models consistently outperform parametric models, effectively capturing the complex, non-linear relationships inherent in highly intermittent and zero-inflated rainfall data.
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Aftab et al. (2026) A Daily Soil Moisture–Temperature Compound Index for Characterising Dry–Hot Extremes
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Giuseppe et al. (2026) Climate in a Mediterranean nature reserve: patterns and trends in the Castelporziano Presidential Estate (Italy)
This study analyzes seasonal and annual patterns and trends of temperature and precipitation, including extreme events, in the Castelporziano Nature Reserve (Italy) from 1980, revealing a significant warming trend, particularly for maximum temperatures and extreme heat indices, influenced by coastal proximity, while precipitation shows no significant trend.
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Hadjipetrou (2026) A review of statistical methods for climate downscaling: the underexplored potential of geostatistical simulation
This review synthesizes developments in statistical and stochastic climate downscaling, critically assessing various methods including regression, weather generators, analogs, and machine learning. It highlights the significant, yet underexplored, potential of geostatistical simulation, particularly Multiple-Point Statistics, to provide spatially coherent and uncertainty-aware fine-scale climate information.
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Galli et al. (2026) Integrating biophysical models and remote sensing to evaluate irrigation practices in four global hubs
This study compares irrigation demand from an agro-hydrological model (WATNEEDS) with irrigation water use from five satellite products across four global irrigation hubs, finding significant correlations (above 0.6 for 3/4 cases) and using discrepancies to identify hydroclimatic and anthropogenic irrigation drivers.
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Al-Timimi et al. (2026) Effects of climate change on temperature and precipitation in the Euphrates-Tigris Basin
This study comprehensively assesses projected climate change impacts on temperature and precipitation across the entire Euphrates-Tigris Basin under four RCP scenarios (2.6, 4.5, 6.0, 8.5) for three future periods, revealing a consistent warming trend and significant spatial redistribution of precipitation, with severe implications for downstream water resources.
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Yan et al. (2026) Attribution of runoff changes in the semi-arid Xiliao River Basin – A Budyko-elasticity approach to deconstructing compound climate and human impacts
This study quantified the contributions of climate change and human activities to runoff reduction in the semi-arid Xiliao River Basin from 1980 to 2022 using a Budyko-elasticity framework, revealing an abrupt runoff decline in 2002 with significant spatial heterogeneity: upstream areas are climate-driven, while middle-downstream areas are human-driven, primarily due to agricultural irrigation.
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Zhang et al. (2026) Impact of spatial scale on the sensitivity of the water supply-demand balance to driving factors
This study develops an integrated water footprint accounting framework to diagnose water stress and its drivers across grid (~1 km²), municipal, and sub-basin scales in the Yellow River Basin from 2000 to 2024. It reveals a widening upper-to-lower reach divergence in water stress, driven by coupled socio-hydrological interactions that are often masked by aggregated analyses, providing scale-differentiated management recommendations.
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Han et al. (2026) SWOT performance in monitoring water level of high-mountain lakes on the Tibetan Plateau
This study introduces a novel Gaussian kernel density estimation approach to retrieve water levels of high-mountain lakes from SWOT observations, demonstrating that SWOT reliably captures variations in water level (average r = 0.72, RMSE = 0.29 m) and has transformative potential for monitoring global small water bodies.
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Kavi (2026) Climate change and variability: an analysis of trends in rainfall and temperature in the Volta and Northern Regions of Ghana
This study analyzed climate change and variability in the Volta and Northern Regions of Ghana over a 39-year period (1984–2023), focusing on temperature and rainfall trends and their implications for agriculture. Findings reveal a statistically significant decreasing rainfall trend in the Northern Region and significant upward trends in both maximum and minimum temperatures across both regions, indicating a warming climate with risks for agricultural productivity.
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Ducco et al. (2026) The role of precipitation and irrigation on groundwater droughts in the Piemonte Plain, Italy
This study investigates the relationship between meteorological and groundwater droughts in the shallow aquifers of the Piemonte Plain, Italy, focusing on the impact of widespread irrigation. It finds that irrigation significantly weakens the correlation between precipitation and groundwater levels, delays groundwater response, and mitigates the propagation of meteorological drought into severe groundwater drought, particularly in rice-cultivated areas.
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SHEN et al. (2026) Estimation of water savings from farmland fallowing in the tarim river basin under food security and ecological security constraints and threshold effects of driving factors
This study developed a framework to estimate water savings from farmland fallowing (FLWC) in the Tarim River Basin under food and ecological security constraints. It found that suitable fallow areas and FLWC peaked in 2015, with cotton exhibiting the highest water-saving potential, and identified mean air temperature and mean relative humidity as primary climatic drivers with threshold effects on FLWC.
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Wu et al. (2026) Comparative analysis of SAR-based soil moisture inversion methods for crop-covered under cloudy, rainy, and irrigation conditions
This study developed a scenario-adaptive framework for soil moisture inversion in crop-covered areas, integrating Radarsat-2 SAR and HJ-2A/B optical data with Random Forest (RF) and the Water-Cloud Model (WCM). It found that direct optical-SAR fusion via RF achieved the highest accuracy (R² = 0.90) under clear conditions, while the VWC-coupled WCM was optimal (R² = 0.61) for cloudy, rainy, or irrigation scenarios.
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Asadzadeh et al. (2026) Water detention structures as a flood mitigation strategy: A case study of the Elgin Creek Basin
This study developed a computationally efficient, system-scale modeling framework to assess flood risks and evaluate water detention structures for mitigating road washouts in data-scarce Prairie basins. It identified five strategically located detention dams that collectively eliminate road washout risk for floods up to the 100-year return period in the Elgin Creek Basin.
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An et al. (2026) Scenario-dependent responses of soil conservation service flow to climate change across karst development gradients in the Pearl River Basin
This study quantifies the scenario-dependent responses of soil conservation service flow (SCSF) to climate change across different karst development degrees (KDDs) in the Pearl River Basin using a coupled Global Climate Model (GCM)-Soil and Water Assessment Tool (SWAT) framework, revealing that SCSF dynamics are significantly influenced by both climate scenarios and karst geomorphology.
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Bhuiyan et al. (2026) Improving coastal water level estimation by merging nadir-only satellite altimetry data into a hydrodynamic model
This study evaluates a novel method to improve coastal water level (CWL) predictions by assimilating nadir-only satellite altimetry data from four missions into the ADCIRC hydrodynamic model along the U.S. East Coast, finding that combined assimilation significantly enhances model performance at over 80% of gauge locations.
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Saini et al. (2026) Development of a Virga Detection Tool and Associated Study of Arctic Virga and Precipitation
## Identification - **Journal:** Journal of Geophysical Research Atmospheres - **Year:** 2026...
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Wang et al. (2026) Quantitative identification of the impact of human activities and climate change on sediment load in the Yellow River Basin of China
This study quantitatively identified the contributions of climate change and human activities to sediment load variations across the Yellow River Basin from 1961 to 2022, revealing a progressive shift from climate-dominated to human-dominated controls, particularly in midstream and downstream reaches.
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Tiwari et al. (2026) Examining the Changes in Precipitation Patterns Across the Western Himalayan Region During the Winter Season
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Wei et al. (2026) Critical role of intraseasonal oscillations in shaping extreme rainfall from tropical cyclones over the South China Sea
This study quantifies the critical role of intraseasonal oscillations (ISOs), including the quasi-biweekly oscillation (QBWO) and Madden–Julian oscillation (MJO), in shaping extreme accumulated rainfall (EAR) from tropical cyclones (TCs) over the South China Sea (SCS), finding that ISOs contribute significantly to the occurrence and characteristics of these extreme events.
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Li et al. (2026) The critical role of soil moisture in compound hazards
This review synthesizes current understanding of soil moisture's critical role in the evolution and onset of diverse compound hazards, highlighting its mechanisms in amplifying drought-heatwave-wildfire events, promoting clustered storms, and driving vegetation die-offs, landslides, and flooding. It also identifies persistent challenges and a roadmap for integrating soil moisture into hazard prediction and early warning systems.
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Wang et al. (2026) Numerical simulation-based study on the response of urban drainage networks to flooding and road risk in typical plain city
This study developed an integrated hydrodynamic model to simulate urban pluvial flooding under various rainfall scenarios in Taocheng District, China, demonstrating that drainage systems significantly reduce surface and road inundation and mitigate flood risk.
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Ackroyd et al. (2026) Spatial assessment of snow grain size from airborne lidar reflectance against coincident imaging spectroscopy retrievals
This study evaluates three methods for deriving snow grain size and albedo from 1064 nm airborne lidar reflectance against coincident imaging spectroscopy retrievals over a snow-covered glacier. It demonstrates that incorporating incidence angle corrections is crucial for accurate lidar-derived snow properties in mountainous terrain, highlighting lidar's potential for high-resolution snow property mapping.
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Pham-Thanh et al. (2026) Seasonal precipitation prediction over Vietnam: evaluation of RegCM dynamical downscaling and statistical bias correction of NCEP CFS forecasts
This study evaluates the performance of dynamically downscaled seasonal precipitation forecasts over Vietnam using RegCM-NH driven by NCEP CFS, and the improvements obtained through statistical bias correction. It finds that multiple linear regression (MLR) significantly enhances forecast accuracy, reducing systematic biases and improving interannual variability representation across Vietnam's climatic sub-regions.
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Vojtek et al. (2026) Transferability of machine/deep learning-based prediction of fluvial flood extent to distinct river sections in Slovakia based on benchmark flood maps and high-resolution spatial data
This study investigates the transferability of machine learning (ML) and deep learning (DL) models for predicting fluvial flood extent across distinct river sections in Slovakia under three flood scenarios. It finds that transferability is most effective between similarly sized river sections, with HAND, distance from river, and slope being the most influential predictors, offering high potential for near real-time flood mapping.
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Cao et al. (2026) Effect of regional marine cloud brightening on land climate
This study uses the CESM Earth system model to investigate the land climate consequences of regional Marine Cloud Brightening (MCB), finding that while MCB stabilizes global temperature and offers benefits like reduced drought stress and increased GPP, its abrupt termination leads to severe and rapid land warming with significant ecological risks.
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Najafzadeh et al. (2026) Assessment of flood susceptibility in Minab County, Iran, through the integration of topographic, climatic, and land-surface indices using ensemble machine learning models
This study developed a high-resolution flood susceptibility map for Minab County, Iran, by integrating multi-source geospatial datasets with seven machine learning models. It found that ensemble tree-based models (CatBoost and Random Forest) provided the most balanced and generalizable performance, identifying short-term precipitation and surface moisture as dominant flood drivers, with approximately 53% of the study area classified as high to very high flood risk.
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Sheil (2026) How Forests May Reduce the Incidence of Destructive Tropical Cyclones, Hurricanes and Typhoons
This review systematically examines whether and how forests influence tropical cyclone frequency, intensity, and behaviour. It finds strong support for post-landfall effects, such as slowing storms and curbing flooding, while pre-landfall influences remain less certain but warrant further investigation.
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Rivoire et al. (2026) The future is in the past? A flexible resampling approach to generate multivariate time series
This paper introduces a straightforward method for generating synthetic climate time series by constrained sampling of observations, demonstrating its ability to preserve physical consistency and multivariate dependencies while simulating multi-day extremes under future climate scenarios.
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Du et al. (2026) A recent significant increase in tropical cyclone-induced precipitation in the North China Plain
This study reveals a significant increasing trend in tropical cyclone (TC) precipitation over the North China Plain from 1981 to 2024, driven by a remarkable surge since 2018 due to elongated TC tracks with deeper inland penetration, linked to an anomalous easterly steering flow.
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Armanuos et al. (2026) Assessing the impact of groundwater abstraction and concrete dam fractures on saltwater intrusion using numerical modeling and interpretable machine learning
This study develops and validates machine learning models to predict the relative saltwater intrusion (SWI) wedge length (L/H) in coastal aquifers, considering groundwater abstraction and fractured underground dams. The XGBoost model demonstrated superior accuracy (R²=0.9978, RMSE=0.216) and identified the relative recharge well rate as the dominant predictor, offering a robust tool for SWI management.
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Che et al. (2026) Temperature sensitivity and rainfall heat flux drive rapid mass loss of low-latitude glaciers in the Southeastern Qinghai–Tibet Plateau
This study investigates the impact of air temperature and rainfall heat flux on the mass balance of low-latitude temperate glaciers in the southeastern Qinghai–Tibet Plateau (QTP), finding that these glaciers exhibit a nearly linear sensitivity to temperature and that rainfall heat flux contributes significantly to their rapid mass loss.
