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Maines et al. (2026) Recent and future variability in 1-day precipitation extremes in Trentino – South Tyrol (Eastern Italian Alps) based on observations (1956-2023) and climate model projections
This study comprehensively assesses 1-day precipitation extremes in Trentino – South Tyrol (eastern Italian Alps) using observations (1956-2023) and climate model projections, finding increasing intensity and frequency in both past observations (especially in the north, summer/autumn) and future projections, with rarer events becoming more probable under higher warming levels.
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Fang et al. (2026) Machine Learning Calibration of Groundwater Table Depth in ELM: Impact on Land Surface Hydrology and Land‐Atmosphere Fluxes
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Sun et al. (2026) Response and adaptation of global terrestrial vegetation production to extreme precipitation
This study quantifies the global terrestrial vegetation's gross primary productivity (GPP) response, adaptation, and recovery to extreme precipitation events (EPEs) using FLUXNET data and modeling, finding that EPEs significantly reduce carbon sequestration but most ecosystems recover within 12 days.
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Lv et al. (2026) Precipitation and soil moisture coupling constrains subseasonal predictability of a prolonged extreme heatwave
This study investigates the subseasonal predictability of the August 2022 Yangtze River Valley heatwave, finding that precipitation-soil moisture coupling is the primary factor limiting forecast accuracy due to its significant influence on surface temperature.
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Tang et al. (2026) Fast response of satellite fluorescence-derived plant physiology to drought stress
This study globally disentangles the sequence and drivers of vegetation physiological and structural responses to drought using satellite data. It reveals that satellite fluorescence-derived plant physiology responds to drought within approximately 3 days, significantly faster than structural changes which emerge after approximately 12 days.
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García-Marquez et al. (2026) Environment90m – globally standardized environmental variables for freshwater science at high spatial resolution
This paper introduces Environment90m, a global, high-resolution dataset of 104 standardized environmental variables aggregated to 726 million freshwater sub-catchments, designed to support large-scale freshwater biodiversity research and conservation efforts.
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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 simulations and a novel clustering algorithm, revealing that extensive droughts exhibit growth and recovery phases, regional variations, and are primarily driven by rainfall deficits often interacting with multiple hydrometeorological processes.
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Qu et al. (2026) Drought propagation as a nonlinear amplifier of ecohydrological damage
This study systematically investigates how meteorological drought propagates to soil and ecological drought, revealing that ecohydrological damage is nonlinearly amplified, reaching 162% to 310% of initial meteorological drought intensity, especially beyond a standardized threshold of 2.18.
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Yue et al. (2026) Improving Daily Precipitation Estimates through Machine Learning-Based Downscaling, Precipitation Event Classification, and Categorical Merging
This study proposes a three-step machine learning framework for multi-source precipitation merging, integrating downscaling, precipitation event classification, and categorical merging. The framework developed a high-resolution (1 km, daily) merged precipitation dataset (MSMP) for the Pearl River Basin, demonstrating significantly improved accuracy, especially for heavy and extreme precipitation, compared to existing products.
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Jia et al. (2026) Advancing ecohydrological modelling: coupling LPJ-GUESS with ParFlow for integrated vegetation and surface-subsurface hydrology simulations
Groundwater sustains vegetation and regulates land-atmosphere exchanges, yet most Earth system models oversimplify its dynamics. This study develops an integrated framework coupling a dynamic vegetation model with the three-dimensional hydrological model ParFlow to explicitly represent groundwater-vegetation interactions, demonstrating that groundwater flow strongly regulates water exchanges and improves simulations of water cycles in Earth system models.
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Kim et al. (2026) Advancing understanding of parameterization effects in global hydrologic models through multi-model, multi-variable evaluation
This study investigates how parameter choices in four Global Hydrological Models (GHMs) affect hydrological simulations by optimizing them with multi-variable data across 228 global watersheds, revealing that optimized parameters generally outperform default settings and highlighting risks of high-flow overestimation with default parameters.
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Trullenque‐Blanco et al. (2026) Projected Evolution of Climatic Aridity in Spain: Robust Signals and Model Uncertainties
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Kucheruk et al. (2026) Flash Drought Climatology Over Southeastern South America: Sensitivity to Index and Reanalysis Selection, and Potential Causes
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Vichot‐Llano et al. (2026) Projected 21st Century Changes in Precipitation and Temperature Over Italy Using CMIP6 CMCC ‐ CM2 ‐ SR5 Model and COSMO ‐ CLM Dynamical Downscaling
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Zakharova et al. (2026) Exploring the potential of Sentinel-3 and Sentinel-6 SAR altimetry measurements for discharge estimation: Case studies from the Rhine and Po rivers
This study investigates the potential of Sentinel-3 and Sentinel-6 SAR altimetry for discharge estimation in medium-sized rivers, using the Rhine and Po as case studies. It demonstrates that enhanced 80 Hz altimetry processing and rigorous data filtering significantly improve water level and discharge accuracy, achieving NRMSE values as low as 4% with empirical methods and 8% with refined physical models.
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Jung et al. (2026) Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data
This paper introduces a fully automated and scalable method for mapping dynamic surface water extents from single-acquisition Sentinel-1 SAR imagery, integrating adaptive thresholding, fuzzy-logic classification, region growing, dark land estimation, and a bimodality test. The approach achieves classification accuracies exceeding 85% globally, providing a robust tool for near-real-time monitoring of floods, droughts, and water resources across diverse environmental conditions.
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Samara et al. (2026) 2021–2023: Extreme Years of Global Drought in the Context of Long- and Short-Term Hydroclimate Trends
This paper performs a global assessment of dryness and wetness using an ensemble of soil moisture datasets and drought indices, revealing consistent drying trends in several regions and identifying 2023 as one of the driest years on record, with the 2021–2023 period marking the longest consecutive extreme global drought since the early 20th century, consistent with anthropogenic climate change.
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Mwangi et al. (2026) Uncertainties in long-term ensemble estimates of contextual evapotranspiration over southern France
This study quantifies uncertainties in long-term contextual evapotranspiration (ET) estimates over southern France using the EVASPA ensemble model, revealing that Land Surface Temperature (LST) inputs and evaporative fraction (EF) formulations are the dominant sources of variability, with uncertainties peaking in summer. The ensemble approach provides reliable flux estimates and a meaningful uncertainty spread, enhancing ET retrieval robustness.
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Zafar et al. (2026) Temporal transferability of spatially derived Manning’s roughness across flood regimes in the Mississippi River
This study evaluated the temporal transferability of spatially derived Manning's roughness values across different flood regimes in the Middle Mississippi River using a 2D hydrodynamic model. It demonstrated that a single set of spatially variable, temporally invariant roughness values provided excellent model performance over a decade, suggesting that hydrological forcing, rather than temporal changes in roughness, is the primary cause of model discrepancies.
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Ren et al. (2026) Global River Forecasting with a Topology-Informed AI Foundation Model
## Identification - **Journal:** arXiv (Cornell University) - **Year:** 2026...
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Huang et al. (2026) Unravelling Groundwater–Precipitation Interactions in Karst Aquifers Under the Dual Pressures of Climate Variability and Human Disturbance
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Ultee et al. (2026) CMIP6 climate model spread outweighs glacier model spread in 21st-century drought buffering projections
This study quantifies 21st-century glacial drought buffering across 75 major river basins using an ensemble of three global glacier models forced by 11 CMIP6 climate models, finding that climate model uncertainty significantly outweighs glacier model uncertainty in projections.
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Gnann et al. (2026) On Utilizing Spatial Gradients to Discover Functional Relationships in Hydrology
This paper reviews functional relationships in hydrology, defining them as variable relationships characterizing hydrological system functions along environmental gradients, and discusses their critical value for advancing hydrological theory, improving observational data, and enhancing computational models.
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Gualdi et al. (2026) Seasonal Predictions and Their Applications in the Mediterranean Region: Part II—Prediction‐Based Services
This review paper examines the development and application of seasonal climate predictions as actionable information for decision-making in the Mediterranean region, illustrating the iterative process of transforming probabilistic forecasts into tailored climate services for key sectors.
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Mashaly et al. (2026) Dynamic Water Budget Modeling to Fill Knowledge Gaps and Improve Water Management in Arid and Semi‐Arid Regions
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Xu et al. (2026) Rapid Prediction of Compound Flood Based on Hydrological-Hydrodynamic Model and Convolution Neural Network
This study proposes a hybrid approach coupling a hydrological-hydrodynamic model (PCSWMM) with a Convolutional Neural Network (CNN) for rapid and accurate prediction of compound flood processes in coastal urban areas, demonstrating a significant increase in computational efficiency while maintaining high prediction accuracy for flood depths.
