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Pawar et al. (2026) Hydrologically enhanced machine learning framework for urban flood inundation mapping using multi-sensor remote sensing data: a case study of Mumbai, India
This study developed a hydrologically enhanced machine learning framework for urban flood inundation mapping in Mumbai using multi-sensor remote sensing data, demonstrating that incorporating hydrologic-topographic predictors significantly improves model performance and physical interpretability. The optimized XGBoost model achieved an overall accuracy of 71.7 % and an AUC of 0.803, with hydrologic predictors increasing discrimination from approximately 0.66 (SAR-only) to 0.80 (complete framework).
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Jeantet et al. (2026) Climate change impacts on groundwater simulated using the AquiFR modelling platform
This study assesses climate change impacts on groundwater levels in French aquifers during the 21st century using the AquiFR hydrogeological platform forced by 36 regional climate projections. It reveals significant variability, but a predominant signal of increasing groundwater levels in northern France and increasing low groundwater levels in southern France under the RCP8.5 scenario, highlighting a discernible North-South distinction.
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Xu et al. (2026) Improving photosynthate allocation dynamic simulations of crops under water stress conditions
This study develops a water-driven, stage-dependent carbon allocation scheme within the Agro-C model to better represent crop responses to soil moisture variability. The optimized model improves the representation of physiologically regulated allocation processes and enhances the realism of crop simulations.
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Lubis et al. (2026) More Frequent Atmospheric Rivers and Associated Precipitation Extremes Induced by the Baroclinic Annular Mode
This study investigates the relationship between midlatitude atmospheric variability, specifically the Baroclinic Annular Mode (BAM), and its impact on global atmospheric river (AR) activity. The research reveals that BAM's positive phase enhances AR frequency and precipitation extremes.
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Cao et al. (2026) Compound marine heatwaves and atmospheric boundary layer jets drive enhanced coastal precipitation
This study investigates the synergistic impacts of marine heatwaves (MHWs) and atmospheric boundary layer jets (BLJs) on coastal precipitation over the South China Sea. The results show that co-occurring MHWs and BLJs drive a 51.9% increase in regional coastal precipitation relative to climatology.
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Zhang et al. (2026) High-resolution global groundwater storage anomalies: a 1 km downscaled dataset
This study developed a global 1 km monthly groundwater storage anomaly (GWSA) dataset for April 2002–December 2020 by downscaling GRACE observations using machine learning and high-resolution environmental predictors. The dataset provides enhanced spatial detail, showing high consistency with original GRACE data and reasonable agreement with in-situ observations, though with regional variations in performance.
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Xu et al. (2026) Dynamic Evolutions and Precursory Meteorological Anomalies of Compound Drought‐Heat Events in Northeast China Under a 3D Connected‐Component Tracking Framework
This study develops an adaptive 3D spatiotemporal tracking framework to analyze compound climate extremes, applying it to compound drought-heat events (CDHEs) in Northeast China from 1960 to 2022. The framework identified 248 CDHE lifecycles, revealing bifurcated spatial evolution patterns and highlighting synergistic precursory meteorological anomalies driving event initiation.
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Zheng et al. (2026) Indian Ocean Dipole Impacts on Autumn Heat and Early-Winter Precipitation over the Arabian Peninsula
This study investigates the season-spanning influence of the Indian Ocean Dipole (IOD) on autumn heat and early-winter precipitation over the Arabian Peninsula, revealing distinct mechanisms for warming and precipitation amplification during positive IOD events and suppressed precipitation during negative IOD.
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Prova et al. (2026) Application of radar remote sensing for cyclone damage mapping in Bangladesh: a coherence-based approach
This study presents a coherence-based Synthetic Aperture Radar (SAR) framework for identifying and quantifying cyclone-induced surface changes in the coastal districts of Bangladesh. The framework, validated with high accuracy (94%), offers a scalable, cloud-independent tool for rapid post-cyclone damage assessment, enhancing disaster monitoring and response.
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Cho et al. (2026) Basin-scale evaluation of the Noah-MP land surface model for runoff and snow generation in the Missouri River Basin: insights and recommendations for parameterization scheme selection
This study evaluated the Noah-Multiparameterization (Noah-MP) land surface model's default and four alternative parameterization schemes for runoff and snow generation in the Missouri River Basin. The findings provide recommendations for scheme selection based on basin characteristics, demonstrating improved hydrological prediction in diverse hydroclimatic regions.