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Yadav et al. (2026) Cooling Effects of Wetlands in a Tropical Megacity: Evidence from the East Kolkata Wetlands, India
This study assesses the complex cooling role of peri-urban wetlands in tropical megacities using a geospatial framework and Landsat imagery, revealing that wetlands create significant thermal gradients with waterbodies as the coolest surfaces and dumping grounds as hotspots. The cooling effect exhibits non-linear distance-decay and directional asymmetry, governed by hydrological connectivity and landscape permeability.
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Arebu et al. (2026) Hydrological Modeling of Reservoir Sedimentation and Evolution of Elevation–Capacity Curve of the Dam Reservoir
This study introduces a novel hydrological approach, integrating the sediment rating curve (SRC) and the dam reservoir elevation-capacity curve (ECC), to accurately estimate reservoir sedimentation and its impact on capacity, demonstrating its effectiveness at the Wadi Fatimah Dam.
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Zhang et al. (2026) A Study on Integration of Topographic Clustering and Physical Constraints for Flood Propagation Simulation
This study develops a high-accuracy and efficient flood evolution simulation method for flood storage and detention basins (FSDBs) by combining terrain clustering and physical propagation constraints. The method achieves errors within 10% for water level and inundation extent, and improves computational efficiency by over 60% compared to traditional methods.
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Chen et al. (2026) Impact of deep-inland typhoon track uncertainty on the 2023 record-breaking rainfall over North China: an ensemble-based analysis
This study applied ensemble sensitivity analysis to ECMWF ensemble data to diagnose the large-scale circulation controls on the unprecedented Beijing–Tianjin–Hebei rainfall event in July-August 2023, finding that rainfall extremes were tightly governed by the precise trajectory of Typhoon Doksuri's remnants, which modulated the critical spatial alignment of moisture flux, typhoon intensity, and terrain-enhanced convergence.
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Jong et al. (2026) Reversal of extreme precipitation trends over the Northeast US in response to aggressive climate mitigation in GFDL SPEAR
This paper assesses projected changes in extreme precipitation over the Northeast US under an aggressive overshoot mitigation pathway, finding that while warm-season extremes decline quickly after greenhouse gas reductions, cold-season extremes exhibit a delayed response and hysteresis, returning to mid-century levels by 2100.
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Bhattacharjee et al. (2026) Spatiotemporal reorganization of drought characteristics across India under changing monsoon variability
This study assesses the spatiotemporal reorganization of drought characteristics across India (1902-2013) using a non-stationary drought index (NSPI) and non-linear trend analysis (EEMD). It reveals a significant post-1950s increase in intrinsic drought duration (~61%) and severity (~62%), driven by changing monsoon variability, with NSPI demonstrating superior detection skill compared to traditional stationary indices.
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Sharifi et al. (2026) Introducing Resiliency as a Novel Metric for Enhanced Drought Monitoring
This study introduces resiliency as a novel metric for dynamic drought monitoring, demonstrating its effectiveness in assessing recovery potential across different drought types (meteorological, hydrological, groundwater, and combined) in the Aleshtar subbasin, Iran, using 30 years of hydroclimatic data.
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Su et al. (2026) SynxFlow-based urban pluvial flood simulation and sensitivity evaluation in the central urban area of Shenzhen
This study assesses the efficacy of SynxFlow, a newly developed open-source hydrodynamic model, for urban pluvial flood simulation and sensitivity evaluation in the central urban area of Shenzhen. It demonstrates SynxFlow's robust performance and highlights the critical role of integrated drainage modules for enhancing flood resilience planning in mega-cities.
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Zhou et al. (2026) Synchronized Heat Extremes in the Northern Hemisphere Based on a Complex Network
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Chu et al. (2026) Future changes in precipitation and temperature using cmip6 model based on topsis method: focus on Songhua river basin
This study projects future precipitation and temperature changes in the Songhua River Basin (SRB) using a Weighted Multi-Model Ensemble (WMME) based on the TOPSIS method with CMIP6 GCMs. It finds significant increases in both variables across the basin, with precipitation rising by 5.7% to 26.6% and temperature by 1.32 °C to 5.44 °C depending on the scenario.
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Aryal et al. (2026) A novel approach for river discharge prediction in Lancang Mekong River Basin: Incorporation of multisource remote sensing and LSTM model
This study developed a Long Short-Term Memory (LSTM) model framework integrating multi-mission satellite altimetry and optical sensor data to predict daily river discharge in the Lancang Mekong River Basin (LMRB). The model achieved robust performance, particularly in downstream reaches, and demonstrated spatial transferability to ungauged locations, though predictive accuracy declined with increasing distance from training sites.
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Fu et al. (2026) Global distribution of the terrestrial moisture dynamics response to the meteorological dry-wet abrupt alternation
This study quantifies the global patterns and trends in the coincidence between meteorological dry-wet abrupt alternation (M-DWAA) and terrestrial dry-wet abrupt alternation (T-DWAA) events. It finds that approximately 17.4% of M-DWAA events globally coincide with a T-DWAA within the subsequent season, with the M-DWAA alternation velocity being the dominant influencing factor.
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Li et al. (2026) Declining Ecological Water Consumption of Marsh Wetlands and the Driving Forces in Semi-Arid Plateau Region: A Case Study in the Bashang Plateau, China
This study investigated the spatiotemporal dynamics and driving forces of marsh wetland ecological water consumption (EWC) in the Bashang Plateau, China, from 1986 to 2021, revealing a significant decline in wetland area and EWC primarily driven by precipitation, surface water area, and indirectly by increased forest EWC due to large-scale afforestation.
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Tang et al. (2026) Elevation‐Dependence of Different Precipitation Phases Concentrations in the Asian Water Tower: Differences in Rainfall and Snowfall Concentration Changes
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Dadaser‐Celik et al. (2026) Future Hydrological Trajectories of Burdur Lake Under Climate Change and Basin‐Scale Human Interventions
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Dong et al. (2026) How Lateral Flow Impacts Heavy Rainfall in Complex Terrain: A Composite Analysis Over the Southern Anhui Mountainous Region
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Wang et al. (2026) Spatiotemporal patterns and zonation of typhoon and non-typhoon extreme rainfall hazards in the typical coastal region of southeastern China
## Identification - **Journal:** Geomatics Natural Hazards and Risk - **Year:** 2026...
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Sulca et al. (2026) An Integrated Analysis to Delineate Groundwater Flow Systems and Recharge Dynamics in the Chili River Sub-Basin, Southern Peru
This study characterizes the poorly understood aquifer systems, recharge mechanisms, and chemical evolution in the arid Chili River sub-basin, Peru. It identifies three aquifer types, distinct groundwater flow systems, and reveals a chemical evolution from high to low elevations, with high-altitude rainfall being the primary recharge source for wells.
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Xu et al. (2026) Compound effects of dams and levees reshape Yangtze flood dynamics and reveal substantial risk misestimations from ignoring levees
This study assesses the compound effects of dams and levees on Yangtze River flood dynamics using the CaMa-Flood model, revealing their distinct and complementary roles in regulating flow and inundation, and demonstrating that ignoring levees leads to a significant overestimation of flood risk.
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Zewdu et al. (2026) Utilizing GIS and fuzzy logic for groundwater resource mapping in gubalafto woreda, Ethiopia: a spatial analysis approach
This study developed a GIS-based fuzzy logic framework to map groundwater potential zones in Gubalafto Woreda, Ethiopia, integrating multiple hydrogeological and topographic factors. The model identified that 17% of the area has high groundwater potential, demonstrating robust predictive accuracy with an AUC of 0.752.
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Sridhara et al. (2026) Historical and future extremes of cauvery basin analysed using cmip6 models and ETCCDI indices
This study validates and ranks 13 CMIP6 models against IMD observations using Multi-Criteria Decision-Making (MCDM) techniques to project historical (1951–2023) and future (2025–2100) extreme temperature and precipitation events in the Cauvery Basin, India, revealing significant increases in heat stress and alternating flood/drought risks under high-emission scenarios.
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Putri et al. (2026) Groundwater recharge from intense rainfall in Indonesia: evidence from East Kalimantan
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Ganiyu et al. (2026) Enhancing flood simulation in data-sparse Niger central hydrological area river basin in Nigeria using machine learning-based data fusion
This study enhances flood event simulation in the data-sparse Niger Central Hydrological Area River Basin in Nigeria by fusing daily downscaled PERSIANN-CDR satellite precipitation with observed rainfall data using machine learning models. The Random Forest (RF) model demonstrated superior accuracy in data fusion, significantly improving precipitation estimates and subsequently leading to more reliable flood simulations with the HEC-HMS hydrological model.
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Gangwar et al. (2026) Consistent increase in Southwest Monsoon rainfall in Telangana, India: insights from bias-corrected CMIP6 simulations
This study projects future changes in Southwest Monsoon rainfall in Telangana, India, using statistically downscaled and bias-corrected CMIP6 models, finding a consistent and significant increase in the frequency and intensity of extreme rainfall events by the end of the 21st century under both moderate and high emission scenarios.
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Çelik et al. (2026) Solar Radiation–Driven Machine Learning for Modelling Reference Evapotranspiration Using ERA5 ‐Land in a Semi‐Arid Microclimatic Basin in Türkiye
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Si et al. (2026) Evolution trends and drivers of glacier and snowmelt induced floods in Upper Yarkant River Basin, Karakoram (1954–2020)
This study developed a multi-temporal framework to classify flood events in the Upper Yarkant River Basin (UYRB) from 1954 to 2020, focusing on the hydrological evolution and driving mechanisms of Glacier and Snowmelt Floods (GSMFs). It found that GSMFs are the predominant flood type, exhibiting increased peak discharge, reduced duration, earlier peak timing, and greater variability, primarily driven by extreme nocturnal warming and the 0 °C isotherm height with a 3-day lag.
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Legese et al. (2026) Spatiotemporal analysis of extreme precipitation and temperature variability, trends, and vulnerability hotspots in coffee growing districts of Ilubabor zone, Ethiopia
This study analyzed 42 years of daily temperature and precipitation data in coffee-growing districts of Ethiopia's Ilubabor Zone to assess spatiotemporal variability, trends, and ecological vulnerability hotspots, revealing significant warming, declining heavy rainfall, increasing dry spells, and identifying Bure and Mettu districts as highly vulnerable.
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George et al. (2026) Submarine groundwater discharge and associated fluxes along the Kanyakumari coast of India using radon and nutrient mass balance approach
This study quantified submarine groundwater discharge (SGD) and associated nutrient fluxes along the Kanyakumari coast of India using radon and nutrient mass balance approaches, revealing significant seasonal variations influenced by monsoonal recharge and tidal dynamics.
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Cui et al. (2026) Coupled dominant factors analysis, dual attention deep learning, and uncertainty quantification for long-term pan evaporation ensemble prediction in the Wuding River Basin, China
This study develops a novel framework integrating dominant factors analysis, dual-attention deep learning, and uncertainty quantification to improve long-term pan evaporation (Epan) ensemble prediction in the Wuding River Basin, China. The framework, utilizing a DA-LSTM model and an improved C-Vine Copula-based multi-model processor (CMMCP), significantly enhances Epan prediction accuracy and reliability by reducing uncertainty.
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Cheung et al. (2026) Expanding Temporal Glacier Observations Through Machine Learning and Multispectral Imagery Datasets in the Canadian Arctic Archipelago: A Decadal Snowline Analysis (2013–2024)
This study presents the first decadal (2013–2024) satellite-derived time series of late-summer snowline altitude (SLA) for six Canadian Arctic Archipelago (CAA) glaciers, revealing that annual peak SLA correlates positively with summer warmth and that glacier hypsometry strongly modulates climatic sensitivity.
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Rahmani et al. (2026) Wetlands set the pace of annual runoff in the northern Great Plains
This study reveals that in North America's Prairie Pothole Region, annual wetland inundation extent, rather than climate drivers, is the dominant factor explaining interannual variability in runoff and high-flow in 69% of 109 studied catchments over 38 years, with most catchments exhibiting threshold-like buffering behavior linked to geographically isolated wetlands.
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Chen et al. (2026) An explainable correction and fusion framework for global bare-earth DTM generation in mountain areas
This study developed an explainable correction and fusion framework to generate high-accuracy global bare-earth Digital Terrain Models (DTMs) in mountainous regions, addressing height biases in existing Digital Surface Models (DSMs). The proposed framework, combining AutoML-SHAP, a CNN-Transformer, and a fusion model, achieved significant vertical accuracy improvements (43.13%–76.86%) over current GDEMs and correction methods.
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Hao et al. (2026) Partitioning precipitation moisture sources in a cold-temperate forest: Seasonal dominance of advection and transpiration in the Greater Khingan Range, China
This study investigated seasonal precipitation moisture sources in the northern Greater Khingan Range using stable isotopes, backward trajectories, and moisture uptake diagnostics. It found that cold-season precipitation is dominated by long-range advection, while warm-season precipitation shows enhanced local recycling primarily driven by transpiration, though advection remains the largest single source.