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Gurazada et al. (2026) ENSO’s influence on co-occurring hot-dry and hot-wet extremes across global croplands
This study investigates the influence of the El Niño Southern Oscillation (ENSO) on co-occurring hot-dry (HD) and hot-wet (HW) extreme events across global croplands. It finds that El Niño years significantly increase the localized risk and global exposure of croplands, particularly for staple crops like rice, to both HD and HW extremes, necessitating integration into agricultural early warning systems.
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Jing et al. (2026) Spatiotemporal Variability of Runoff Coefficients and Rainfall–Runoff Responses in a Mountainous Basin Using Integrated Hydrological Analogy and SCS–CN Models
This study developed a three-stage coupling framework integrating hydrological analogy, SCS-CN calibration, and reciprocal modeling to quantify spatiotemporal runoff coefficient variability in a mountainous basin, demonstrating enhanced predictive reliability and mechanistic interpretability for runoff estimation under data-scarce conditions.
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Lee et al. (2026) A novel approach for soil moisture retrieval from Sentinel-1 SAR via temporal stability-based backscatter analysis
This study developed a novel temporal stability analysis (TSA)-based masking method for Sentinel-1 SAR to improve high-resolution soil moisture retrieval by effectively filtering noisy pixels. The TSA method significantly enhanced correlations and reduced errors compared to existing methods across diverse monitoring networks.
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Wang et al. (2026) Improved estimation of evapotranspiration and gross primary productivity by incorporating soil moisture feedbacks into a coupled ecosystem model
This study developed a fully coupled ecosystem model that explicitly incorporates dynamic soil moisture feedbacks to improve the consistent estimation of gross primary productivity (GPP) and evapotranspiration (ET). The model demonstrated robust performance across 32 diverse sites, significantly enhancing GPP and ET predictions, especially under dry soil conditions, compared to non-coupled approaches.
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Gao et al. (2026) Applicability of the Richards equation in infiltration simulation: A comparative study with the two-phase flow model
This study systematically evaluates the applicability of the Richards equation (RE) for simulating water infiltration in the vadose zone by comparing its performance against a two-phase (TP) flow model, identifying specific hydrogeological conditions where RE overestimates infiltration due to restricted air escape.
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Li et al. (2026) Skills in sub-seasonal to seasonal terrestrial water storage forecasting: insights from the FEWS NET land data assimilation system
This study evaluates subseasonal to seasonal (S2S) terrestrial water storage (TWS) forecasts over Africa from the Famine Early Warning Systems Network (FEWS NET) land data assimilation system (FLDAS) using GRACE/FO observations. It finds that the NASA Catchment Land Surface Model (CLSM) generally outperforms Noah-MP, primarily due to its more accurate reanalysis-based initial conditions and stronger representation of TWS interannual variability.
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Wu et al. (2026) Global Desert Variations During 1985–2024 Associated With Effective Water Availability
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Jeung et al. (2026) Sensitivity of hydrological machine learning prediction accuracy to information quantity and quality
This study investigates how the quantity and quality of information in training data influence the prediction accuracy of hydrological machine learning (ML) models. It demonstrates that while the highest accuracy is achieved with all available data, incorporating high-quality outputs from calibrated mechanistic models most efficiently improves ML prediction accuracy.
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Makhasana et al. (2026) Understanding Land-Atmosphere Interactions During Coupling Whiplash Events
This study investigates the role of Land-Atmosphere (L-A) interactions in driving hydrometeorological whiplash events, characterized by abrupt dry-to-wet and wet-to-dry transitions. It identifies that lower atmospheric moisture availability is critical for whiplash intensity, while convective potential responds to seasonal thermal changes, revealing distinct L-A feedback mechanisms and global high-risk regions.
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Chen et al. (2026) Enhancement of Global Flood Risk Due To Greater Flood Magnitude and Variability Under Anthropogenic Activities
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Debnath et al. (2026) Impacts of Climate Change and Demographic Growth on Future Water Supply and Demand Gap in a River Basin
This study developed a Water Evaluation And Planning (WEAP) model for the Kesinga Sub Catchment of the Mahanadi River basin in India to assess future water supply-demand gaps under climate change and demographic growth, finding significant unmet domestic water demand (up to 20%) in specific demand sites by the 2030s, particularly during lean periods.
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Yang et al. (2026) Enhancing Multi-Step Ahead Daily Runoff Prediction via HydMoE Model with Local-Global Hybrid Attention
This study proposes HydMoE, a deep learning model integrating a Mixture of Experts architecture with Time2Vec temporal embedding and Local-Global Hybrid Attention, to enhance multi-step ahead daily runoff prediction and provide interpretability for diverse hydrological patterns. It achieves superior performance over baselines for 1 to 7-day lead times on the CAMELS dataset.
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Zhang et al. (2026) Fill-spill process-guided hydrologic modeling: enhanced identification of hydrologically sensitive zones and simulations in semi-arid basins
This study proposes the Hydrologically Sensitive Fill-Spill Zone (HSFSZ) concept and develops the CASC2D-HSFSZ model to improve flood simulation accuracy in semi-arid regions by explicitly incorporating depression-storage and threshold-activated connectivity, demonstrating enhanced performance over the original CASC2D model.
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Sumith et al. (2026) Comparative Analysis of Deep Learning and Machine Learning Models for Evapotranspiration Prediction in Semi-Arid Regions: Statistical Model Evaluation with a Paired t-Test and Bootstrap Resampling
This study comprehensively evaluates deep learning (LSTM, RNN, GRU) and traditional machine learning models (DT, RF, SVM, ANN, GBM) for evapotranspiration prediction in semi-arid regions using climatic factors. The Long Short-Term Memory (LSTM) model demonstrated superior predictive accuracy and statistical significance, establishing it as the most effective and reliable model for this application.
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Qi et al. (2026) Waterlogging Simulation Model Based on Bidirectional Coupling Between Runoff Production and Confluence in Plain Areas
This paper develops a raster-based waterlogging simulation model for plain areas, introducing a novel bidirectional coupling mechanism between runoff production and confluence to overcome the limitations of traditional unidirectional models. The model significantly improves the accuracy of plain waterlogging simulation, with peak flow errors within ±15% and Nash-Sutcliffe Efficiency (NSE) values over 0.84, providing enhanced tools for flood warning and water resource management.
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Cannon et al. (2026) Deep Learning for Multi-Satellite Precipitation Retrievals: Impact of Tomorrow.io’s Microwave Sounders
This study introduces a novel satellite-based precipitation retrieval system that integrates publicly available geostationary and polar-orbiting satellite data with commercial microwave sounders, utilizing a convolutional neural network to provide near-surface precipitation rates every 10 minutes at 4 km resolution, demonstrating significant accuracy improvements over existing products.
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Zhang et al. (2026) Scale-dependent model-observation inconsistencies in global terrestrial water storage models
This study evaluates scale-dependent model-observation inconsistencies in seven global terrestrial water storage models against GRACE observations across global, climate-zone, and basin scales, finding that satellite observation-constrained assimilation significantly reduces these inconsistencies compared to model-driven products.
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Lyu et al. (2026) Warming overwhelms CO2-driven drought mitigation in alpine vegetation on the Qinghai-Tibetan Plateau
This study investigates the combined effects of rising atmospheric carbon dioxide (CO₂) and warming on alpine vegetation drought responses on the Qinghai-Tibetan Plateau. It finds that while CO₂ rise alone mitigated drought-induced productivity losses by 5.7%, concurrent warming reversed this benefit, intensifying drought stress by 5.2% due to increased plant water demand.
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Song et al. (2026) Decoupling of gross primary productivity and transpiration revealed through a daily remote sensing modelling approach in arid irrigated farmland
This study investigated the relationship between gross primary productivity (GPP) and transpiration (T) in arid, irrigated farmlands, revealing a weak coupling and significant decoupling where transpiration rates were disproportionately high relative to photosynthesis, especially during the middle to late growing season.
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Dai et al. (2026) Analysis of Seasonal Propagation Dynamics and the Potential Driving Factors from Meteorological to Soil Moisture Drought
This study investigates the seasonal propagation dynamics from meteorological drought to soil moisture drought (SMD) and their driving factors in the Luanhe River Basin, China, using the Soil and Water Assessment Tool (SWAT) model and copula functions. It reveals distinct seasonal propagation times, with the shortest in summer and longest in spring, and identifies hydrometeorological, teleconnection, and land use factors as significant drivers of these dynamics.
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Gorsevski (2026) Predicted Streamflow Sensitivity to Climate Change Using TOPMODEL with CLIGEN Weather Generator in a Data-Sparse Medium-Sized Mediterranean Watershed
This study develops a transferable framework to assess future streamflow sensitivity to climate change in data-sparse Mediterranean regions, confirming historical declines and predicting significant future reductions of 32% to 50% under 1.5 °C and 3.0 °C temperature increases, respectively.