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Liu et al. (2026) Dust Aerosol Effects on Charge Structure and Lightning Activity of a Continental Thunderstorm: A Modeling Study With Thompson‐Electrification Scheme
This study established a mesoscale electrification-coupled model to investigate the individual and synergistic impacts of dust aerosols (as cloud condensation nuclei (CCN) and ice nuclei (IN)) on the microphysics, charge structures, and lightning activity of a continental thunderstorm. The research found that dust significantly modifies hydrometeor distributions, converts charge structures from classical tripole to quadrupolar, and increases the prevalence of positive cloud-to-ground (+CG) lightning.
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Bao et al. (2026) An edge-effect-aware framework for uncertainty-resolved crop mapping and planting-structure monitoring in arid irrigation landscapes
This study developed an edge-effect-aware framework for uncertainty-resolved crop mapping and planting-structure monitoring in arid irrigation landscapes. It found that a structurally simple Standard Neural Network (SNN) combined with spatial filtering achieved superior accuracy (0.953 Overall Accuracy) and robustness against mixed-pixel interference, revealing a "sunflower matrix and maize aggregation" pattern driven by economic incentives and water constraints.
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Hageltom et al. (2026) Adapting Noah-MP-Crop for sorghum modeling in a semi-arid environment: a case study of Gedaref region in East Sahel
This study adapted the Noah-MP-Crop model for rainfed sorghum in Sudan's semi-arid Gedaref region by developing sorghum-specific parameters and integrating satellite-derived dynamic sowing dates. The adapted model, particularly with dynamic sowing, significantly improved simulations of sorghum canopy development and yield compared to a fixed sowing date approach.
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Paciolla et al. (2026) Irrigation quantification through ensemble physical modelling
This study developed a Monte Carlo ensemble physical modeling methodology to quantify irrigation incidence and volume by integrating ground and satellite data with an energy-water balance model. The algorithm proved most effective for fields with fewer irrigation events and during high-evapotranspiration periods, though its accuracy is sensitive to satellite data uncertainty, particularly in heterogeneous agricultural landscapes.
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Guo et al. (2026) Diagnosing drought–flood vulnerability of rice systems across growth stages using Sentinel-1/2 and CHIRPS time series
This study developed a Stage-resolved Drought–flood Rice Vulnerability (SDRV) framework using Sentinel-1/2 and CHIRPS time series to diagnose stage-specific vulnerability of single-season rice to drought-flood alternation in the middle and lower Yangtze River region, revealing that vulnerability is highly stage-dependent and amplified in specific growth stages (S2-S3).
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Emmanuel* et al. (2026) Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting
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Laffitte et al. (2026) Interpretable Machine Learning Reveals Global Nonlinear Responses of Soil Respiration to Climate
This study used a SHAP-interpreted Random Forest model on 8,111 observations to identify global response patterns of soil respiration (Rs) to key environmental drivers. It revealed a peaked temperature association with a maximum at 27.7°C, plateauing precipitation contributions, and linearly increasing leaf area index contributions, highlighting nonlinearities often overlooked by monotonic assumptions in Earth system models.
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Goudiaby et al. (2026) Evaluating the impact of satellite precipitation data uncertainties on streamflow and actual evapotranspiration simulations
This study investigates the uncertainty associated with satellite precipitation data on GR4J's performance in simulating discharge and actual evapotranspiration in West Africa, using multi-objective calibration strategies.
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Barthe et al. (2026) Meso-NH-ISO v1.0: a water stable isotopologue scheme in the non-hydrostatic mesoscale atmospheric model Meso-NH – application to a 2D West African squall line
This study implements water stable isotopologues in the non-hydrostatic mesoscale atmospheric model Meso-NH, enabling simulations of realistic case studies with various mesoscale convective systems. The implementation is validated on a 2D simulation of a Sahelian squall line observed during the COPT 81 field campaign.
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Pu et al. (2026) Flood Responses to Climate Warming on Tribal Lands Using High‐Resolution Hydrodynamic Modeling
This study addresses the critical equity gap in climate adaptation by developing a climate-to-local-inundation framework for the Santee Sioux Nation, Nebraska. The research finds that projected changes in inundation frequency and flood depth are limited, with localized adjustments in floodplain activation and tributary connectivity.
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Harbord et al. (2026) Congo Air Boundary Breakdown Timing and Its Connection to Southern African Rainfall Onset in Reanalyses and Historical CMIP6 Models
This study evaluates how well 27 CMIP6 models simulate the relationship between the Congo Air Boundary (CAB) breakdown and rainfall onset in southern Africa. It finds that while models show wider variability in breakdown timing compared to reanalyses, they successfully reproduce the core association between CAB breakdown and rainfall onset, which is crucial for assessing future rainfall projections.