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Qin et al. (2026) Gain–phase characteristics of groundwater responses to barometric pressure: interpreting subsurface confinement in layered systems
This study applies frequency-domain barometric response functions (BRF) to analyze groundwater response to atmospheric pressure in a layered aquifer. It demonstrates the utility of BRF analysis for interpreting pressure transmission and confinement in vertically heterogeneous aquifers, revealing distinct gain-phase relationships for different confinement types.
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Hiraga et al. (2026) Numerical experiments of cloud seeding for mitigating localization of heavy rainfall: a case study of Mesoscale Convective System in Japan
This study numerically investigated the potential of cloud overseeding to mitigate localized heavy rainfall from a mesoscale convective system in Japan, finding that an optimal seeding configuration could reduce area-averaged 3-hour accumulated rainfall by 11.5% and maximum rainfall by 32% in the heavy rainfall region.
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Hassan et al. (2026) Climate adaptation-aware flood prediction for coastal cities using Deep Learning
This study develops a novel, lightweight Convolutional Neural Network (CNN)-based model, CASPIAN-v2, for rapid and accurate prediction of high-resolution coastal flooding in urban areas under various sea-level rise scenarios and shoreline adaptation strategies. The model significantly outperforms state-of-the-art methods, reducing mean absolute error by nearly 20%, and offers a scalable tool for climate adaptation planning.
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Guo et al. (2026) Wind-water energy characteristics and sediment transport prediction in sandy coarse sand basin
This study developed an energy-based wind-water composite watershed erosion and sediment transport model for the sandy coarse sand area of the Yellow River, demonstrating its high accuracy in predicting sediment yield across different soil types and revealing significant spatiotemporal differentiation of erosion energies.
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Zhao et al. (2026) Integrated linear and non-linear assessment of remote sensing drought indices for soil moisture monitoring across multiple temporal scales in China
This study systematically evaluated remote sensing drought indices (Vegetation Condition Index, Vegetation Water Index, Temperature Condition Index) against soil moisture across China using linear correlation and a Copula-based framework, revealing that optimal index performance varies significantly by ecosystem, temporal scale, and drought severity, especially under extreme conditions.
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Mutar et al. (2026) Assessment of Haditha Dam’s Operation Under Historical Hydrological Conditions: Comparison Between Actual and Simplified Operation Using the HEC-HMS Model in Different Scenarios
This study evaluated the HEC-HMS model's applicability for simulating inflow hydrographs and supporting reservoir operation at Haditha Reservoir, Iraq, under historical hydrological conditions. It found that a rule-based operation scenario significantly improved reservoir storage and water levels during dry periods compared to existing and hydraulic-based operational policies.
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Kajiyama et al. (2026) City boundaries for global urban water scarcity assessment
This study introduces HydroUrbanMap (HUM), a global gridded dataset of hydrologically-informed city boundaries for 1,604 cities at 5 arcmin resolution, designed to improve urban water scarcity assessments by accurately delineating water-served populations and identifying accessible surface water sources. It demonstrates that HUM's approach overcomes limitations of existing urban delineation methods, providing a more realistic basis for city-specific water resource assessments.
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Nandi et al. (2026) Combined hydro-meteorological drought assessment of Ganga-Brahmaputra Basin: insights of the control of total water storage anomaly in drought occurrence
This study assesses hydro-meteorological drought in the Ganga-Brahmaputra Basin (GBB) using a combined drought index (CCDI) derived from GRACE-based Terrestrial Water Storage Anomaly (TWSA) and precipitation, revealing significant TWSA declines and identifying TWSA as the dominant driver of drought severity in the Upper Ganga and Yamuna-Chambal Basins.
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Santos (2026) Projected Intensification of Hydroclimatic Extremes in Rio Grande do Norte, Brazil, Under CMIP6 Scenarios
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Becker et al. (2026) A map of high-altitude wetlands in the world’s major mountain regions
This study presents the first global high-resolution (30 meters) map of high-altitude wetlands across the world's major mountain regions, identifying a total area of over 134,700 square kilometers of these critical ecosystems.
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Khosravi et al. (2026) A geographically weighted XGBoost framework for Pixel-Level modeling of vegetation responses using Multi-Source Earth Observation data
This study introduces Geographically Weighted XGBoost (GW-XGBoost), a hybrid and interpretable framework, to model pixel-level vegetation responses to climate extremes in the Middle East. The model, calibrated with 30 years of multi-source Earth Observation data, outperforms baseline models and reveals a significant ecological transition where vegetation sensitivity has shifted from cold/precipitation constraints to warm temperatures and episodic moisture pulses.
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Dixit et al. (2026) Integrating SMART principles in flood early warning system design in the Himalayas
This study integrates SMART principles with low-cost, real-time hydrometeorological monitoring to design an urban flood early warning system (EWS) in the data-scarce Lesser Himalayas. It demonstrates how community-engaged monitoring captures crucial spatiotemporal rainfall variability and watershed dynamics, which are poorly represented by secondary datasets, providing foundational insights for effective, community-centered EWS implementation.
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Pérez et al. (2026) Regional synchronization patterns between climate indices and colombian hydroclimatic variables
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Seo et al. (2026) Global 0.25-degree gridded Snow water equivalent data derived from machine learning using in-situ measurements
This study developed SWEML, a novel global daily snow water equivalent (SWE) product at 0.25° (~25 km) resolution for 1980–2020, utilizing a machine learning-based Random Forest algorithm trained on in-situ measurements. SWEML demonstrated superior accuracy (overall RMSE 10.33 mm) compared to ten existing reference datasets, particularly in high-elevation regions, and showed robust performance even in data-sparse areas like the Andes.
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Uysal et al. (2026) A data-assimilated SEAS5 forecasting framework for seasonal hydropower inflows in a snow-dominated basin
This study developed a seasonal hydropower inflow forecasting framework for a snow-dominated basin by integrating a variational data assimilation (VarDA) scheme into the HBV hydrological model, demonstrating significant improvements in inflow and snow water equivalent predictions, especially at short lead times.
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Leistert et al. (2026) Accumulation-based Runoff and Pluvial Flood Estimation Tool (AccRo v.1.0)
This paper introduces AccRo (Accumulation-based Runoff and Flooding), a computationally efficient model designed to estimate maximum inundation depth, flow velocity, and specific discharge for pluvial flood events at larger spatial scales. The model demonstrates high accuracy in reproducing analytical solutions for design cases and closely matches the results of state-of-the-art 2D hydrodynamic models for real-world scenarios, offering a robust alternative for flood hazard assessment.
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Brum et al. (2026) Assessing the environmental costs of multi-scale recurrent neural networks for sustainable extreme rainfall nowcasting
This study evaluates the Multi-scale Recurrent Neural Network (MS-RNN) framework for improving computational efficiency and predictive accuracy in extreme precipitation nowcasting using real weather radar data. It quantifies the environmental costs (energy, CO2 equivalent emissions, and water usage) of deep learning models to support sustainable and accessible AI solutions for climate resilience in resource-limited regions.
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Leopardi et al. (2026) A satellite-based approach for estimating runoff and river discharge in the Pan-Arctic region from 2003 to 2022
This study presents daily, long-term (2003–2022) satellite-based estimates of river discharge and gridded runoff at 0.25° × 0.25° spatial resolution across the continental Pan-Arctic region. Integrating various satellite observations into the adapted STREAM hydrological model, it demonstrates high performance (median Kling-Gupta Efficiency of 0.83) and quantifies freshwater fluxes to the Arctic Ocean at 4760 ± 619 km³ yr⁻¹.
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Chen et al. (2026) Rapid recovery from permafrost thaw subsidence after extreme warmth inferred from InSAR
This study examines the response of ice-rich permafrost in Northwest Alaska to the 2019 extreme warmth, revealing a short-lived subsidence of approximately 6 cm followed by a partial recovery of about 3 cm over three years, suggesting substantial resilience rather than widespread sustained degradation.
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He et al. (2026) Contrasting trends in climatic and ecohydrological aridity over one-fifth of global drylands
This study reveals that nearly one-fifth (22.3%) of global vegetated drylands exhibited contrasting trends in climatic and ecohydrological aridity over the past four decades, primarily driven by the opposing effects of elevated atmospheric CO2 on vegetation structure and stomatal conductance.
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Song et al. (2026) Simulation of Nitrogen Migration and Output Loads Under Field Scale in Small Watershed, China
This study investigated nitrogen transport dynamics in an agricultural watershed using high-resolution UAV-derived digital elevation models (DEMs) and coupled hydrological–erosion modeling. It found that decimeter-scale DEMs are essential for accurately capturing microtopographic regulation, which predominantly controls nitrogen migration and spatial heterogeneity of exports.
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Gaona et al. (2026) Spatial Downscaling of the CHIRPS Rainfall Product Using Machine Learning Methods: The Catamayo–Chira Transboundary Basin (Ecuador-Peru) Case
This study spatially downscaled the 5 km CHIRPS rainfall product to 1 km for the Catamayo–Chira Transboundary Basin (Ecuador-Peru) using various single-variable and multivariable machine learning methods, demonstrating significant improvement in precipitation estimates and successfully capturing "El Niño" event differences.
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Wang et al. (2026) Evaluating WRF simulated temperature uncertainties across Northern Hemisphere climate zones with different land surface models and land cover datasets
This study systematically evaluated the temperature simulation performance of four land surface models (LSMs) and three land cover (LC) datasets within the WRF model across the Northern Hemisphere. The CGLC-Noah combination demonstrated the best overall performance, though significant temperature underestimations were found in tropical and polar regions.
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El-Yazidi et al. (2026) Climate Change Projections: Application of the Statistical Downscaling Model in the Souss-Massa Watershed
This study analyzed historical (1982–2022) and projected future (2025–2099) climate variability in the semi-arid Souss-Massa watershed, finding a statistically significant increase in mean annual temperature historically and projecting substantial future warming and precipitation decreases, indicating a trend towards arid conditions.
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Lu et al. (2026) Future water erosion on the Tibetan Plateau: Projections from coupled model intercomparison project phase 6 (CMIP6)
This study projects future water erosion on the Tibetan Plateau using CMIP6 models, revealing that 'hot' models (high climate sensitivity) in unconstrained ensembles predict higher mean annual soil erosion rates (up to 30.37 t⋅ha⁻¹⋅a⁻¹) compared to constrained ensembles, highlighting the critical role of model selection and the need for adaptive soil conservation strategies.
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Mahamat-Nour et al. (2026) Hydrogeological functioning of the Massenya floodplain, Lake Chad Basin: insights from stable isotopes and hydrochemistry
This study investigates the hydrogeological functioning and water quality of the Massenya floodplain in the Lake Chad Basin using hydrodynamics, stable isotopes, and hydrochemistry. It reveals a dual groundwater recharge system, with recent precipitation and floodwater replenishing shallow aquifers and older, fossil waters in deeper horizons, highlighting the critical role of floodplains in sustaining water resources despite anthropogenic pressures and climate variability.
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Massart et al. (2026) Filling the Gap: Elevation-Based Sentinel-1 Surface Soil Moisture Retrieval over the Austrian Alps
This study introduces a novel approach to retrieve surface soil moisture (SSM) across the complex topography of the Austrian Alps by aggregating Sentinel-1 backscatter into 100 m elevation bands. The resulting product provides consistent and elevation-stratified SSM information across over 80% of the region, demonstrating satisfactory agreement with ERA5-Land and capturing precipitation-driven anomalies.
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Cui et al. (2026) Diagnosing interaction between vegetation greening and terrestrial water storage changes in the arid and semi-arid Mongolian plateau
This study investigates the complex, often bidirectional, interactions between vegetation greening and terrestrial water storage anomaly (TWSA) in the arid and semi-arid Mongolian Plateau. It reveals that vegetation greening intensifies groundwater depletion by reducing soil moisture recharge, while limited deep subsurface water recharge restricts vegetation greening, emphasizing the critical role of subsurface water in restoration efforts.
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Bidabadi et al. (2026) Spatial risk assessment of drought-induced operational failures in interconnected irrigation canals: application to the Mahyar–Jarghooye district, Iran
This study develops a stakeholder-scale, map-based risk assessment framework to evaluate drought-induced operational failures in interconnected irrigation canals under water shortages (WS) and inflow fluctuations (IF). Applied to the Mahyar–Jarghooye district in Iran, it generates spatial vulnerability, consequence, and risk maps to identify hotspots and inform management.
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Gyamfi et al. (2026) Physics-informed spatio-temporal graph neural networks for evapotranspiration prediction: Case of the Korean Peninsula
This study develops a physics-informed spatio-temporal graph neural network for evapotranspiration prediction across the Korean Peninsula, integrating climate variables, soil moisture, and a surface energy-balance constraint. The model demonstrates strong skill, particularly under dry conditions, and projects substantial increases in evapotranspiration under future climate scenarios, highlighting increasing evaporative demand and water stress.