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Zou et al. (2026) Identifying hotspots and impact factors of multi-type compound events over major global river basins
This study identifies hotspots and impact factors of 12 types of compound events (CEs) across 520 major global river basins, revealing their spatial heterogeneity and the significant influence of atmospheric circulation anomalies.
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Türk et al. (2026) Catchment transit time variability with different SAS function parameterizations for the unsaturated zone and groundwater
This study investigated whether stable water isotope (δ2H) measurements in streamflow can effectively represent preferential flow in the unsaturated zone and groundwater using StorAge Selection (SAS) functions. It found that δ2H data are sensitive to preferential flow in the unsaturated zone but insufficient to constrain groundwater preferential flow due to the damping effect of large passive groundwater storage.
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Pinto et al. (2026) User‐Relevant Climate Indices and Associated Uncertainties From Transient Convection‐Permitting Climate Model Projections
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Évin et al. (2026) Uncertainty sources in a large ensemble of hydrological projections: Regional Climate Models and Internal Variability matter
This study quantifies uncertainty sources in a large ensemble of hydrological projections for Metropolitan France using the QUALYPSO method. It finds that low flows are projected to decrease in southern France, with emission scenarios and regional climate models being dominant uncertainty sources, and highlights that internal variability is often as significant as climate change response uncertainty.
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Graham et al. (2026) The Met Office Unified Model Global Atmosphere 8.0 and JULES Global Land 9.0 configurations
This paper describes the Global Atmosphere 8.0 and Global Land 9.0 (GA8GL9) configurations of the Met Office Unified Model and JULES land surface model, detailing their scientific advancements over previous versions (GA7GL7) and evaluating their improved performance across weather and climate timescales. GA8GL9 demonstrates reduced errors, enhanced spatial structure in numerical weather prediction, and improved mean climate, particularly in top-of-atmosphere outgoing shortwave radiation.
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Stahl et al. (2026) Towards an operational European Drought Impacts Database (EDID)
This paper details the development and implementation of the European Drought Impact Database (EDID) for operational application within the Copernicus European Drought Observatory, providing a comprehensive baseline of drought impacts across Europe and revealing spatial and temporal patterns. The study finds that agriculture, public water supply, and aquatic ecosystems are the most frequently impacted systems, with an increasing trend in reported impacts and severity over time.
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Cheng et al. (2026) Hydrologically driven coordination of transboundary floods
This study develops a hydrologically driven coordination framework integrating physical flood routing into an open-loop differential game for transboundary flood management in the Yarlung Tsangpo–Brahmaputra River Basin. It demonstrates that cooperative strategies, accounting for hydrologic travel time, significantly reduce downstream flood peaks and suprathreshold flow durations compared to noncooperative approaches.
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Sun et al. (2026) A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation
This paper proposes HyDPNet, a novel hybrid deep learning model integrating a differential Xinanjiang model with dynamic parameters and an LSTM post-processor, demonstrating superior flood simulation accuracy in the Lushui River basin compared to benchmark models.
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Kim et al. (2026) Comparative Simulation of Hillslope Runoff Using Two Infiltration Equations
This study compared the performance of Horton and Green-Ampt infiltration equations, coupled with a physically-based overland flow model explicitly accounting for rill-interrill microtopography, in simulating hillslope runoff against field data. It found the Green-Ampt equation superior in validation due to its consideration of antecedent soil moisture, and highlighted that runoff is more sensitive to infiltration parameters than to surface friction.
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Zhao et al. (2026) Quantifying the drivers of river thermal regimes in the Hanjiang River Basin under climate change and reservoir construction
This study developed an integrated SWAT-HHO-LSTM modeling framework to disentangle the impacts of climate change and dam-heightening on river water temperature in the Hanjiang River Basin, revealing a spatial transition from dam-dominated cooling upstream to climate-driven warming downstream.
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Hamel et al. (2026) River temperature response to atmospheric heatwaves is modulated by discharge and meltwater
This study investigates how river water temperature in Alpine regions responds to atmospheric heatwaves, revealing that only 47% of atmospheric heatwaves lead to riverine heatwaves, with discharge and meltwater anomalies significantly modulating this response.
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Zhou et al. (2026) A high-resolution dataset revealing the dynamical variations of the relative humidity in China
This study developed a high-resolution (1 km × 1 km) daily relative humidity dataset for China (1951–2020) using Random Forest interpolation and analyzed its spatiotemporal variations, revealing a significant downward trend of −0.26% per decade from 1956 to 2020.
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Yan et al. (2026) Determination of irrigation water use from multiple soil moisture observations at a fine spatial resolution: Preferred model optimization and fusion strategy
This study proposes a framework to determine high-resolution irrigation water use (IWU) across China using multiple soil moisture (SM) observations, identifying the optimal SM depth and an effective data fusion strategy for improved estimation. The framework demonstrates that surface SM (0–10 cm) combined with an optimal fusion strategy yields the most accurate IWU estimates at a 1 km resolution.
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Zhu et al. (2026) HieraBoost-Q: interpretable karst discharge prediction from multi-site electrical conductivity with SHAP-based mechanism insights
This study proposes HieraBoost-Q, an interpretable hybrid framework integrating multi-site electrical conductivity and hierarchical XGBoost with SHAP, to improve discharge prediction and elucidate recharge mechanisms in karst catchments. It significantly enhances prediction accuracy and provides insights into the spatiotemporal dynamics of karst recharge.
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Kayhomayoon et al. (2026) Developing the fusion of MODFLOW simulation and data-driven approaches for river-aquifer recharge modeling
This study developed a hybrid MODFLOW-machine learning approach to simulate and predict river-aquifer recharge in the Guilan aquifer, Iran, demonstrating its effectiveness for complex groundwater management and potential to reduce water loss by up to 30%.
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Maddison et al. (2026) Using seasonal forecasts to enhance our understanding of extreme wind and precipitation impacts from extratropical cyclones
This study utilizes nearly 700 years of seasonal forecast model output to quantify the likelihood of unprecedented wind and precipitation impacts from European extratropical cyclones (ETCs). It finds that the probability of an ETC having an impact more extreme than any observed is generally between 0.5 % and 1.6 % for wind and 0.2 % and 0.7 % for precipitation, with the North Atlantic Oscillation strongly influencing wind impact likelihood.
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Springer et al. (2026) A review of current best practices and future directions in assimilating GRACE/-FO terrestrial water storage data into numerical models
This review synthesizes insights from approximately 60 GRACE/-FO data assimilation studies to identify best practices and future directions for integrating terrestrial water storage anomaly data into numerical models, revealing that effective strategies leverage ensemble Kalman filters, localization, and explicit accounting for correlated observation errors.
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Mu et al. (2026) Understanding the tradeoff between the flood risk and the hydropower benefit from the integrated flood control operation of a reservoir group and a flood detention basin
This study proposes a framework to analyze the tradeoff between flood risk and hydropower benefit from the integrated operation of a reservoir group and a flood detention basin (FDB). It finds that integrated operation enhances this tradeoff for extreme floods but weakens it for non-extreme floods due to FDB operational inflexibility.
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Vergnes et al. (2026) Assimilation de données pour la prévision des débits d’étiage
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Cheng et al. (2026) Dynamic water delay time estimation using dispatch data for improved inflow forecasting in cascade hydropower reservoirs
This study proposes an operation-data-driven probabilistic framework to improve inflow forecasting in cascade hydropower systems by dynamically estimating and quantifying the uncertainty of water delay time. Applied to the Lancang River cascade, the method significantly reduces forecasting errors compared to benchmark models, providing a practical, self-sufficient, and uncertainty-aware solution for dispatch centers.
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Xin et al. (2026) A multi-scale assessment of the effects of runoff/sediment discharge in karst catchments as shown by vegetation-related remote sensing indicators
This study assessed the effectiveness of five satellite-derived vegetation indicators in capturing runoff and sediment discharge variability across 15 karst catchments globally from 2003–2020, identifying Solar-Induced chlorophyll Fluorescence (SIF) as the most effective explanatory variable.
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Zhu et al. (2026) A global framework for subsurface soil moisture estimation: Coupling fractal Richards equation with Bayesian optimization
This study develops a global, satellite-based framework (ExpF-FRE) to extend near-surface soil moisture (SM) measurements (0–5 cm) to subsurface depths of 20 cm and 50 cm at 400 m daily resolution, demonstrating robust performance against in situ observations and reanalysis products without site-specific calibration.