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Borkotoky et al. (2026) Influence of North American Weather Regimes on Regional Extreme Precipitation Events over CONUS
This study defines seasonally-evolving North American Weather Regimes (WRs) and investigates their influence on extreme precipitation events across CONUS watersheds, finding that specific WRs (e.g., persistent upper-level troughs) often trigger extreme precipitation in certain regions.
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Baste et al. (2026) The need for uncertainty: why probabilistic LSTMs are key to improving flood predictions and enabling learned warning rules
This study investigates whether probabilistic Long Short-Term Memory (LSTM) models improve the representation of extreme events in rainfall–runoff simulations and if reinforcement learning can translate these into actionable flood warnings. It finds that probabilistic LSTMs significantly enhance the capture of extreme flood events compared to deterministic LSTMs, and a reinforcement learning agent (FRiCA) effectively learns risk-aware flood warning rules, outperforming static heuristics.
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García-Hernández et al. (2026) Assessing silvicultural treatment effects on catchment hydrology under current and future climate scenarios
This study used eco-hydrological modeling to assess the impact of two forest thinning strategies (Uniform Shelterwood System and Group Selection System) on ecohydrological responses (Leaf Area Index, Evapotranspiration, and streamflow) in a Mediterranean mountain catchment under current and future climate scenarios, finding that the Group Selection System significantly increased water yield under milder scenarios, while extreme climate attenuated management effects.
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Guo et al. (2026) A knowledge-guided machine learning framework for reconstructing climate-driven subsurface water dynamics from time-lapse electrical resistivity tomography
This study develops a knowledge-guided machine learning framework using time-lapse electrical resistivity tomography (TL-ERT) and meteorological forcing to reconstruct climate-driven subsurface water dynamics on a hillslope. The framework successfully models the seasonal evolution of subsurface resistivity, providing a proof of concept for hydrologic interpretation under climate scenarios.
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Pasquini et al. (2026) Assessing the ability of a stretched‐grid deep‐learning weather prediction model to capture physical balances
This study compares the performance of a deep learning weather prediction model, Bris, with a traditional numerical weather prediction model, MEPS, in forecasting an extreme extratropical cyclone event. The results show that Bris struggles to capture key mesoscale features and disrupts atmospheric balances due to fine-scale noise.
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Xiao-hu et al. (2026) Spatial Structure and Lifecycle Variability of Compound Dry‐Hot and Heavy Rainfall Events Over the North China Plain
This study developed an object-based framework to detect and track paired dry-hot and heavy rainfall patches over the North China Plain, revealing recurrent spatial and lifecycle structures in these events.
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Chegnizadeh et al. (2026) Non-linear stochastic optimization for agricultural planning under future surface-water scarcity in the area downstream of the Karkheh Dam, Iran
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Gou et al. (2026) A performance-based multivariable weighting framework for WRF parameterization-scheme selection
This study proposes a performance-based multivariable weighting framework for WRF parameterization-scheme evaluation. The framework combines mean relative error and the coefficient of variation of absolute errors to give greater influence to variables that are both difficult to simulate and informative for distinguishing candidate schemes.
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Hara et al. (2026) Observation of Daily Flood Hazard During the SWOT Cal/Val Orbit
This study utilizes daily observations from the SWOT mission to track a short-lived flood along the Du Gouffre River in Canada, demonstrating the value of high-frequency wide-swath radar altimetry for monitoring transient floods.
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Yang et al. (2026) Regime Shifts During Compound Drought‐Heatwave Events Amplify Ecosystem and Heat Stress Risks
This study develops a daily regime-based framework to characterize global summer Compound Drought-Heatwave Events (CDHEs) and identifies key transition pathways that influence event severity and duration.
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Okajima et al. (2026) Rectified Effect of Synoptic‐Scale Atmospheric Eddies on Global Ocean‐Atmosphere Heat Exchanges
This study investigates the long-term effect of synoptic-scale atmospheric eddies on turbulent heat flux between the ocean and atmosphere, revealing a significant enhancement in midlatitude regions.
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Shi et al. (2026) Remote sensing of aquatic nutrients from proxy estimation to integration with process models
This study reviews remote sensing approaches for estimating aquatic nutrients, focusing on their integration with process-based models. The review highlights the limitations of proxy-based estimation methods and emphasizes the need for direct mechanistic inference of nutrient cycling processes.