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You et al. (2026) Quantifying climate-induced cascading effects on runoff in a cold region using a glacier-enhanced Budyko framework
This study introduces a novel Budyko-based attribution framework, integrating glacier mass balance and ridge regression, to comprehensively separate the impacts of direct climate change, cascading climate effects, and human activities on runoff in cold regions. Applied to an alpine watershed, the framework robustly quantified these drivers, revealing an antagonistic effect where direct climate increased runoff while cascading effects and human activities suppressed it.
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Li et al. (2026) Weakening vegetation control on global terrestrial evapotranspiration in a warmer world
This study quantifies future changes in the sensitivity of terrestrial evapotranspiration (ET) to leaf area index (LAI) under projected warming scenarios, finding that vegetation control on ET will weaken globally by 2100 due to reduced stomatal conductance outweighing CO₂ fertilization. This weakening will diminish LAI-driven evaporative cooling, leading to enhanced water conservation.
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Besnier et al. (2026) Crossing the Threshold: Land Cover Change Triggers Hydrological Regime Shift in Brazil’s Itaipu Hydropower Region
This study investigates hydrological transitions and their statistical associations with land cover changes in the Itaipu study region from 2002 to 2023. It identifies a significant basin-wide shift in Terrestrial Water Storage Anomalies (TWSAs) in mid-2009, strongly coupled with agricultural expansion and land cover changes, leading to increased runoff generation.
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HONG et al. (2026) Characteristics and influencing factors of soil moisture memory across mainland China
This study utilized 2,218 daily in situ soil moisture observations across mainland China to characterize the spatial patterns and influencing factors of soil moisture memory (SMM) using an exponential drydown model, highlighting its heterogeneity and discrepancies with satellite/reanalysis products.
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Wang et al. (2026) Modeling Climate Change Impacts on Large and Small Lakes of the Tibetan Plateau: Responses and Drivers
This study evaluates the FLake model's performance in simulating thermal structure and heat fluxes in large and small lakes on the Tibetan Plateau using in situ observations. It finds that the model generally reproduces seasonal variations but underestimates diurnal amplitudes, and that long-term warming trends are primarily driven by downward longwave and shortwave radiation and air temperature.
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Nana et al. (2026) Assessing the ability of the ECMWF seasonal prediction model to forecast extreme September–November rainfall events over Equatorial Africa
This study assesses the European Centre for Medium-Range Weather Forecasts seasonal prediction system 5.1 (ECMWF-SEAS5.1) in forecasting extreme September–November (SON) rainfall events over Equatorial Africa (EA). It finds that the model generally reproduces observed rainfall patterns and teleconnections with tropical sea surface temperatures well, with better skill for September initial conditions, but tends to underestimate the magnitude of extreme events and shows limitations in representing certain atmospheric features at longer lead-times.
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Rehbein (2026) Reconstructing nineteenth-century Danube river water levels with transformer-based computer vision
This study developed a semi-automated workflow using transformer-based computer vision to convert nineteenth-century hand-drawn Bavarian Danube gauge charts into daily water-level series. The method achieved high accuracy (mean composite score 0.979) across three representative gauges while reducing manual effort by an order of magnitude, providing openly available, transparently documented historical hydrological data.
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Li et al. (2026) Ecological flow guarantee rate along the Xijiang River mainstream at different scales based on multiple probability distributions
This study quantifies ecological flow guarantee rates along the Xijiang River mainstream by reconstructing quasi-natural runoff using a random forest model and applying a probabilistic framework with multiple distribution functions. It finds that ecological flow guarantee rates decreased during the change period, particularly in the upper reaches and during the flood season (July to October), identifying these as priority areas and sensitive periods for ecological flow management.
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Lin et al. (2026) Is the recently enhanced mesoscale convective systems in East Asia due to global warming or decadal variability?
This study investigates the drivers of increased mesoscale convective system (MCS) precipitation in the Yangtze River Basin, finding that decadal variability, primarily through increased MCS frequency, is the main cause, with anthropogenic warming playing a smaller, reinforcing role.
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Li et al. (2026) Spectral albedo, vegetation greenness, and radiative forcing responses of the Amazon to drought and wet conditions from 2005 to 2016
This study investigates the responses of spectral albedo, vegetation greenness, and albedo-driven radiative forcing to drought and wet conditions in the Amazon (2005-2016) across evergreen broadleaf forest, grassland, and savannas. It finds that visible and shortwave albedo negatively correlate with wetness over grasslands and savannas, while evergreen broadleaf forests show less pronounced and more complex responses, with significant implications for surface radiative forcing.
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Dutta et al. (2026) Correction: Effect of doppler radar reflectivity and radial velocity assimilation on lightning and rainfall prediction of a severe thunderstorm over Odisha, India
This document is a correction notice for an article that investigated the impact of assimilating Doppler radar reflectivity and radial velocity data on the prediction of lightning and rainfall associated with a severe thunderstorm over Odisha, India.
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Martí et al. (2026) Implementation of a dry surface layer soil resistance in two contrasting semi-arid sites with SURFEX-ISBA V9.0
This study evaluates and improves the SURFEX-ISBA V9.0 land surface model's estimation of latent heat fluxes in semi-arid environments by implementing a dry surface layer (DSL) soil resistance. The DSL resistance successfully reduced the overestimation of bare soil evaporation, leading to a 29% to 32% reduction in the daily Root Mean Square Error of latent heat flux at two contrasting sites.
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Elsahabi et al. (2026) Evaluating evaporation and seepage losses in lakes using sentinel images and the water balance equation
This study assessed evaporation and seepage losses in Aswan High Dam Lake (AHDL) by integrating Sentinel-3 imagery, field data, and the water balance equation, demonstrating the method's reliability for estimating these water losses and evaporation rates.
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Ethaib et al. (2026) Water Resources in South of Iraq: Current State, Future Evolutions, Challenges, and Potential Solutions
This paper provides an in-depth review of the factors contributing to the severe water resources crisis in southern Iraq, analyzing the current situation using indicators like marsh water availability, Shatt al-Arab salinity, and cultivated area, and identifying key challenges and potential solutions.
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Li et al. (2026) A tailored deep learning method to improve spatial rainfall downscaling
This study developed a tailored deep learning model, RM-ResNet, incorporating a spatial correction algorithm to downscale satellite rainfall data from 8 km to 1 km resolution. The method successfully improved the representation of rainfall spatial patterns, including extreme events and storm centers, demonstrating consistency with observations.
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Zhao et al. (2026) A novel hybrid approach for enhancing precipitation data fusion: Bayesian and geographical regression integration for hydrological applications
This study proposes and validates a novel three-stage hybrid precipitation fusion framework, integrating Mixed Geographically Weighted Regression (MGWR) and Bayesian Three-Cornered Hat (BTCH) methods, to generate high-quality, high-resolution precipitation data. The "Correct-then-Combine" (MGWR-BTCH) pathway significantly improved precipitation accuracy and hydrological utility in the data-sparse Shahe Basin.
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Rivoire et al. (2026) Identification of hydro-meteorological drivers for forest low greenness events in Europe
This study presents a novel, large-scale, spatially explicit analysis of forest browning drivers across Europe using a random forest modeling framework. It identifies key hydro-meteorological predictors, including warm and dry spring/early summer conditions and multi-year soil moisture and temperature anomalies, as crucial for explaining low Normalized Difference Vegetation Index (NDVI) events.
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Manspeizer et al. (2026) Tracking a semi-arid Eastern Mediterranean ecotone through integration of terrestrial and atmospheric earth observation data (2000–2024)
This study developed an Earth observation method to monitor semi-arid Eastern Mediterranean shrublands in relation to climate change and subtropical high migration, finding a 13.3% decrease in aridity and a doubling in the rate of atmospheric water vapor increase between 2014 and 2024. It proposes that ecological equilibrium occurs at plagioclimax following disturbance, challenging traditional succession theories.
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Chen et al. (2026) Comparative analysis of high-resolution GCMs and RCMs ensembles in simulating and projecting compound extreme events in China
This study compares high-resolution CMIP6 Global Climate Model (GCM) and CMIP5 Regional Climate Model (RCM) multi-model ensembles for simulating and projecting Compound Extreme Heat-Precipitation Events (CHPEs) over China, finding that GCM ensembles generally demonstrate better capability in reproducing these events.
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Mohammed et al. (2026) Performance evaluation of CMIP6 climate models for rainfall and erosivity in the thamirabharani basin, India
This study evaluates the performance of 35 CMIP6 Global Climate Models (GCMs) for rainfall and erosivity in the Thamirabharani River Basin, India, identifying the best-performing models through a multi-criteria decision-making framework. The research projects significant increases in seasonal and annual rainfall (up to 93.3%) and rainfall erosivity (up to 71.7%) by the end of the century under high-emission scenarios, implying heightened risks for soil erosion and water resource management.
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Yang et al. (2026) Assessment of Hybrid Grey-Green Infrastructure for Waterlogging Control and Environmental Preservation in Historic Urban Districts: A Model-Based Approach
This study developed a quantitative assessment framework using a 1D-2D hydrodynamic model for a historic urban district to evaluate waterlogging risks and proposed a hybrid grey-green infrastructure (HGGI) system that effectively reduces waterlogged areas while minimizing intervention in cultural heritage.
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Aboelnour et al. (2026) Mapping tomorrow’s flood: a probabilistic, equity-centered risk assessment for the Indianapolis Metropolitan Area
This study develops a high-resolution, probabilistic framework to map current and future urban flood risk in the Indianapolis Metropolitan Area by integrating stochastic precipitation, surface runoff, and a Composite Flood Risk Index (CFRI) that includes social poverty vulnerability and exposure. It finds that climate change will significantly intensify and redistribute flood risk, increasing very-high CFRI zones sevenfold by century's end, especially in previously low-risk suburban areas.
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Hacker et al. (2026) Multidecadal reconstruction of terrestrial water storage changes by combining pre-GRACE satellite observations and climate data
This study reconstructs multidecadal terrestrial water storage anomalies (TWSA) for global land from 1984 to 2020 by optimally combining pre-GRACE geodetic satellite observations (SLR and DORIS) with climate data-driven regression models, providing a long-term consistent dataset (TWSTORE) for climate change attribution and hydrological studies.
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Li et al. (2026) Evaporation, surface energy balance, and water-heat-salt transport under saline shallow groundwater: Lysimeter and modeling insights across soil textures
This study investigated the coupled water, heat, and salt transport in two soil textures (silt loam and sand) under shallow saline groundwater and natural conditions using field lysimeters and numerical modeling, revealing texture-dependent salt precipitation patterns that influence evaporation resistance and soil temperature.
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Adnan et al. (2026) Assessing the transferability of LSTM-based streamflow models under varying source basin diversity and target data availability (Mangla Basin, Pakistan)
This study evaluates the transferability of LSTM-based streamflow models in the data-scarce Mangla Basin, Pakistan, demonstrating that transfer learning significantly improves predictions, especially with limited local data, though its advantage lessens as local data availability increases.
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Hancock et al. (2026) 21st century hydrological trends in the Mississippi River basin intensify the east to west moisture gradient
This study validates 19 CMIP6 models against historical observations to project future monthly hydroclimate changes in the Mississippi River system under the SSP3-7.0 pathway. It finds consistent increases in precipitation but decreases in soil moisture due to enhanced evaporative demand, with highly divergent and regionally varied trends for runoff and discharge driven by large-scale atmospheric and oceanic patterns.
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Lei et al. (2026) Analysis of the Impact of Water Conservancy Projects on Water Resource Use Efficiency and Vegetation Net Primary Productivity in an Arid Inland Basin
This study investigated the mechanisms by which ecological water conveyance impacts Net Primary Productivity (NPP) in the Aiding Lake Basin, finding that despite an overall declining trend in mean annual NPP, water conveyance significantly and positively enhanced regional vegetation productivity.
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Ngai et al. (2026) Diurnal rainfall variability over the Maritime Continent: Evaluation and future projections from CORDEX-SEA simulations
This study systematically evaluates present-day diurnal rainfall simulations over the CORDEX-SEA domain and projects future changes under the RCP8.5 scenario, finding that regional climate models significantly improve the simulation of diurnal rainfall characteristics and project a widespread weakening of amplitude over land and reduced coastal propagation in the late 21st century.
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Ye et al. (2026) Leveraging water vapor to extend forecast horizons for forecast-informed reservoir operations: a vapor-precipitation-streamflow three-line defense
This study proposes a vapor-precipitation-streamflow (VPS) Forecast-Informed Reservoir Operations (FIRO) scheme that leverages precipitable water vapor (PWV) to extend forecast horizons. The VPS-FIRO scheme enables earlier prerelease operations, reducing spilled water volume by 5.6% and decreasing the duration of excessive outflow from 1% to 0.3% compared to traditional precipitation-streamflow (PS) FIRO.