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Chen et al. (2026) Emergent constraints on the hydrological impacts of land use and land cover change
This study applies an observation-based emergent constraint framework to correct Earth system model (ESM) estimates of evapotranspiration changes due to land use and land cover changes (LULCC), revealing significant biases in unconstrained models and projecting stronger evapotranspiration enhancements in future afforestation scenarios.
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Fischer et al. (2026) Quantifying evaporation of intercepted rainfall: a hybrid correction approach for eddy-covariance measurements
This study quantifies evaporation of intercepted rainfall at a coniferous forest site, revealing a systematic underestimation by eddy-covariance measurements (24% of precipitation) compared to model estimates (45%). A novel hybrid correction approach is proposed to reconcile eddy-covariance data with both energy and water budgets during interception events.
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Ogunrinde et al. (2026) Evaporative demand drought index for monitoring and analyzing drought conditions in arid regions of Asia and Africa
This study introduces and evaluates the Evaporative Demand Drought Index (EDDI) as a complementary tool for monitoring flash droughts and evapotranspiration-driven moisture stress in arid regions of Asia and Africa, demonstrating its ability to detect drought onset weeks earlier than precipitation-based indices.
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Chan et al. (2026) UK Hydrological Outlook using Historic Weather Analogues
This study assesses the skill of a new Historic Weather Analogues (HWA) method for seasonal hydrological forecasts across 314 UK catchments, benchmarking it against the standard Ensemble Streamflow Prediction (ESP) and climatology. The HWA method significantly improves winter river flow forecasts nationally, especially in upland, fast-responding catchments, while maintaining comparable skill to ESP in other regions and seasons.
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Sanchez et al. (2026) Hotspots and hot moments of metal mobilization: dynamic connectivity in legacy mine waters
This study reveals that metal(loid) mobilization in abandoned underground mines is governed by episodic shifts in subsurface hydrological connectivity, particularly during low-flow and pre-flush periods, leading to disproportionate contaminant release from localized storage zones that are often overlooked by conventional monitoring.
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Roux et al. (2026) Hydrological regime shifts in Sahelian watersheds: an investigation with a simple dynamical model driven by annual precipitation
This study investigates hydrological regime shifts in Sahelian watersheds using a simple dynamical model driven by annual precipitation. It finds that four studied watersheds (Gorouol, Nakanbé, Dargol, Sirba) experienced regime shifts during the droughts of the 1970s–1980s, transitioning from a low to a high runoff coefficient regime.
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Calvo‐Sancho et al. (2026) Human-induced climate change amplification on storm dynamics in Valencia’s 2024 catastrophic flash flood
This study uses a kilometer-scale pseudo-global warming storyline approach to attribute the catastrophic October 2024 Valencia flash flood to anthropogenic climate change, finding that present-day conditions significantly amplified rainfall intensity and the event's overall severity through enhanced moisture and convective processes.
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Wu et al. (2026) Linear time-lag effects and nonlinear interactions of global drought-flood abrupt alternation in responses to multiple factors
This study investigated the linear time-lag effects and nonlinear interactions of multiple factors, including surface energy fluxes, on global drought-flood abrupt alternation (DFAA) events. It found that accounting for time-lag effects significantly increased the explanatory power of these factors on DFAA from 33.03% to 70.05%, revealing complex spatial heterogeneity and nonlinear threshold regulations.
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Danesh‐Yazdi et al. (2026) Fields of opportunity: satellite imaging uncovers water stress and potential for water savings from irrigation
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Yılmaz et al. (2026) Neural circuit policy and hybrid deep learning models for enhanced meteorological drought forecasting performance
This study introduces the novel Neural Circuit Policy (NCP) deep learning model for meteorological drought forecasting using the Standardized Precipitation Index (SPI) at multiple time scales, demonstrating its superior performance, especially when integrated into hybrid models, for both forecasting accuracy and drought category classification.
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Suresh et al. (2026) Physics-Guided Deep Learning with Bayesian Optimization for Enhanced River Streamflow Prediction
This study introduces PIDeepONet, a novel hybrid deep learning model integrating Physics-Guided Loss (PGL) and Bayesian Optimization (BO), to enhance the accuracy and physical plausibility of river streamflow predictions using only observational data. The model effectively bridges the gap between traditional physics-based and purely data-driven approaches, demonstrating superior performance in both random and temporal data splits for two Indian river basins.
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Shahariar et al. (2026) Enhanced Streamflow Prediction Using a Hybrid CNN-LSTM Model: Integrating Climate-Driven Spatio-Temporal Features in a Deep Learning Framework
This study developed a climate-driven hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model for daily streamflow prediction in the Brahmaputra River Basin, relying solely on precipitation and temperature. The model demonstrated superior performance over standalone deep learning models and achieved comparable or superior skill to a calibrated SWAT model, particularly for low-flow and high-flow extremes.
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Shakib et al. (2026) FloodWatch: An Automatic Machine Learning Tool for Flood Forecasting and Segmentation using Weather Data and Images
This study introduces "FloodWatch," an automatic machine learning (AutoML) tool designed for comprehensive flood forecasting and segmentation. It integrates various machine learning and deep learning algorithms, Explainable AI (XAI), Generative Adversarial Networks (GANs) for data augmentation, and GIS visualization into a user-friendly, no-code web application to enhance flood prediction accuracy and accessibility for disaster management.
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Sivan et al. (2026) Evaluation of compound agrometeorological drought and hot events using a copula-based standardized index
This study introduces a novel Standardized Agrometeorological Drought and Hot Index (SADHI) based on a vine copula framework to analyze compound agrometeorological drought and hot events (CADHE) in Kerala, India, from 1955 to 2020, revealing a significant increase in their frequency and severity, particularly after 1980, with the 2016 event being the most widespread and extreme.
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Malmquist et al. (2026) Effects of water infrastructure development on water balance in a temperate agricultural landscape
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Höckh et al. (2026) Screening the Future Security of Groundwater-based Water Supply on the Scale of a Regional Water Supplier
This study develops a simple, low-cost screening framework using a novel Groundwater Use Index (GWUI) to assess the future security of groundwater-based water supply under climate change. Applied to a regional water supplier in southwest Germany, the framework projects significant water stress and potential shortages by 2070, even in a historically humid region, primarily due to declining groundwater recharge.
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Mure-Ravaud et al. (2026) Physically-based transposition of a mesoscale convective system for estimating probable maximum precipitation
This paper introduces a novel physically-based storm transposition approach, leveraging numerical weather prediction model internal variability, to objectively define transposition regions and reduce boundary condition shifts for probable maximum precipitation estimation, applied to a mesoscale convective system over the Raccoon River Watershed in Iowa.
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Akkimi et al. (2026) FlDepth: A New Method for Estimating Fluvial and Pluvial Flood Depths from Near Real-Time Satellite-Derived Inundation Map and Topography
This study introduces FlDepth, a novel GIS-based method for estimating fluvial and pluvial flood depths using near real-time satellite inundation maps and topographic data. The method demonstrated high accuracy (errors < 20 cm, NSE ~ 1) compared to hydrodynamic models and ICESat-2 data, outperforming existing tools like FwDET in diverse terrains.
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Singh et al. (2026) Modelling framework for asynchronous land-atmosphere coupling using NASA GISS ModelE (NASA-GISS E2.1) and LPJ-LMfire (v1.4.0): design, application and evaluation for the 2.5 ka period
This study develops and evaluates a framework for asynchronously coupling the NASA GISS ModelE climate model with the LPJ-LMfire dynamic global vegetation model to simulate paleoclimate for the 2.5 ka period, demonstrating the critical importance of bias correction for accurate land-atmosphere feedback representation and consistency with proxy data.
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Sun et al. (2026) Assessing and optimizing high-resolution global river streamflow estimates with triple collocation analysis
This study evaluates three global hydrological models (CWatM, PCR-GLOBWB, H08) at high and low spatial resolutions and optimizes streamflow estimates using Triple Collocation and simple averaging. It finds that high-resolution CWatM and Triple Collocation-based data fusion significantly improve global streamflow simulation accuracy, offering practical guidance for hydrological assessments.
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Wu et al. (2026) Modeling runoff with incomplete data: a comparison of hydrological, deep learning, and hybrid approaches
This study systematically evaluates hydrological, deep learning, and hybrid runoff models under various data scarcity scenarios across forty catchments. It finds process-based models more reliable in data-scarce conditions, while hybrid models effectively combine physical knowledge with data-driven flexibility, underscoring the importance of model selection based on data availability.
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Liu et al. (2026) Mapping drawdown-zone bathymetry using SWOT observations: implications for global monitoring of lake inundation and storage changes
This study develops a novel Spatial Iterative Filtering and Weighted Average Fusion (SIF-WAF) method to integrate multi-temporal SWOT observations for high-precision mapping of lake drawdown zone bathymetry, demonstrating its potential for global monitoring of lake inundation and storage changes.