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Casson et al. (2026) Propagating meteorological uncertainty in physically based mountain snow modeling
This study generates meteorological forcing ensembles designed for mountainous terrain and applies them to physically based snow modeling. The approach explicitly corrects for precipitation undercatch, generates spatially and temporally coherent high-resolution meteorological ensembles, and propagates these uncertainties through the SUMMA energy-balance snow model.
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Grassi et al. (2026) A Machine Learning Framework for Objective Definition and Projection of Meteorological Seasons: An Application to the Indian Subcontinent
This study presents a machine learning framework that objectively detects meteorological seasons and projects their future evolution, focusing on the Indian subcontinent as a proof-of-concept region.
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Guo et al. (2026) Shortened Wet Seasons in the Southern Hemisphere Land under Global Warming
This study investigates changes in wet season length under global warming, revealing a robust shortening in Southern Hemisphere land regions, primarily driven by a greater delay in wet season onset than cessation due to asymmetric changes in latent heat flux.
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Zafar et al. (2026) Comparative Assessment of Machine Learning and Statistical Models for Agricultural Drought Forecasting and Driver Attribution in the North China Plain
This study provides a spatiotemporal analysis and prediction of agricultural drought in the North China Plain (NCP) from 2001 to 2024 using the Vegetation Health Index (VHI). It identifies air temperature as a dominant negative driver of vegetation health, while soil moisture and vegetation greenness (EVI) are significant positive influences, and demonstrates that Long Short-Term Memory (LSTM) models offer the best performance for drought prediction.
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Sang et al. (2026) Observation-constrained assessment of long-term cropland productivity trends and drivers at the global scale
This study provides a global, observation-constrained assessment of cropland gross primary productivity (GPP) trends and their drivers from 1982 to 2016. It reveals a significant increase in cropland GPP, primarily driven by vegetation structural changes and a residual term combining CO₂ fertilization and agricultural management, with climate variability imposing a negative effect.
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Broekhoven et al. (2026) Changes in groundwater-surface water interactions following two centuries of irrigation practices and groundwater use in the Upper Ganges-Yamuna interfluve, North India
This study quantifies two centuries of changes in groundwater recharge and abstraction in the Upper Ganges-Yamuna interfluve, North India, revealing a hydrological regime shift. Initially, canal construction (post-1830) increased groundwater levels and discharge to rivers, but intensive groundwater abstraction (post-1970) has since lowered groundwater tables below pre-canal conditions, leading to local rivers shifting from draining to infiltrating conditions around 2000.
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Liu et al. (2026) Evidence of vertical soil hydraulic heterogeneity regulating hydrothermal simulations in Qinghai–Tibetan Plateau wetlands
This study provides the first process-based and depth-resolved characterization of layered soil hydraulic properties in Qinghai–Tibetan Plateau wetlands using laboratory experiments. It demonstrates that implementing this vertical hydraulic heterogeneity in the Noah-MP land surface model significantly alters water and energy partitioning, highlighting its crucial role in regulating hydrothermal simulations.
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Goshtasbpour et al. (2026) Areal reduction factors from high-resolution rain gauges in Austria
This study empirically derives Areal Reduction Factors (ARFs) for the Feldbach region in southeastern Austria using a high-resolution rain gauge network (WegenerNet) to convert point design precipitation into areal values. The findings reveal that ARFs are necessary even for areas as small as 1 square kilometer, exhibit unique dependencies on return period for sub-hourly durations, and show weak correlation with topography.
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Wang et al. (2026) Spatiotemporal evolution and drivers of compound dry-hot and wet-hot events in a changing climate
This study assesses the global spatiotemporal evolution and drivers of Compound Dry-Hot Events (CDHEs) and Compound Wet-Hot Events (CWHEs) from 1961 to 2100 using CMIP6 simulations. It finds that both event types have intensified since the 1990s, with CWHEs projected to become increasingly dominant and severe by the late 21st century, primarily driven by hot conditions.
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Qiao et al. (2026) Turbulent mixing by non-breaking surface waves in the ocean: theory, observation and modeling
This study provides a theoretical foundation for turbulence generated by non-breaking surface waves in the ocean, showing that the resulting vertical mixing extends to a depth of the horizontal wavelength of the surface wave. The mechanism of stretching and tilting of turbulence vortex tubes by the wave's orbital velocity is responsible for the growth of turbulence from background noise.
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Sabut et al. (2026) Global expansion of compound drought–heatwave risk
This study investigates the evolution and spatial reorganization of compound drought-heatwave (CDHW) events globally, revealing that these events systematically displace over time rather than remaining stationary, thereby expanding the footprint of compound stress.