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Harley et al. (2026) California Temperature Since 1520 CE Shows Interactions in Extremes of Heat, Drought, and Fire
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Bäthge et al. (2026) A Global-Scale Time Series Dataset for Groundwater Studies within the Earth System
This paper introduces GROW, a global-scale, quality-controlled dataset integrating over 200,000 groundwater depth and level time series with 36 associated Earth system variables to facilitate understanding of groundwater dynamics and model evaluation. It provides an analysis-ready foundation for studying large-scale groundwater processes in space and time within the Earth system.
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Wang et al. (2026) Toward drivers of the interannual variability of warm-season extreme rainfall over the Bohai Rim, China
This study investigates the climatic drivers and physical mechanisms for interannual variations in warm-season rainfall extremes over the Bohai Rim (BHR) region of China from 1979 to 2022. It reveals that increased extreme rainfall is primarily driven by a zonally oriented dipole circulation pattern and a significant lagged influence of El Niño-like Pacific sea surface temperature warming, which induces a subtropical Western North Pacific Subtropical High (WNPSH)-resembling anomaly gyre.
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Liu et al. (2026) Optimality-Based Active Region Model (ARM) for Fingering Flow in the Vadose Zone: Recent Theoretical Progress
This paper presents the latest theoretical developments of the optimality-based active region model (ARM), a macroscopic framework designed to accurately describe gravitational fingering flow in the vadose zone, by providing an updated mathematical derivation and extending it to a dual-flow field model for enhanced rigor and realism.
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Maftei et al. (2026) Ecohydrology in the Context of Climate Change: Strategies for Management, Monitoring, and Modeling
This editorial synthesizes research from a Special Issue on ecohydrology, focusing on strategies for management, monitoring, and modeling in the context of climate change, highlighting advancements in understanding hydroecological coupling and adaptive resource governance through technological convergence and analytical innovation.
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Wang et al. (2026) Beyond Annual Averages: Multi‐Scale Rainfall Variability, Drought Indicators, and Seasonal Shifts Under a Changing Climate
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Singh et al. (2026) Projected intensification of precipitation extremes in the Kosi Basin using CMIP6 models
This study evaluates and ranks thirteen statistically downscaled CMIP6 models for reproducing eight ETCCDI precipitation indices over the Kosi River Basin, identifying an optimal eight-member ensemble (AMME8) that projects a significant intensification of precipitation extremes under future warming scenarios.
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Shu et al. (2026) High-resolution urban flooding inundation forecasting through hydrodynamic interaction and multimodal deep learning
This research proposes a multimodal deep learning model (SMDFN) tightly coupled with a hydrological-hydrodynamic model to improve high-resolution urban pluvial flooding inundation forecasting by capturing hydrodynamic interactions and enabling multimodal feature extraction. The model demonstrates superior performance, reducing RMSE by 13.8%, and offers a multi-region collaborative forecasting solution.
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Arbai et al. (2026) Projected Annual and Monsoonal Precipitation Trends of CMIP 6 Over P eninsular M alaysia
This study investigated historical (1973–2022) and projected (2051–2100) precipitation trends over Peninsular Malaysia using ground-based records and CMIP6 GCMs, revealing spatially heterogeneous patterns influenced by monsoons, with future projections indicating modest increases under SSP2-4.5 but widespread declines under SSP5-8.5.
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Tsiros et al. (2026) Variability of the Climate in Athens‐Greece Over the Last 165 Years of the Period 1858–2023: An Assessment Based on Thornthwaite's Climate Classification and Relevant Indices
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Liang et al. (2026) WetFramework: A deep learning framework for coastal wetland boundary extraction and inundation frequency estimation
This paper introduces WetFramework, a novel deep learning framework that integrates Transformer, Mamba, and wavelet transforms to accurately extract coastal wetland boundaries and quantitatively estimate inundation frequency at microscales, demonstrating superior performance and generalization across diverse coastal regions.
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Ma et al. (2026) Response of sediment delivery ratio to water-sediment and riverbed boundary conditions during flood events in the lower yellow river since 2000
This study investigates the response of the sediment delivery ratio to water-sediment and riverbed boundary conditions in the Lower Yellow River since 2000, developing a theoretical equation that incorporates riverbed characteristics for improved accuracy in predicting sediment transport capacity during flood events. The findings highlight the crucial role of riverbed boundaries and offer practical recommendations for river management.
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Li et al. (2026) Deriving phase-contingent dynamic drought-limited water levels: An adaptive framework for managing megadrought evolution
This study develops an adaptive framework to derive dynamic, phase-contingent Drought-Limited Water Levels (DLWLs) for managing megadroughts in reservoirs, addressing the limitations of static thresholds. It demonstrates that a supervised Random Forest model, anchored in physically constrained hydrological benchmarks, reliably classifies drought severity across four evolutionary phases, enabling improved, resilient reservoir operation.
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Xu et al. (2026) Time-lag and cumulative drought effects decouple vegetation sensitivity from damage risk in the upper Yangtze River basin
This study analyzed vegetation response to drought in the upper Yangtze River basin (1990-2022) using NDVI and multi-scale SPEI, developing a composite drought sensitivity index and quantifying loss risk with a Copula-Bayes framework, revealing that drought sensitivity does not always align with actual vegetation loss probability.
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Wang et al. (2026) Shifts in the Decoupling and Driving Mechanisms of Grassland Greening and Water Availability in the Northern Hemisphere
This study systematically assessed the spatiotemporal evolution and trend divergence of grassland greening (leaf area index, LAI) and water availability (WA) across the Northern Hemisphere from 2000 to 2100, revealing a historical widespread decoupling (greening with declining WA) that is projected to reverse in the future, with shifts in dominant climatic drivers.
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Baker (2026) Identification of synoptic climate and drought controls on rainfall stable water isotopic composition in the Macleay karst region of eastern Australia
This study investigates the stable water isotopic composition of precipitation, karst springs, and rivers in the Macleay region of eastern Australia to understand the influence of synoptic climate and drought on rainfall isotopes and to estimate groundwater recharge thresholds. It found that offshore low-pressure systems deliver isotopically depleted rainfall, which preferentially recharges groundwater, with a daily recharge threshold estimated between 11 mm and 40 mm.
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Miao et al. (2026) SM2RAIN–dual: a global rainfall fusion product derived from multi-source satellite soil moisture observations
This study addresses regional disparities in SM2RAIN-derived rainfall estimates by developing a rainfall data fusion scheme using multi-source satellite soil moisture products. The research generated a global, more reliable rainfall product called SM2RAIN–Dual, which combines SMAP and ASCAT data.
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Dong et al. (2026) Surface Soil Moisture Drydown over the Tibetan Plateau from SMAP: Consistency with In Situ Observations, Spatial Patterns and Controls
This study evaluates the consistency of SMAP satellite-derived surface soil moisture drydown timescales (τ) with in situ observations over the Tibetan Plateau, maps its spatial patterns, and identifies dominant environmental controls. It finds that SMAP systematically yields shorter drydown timescales than in situ measurements, primarily due to differences in effective sensing depth and spatial representativeness, with τ exhibiting a clear southeast-to-northwest gradient driven by elevation, soil sand fraction, and vegetation.
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Goodman et al. (2026) Modeling cumulative hydrologic effects of multiple floodplain restoration projects in a 4th-order river channel network
This study used HEC-RAS to evaluate the cumulative hydrologic effects of floodplain restoration projects on flood propagation in a generic 4th-order river channel network. It found that flood attenuation generally increased with restored channel length, but project location and existing restoration significantly influenced benefits, sometimes even exacerbating flooding due to peak flow synchronization.
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Esmond et al. (2026) A multi-tracer approach to constraining water sources of culturally and ecologically significant natural springs: Combining environmental isotopes and environmental DNA
This study developed an eco-hydrogeological approach, integrating geochemical tracers (hydrochemistry, stable and radio-isotopes) with environmental DNA (eDNA), to create a robust conceptual model of groundwater flow paths and water sources for Great Artesian Basin springs in Carnarvon Gorge, Australia. The findings revealed that vertical inter-aquifer flow and multiple recharge zones control spring dynamics, with eDNA proving more sensitive than isotopes in distinguishing hydraulically separated flow paths and recharge areas.
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Wen et al. (2026) Preferential flow reduces overland flow on slopes: insights from a field experiment on the Chinese Loess Plateau
This study investigated how preferential flow influences slope runoff under various vegetation restoration conditions on the Chinese Loess Plateau, revealing that vegetation restoration significantly increases preferential flow, thereby reducing overland flow.
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Kröcher et al. (2026) Monitoring changes in the landscape water balance: validation of satellite- and model-based evapotranspiration data in Lusatia, Germany
This study systematically validates three satellite- and model-based evapotranspiration (ET) products (CERv2, MODIS, Landsat) against long-term in situ measurements in Lusatia, Germany. It finds that while all products consistently capture spatio-temporal ET patterns, their accuracy in absolute ET values varies significantly, particularly under water-limited conditions.
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Dobrovolný et al. (2026) Spatiotemporal Changes in Precipitation Concentration in the Atlantic‐European Region
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Bagheri et al. (2026) RetroSight and ForeSight ensemble model (ReForM) for improved time series prediction: A case study on river temperature prediction
This study introduces ReForM, a novel data-driven and physics-informed ensemble model that leverages both historical data and future physics-based simulations to significantly improve time series predictions. Applied to river temperature forecasting, ReForM demonstrates superior accuracy, especially for long-term predictions, outperforming state-of-the-art machine learning benchmarks.
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Adhikari et al. (2026) Design of stormwater bioretention systems for improved volume and peak runoff reduction
This study investigated 54 bioretention system design combinations using a calibrated SWMM model to optimize hydrologic performance for both common and intense rainfall events. It found that a storage connection consistently improved performance, while higher filter media fractions enhanced volume reduction during common events, and lower fractions were more beneficial for reducing overflows during high-intensity rainfall.
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Chen et al. (2026) Combined effect of tides, irregular waves and beach recovery on groundwater flow and marine-sourced salt transport in coastal unconfined aquifers
This study numerically investigates the combined impact of tides and irregular waves on groundwater dynamics and marine-sourced salt transport during beach recovery. It reveals that wave action, particularly overtopping waves, significantly amplifies subsurface mixing and solute transport, leading to substantial increases in intertidal saline infiltration and submarine groundwater discharge.
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Wang et al. (2026) Projected Future Changes of Atmospheric Rivers by a High- and Low-resolution CESM
This study evaluates atmospheric river (AR) simulations and projections using high-resolution (HR) and low-resolution (LR) Community Earth System Model (CESM), finding that LR CESM systematically underestimates AR frequency, intensity, and precipitation, particularly for extreme events, while HR CESM significantly improves historical simulations and provides more robust projections of increased extreme ARs under warming.
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Yuan et al. (2026) Integrating ecosystem adaptability into drought resilience assessment: a case study of the Yellow River Basin, China
This study developed an integrated framework to assess ecosystem drought resilience by incorporating adaptability as a third dimension alongside resistance and recovery. Applying this framework to the Yellow River Basin (1982–2017), the research found opposite trends and trade-offs between resistance and recovery, with overall resilience increasing over time.
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Zhou et al. (2026) A theoretical index for understanding distinct land relative humidity trends in observations, reanalyses, and models
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ADOMBI (2026) DeepDiscover: towards autonomous discovery of bucket-type conceptual models – a proof of concept applied to hydrology
This study introduces DeepDiscover, a physics-embedded machine learning framework designed to autonomously infer bucket-type conceptual hydrological models from data. It demonstrates the feasibility and superior predictive performance of this approach compared to traditional benchmarks, reducing reliance on expert-defined model formulations.
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Wu et al. (2026) A two-level attribution method for water resource changes based on water budget balance and distributed simulation
This study develops and applies a novel two-level attribution method for water resource changes, integrating water balance principles and a distributed human-water relationship model. The method effectively clarifies the driving mechanisms of water resource changes across scales, revealing that climatic factors dominated runoff changes in the Qin River Basin (2001–2022), while human activities had complex effects on natural and actual runoff.
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Programme (2026) Global Groundwater Vulnerability Map to Floods and Droughts
This paper presents the "Global Map of Groundwater Vulnerability to Floods and Droughts," a dataset indicating the intrinsic vulnerability and resistance of global groundwater resources to natural disasters. It serves as a crucial tool for emergency management and global water resource discussions.
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Ma et al. (2026) The qualitative and quantitative relationship between the spatiotemporal variations of potential evapotranspiration and meteorological variables in the Hexi corridor, Northwest China
This study investigated the spatiotemporal variations of potential evapotranspiration (ET0) and its response to meteorological factors in the Hexi Corridor, Northwest China, from 1960 to 2019, finding that ET0 showed a fluctuating increase primarily driven by increasing mean temperature.