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Davis et al. (2026) Physics‐Based Versus AI Weather Prediction Models: A Comparative Performance Assessment of Atmospheric River Prediction
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Huang et al. (2026) How does gross primary production uncertainty impact evapotranspiration prediction within the carbon–water coupled model?
This study quantitatively assessed the impact of remote sensing-based Gross Primary Production (GPP) uncertainty on Evapotranspiration (ET) predictions within carbon-water coupled models, revealing significant negative effects on both accuracy and spatiotemporal patterns of ET estimates.
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Li et al. (2026) Soil water vapor adsorption and condensation governed by groundwater depth and vadose zone lithology in arid and semi-arid regions
This study quantitatively analyzed the spatiotemporal dynamics of soil water vapor adsorption and condensate formation in arid regions, revealing the distinct impacts of groundwater depth and vadose zone lithology, and highlighting an overestimation in conventional condensation measurements due to unseparated adsorption.
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Zhang et al. (2026) A physics-driven hybrid transformer model for hydrologic simulation under nonstationary environmental conditions
This study proposes a Physics-Driven Hybrid Transformer Hydrologic Model (PD-HTHM) that integrates deep learning with the conceptual SIMHYD model to generate time-varying parameters, improving hydrologic simulation under nonstationary environmental conditions. It demonstrates that dynamically adjusting a few key parameters significantly enhances model robustness and predictive accuracy compared to static parameterizations.
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Pan et al. (2026) Evaluating saturated hydraulic conductivity schemes: impacts on soil moisture simulations and soil texture-dependent applicability
This study evaluated five saturated hydraulic conductivity (Ksat) schemes against laboratory measurements and then integrated them into the Community Land Model (CLM5.0) to simulate soil moisture on the Tibetan Plateau, finding that the optimal Ksat scheme for soil moisture simulation is dependent on soil texture and organic matter.
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Sharma et al. (2026) MAUNet-Light: A Concise MAUNet Architecture for Bias Correction and Downscaling of Precipitation Estimates
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Soares et al. (2026) River intermittency: mapping and upscaling of water occurrence using unmanned aerial vehicle, Random Forest and remote sensing landscape attributes
This study maps and models the spatio-temporal dynamics of an intermittent river using UAV surveys to classify water occurrence and Random Forest models with landscape attributes, dam presence, and satellite indices. Model (a), incorporating Sentinel MNDWI, proved most successful in simulating intermittency both temporally and spatially with approximately 80% accuracy.
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Feigl et al. (2026) Distilling hydrological and land-surface model parameters from physio-geographical properties using text-generating AI
This study demonstrates the use of variational autoencoders (VAEs) as text-generating AI models to automatically derive interpretable parameter transfer functions for distributed hydrological and land-surface models. This novel approach significantly improves runoff predictions in ungauged basins across 162 German catchments compared to traditional regionalization methods and deep learning models, while maintaining physical interpretability.
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Jones et al. (2026) Collective risk modelling of multi-peril events: correlation of European windstorm gust and precipitation annual severity
This study develops and tests three collective risk frameworks to model the correlation between annual aggregated wind gust and precipitation severities from European windstorms. It finds that interannual modulation of hazard variables is crucial to accurately capture the observed negative correlations at high severity thresholds.
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Liu et al. (2026) Multi-satellite data fusion for improved field-scale evapotranspiration mapping on Google Earth Engine
This study developed a Google Earth Engine (GEE)-based framework to improve field-scale evapotranspiration (ET) mapping by fusing thermal infrared (TIR) observations from ECOSTRESS and VIIRS with Harmonized Landsat-Sentinel (HLS) data. The integration of multi-satellite data generally enhanced ET estimation accuracy, reducing average Mean Absolute Error (MAE) by 8.64% (daily), 14.40% (weekly), and 16.37% (monthly) compared to Landsat-only baselines.
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Blanch et al. (2026) AI image-based method for a robust automatic real-time water level monitoring: a long-term application case
This study presents a robust, automated camera gauge for long-term, near real-time river water level monitoring, employing artificial intelligence for image-based segmentation and ground control point identification combined with photogrammetric techniques. Tested over 2.5 years at four sites, the system achieved high performance with mean absolute errors ranging from 0.96 to 2.66 cm, demonstrating resilience to adverse conditions and enabling continuous 24/7 monitoring.
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Webb et al. (2026) Antecedent moisture enhances early warning of atmospheric river flood hazards
This study demonstrates that incorporating antecedent soil moisture conditions into the atmospheric river (AR) scale significantly improves its ability to predict flood hazards in California and central Chile, nearly doubling its correspondence with peak streamflow.
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Douville (2026) How Do Projections of Meteorological Droughts Vary Across Models and Regions?
This study quantifies future changes in meteorological drought properties using Earth system models and the standardized precipitation index, identifying regional "dry spots" where drought severity is projected to increase, primarily due to prolonged duration.
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Muñoz‐Castro et al. (2026) How well do hydrological models simulate streamflow extremes and drought-to-flood transitions?
This study evaluates how well four conceptual hydrological models simulate streamflow extremes and drought-to-flood transitions across 63 catchments in Chile and Switzerland, assessing the impact of model structure and calibration choices. It finds that general model performance (KGE) does not guarantee accurate detection of extremes, and model structure is the most critical factor, with transitions being poorly represented, especially in semi-arid and high-mountain regions.
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Gu et al. (2026) Has the latest IMERG V07 from GPM improved the performance of precipitation estimation of regional-scale compared to its predecessor?
This study systematically evaluates and compares the performance of the latest IMERG V07 precipitation product against its predecessor V06 across the Yellow River Basin. It finds that IMERG V07 significantly improves precipitation estimation and rain/no-rain detection capabilities, demonstrating reduced sensitivity to environmental factors compared to V06.
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Schmitt et al. (2026) AGILE v0.1: The Open Global Glacier Data Assimilation Framework
This paper introduces AGILE v0.1, an open global glacier data assimilation framework that uses a time-dependent variational method with automatic differentiation to efficiently optimize glacier bed topography and distributed ice volume, demonstrating significant improvements over initial guesses in idealized experiments.
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Tallapragada et al. (2026) Evaluation of GFSv16 for Near‐Real‐Time Data Impact Studies During the Atmospheric River Reconnaissance Program 2022
This study investigated the impact of assimilating dropsonde data from AR Reconnaissance on improving winter 2022 precipitation forecasts for landfalling Atmospheric Rivers (ARs) on the U.S. West Coast, finding that targeted dropsonde observations significantly enhance forecast accuracy for medium to strong AR events.
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Seo et al. (2026) Implementation of a multi-layer snow scheme in the GloSea6 seasonal forecast system: impacts on land–atmosphere interactions and climatological biases
This study implements a multi-layer snow scheme in the GloSea6 seasonal forecast system, demonstrating improved simulation of snow seasonality, land-atmosphere interactions, and reduced climatological biases in temperature and precipitation over the Northern Hemisphere by delaying snowmelt and enhancing evaporative cooling.
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Bai et al. (2026) Persistent and intensifying heat extremes in global deeper soils
This study investigated global heatwave evolution across near-surface air, land surface, and subsurface soils from 1980–2024, finding that heatwaves have strengthened across all layers, with soils exhibiting greater exposure and severity than air or land surface, implying air temperature alone underestimates heatwave risks to soil ecosystems.
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Zhou et al. (2026) Integrating distributed hydrologic simulation with low-flow resilience: a spatiotemporal perspective
This study utilizes a fully distributed watershed hydrologic model to investigate low-flow resilience in a poorly gauged basin, revealing distinct spatiotemporal patterns where low flow emerges after approximately 120 days of minimal rainfall, and downstream reaches exhibit higher resilience compared to upstream areas.
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Homtong et al. (2026) Mapping spatiotemporal agricultural droughts from 2019 to 2024 in Northeast Thailand using multi-temporal and multiple sensor data together with random forest algorithm
This study mapped spatiotemporal agricultural droughts in Northeast Thailand from 2019 to 2024 using multi-temporal Sentinel-2 imagery and a Random Forest Regression algorithm, with a Soil Moisture Index (SMI) derived from Landsat 8 as reference data. The models achieved high accuracy (R > 0.65), revealing consistent severe drought events between March and May annually, with irrigated areas showing lower severity.
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Gnann et al. (2026) Uncertainty, temporal variability, and influencing factors of empirical streamflow sensitivities
This study systematically evaluates empirical methods for estimating streamflow sensitivities to precipitation and potential evaporation, revealing high uncertainties, particularly for potential evaporation. It demonstrates that these sensitivities are not static but decrease significantly over time (15 %–70 % over 50 years) as aridity increases, urging caution in their use for climate change impact assessments.
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He et al. (2026) Coupling data assimilation and machine learning to improve land surface conditions and near-surface temperature and humidity forecasts
This study coupled a hybrid data assimilation-machine learning framework (DL) with the Weather Research and Forecasting (WRF) model to quantify the impacts of incorporating soil moisture (SM) and vegetation data on land surface initialization and near-surface weather forecast accuracy. The results indicate that optimizing leaf area index (LAI) and SM significantly improves the simulation of water table depth, evapotranspiration, air temperature, and humidity, and refines land surface initial conditions for improved near-surface weather forecasts.
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Bong et al. (2026) Water Isotope Model Intercomparison Project (WisoMIP): Present‐Day Climate
This paper presents the first results of WisoMIP Phase 1, intercomparing isotope-enabled atmospheric general circulation models nudged to ERA5 reanalysis to isolate differences in water isotope behavior. The study finds that the ensemble mean of these models best matches observations, providing a benchmark dataset and revealing uncertainties in physical processes of the global water cycle.
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Yung et al. (2026) Flood monitoring: An innovative application of multisource image fusion and transfer learning
This study proposes a cross-sensor framework for robust water body mapping using multitemporal optical imagery, integrating relative radiometric normalization, spatiotemporally invariant feature extraction, a PSO-RF classifier, and cross-sensor sample transfer strategies to significantly improve flood monitoring accuracy and efficiency.
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Shokri et al. (2026) Better continental-scale streamflow predictions for Australia: LSTM as a land surface model post-processor and standalone hydrological model
This study evaluates two Long Short-Term Memory (LSTM)-based models (standalone and hybrid with AWRA-L) for continental-scale streamflow prediction in Australia, demonstrating their superior performance over traditional land surface and conceptual hydrological models across various validation scenarios. The findings highlight the potential of deep learning to enhance water resource management and climate adaptation strategies.
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Wang et al. (2026) Long‐Term Trends of Multiple Drought Types and Their Characteristics From ERA5 Since 1940 in the Yangtze River Basin
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Kan et al. (2026) Latitudinal divergence in runoff responses to global forestation due to forest-atmosphere feedbacks
Global potential forestation leads to a latitudinal divergence in runoff responses, increasing runoff in tropical regions but decreasing it in boreal regions, a pattern primarily driven by forest-atmosphere feedbacks rather than direct effects of forest expansion.
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Jiao et al. (2026) Multi attribute refined identification of flood-affected bodies based on multi-source data fusion
This study develops a multi-attribute diagnostic framework for flood-affected bodies by fusing multi-source data, addressing challenges in urban land function identification and dynamic population distribution characterization. It proposes an ensemble learning model for optimal urban land function identification and a human-land relationship matching method for high spatiotemporal resolution population mapping, demonstrating reliable support for comprehensive flood disaster assessment.
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Pandey et al. (2026) AI-Driven Predictive Modelling for Groundwater Salinization in Israel
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Li et al. (2026) Retrieval of snow depth using synthetic aperture radar: past, current, and future
This paper provides a comprehensive review of snow depth retrieval using synthetic aperture radar (SAR) techniques, detailing the interaction between SAR signals and snow, various backscattering models, and methods like PolSAR, InSAR, PolInSAR, and TomoSAR, to offer a future outlook on this critical parameter.
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Zhu et al. (2026) Estimating daily seamless 20-m resolution evapotranspiration using data fusion and TSEB
This study developed an efficient framework integrating cloud-filling, a high-performance spatiotemporal fusion model, multi-source remote sensing data, and the Two-Source Energy Balance (TSEB) model to produce daily seamless evapotranspiration (ET) estimates at 20 m resolution. The framework achieved robust performance, with daily ET estimates showing a coefficient of determination (R²) of 0.56, a mean bias (BIAS) of –0.08 mm/d, and a root mean square error (RMSE) of 1.05 mm/d, providing a powerful tool for precision agricultural water resource management.
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Cao et al. (2026) RRV (reliability, resilience and vulnerability)-based assessment of groundwater extraction sustainability in an over-exploited piedmont plain
This study evaluates groundwater extraction sustainability in the overexploited Beijing Plain, revealing that precipitation accounts for 86% of groundwater storage variations, and sustainability assessments based solely on groundwater storage changes significantly overestimate actual sustainability compared to those considering precipitation deficits.
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Ogle et al. (2026) Image-based classification of stream stage to support ephemeral stream monitoring
This study develops a low-cost, image-based machine learning method to classify relative stream stage (no, low, or high water levels) in ephemeral streams using field camera imagery from 2017-2023 in the upper Russian River watershed, California, demonstrating its utility for monitoring and quality control in data-scarce intermittent river systems.
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Hou et al. (2026) Global rainfall simulator studies: review, challenges and perspectives
This review paper comprehensively examines the technological progression, types, applications, limitations, and future prospects of global rainfall simulators, emphasizing the critical need for standardization to enhance their reliability and comparability in soil erosion and hydrological research.
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Hengl et al. (2026) OpenLandMap-soildb: global soil information at 30 m spatial resolution for 2000–2022+ based on spatiotemporal Machine Learning and harmonized legacy soil samples and observations
This paper presents OpenLandMap-soildb, a global dataset providing dynamic predictions of key soil properties and types at 30 m resolution for 2000–2022+ using spatiotemporal Machine Learning. It reveals a global loss of at least 11 Pg of soil organic carbon in the topsoil over the past 25 years, primarily due to deforestation.
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Gaines et al. (2026) Impact of Spatial Scale on Optical Earth Observation‐Derived Seasonal Surface Water Extents
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Yoon et al. (2026) A heterogeneous weighting strategy for leveraging Cross-Basin data enhances the Usability of deep learning hydrological models
This study develops a novel heterogeneous weighting strategy for deep learning hydrological models to effectively leverage cross-basin data, demonstrating improved predictive performance over conventional homogeneous weighting by accounting for basin-specific characteristics. The proposed method enhances the usability of deep learning models for hydrological prediction by mitigating local performance degradation often seen in regional pooling models.
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Ferguson et al. (2026) Renewability of fossil groundwaters affected by present-day climate conditions
This study compares hydraulic response times (HRT) and aquifer residence times (ART) across 31 major aquifer systems globally, revealing that the presence of fossil groundwater does not necessarily mean current water levels are controlled by past climates, as HRT often dictates the system's adjustment to modern conditions. It concludes that HRT is crucial for assessing groundwater renewability and sustainable management, even in aquifers containing very old water.
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Richter et al. (2026) Technical note: Literature based approach to estimate future snow
This study develops a resource-efficient, literature-based approach to project future snow depths and season lengths in regions lacking downscaled climate projections. By synthesizing existing literature and parameterizing reduction curves based on temperature scenarios and elevation, the method reveals significant declines in snow depth and season length across Swiss Alpine stations under a +2°C warming scenario, particularly at lower elevations.
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Zhang et al. (2026) A framework for identifying discriminative model, key factors, and precipitation blocking threshold on triggering drought propagation in the Xijiang River Basin (XRB)
This study developed a GANs-enhanced machine learning framework to identify key factors and precipitation thresholds for meteorological-to-agricultural drought propagation in the Xijiang River Basin, finding that non-effective precipitation days, drought duration, and spatial complexity are critical, and daily precipitation exceeding 3 mm can mitigate propagation.
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Zhou et al. (2026) An impact-based drought classification method using real-world agricultural drought records and explainable automated machine learning
This study introduces a novel impact-based framework combining causal inference with explainable Automated Machine Learning (AutoML) to classify drought severity and identify its primary drivers in China. The framework, leveraging real-world impact records, outperforms conventional methods, revealing that non-climatic factors (latitude, geopotential height) and climatic factors (soil moisture, evaporation) are key drivers, and indicating a significant intensification of drought severity across China from 1980 to 2024.
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Pradhan et al. (2026) Imprints of terrestrial water fluxes on tropospheric stable water isotopes revealed by satellite observations and complex network analysis
This study investigates the relationship between the isotopic composition of atmospheric water vapor (δD) and the surface water balance (evapotranspiration minus precipitation, ET-P) using satellite observations and model data. It reveals strong positive correlations and complex network teleconnections, demonstrating the utility of water vapor isotopes for climate network analysis and model evaluation.
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Zotta et al. (2026) Improving AMSR2 vegetation optical depth retrievals via land parameter retrieval model parameter optimisation
This study improves Vegetation Optical Depth (VOD) estimates from AMSR2 X-band observations by optimising key Land Parameter Retrieval Model (LPRM) parameters (surface roughness, effective temperature, single scattering albedo) through minimising brightness temperature residuals, demonstrating enhanced VOD-LAI seasonal agreement, especially in forests, but revealing trade-offs with soil moisture retrieval skill.