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Mhanna et al. (2026) Hydrological and ecological consequences of the Kakhovka dam collapse
This study assesses the cascading hydrological impacts of the Kakhovka dam destruction in June 2023, revealing significant decreases in total water storage, amplified river variability, episodic flooding, and long-term challenges for re-emerging wetlands due to groundwater decline.
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Yang et al. (2026) Quantitative assessment impact of anthropogenic heat flux on global urban evapotranspiration retrieval at multiple temporal scales
This study quantified the global impact of anthropogenic heat flux (AHF) on urban evapotranspiration (ET) estimation by comparing remote sensing-derived ET with and without AHF across 668 cities worldwide. It found that neglecting AHF leads to significant ET underestimation, particularly in cold regions and megacities, with discrepancies peaking in summer.
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Sîrbu et al. (2026) Short-Term Streamflow Forecasting for River Management, Using ARIMA Models and Recurrent Neural Networks
This study conducts a controlled comparison between SARIMA and stacked LSTM models for 7-day-ahead daily water-depth forecasting using synthetic hydrographs across normal, drought, and flood regimes, concluding that both approaches exhibit statistically comparable median performance.
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Zhang (2026) Modulating Effects of Soil Thickness on the Spatiotemporal Evolution of Hydrological Connectivity in Heterogeneous Karst Hillslopes
This paper investigates how soil thickness influences the spatiotemporal evolution of hydrological connectivity within heterogeneous karst hillslopes.
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Luo et al. (2026) PSiam-HDSFNet: A Pseudo-Siamese Hybrid Dilation Spiral Feature Network for Flood Inundation Change Detection Based on Heterogeneous Remote Sensing Imagery
This paper proposes a novel pseudo-Siamese hybrid dilation spiral feature network (PSiam-HDSFNet) to improve flood change detection accuracy from heterogeneous SAR and optical remote sensing images, specifically addressing challenges in distinguishing small ground objects from actual inundated regions. The method significantly enhances change detection accuracy, with F1 scores improving by up to 7.704% compared to suboptimum methods.
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Dou et al. (2026) Analysis of the performance of a virtual gauge-based method in hydrological modeling of basins with no precipitation stations
This study evaluates the hydrological performance of the virtual gauge-based method (VG) for flood forecasting in basins without precipitation stations, demonstrating that VG-driven simulations achieved up to approximately 50% higher flood volume prediction accuracy and superior flood simulation capabilities compared to traditional methods.
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Taylor et al. (2026) Wind shear enhances soil moisture influence on rapid thunderstorm growth
This study reveals that wind shear significantly enhances the influence of soil moisture (SM) contrasts on the rapid growth of thunderstorms, particularly for extreme events, by modulating mesoscale circulations that promote deep convection. Analyzing 2.2 million afternoon events across sub-Saharan Africa, the authors found 68% more extreme initiations under favorable soil conditions when wind shear was strong.
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Cerbelaud et al. (2026) Wide-swath altimetry maps bank shapes and storage changes in global rivers
This study leverages the first water year of the Surface Water and Ocean Topography (SWOT) mission to provide near-global observations of active river channel geometry and monthly water storage changes across 126,674 river reaches, revealing a global annual river storage variability of 313.1 ± 129.5 km³, which is approximately 28% lower than the lowest previously modelled estimates.
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Ferguson (2026) Do wet or dry soils trigger thunderstorms? It depends on how the wind blows
This News & Views article discusses how the interaction between soil moisture and vertical wind shear significantly influences thunderstorm initiation, enabling more precise short-term forecasting of intense storms.
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Li et al. (2026) Modeling Land Use Dynamics under Climate and Hydrological Changes: An Integrated Hydro–Land Framework
This paper introduces LaHyFr, a cascaded land-hydrology coupled modeling framework, to simulate bidirectional soil-water interactions and land-use dynamics under climate change, demonstrating significantly improved simulation accuracy in the Hanjiang River Basin.
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Haberlandt et al. (2026) Assessing the maximization potential for historical floods by spatio-temporal simulation of precipitation
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Medina‐Roldán et al. (2026) Comparison of National and Regional Assessments of Soil Loss Rates by Water Erosion and Soil Erosion Control: An Application to the Tuscany Region (Italy)
This study compares regional and European-scale Revised Universal Soil Loss Equation (RUSLE) applications for Tuscany, Italy, revealing that regional high-resolution data estimates significantly higher soil erosion rates and better identifies high-risk areas compared to broader European datasets.
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Yang et al. (2026) 20 years of trials and insights: bridging legacy and next generation in ParFlow and Land Surface Model Coupling
This study reviews two decades of ParFlow-land/atmosphere coupled modeling, presents a renewed recoupling of ParFlow with the updated Common Land Model (CoLM) demonstrating improved performance, and proposes a sustainable coupling framework and a community-led model intercomparison project (PLCMIP) for future development.
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Merlo et al. (2026) Tracking shifts in European drought hotspots
This study develops novel impact-based Combined Drought Indices (iCDIs) using a machine learning framework to directly link hydroclimatic drivers to remotely sensed vegetation stress across Europe. The iCDIs outperform traditional indices and project a significant northward shift in future drought impacts, identifying Central Europe as an emerging hotspot, contrary to conventional views.
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Sun et al. (2026) Land use and land cover change intensified soil moisture drought: evidence from CMIP6-LUMIP
This study quantifies the long-term impacts of historical land use and land cover change (LULCC) on global soil moisture drought (SMD) characteristics from 1901-2014, finding that LULCC significantly intensifies SMD over more than half of the global land area by altering surface energy partitioning and depleting soil water storage.
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Kwon et al. (2026) Synergistic impact of simultaneously assimilating radar- and radiometer-based soil moisture retrievals on the performance of numerical weather prediction systems
This study evaluates the synergistic impact of simultaneously assimilating radar-based (ASCAT) and radiometer-based (SMAP) soil moisture retrievals into the Korean Integrated Model (KIM) using a weakly coupled data assimilation system. The findings demonstrate that multi-sensor soil moisture assimilation leads to more balanced and improved analyses and forecasts of specific humidity, air temperature, and precipitation compared to single-sensor assimilation.
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Zhang et al. (2026) Long-Term Evolution of Permafrost across the Qinghai-Tibet Plateau: Perspectives from Multi-Model Ensembles and Machine Learning
This study combined CMIP6 data with machine learning models to project permafrost extent and maximum seasonal soil freeze depth (SFD) across the Qinghai-Tibet Plateau (QTP) from 2025 to 2100 under various SSP scenarios. Results indicate continuous permafrost degradation into seasonally frozen ground, with SFD declining significantly, and specific high-risk zones identified, with the Deep Neural Network (DNN) model demonstrating superior performance.
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Ravazzolo et al. (2026) Towards integrated short-term Rain-on-Grid modeling and long-term RUSLE estimates for improved erosion susceptibility assessment in the Oltrepò Pavese hills of Northern Italy
This study evaluates the complementary use of the empirical Revised Universal Soil Loss Equation (RUSLE) and a two-dimensional Rain-on-Grid (RoG) hydrodynamic model for erosion susceptibility assessment in Northern Italy. The models showed over 50% spatial overlap in identifying erosion-prone areas, with RoG better reproducing event-based erosion zones and RUSLE capturing land-cover effects, offering a practical integrated framework for data-scarce catchments.
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Bi et al. (2026) A 0.1° monthly potential evapotranspiration dataset based on the optimal models over global vegetation zones
This study developed a global 0.1° monthly potential evapotranspiration (PET) dataset for 1992–2022 by calibrating and selecting optimal PET models (Priestley-Taylor and Milly-Dunne) using observations from 124 eddy covariance sites, aiming to reduce uncertainties in existing PET products.
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Eliades et al. (2026) Forests in a semi-arid climate die with a memory: satellite signals predict forest mortality years after drought
This study investigates the relationship between satellite-derived vegetation indicators and meteorological drought indices to understand tree mortality mechanisms in semi-arid Cypriot forests, revealing that severe drought conditions trigger mortality and that vegetation response is linked to multi-year climate memory effects, with indicator effectiveness varying by species and post-mortality stage.
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zhang (2026) CATENA data
This entry describes the CATENA dataset, which provides volumetric water content data, categorized under 'Vegetable' and 'Moisture'.
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Athukoralalage et al. (2026) The impact of a mega-flood event on the water quality of the southern Murray-Darling Basin, Australia
This study investigated the impact of a 2022–2023 mega-flood and five other major flow events on Total Nitrogen, Total Phosphorus, and Dissolved Organic Carbon dynamics in the southern Murray-Darling Basin, Australia. It found that the mega-flood significantly increased nutrient loads and prolonged water quality degradation, particularly in downstream areas due to extensive floodplain inundation and delayed nutrient release.
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Sellami (2026) Spatiotemporal Droughts Propagation and Direct Driving Variables Under Climate Change Projections: A Case Study of Tunisia
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Aich et al. (2026) Conditional diffusion models for downscaling and bias correction of Earth system model precipitation
This paper introduces a machine learning framework utilizing conditional diffusion models for simultaneous bias correction and downscaling of Earth System Model (ESM) precipitation. The approach outperforms existing statistical and deep learning methods, particularly for extreme events, by improving spatial structure and statistical fidelity while preserving climate change signals.
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Wang et al. (2026) A wetland partitioning method based on the hydrological connectivity and the underlying causes of their occurrence
This study developed a wetland partitioning method based on hydrological connectivity using hydrodynamic modeling and clustering, demonstrating its effectiveness in delineating subareas in the Zhalong Wetland and revealing how human activities and topography influence connectivity across different hydrological years.
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Ling et al. (2026) An improved Hydrology-Informed attention LSTM(HIA-LSTM) model for runoff simulation with seasonal snowmelt
This study proposes a Hydrology-Informed Attention LSTM (HIA-LSTM) that embeds physical inductive biases into its neural architecture to improve runoff simulation in alpine basins with complex cryospheric processes. The HIA-LSTM significantly outperforms conventional deep learning models, achieving superior accuracy and interpretability, especially in melt-driven runoff scenarios.
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Alsumaiei (2026) Complexity-efficiency dynamics of metaheuristic-optimized recurrent neural network models for drought forecasting in hyper-arid Kuwait
This study develops and benchmarks metaheuristic-optimized recurrent neural network models (LSTM, GRU) for drought forecasting in hyper-arid Kuwait using the distribution-free Precipitation Index (PI12, PI24), finding that longer aggregation windows enhance stability and that compact architectures often achieve comparable accuracy to more complex, optimized models with greater efficiency.
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Chen et al. (2026) Contrary effects of soil moisture-atmosphere feedback on dry and humid heatwaves
This study investigates the distinct impacts of soil moisture-atmosphere feedback (SAF) on dry and humid heatwaves globally, revealing that SAF amplifies dry heatwaves but has spatially divergent effects on humid heatwaves, reducing their severity in low-to-mid latitudes while intensifying them in high latitudes. These contrary effects are primarily driven by the competition between SAF-induced thermal warming and moisture depletion, which modulate mean wet-bulb temperatures.
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Yang et al. (2026) High-resolution mapping of saturated soil hydraulic conductivity across China’s drylands
This study developed a novel machine learning approach integrating multi-sensor Sentinel-1/2 remote sensing data and environmental covariates to generate high-resolution (90 m) saturated soil hydraulic conductivity (Ks) maps across China's drylands, demonstrating superior accuracy and spatial detail compared to existing datasets.
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Liu et al. (2026) Determination of Suitable Ecological Intervals for Arid Terminal Lakes via Multi-Source Remote Sensing: A “Morphometry–Security–Efficiency” Framework Applied to Ebinur Lake
This study develops a novel framework integrating morphometric stability, ecological security reliability, and resource use efficiency to define the suitable ecological interval for Ebinur Lake, revealing a significant shrinking trend and proposing a "Spring Surplus and Autumn Deficit" water regulation strategy to optimize ecosystem services.
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Khan et al. (2026) A scalable framework for flash flood hazard assessment in data-scarce catchments using coupled modeling
This study developed a scalable framework for flash flood hazard assessment in data-scarce catchments by coupling HEC-HMS and HEC-RAS 2D with remotely sensed data and transposed rainfall. The framework successfully mapped flood hazards for various return periods, revealing a significant increase in extreme hazard zones from 514 hectares (2%) to 2,498 hectares (7%) between 10-year and 100-year return period floods.
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Miazza et al. (2026) Technical note: Transit times of reactive tracers under time-variable hydrologic conditions
This study derives and explores novel analytical solutions for the transit time distributions (TTDs) of reactive tracers in randomly sampled hydrological systems, demonstrating how processes like sorption, degradation, and evapotranspiration, along with input patterns, cause tracer TTDs to differ significantly from water TTDs.