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Alijanian et al. (2026) A flexible semi-continuous time series framework for hydrological water balance analysis using multi-source data
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Zuo et al. (2026) Decoding surface and root-zone soil moisture dynamics for agricultural drought assessment using multi-source climate records (1990–2019)
This study investigates the dynamics of surface and root-zone soil moisture using 30 years of ESA-CCI data to assess agricultural drought characteristics in three US states and develops a novel knowledge-guided machine learning model for improved drought prediction. It reveals distinct soil moisture responses to precipitation during prolonged versus short-duration droughts and demonstrates an 8% improvement in root-zone soil moisture prediction accuracy with the new model.
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Chaves et al. (2026) When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models
This study introduces an information theory-based metric to quantitatively evaluate the relative contributions of physics-based conceptual constraints and data-driven components in hybrid hydrological models. It finds that performance predominantly relies on the data-driven component, which often compensates for or even overwrites physics-based constraints, challenging the assumption that integrating physics inherently enhances model performance or interpretability.
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Sun et al. (2026) Climate warming-induced glacier mass loss driving peak runoff variability and cryosphere service value decline
This study investigates glacier responses to past and future climate change using an integrated ice-dynamic model. It reveals that climate warming will cause substantial and irreversible glacier mass loss, leading to declining glacier service values and shifts in peak runoff timing, particularly in High Mountain Asia.
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Blanco et al. (2026) On two definitions for precipitation intensity: differences, artifacts and ambiguities
This study assesses the differences between two widely used precipitation intensity definitions (I and I_wet) in arid and semi-arid regions using global precipitation data. It finds that I consistently inflates values compared to I_wet, with differences exceeding 100% in arid regions, and advocates for the use of I_wet for more accurate quantification.
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González-Leiva et al. (2026) When will meteorological droughts become the new normal climatic condition in Central-Southern Chile?
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Brenner et al. (2026) GeoDS (v.1.0): a simple Geographical DownScaling model for long-term precipitation data over complex terrains
This paper introduces GeoDS (v.1.0), a simple, topography-based geographical downscaling model for long-term precipitation data over complex terrains, designed for paleoclimate studies. It demonstrates GeoDS's ability to capture fine-scale precipitation patterns and improve statistical agreement with observations in the European Alps and Greenland, while being computationally inexpensive and robust.
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Hussainzada et al. (2026) Comprehensive framework for agricultural water management in data-scarce regions: Integration of hydrological models and remotely sensed crop type data
This study proposes a comprehensive framework for agricultural water management in data-scarce regions by integrating hydrological modeling (WRF-Hydro) with remotely sensed crop type data and machine learning, demonstrating significant potential for water savings through improved irrigation efficiency in the Amu River Basin.
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Li et al. (2026) Integrated evaluation of snow density reanalysis products in the Northern Hemisphere
This study systematically evaluated the accuracy and applicability of snow density from five reanalysis datasets (ERA5-Land, GLDAS-Noah, GLDAS-CLSM, GLDAS-VIC, JRA-3Q) across the Northern Hemisphere using 4,319 in-situ stations, finding that while ERA5-Land and GLDAS-Noah performed best in spatial and temporal representation, all reanalysis products failed to accurately reproduce observed long-term interannual snow density trends.
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Liu et al. (2026) Projecting future exposure to compound precipitation and wind extremes using Copula methods with Bayesian model averaging
This study developed a novel framework using Bayesian Model Averaging and Copula methods to project future population and economic exposure to compound precipitation and wind extremes (CPWEs). It found that CPWE intensity and exposure risk are projected to increase, particularly around mid-century, with high-risk areas persisting in the North China Plain and southeastern coast under climate change scenarios.
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wu et al. (2026) Evaluating surface fluxes in WRF using eddy-covariance flux measurements in the Western and Eastern U.S.
This study evaluates the Weather Research and Forecasting (WRF) model's surface flux simulations, specifically using the Pleim-Xiu land surface model (PX LSM), against year-long eddy-covariance measurements from 16 sites in the San Joaquin Valley (SJV) and Multi-state Mid-Atlantic (MMA) regions, revealing significant heat flux biases in the SJV primarily linked to irrigation.
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Feng et al. (2026) Subsurface stormflow concentration-discharge relationships reveal DOC and nitrate transport mechanisms across land uses in karst hillslopes
This study investigated dissolved organic carbon (DOC) and nitrate transport dynamics and concentration-discharge relationships in subsurface and epikarst flows across four land-use types in karst hillslopes. It revealed epikarst flow as the dominant nutrient export pathway and highlighted the impact of land use and rainfall patterns on carbon and nitrogen fluxes.
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Grandjouan et al. (2026) An original approach combining biogeochemical signatures and a mixing model to discriminate spatial runoff-generating sources in a peri-urban catchment
This study developed an original approach combining biogeochemical signatures and a Bayesian mixing model to spatially decompose streamflow into eight runoff-generating sources in a peri-urban catchment, revealing highly variable source contributions influenced by hydro-meteorological conditions and land use.
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Huynh et al. (2026) A hybrid physics–AI approach using universal differential equations with state-dependent neural networks for learnable, regionalizable, spatially distributed hydrological modeling
This study introduces a hybrid physics–AI framework that integrates state-dependent neural networks into a spatially distributed, regionalizable, and fully differentiable hydrological model using universal differential equations (UDEs). The framework demonstrates consistently strong performance in streamflow simulations, particularly for flood modeling, by refining internal water fluxes and improving parameter regionalization.
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Li et al. (2026) Observed declining strength of vegetation-atmosphere coupling
This study investigates the global patterns and driving mechanisms of vegetation-atmosphere coupling (VC) strength using a novel physically-based index, revealing a widespread declining trend in VC across 38.84–61.98 % of global land, primarily driven by changes in leaf area index and wind speed.
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He et al. (2026) The general formulation for mean annual runoff components estimation and their change attribution
This study proposes a general and concise Modified Ponce-Shetty (MPS) model to quantify and attribute mean annual surface flow, baseflow, and total runoff. Applied across 662 catchments in China and the contiguous United States, the model accurately depicts spatial variability and simulates multi-year runoff components, revealing that surface flow is jointly controlled by precipitation and environmental factors, while baseflow is primarily influenced by environmental factors.
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Achahboun et al. (2026) LSTM and Temporal Fusion Transformers for daily evapotranspiration estimation using FLUXNET2015 and Google Earth Engine
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Zhang et al. (2026) Drying Soil Moisture Dominates Enhancing Summer Soil Moisture‐Temperature Coupling Under Climate Change
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Yao et al. (2026) Persisting Modulation of Interdecadal Pacific Oscillation on Near‐Future Winter Precipitation Projections in Northern Europe
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Alkanjo et al. (2026) Machine Learning as a Tool to Predict Reference Evapotranspiration
This study predicted monthly reference evapotranspiration (ET) in Siirt, Türkiye, using various machine learning and statistical regression models, along with a Design of Experiments (DoE) approach. The DoE model achieved the highest accuracy (R²=0.987), identifying average temperature as the most influential variable.
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Akhter et al. (2026) Quantification of Different Uncertainties in Streamflow Simulations and Their Propagation Across Multiple Catchments
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Arnáez et al. (2026) Hydrological Challenges and Competing Demands in the Mediterranean Region
This review examines the severe hydrological challenges in the Mediterranean region, highlighting the growing conflicts between declining water resources in mountainous headwaters (due to climate change and revegetation) and increasing water demand in lowlands (driven by agriculture, tourism, and urban expansion), which leads to environmental and social tensions.
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Hoseingholi et al. (2026) Optimal Hedging Rules Determination for Dam Reservoir Operation Under Climate Change
This study proposes a novel approach combining hydrological modeling, optimization, and climate projections to determine optimal discrete and continuous hedging rules for the Marun Dam reservoir in Iran under climate change. It finds that the discrete hedging rule significantly outperforms standard operation policy and continuous hedging rules in maintaining reservoir storage and reducing severe water shortages, offering a more robust strategy for future water resource management.
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Noffz et al. (2026) Development of a Dual‐Domain Karst Flow Model Under Consideration of Preferential Film‐Flow Dynamics and Analysis of Compartment‐Specific Parameter Sensitivities
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Sayl et al. (2026) Sustainable Water Management for Agriculture to Mitigate Climate Change Effects
This study developed an integrated methodology using the WATSUIT model and Geographic Information System (GIS) to mitigate drought impacts in Iraq by assessing the suitability of saline water from the Main Outfall Drain (MOD) for irrigating salt-tolerant crops, complemented by rainwater harvesting for soil washing. The findings indicate that while Basra's MOD water is too saline, other regions along the MOD can utilize this water with appropriate leaching fractions and rainwater harvesting to support sustainable agriculture.
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Zhan et al. (2026) Integrating SWOT With Multi‐Source Satellite Observations for Near‐Daily Reservoir Water Level Monitoring
## Identification - **Journal:** Water Resources Research - **Year:** 2026...
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Li et al. (2026) Observation‐Driven Forecast of Global Terrestrial Water Storage and Evaluation for 2010–2024
This study develops a machine learning approach to forecast GRACE-like terrestrial water storage changes (TWSC) up to 12 months ahead, addressing the latency of GRACE/GRACE-FO products. The method demonstrates improved accuracy and robustness compared to ECMWF's seasonal forecasts, providing a viable data-driven solution for operational TWSC forecasting.
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Rezaie et al. (2026) Evaluating the Impact of Climate and Land-Use Change on Flood Susceptibility on a Global Scale
This global-scale study investigates the combined impacts of climate and land-use change on flood susceptibility in the 21st century using SSP-RCP scenarios and machine learning, finding that Random Forest outperforms other models and projects an increase in high/very high flood susceptibility areas, particularly in Oceania, Europe, and parts of Asia and Africa.
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Fadaee et al. (2026) Explainable Artificial Intelligence in Hydrology: A Review
This paper presents the first systematic and critical review of Explainable Artificial Intelligence (XAI) applications in hydrology and hydrogeology, synthesizing over 180 peer-reviewed studies. It concludes that XAI significantly enhances the transparency and trustworthiness of AI models while deepening the understanding of underlying physical hydrological processes.
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Viviroli et al. (2026) Cascading downstream impacts of water cycle changes in mountain regions
This perspective synthesizes the cascading downstream impacts of climate-induced water cycle changes in mountain regions, highlighting how these alterations affect diverse social-ecological systems and identifying critical research gaps and adaptation challenges.
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Ougahi et al. (2026) Investigating Deep Learning Knowledge Transfer in Streamflow Prediction From Global to Local Catchment
This study evaluates transfer learning approaches using Long Short-Term Memory (LSTM) models to improve streamflow prediction in data-scarce regions by leveraging data from data-rich areas and catchment characteristics. It demonstrates that pre-training LSTMs on hydrologically similar basin clusters, followed by fine-tuning with limited local data, significantly enhances prediction accuracy in data-poor regions.
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Toum et al. (2026) A New Process‐Based Approach for Evaluating Gridded Precipitation Products in Mountain Watersheds: Test Cases From the Central Andes of Argentina
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Giggy et al. (2026) Changes in Dominant Streamflow Drivers as Network‐Scale Flow Regime Shifts From Intermittent to Ephemeral Across a Multi‐Year Drought
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Cecchetto et al. (2026) Beyond Water: Mapping Sediment Bars to Enhance Satellite Monitoring of River Dynamics
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Ghaneei et al. (2026) An Effective Monitoring of Evolving Groundwater Drought via Multivariate Data Assimilation and Machine Learning
This study introduces an observation-informed approach to produce daily groundwater drought maps at 1/8° resolution across the contiguous United States, revealing distinct and persistent dry clusters, particularly severe in the Western and Northeastern regions.
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Neto et al. (2026) Streamflow Elasticity to Precipitation Distribution and Potential Evapotranspiration Across South America
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Jäger et al. (2026) On the Robustness of Modeled Non‐Local Temperature Effects of Historical Land Use Changes
## Identification - **Journal:** Journal of Advances in Modeling Earth Systems - **Year:** 2026...
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Song et al. (2026) Physics‐Informed, Differentiable Hydrologic Models for Capturing Unseen Extreme Events
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Markhali et al. (2026) Regionalization of a Distributed Hydrology Model Using Random Forest
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Kumar et al. (2026) Developing a Comprehensive and Spatially Explicit GIS–Fuzzy TOPSIS Framework for Drought Vulnerability Assessment
This study developed a comprehensive and spatially explicit GIS–Fuzzy TOPSIS framework for drought vulnerability assessment in Maharashtra, India, integrating diverse environmental and socio-economic indicators. The framework revealed that nearly half of the study area (47.52%) falls into high and very high vulnerability zones, primarily driven by low rainfall, depleted groundwater, and persistent soil moisture deficits.
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Mohril et al. (2026) Exploring Response Time Influence on Rising Limb of a Hydrograph With Emphasis on Raindrop Characterisation: An Experimental Study
This study evaluates a rainfall simulator's ability to replicate natural rainfall conditions and then uses it to systematically analyze the influence of dynamic (moving) rainfall patterns on surface flow and hydrograph components. A nonlinear regression model effectively captures these rainfall-runoff interactions, demonstrating high accuracy in both calibration and validation.
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Liang et al. (2026) Understanding Multiscale Hydrological Interactions From Spectral Perspective: A Large Sample Investigation Across the United States
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Verma et al. (2026) CTRIP‐HyDAS: A Global‐Scale Data Assimilation Framework for SWOT‐Derived Discharge Using Synthetic Observations at High Resolution (1/12°)
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Wit et al. (2026) Subsurface Irrigation in Regional Water Management: a System Dynamics Approach to Support Decision Making
This study developed a system dynamics model (SDM) to simulate the non-linear hydrological impacts of upscaling controlled drainage with subirrigation (CDSI) from local to regional scales, demonstrating its utility as a decision support tool for water management authorities in assessing feasibility and supporting decision-making.
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Watson et al. (2026) Evaluating CORDEX ‐ CORE Climate Projections for Simulated Hydrological Processes in a Mesoscale Catchment, South Africa
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Cai et al. (2026) Contrasting responses of Eurasian extreme precipitation to climate change: a multi-regional assessment using NEX-GDDP-CMIP6
This study evaluates the NASA-NEX-GDDP-CMIP6 dataset's performance in simulating six extreme precipitation indices across four Eurasian regions (1985–2014) and projects future changes under Shared Socioeconomic Pathways (2071–2100), revealing contrasting hydroclimatic responses from increased monsoon extremes in South Asia to hydroclimatic whiplash in Central Asia.
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Hou et al. (2026) Advancing Near‐Real‐Time Flood Inundation Mapping in Australia
This study develops and evaluates a near real-time, 5-meter spatial resolution flood monitoring framework for Australia, integrating gauge data, hydrological models, and multi-source satellite observations. It demonstrates the critical role of low-latency gauge data and high-resolution LiDAR DEMs, while also showing the effectiveness of ensemble modeling and multi-source remote sensing for ungauged areas.
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Deepthi et al. (2026) Chaos Theory-Based Downscaling for Future Rainfall Projections from Climate Models
This study applies a novel chaos theory-based downscaling method to project future monthly rainfall across India (0.25° × 0.25° resolution) using five CMIP6 General Circulation Models (GCMs) under four Shared Socioeconomic Pathway (SSP) scenarios (2015-2099), revealing significant regional shifts in both southwest and northeast monsoon rainfall patterns.
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Wang et al. (2026) Event‐Type‐Based Multi‐Dimensional Diagnostics of Process Limitations in Hydrological Models
## Identification - **Journal:** Water Resources Research - **Year:** 2026...
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Li et al. (2026) ELM‐MOSART‐DOC: A Large‐Scale Riverine Dissolved Organic Carbon Model and Its Application Over the United States
## Identification - **Journal:** Journal of Advances in Modeling Earth Systems - **Year:** 2026...
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Choudhary et al. (2026) Simulating Streamflow Scenarios Using Hydrological Modeling Integrated with Future Land Use and CMIP6 Climate Projections
This study develops a unique modeling framework integrating an ensemble of CMIP6 General Circulation Models (GCMs) with future land use land cover (LULC) projections to assess their combined impacts on the hydrology of the Upper Krishna River sub-basin. It projects significant increases in temperature, precipitation, surface runoff, and water yield, leading to heightened and prolonged flood risks by the end of the century, particularly under high-emission scenarios.
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Keserci (2026) Comparative Performance of Global Datasets and Ground‐Based Precipitation and Temperature Products in the Eastern Mediterranean Basin: The Case of Türkiye
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Li et al. (2026) Parsimonious and Transferrable Parameterization of Reservoir Operations: A Modular Approach for Large‐Scale Modeling
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Jia et al. (2026) Enhancing Streamflow Prediction Using Cutting-edge Deep Learning Models and Seasonal-Trend Decomposition
This study systematically evaluates three cutting-edge deep learning models (FITS, FGN, PatchTST) against traditional models (LSTM, CNN, GRU) for streamflow prediction, both with and without four seasonal-trend decomposition (STD) techniques. It demonstrates that advanced models, particularly FITS, offer superior accuracy, robustness, and computational efficiency, while STD significantly improves traditional models but has limited impact on the advanced ones.