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Liu et al. (2026) Mapping Synchronous Heatwaves in the Northern Hemisphere: Insights from Climate Network Analysis
This study identifies hotspot regions and dominant synchronization patterns of summertime synchronous extreme heatwaves across the Northern Hemisphere using a climate network method. It reveals connections to large-scale atmospheric circulation patterns, including Rossby waves and zonal wave trains, and highlights the role of positive soil moisture feedback in intensifying these events.
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Woreket et al. (2026) Remote sensing for estimating crop water productivity: a systematic review of concepts and methods
This systematic review synthesizes 93 studies (2020-2025) to critically examine remote sensing concepts and methods for estimating Crop Water Productivity (CWP) by analyzing approaches for crop yield and actual evapotranspiration (ETa), aiming to provide a consolidated reference for advancing CWP assessment.
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Huang et al. (2026) Reconstructing Lake Storage for the Major Water Bodies in the Aral Sea Basin Using Multi-DEM Hypsometry
This study developed a multi-digital elevation model (DEM) hypsometry framework to reconstruct near-monthly lake storage in arid zones, demonstrating its superior accuracy in recovering storage during low-level periods and hydrological disconnection compared to conventional methods.
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Premier et al. (2026) Assessing the impact of Earth Observation data-driven calibration of the melting coefficient on the LISFLOOD snow module
This study evaluates the LISFLOOD hydrological model's snow module and the impact of calibrating its snowmelt coefficient using Earth Observation (EO) snow cover fraction (SCF) data across nine European basins. It demonstrates that while EO-based calibration significantly improves snow cover representation, its impact on basin-level discharge simulations is minimal, suggesting that standard discharge-based calibration adequately captures snow dynamics for streamflow.
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Poschlod et al. (2026) Climate change effects on river droughts in Bavaria using a hydrological large ensemble
This study investigates the impact of climate change on rare and extreme river droughts in two Bavarian catchments using a unique hydrological large ensemble. It projects a drastic increase in the frequency and intensity of summer droughts, with historical 100-year events becoming significantly more common by the far future (2070–2099) under a high-emission scenario.
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Mliyeh et al. (2026) Advancing hydrological modeling in the Mediterranean: Multi-objective calibration of the SWAT+ model using open-source data and tools
This study evaluated multi-variable calibration strategies for the SWAT+ model in the Upper Oum Er Rbia watershed, Morocco, integrating streamflow and remote sensing evapotranspiration data. The multi-variable approach achieved satisfactory and balanced performance for both streamflow (NSE = 0.75, KGE = 0.77) and evapotranspiration (NSE = 0.51, KGE = 0.64), highlighting the value of open-access remote sensing ET data in refining hydrological model parameters for data-scarce regions.
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Beguería et al. (2026) Water balance components of the Pyrenees: A 30-year modelling study in a transboundary context
This study reconstructed the regional water balance for the Pyrenees over the 1981–2010 historical baseline using two contrasting hydrological models, SASER and SWAT. Results reveal strong hydroclimatic gradients and highlight evapotranspiration, recharge, and snowmelt timing as key sources of structural uncertainty, establishing the first integrated, transboundary hydrological baseline for the region.
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Mamani et al. (2026) Irrigation parameterization for the ICON model
This study developed an irrigation parameterization for the ICON model to quantify the long-term impact of irrigation on surface and atmospheric variables over the EURO-CORDEX domain. Results indicate that irrigation leads to a cooling effect, increased latent heat flux and evapotranspiration, and decreased sensible heat flux in irrigated areas.
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Wei et al. (2026) A framework for long-term vegetation latent heat estimation and forecasting combining ERA5-land and Landsat data
This study developed a globally applicable framework integrating ERA5-Land reanalysis and Landsat data with machine learning to estimate and forecast monthly vegetation latent heat (LE) at 30 m resolution from 1984 to the present. It found Random Forest performed best for estimation and proposed two forecasting frameworks, LE-ML and LE-Direct, with varying performance based on training data availability.
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Ibrahim et al. (2026) An integrated approach to unravel the deep-shallow aquifer connectivity in the Eastern Sahara
This study integrates remote sensing, geophysical, and isotopic data to investigate deep-shallow aquifer connectivity in the Eastern Sahara, revealing that significant vertical upwelling from the deep Nubian Aquifer System (NAS) to overlying shallow aquifers occurs along intersecting structural trends in southern and middle Egypt, with contributions ranging from 10% to 98%.
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Marson et al. (2026) The Explore2-2022 climate projections dataset for impact studies over France.
This paper introduces the Explore2-2022 dataset, a new set of bias-corrected regional climate projections for France, sub-sampled from the EURO-CORDEX (EUR11) ensemble and consistent with CMIP6, designed to support impact studies, particularly on water resources, and characterize climate change uncertainties.
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Shahnazi et al. (2026) A novel implementation of a decomposition-enhanced hybrid GWO–KELM model with LUBE for constructing prediction intervals of groundwater drought
This study developed a novel decomposition-enhanced hybrid Grey Wolf Optimizer (GWO)–Kernel Extreme Learning Machine (KELM) model with Lower–Upper Bound Estimation (LUBE) for multi-horizon point and interval forecasting of groundwater drought (Standardized Groundwater Index, SGI). The Variational Mode Decomposition (VMD)–GWO–KELM model consistently outperformed other approaches, especially for short-term forecasts, providing reliable and sharp prediction intervals.
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TIAN et al. (2026) Intensifying droughts, heatwaves, and compound drought–heatwave events and their spatiotemporal patterns in Africa (1979–2024)
This study systematically evaluates the spatiotemporal patterns of heatwaves, droughts, and compound drought–heatwave (CDHW) events across Africa from 1979 to 2024, revealing significant intensification of all three, with CDHWs accelerating since the 2000s, particularly in Eastern and Southern Africa.
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Häberli et al. (2026) Unprecedented extreme meteorological droughts simulated in Fenno-Scandinavia with high-resolution climate models
This study assesses future meteorological drought probabilities in Fenno-Scandinavia using high-resolution convection-permitting regional climate models (CPRCMs) and a novel multi-threshold Standardized Precipitation Index (SPI) method. It projects a decrease in moderate droughts but a significant increase in unprecedented extreme droughts, particularly during the critical growing season, highlighting the added value of CPRCMs.
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Janzing et al. (2026) Spatiotemporal Dynamics of Streamflow Drought in the Larger Alpine Region
This study analyzes the spatiotemporal dynamics of streamflow droughts across the larger Alpine region using high-resolution hydrological model simulations and a novel clustering algorithm, revealing that extensive droughts exhibit growth and recovery phases, regional differences in behavior, and are primarily driven by rainfall deficits, though often by multiple interacting processes.
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Ebrahimi et al. (2026) GIS-based assessment of groundwater suitability for agricultural irrigation in central Iran
This paper focuses on a GIS-based assessment to determine the suitability of groundwater for agricultural irrigation in central Iran. The main findings are not available in the provided pre-proof.
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Zhang et al. (2026) Can Conceptual Rainfall‐Runoff Models Capture Multi‐Annual Storage Dynamics?
This study investigated if specific structural components enable conceptual rainfall-runoff models to capture multi-annual storage dynamics during droughts. It found that models incorporating a long-term store, its disconnection from direct streamflow, and a water loss mechanism from it were significantly more successful in representing long-term hydrological memory and drought response.
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Deger (2026) Exploring Monthly, Seasonal and Annual Spatio-Temporal Variability of Temperature and Precipitation Series by Classical and Innovative Techniques
This study comprehensively analyzed the spatio-temporal variability and trends of temperature and precipitation across 12 stations in Southeastern Anatolia, Türkiye, revealing a dominant significant increasing trend in temperature and a prevalence of non-significant decreasing trends in precipitation.
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Demirbaş et al. (2026) Modelling the Impact of Climate Change on the Reservoir Filling Rates of Dams Used for Drinking Water Supply Through Artificial Neural Networks
This study models the impact of climate change on the reservoir filling rates of drinking water supply dams in Ankara, Istanbul, and Izmir, Türkiye, using Artificial Neural Networks (ANNs). It quantifies the divergence between expected and observed precipitation, revealing significant water losses and the susceptibility of these urban water systems to climate change.
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Mohomi et al. (2026) Projections of extreme rainfall in South Africa using CMIP6 ISIMIP models
This study projects extreme rainfall in South Africa using CMIP6 ISIMIP global climate models under SSP1-2.6 and SSP5-8.5 scenarios, finding an overall increase in extreme rainfall events, with a trend towards a drier west and a wetter east, posing significant risks to water resources and infrastructure.
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Adekilae et al. (2026) Pseudo‐diffusivity characteristic curves for surface–rootzone soil hydrologic connectivity
Undeterminable due to corrupted input text. The provided paper text is unreadable, consisting primarily of garbled characters, preventing extraction of its core objective and main findings.
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Martin et al. (2026) Corrigendum to “Estimating irrigation consumptive use for the conterminous United States: coupling satellite-sourced estimates of actual evapotranspiration with a national hydrologic model” [J. Hydrol. 662 (2025) 133909]
This document is a corrigendum to the original paper "Estimating irrigation consumptive use for the conterminous United States: coupling satellite-sourced estimates of actual evapotranspiration with a national hydrologic model," providing necessary corrections to author affiliations, definitions, references, numerical values, and figure/table captions.
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Fu et al. (2026) Climate change enhances the propagation from meteorological to lake drought
This study quantified the propagation time and probability from meteorological to lake droughts for 153,643 global lakes from 1985 to 2018, revealing that climate change is enhancing this propagation, particularly in arid regions and North America due to rising temperatures and vapor pressure deficit.
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Gianotti et al. (2026) Meteorological to Agricultural Drought Transitions Compounded by Heat Waves in Historical and Future Climates
## Identification - **Journal:** Water Resources Research - **Year:** 2026...
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Guo et al. (2026) Simulation and Rapid Prediction of Water Quantity and Quality Processes Based on Numerical Models and Deep Learning
This study develops a coupled 1D-2D numerical model (GAST-SWMM) to simulate urban water quantity and quality processes, generating a training database for a Long Short-Term Memory (LSTM) deep learning model. The LSTM model provides rapid and accurate predictions of pollutant concentrations on urban surfaces and within sewer networks, outperforming other machine learning models and significantly reducing computational time.
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Man et al. (2026) Multi-Target Water Demand Forecasting with Graph Neural Networks: A Comparative Study
This study systematically evaluates Graph Neural Networks (GNNs) for multi-target water demand forecasting (MTF), demonstrating their superior accuracy and robustness compared to traditional sequence-based models. Self-learning GNNs, specifically MTGNN and MTGODE, achieved enhanced accuracy and stability, particularly under data irregularities and for multi-step predictions.
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WU et al. (2026) Dominant drivers for geographic patterns and multi-scale variability of global land‒atmosphere coupling
This study systematically assesses global land-atmosphere (L-A) coupling from 1958-2022, identifying five distinct regional patterns and their multi-scale temporal variability, and determining the dominant physical drivers for each region using machine learning and process network analysis. The findings reveal that while interannual signals generally dominate L-A coupling variability, specific regions like the Hot Evaporative Region exhibit strong decadal signals, with dominant drivers varying significantly across regions and seasons.
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Radwin et al. (2026) Multispectral Surface Reflectance as an Indicator of Groundwater Depth for Salt Crust Systems: Insights From the Bonneville Salt Flats, Utah
## Identification - **Journal:** Earth and Space Science - **Year:** 2026...
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Arabacı et al. (2026) A modelling framework for simulating 50-year thermal variation under lake drying and urban expansion: insights from Ramsar lake Burdur, Türkiye
This study developed an integrated modeling framework to simulate the long-term thermal variations around Lake Burdur, Türkiye, under lake drying and urban expansion scenarios. It found that the lake's cooling capacity will substantially weaken by 2075, significantly reducing the thermal resilience of nearby urban areas.
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Bril et al. (2026) Assessing the Effectiveness of Nature‐Based Solutions and Building‐Level Flood Risk Reduction Measures: An Open‐Source Coupled Model
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Finkel et al. (2026) Rare Event Sampling for Moving Targets: Extremes of Temperature and Daily Precipitation in a General Circulation Model
Not discernible from the provided corrupted text.
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Moumen et al. (2026) Riverine Flood Mapping Methods and Criteria: A Meta-Analysis Review and Synthesized Guidelines
This meta-analysis statistically evaluates the influence of methods, topography, area extent, reference dataset size, and criteria on riverine flood mapping (RFM) accuracy across 142 studies, synthesizing guidelines for objective and context-appropriate method and criterion selection. It finds remote sensing and machine/deep learning methods generally most accurate, with performance varying significantly by topography and area extent, and identifies distance from river, elevation, and slope as the most influential criteria.
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Wang et al. (2026) A DeepONet surrogate for accelerating distributed hydrological model simulations
This paper introduces a DeepONet surrogate model designed to significantly accelerate the simulation speed of distributed hydrological models.
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Wang et al. (2026) Advancing Physical Realism in Hydrological Modelling: Selection and Integration — A Review and Synthesis
This review synthesizes 30 hydrological models and 186 peer-reviewed studies to propose a decision-oriented framework for model selection and integration, aiming to enhance physical realism, address data scarcity, and improve long-term and sub-daily simulations for resilient water resources management.
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Yaldiz et al. (2026) A Step Toward Rainfall Erosivity Mapping Over Türkiye Using Kriging With External Drift
This study mapped the rainfall erosivity (R) factor over Türkiye using Kriging with External Drift (KED) and satellite-derived Modified Fournier Index (MFI). The KED model significantly outperformed linear regression, ordinary kriging, and a global erosivity product, achieving a Kling-Gupta Efficiency (KGE) of 0.68.
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Dioha et al. (2026) Future projections of aridity change across Africa's climatic regions
This study aims to project future changes in aridity across various climatic regions of Africa, providing insights into the potential impacts of climate change on the continent.
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Minea et al. (2026) Coupled evolution of meteorological and hydrological drought until 2100 based on changes in climate scenarios
This study analyzed the coupled evolution of meteorological and hydrological droughts in Eastern Romania from 1971-2100 using historical data and future climate scenarios, revealing strong correlations between drought types and a projected increase in severe and extreme hydrological droughts by the end of the century.
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Risser et al. (2026) Correction: A framework for detection and attribution of regional precipitation change: application to the United States historical record
This paper develops and applies a framework for the detection and attribution of regional precipitation changes, specifically using the historical record of the United States.
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Zhang et al. (2026) A high-order Model-free Dynamic Framework for Accurate Daily Streamflow Prediction
This paper introduces a high-order lightweight dynamic framework (HoLDF) for daily streamflow forecasting, which integrates high-order structural information identified by an improved Granger causality inference approach into a reservoir computing paradigm. HoLDF significantly outperforms baseline deep learning models in accuracy, robustness, and computational efficiency, making it suitable for operational deployment.
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Ferreiro-Crespo et al. (2026) AWARE historic and 2024 characterization factors for Spain
This study developed an improved AWARE-based methodology for water scarcity assessment in Spain, integrating current reservoir data and refined demand estimates to provide temporally responsive and spatially resolved characterization factors. The application to 2024 data revealed an average 8.3% increase in water scarcity factors nationally, with significant regional variations highlighting entrenched hydrological polarization.
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Zhang et al. (2026) Longitudinal Mean Velocity and Turbulent Kinetic Energy Within an Emergent Canopy in Nonuniform Flows
This study investigates the longitudinal velocity and turbulent kinetic energy (TKE) dynamics in emergent canopies under streamwise varying flow conditions using laboratory flume experiments. It found that both time-mean longitudinal velocity and TKE significantly enhance downstream, and developed an analytical model revealing an effective power-law exponent of 2/3 between longitudinal TKE and mean velocity due to flow nonuniformity.
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Risser et al. (2026) Correction: Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation
This correction notice addresses and rectifies a misspelling of an author's name, Christina M. Patricola-DiRosario, in a previously published article.
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Ma et al. (2026) Monitoring Reservoir Storage Using SWOT Satellite Observations and a Reservoir Operation Model
This study evaluates the accuracy of reservoir storage estimates derived from the new Surface Water and Ocean Topography (SWOT) satellite mission against in situ observations for 12 Western U.S. reservoirs, finding that SWOT provides highly accurate water surface elevation and storage data that can effectively constrain hydrological models to fill temporal gaps.
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Jiang et al. (2026) Integrating socio-hydrological modeling and climate change projections for sustainable water resource management in agricultural systems
This paper integrates socio-hydrological modeling with climate change projections to develop sustainable water resource management strategies specifically for agricultural systems.
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Baioni et al. (2026) A regionally based method to identify lithology-specific hydraulic conductivity distributions in shallow aquifers using catchment-scale effective values
This paper introduces a novel method (HCDM) to infer lithology-specific hydraulic conductivity distributions in shallow aquifers using catchment-scale effective values. Validated with synthetic data and applied to 113 catchments in the Armorican Massif, the method demonstrates high predictive accuracy, with 85% of modeled conductivities falling within a 90% confidence interval of observed values.
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Tajima et al. (2026) Climate Change Alters Post‐Surge Recovery of Coastal Aquifers
This study quantifies the combined effects of increasing storm-surge intensity and decreasing frequency on coastal aquifers using integrated numerical simulations. It reveals two distinct long-term regimes—full recovery or shifted equilibrium with persistent salt accumulation—determined by critical thresholds of storm intensity and frequency, which can be predicted by a dimensionless number.
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Riche et al. (2026) Predicting LULC Changes and Assessing their Impact on Surface Runoff with Machine Learning and Remote Sensing Data
This study developed an approach integrating remote sensing and machine learning to predict future land use and land cover (LULC) changes and assess their impact on surface runoff in a semi-arid Mediterranean watershed. It found that urbanization significantly increases runoff, while forests mitigate it, with land factors having limited influence during intense rainfall events.
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Ma et al. (2026) Divergence or Convergence? A Comparison of InVEST and SWAT in Simulating Water Conservation Patterns and Drivers
This study quantitatively compares the InVEST and SWAT models in simulating water conservation patterns and drivers in the Liupan Mountain region from 2003 to 2022, finding both models show an increasing trend and consistent spatial heterogeneity, but with complementary strengths for different application contexts.
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Neri et al. (2026) On the Choice of Optimal Reservoir Operating Rules in a Changing Climate for the Sustainable Management of Drinking Water Sources
This study develops a multi-objective optimization framework to define optimal reservoir withdrawal rules for a multi-basin drinking water supply system in Northern Italy, assessing their adaptation to climate change under historical and future meteorological forcings to maximize production and minimize deficits. The research quantifies future expected relative changes in optimal operating rules and outlines corresponding patterns of withdrawal volumes and potential water system failures throughout the century.
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Lechner et al. (2026) Hydrological drivers of surface runoff during high intensity rainfall experiments in Alpine ski regions
This study investigates surface runoff behavior in 12 Eastern Alpine ski regions using 74 rainfall simulation experiments, revealing significantly higher surface runoff coefficients on ski slopes (median 0.57) compared to reference areas (median 0.07). A random forest model identified geological factors as the strongest predictors on ski slopes, while soil and land use variables were more influential on reference areas.
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Lapides et al. (2026) Potential Impacts of Groundwater Pumping on Stream Temperature Are Greatest in Streams With Substantial Cold Groundwater Inflows
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Calvi et al. (2026) Origin, Age, and Flow Path of Groundwater Associated With High‐Mountain Springs in Arid Andean Regions
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Wolkeba et al. (2026) Water scarcity indicator based on GRACE derived total water storage for fast water scarcity monitoring
This study introduces a novel water scarcity indicator derived from GRACE total water storage anomaly, offering a robust and efficient alternative to traditional Global Hydrological Model (GHM)-based assessments. The new indicator demonstrates strong alignment with established blue water scarcity metrics and provides comparable estimates of populations and land areas under scarcity.
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Yang et al. (2026) A Global Assessment of Climate Change and Anthropogenic Effects on Changes in Streamflow
This study globally assessed spatiotemporal streamflow changes in 2264 catchments from 1961–2014, quantifying the contributions of precipitation, potential evapotranspiration, and landscape characteristics using the Budyko hypothesis. It found that precipitation was the dominant factor for streamflow changes in most catchments, with significant regional variations in trends and sensitivities to climate and anthropogenic factors.
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Pan et al. (2026) A Study on Rapid Dynamic Flood Forecasting in Small Watersheds Using a GNN-Transformer Approach Integrated with Spatial Physical Information
This study develops a novel GNN-Transformer deep learning model for rapid flood forecasting in small watersheds, integrating static physical information and dynamic rainfall data. The model achieves high accuracy (NSE > 0.99, RMAE < 7%) and significantly improved computational efficiency (100-200 times faster) compared to traditional hydrodynamic models.
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Wu et al. (2026) Evaluating Evapotranspiration Simulation Performance in 30 Conceptual Hydrological Models: Insights Into ET Representation Across Diverse Climates
This study investigates 30 conceptual hydrological models to assess their evapotranspiration (ET) representations and ability to reproduce state-of-the-art ET products across 507 diverse CAMELS-US catchments, providing guidance for appropriate ET modeling based on climate.
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Sánchez‐Gómez et al. (2026) Climate Change in the Upper Tagus River Basin: Impacts on Climate Variables and Hydrological Processes
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Negro et al. (2026) Integrating Satellite Data Into Meso‐Scale Habitat Modeling for Non‐Perennial Rivers and Streams
This study introduces a novel methodology using the MesoHABSIM model and satellite imagery to assess aquatic habitat dynamics in a non-perennial river, revealing species-specific vulnerabilities of fish and macroinvertebrates to flow intermittency and informing ecological flow strategies.
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Dueñas-Tovar et al. (2026) Integration of spectral indices and precipitation data to assess river morphometric features in a tropical semi-humid environment
This study developed a reproducible remote sensing workflow using eight optical indices and a Random Forest algorithm to assess river channel mobility (lateral shift and sinuosity) in a data-limited tropical semi-humid environment. The workflow successfully identified episodic, reach-specific channel adjustments, with lateral shifts up to 500 meters, and revealed a short-term negative correlation between antecedent precipitation and lateral displacement.
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Azghandi et al. (2026) Machine Learning–Based Characterization of Groundwater Recharge in Semi-Arid Drylands
This study characterized groundwater recharge dynamics in the semi-arid Karkheh Plain (Iran) from 2001–2024 using satellite-based water balance and machine learning, finding that ΔSoil Moisture is the dominant driver and that positive recharge peaks have significantly declined, indicating increasing groundwater vulnerability.
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Ghasemifar et al. (2026) Widespread extreme precipitation events over Iran: Large-scale patterns and their associated global indices
This study characterizes widespread extreme precipitation events (WEPEs) over Iran, identifying their frequency, intensity, duration, and regional patterns using 25 years of satellite data. It reveals that WEPEs are primarily driven by deep troughs over the Red Sea/Arabian Peninsula and are strongly linked to the Circumglobal Wave Train (CGT), with the North Atlantic Oscillation (NAO) indirectly modulating CGT variability.
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Özel et al. (2026) Multi-dimensional Assessment of the Water-Food Nexus in a Semi-Arid Watershed
This study holistically assesses the water-food nexus in the semi-arid Upper Sakarya Watershed, Türkiye, by integrating hydrological modeling, economic analysis, and stakeholder perspectives to evaluate agricultural water management scenarios. It finds that effective scenarios, particularly crop pattern changes, can significantly reduce irrigation water use (up to 60 million m³ per year) while increasing farmers' net income per cubic meter of water, but implementation faces technical, practical, and political constraints.
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Yang et al. (2026) A hybrid method coupling physical process-driven model with generative deep learning for probabilistic flood forecasting
This paper proposes a novel hybrid method that integrates a physical process-driven model with generative deep learning to enhance the accuracy and reliability of probabilistic flood forecasting.
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Bernal‐Mujica et al. (2026) The Impact of Deciduous Forest and Topography on Snowpack Dynamics in a Headwater Catchment in the Southern Andes Cordillera
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Rahman et al. (2026) Comprehending the impact of hydro-meteorological droughts on ecosystem vulnerability and resilience across the Indus River Basin in Pakistan
This study develops a catchment-based integrated drought index (CIDI) for the Indus River Basin by integrating the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Water Availability Index (SWAI), and assesses ecosystem vulnerability and resilience, finding CIDI to be robust and identifying extreme vulnerability in the Middle and Lower Indus Basins.
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Khan et al. (2026) Mapping agricultural drought hotspots in Pakistan: a remote sensing-based climate–vegetation nexus
This study analyzes agricultural drought dynamics in Pakistan from 2001 to 2023 using multisensor remote-sensing indices, revealing spatially heterogeneous and seasonally structured drought occurrences with northern regions being resilient and southern/western regions highly vulnerable, necessitating region- and season-specific adaptation strategies.
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Hanumantha et al. (2026) A Spatially Explicit Water Balance Model for Assessing Recharge Sensitivity to Climate and Land Cover Change in Central Mexico
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Singh et al. (2026) Global shifts in rainfall drought relationship: weakening association in tropics
This study examines global meteorological drought dynamics from 1951 to 2016, revealing a sixfold increase in global drought frequency, particularly in tropical and subtropical regions. It finds that increased rainfall variability, rather than just rainfall deficit, is increasingly driving these droughts, leading to a 60 % rise in drought likelihood even during surplus rainfall years.
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Eatesam et al. (2026) Quantifying the attribution of ecohydrological degradation: a comparative deep learning approach in a changing environment
This paper aims to quantify the attribution of ecohydrological degradation in a changing environment using a comparative deep learning approach.