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Bédard et al. (2026) Satellite radiance observation error inflation and spatial thinning: Reconciling idealized theory with near‐operational applications
This study investigates the joint effects of spatial thinning and observation error standard deviation inflation in satellite radiance assimilation, revealing a robust asymptotic behavior in optimal inflation. The results demonstrate statistically significant improvements in forecast scores when thinning reduction is paired with appropriate inflation.
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Rexroade et al. (2026) Seasonal wetting and drying dynamics drive greenhouse gas emissions across Australia’s river network
This study integrates riverine and dry riverbed carbon dioxide and methane data with geospatial data to predict greenhouse gas emissions across Australia's river network. The results show that seasonal wetting and drying dynamics drive greenhouse gas emissions.
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Abbasian et al. (2026) Changes in Flood Frequency and Seasonality Across Midwestern U.S. Watersheds Under a Warming Climate: Insights From Convection‐Permitting Climate Simulations and Physics‐Based Hydrologic Models
This study combines convection-permitting climate simulations with the WRF-Hydro model to investigate how climate warming affects flood frequency and seasonality in the Midwestern United States. The research reveals a regional shift toward more extreme warm-season, rainfall-driven floods under both simulated warming and historical records.
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Zhao et al. (2026) Urban vegetation experienced less damage than rural vegetation during the 2022 compound hot–dry event in eastern China
This study examines the impact of compound hot–dry events on vegetation growth in urban and rural areas of the Yangtze River Basin in China. The results show that urban vegetation is more resilient to these extreme events than rural vegetation.
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Li et al. (2026) Asymmetric impacts and legacy effects of dry-wet abrupt alternation on global terrestrial vegetation productivity
This study systematically assessed the asymmetric impacts and legacy effects of dry-wet abrupt alternation (DWAA) on global vegetation productivity. It found that DWAA causes spatially heterogeneous and direction-dependent gross primary productivity (GPP) losses, with dry-to-wet transitions leading to greater initial losses and wet-to-dry transitions resulting in longer-lasting losses and distinct recovery patterns.
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Li et al. (2026) Unprecedented late-summer mega-heatwave over the Yangtze River Basin in 2024: dynamical fingerprint of extreme anomalous atmospheric circulation variability
This study investigates the unprecedented late-summer 2024 heatwave over the Yangtze River Basin, revealing that its record magnitude was primarily driven by an extreme realization of a recurrent Eurasian circulation mode superimposed on long-term warming.
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Hsu et al. (2026) Sub‐Seasonal Forecasting of Precipitation and Soil Moisture in CFSv2 Near Gulf Coast Tropical Cyclone Landfall Locations
This study assesses the Climate Forecast System Version 2's (CFSv2) ability to represent near-surface soil moisture and associated precipitation at various lead times over the U.S. Gulf coast, revealing decreasing predictability with lead time and a failure to capture intense precipitation events, which limits its subseasonal-to-seasonal forecast utility for tropical cyclone impacts beyond a week.
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Admasu et al. (2026) An assessment of Northern Hemisphere warm spell trends across seasons: spring warm spells increasing faster than projected
This study assesses trends in warm spells across all seasons in Northern Hemisphere mid-latitude land using observational data and CMIP6 climate models. It finds that observational data show increasing warm spell days in all seasons, but CMIP6 models significantly underestimate spring trends and overestimate fall trends, with these biases linked to seasonal mean temperature, surface albedo, and soil moisture biases.
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Bian et al. (2026) Improved Parameterization of Snow Albedo in WRF/Noah-MP: Methodology Based on Snow Ablation in Upper Yellow River
This study developed an improved snow albedo scheme for the Noah-MP land surface model, dynamically integrating snow age, depth, and temperature, and demonstrated its superior accuracy in simulating a snow event in the Upper Yellow River basin compared to default schemes.
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Meza et al. (2026) High-resolution actual evapotranspiration in almond orchards by integrating physiological and TSEB models
This study developed and applied a novel physiological-surface energy balance-based thermal infrared (TIR) temperature correction and a daily evapotranspiration (ETa) upscaling method to improve high-resolution ETa estimates in almond orchards. It demonstrated that integrating this correction with the Two-Source Energy Balance (TSEB) model significantly enhances the accuracy of instantaneous and daily ETa, supporting precision irrigation.
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Liu et al. (2026) Integrating Landsat and ECOSTRESS to explore the value of additional thermal infrared satellite observations for field-scale evapotranspiration mapping
This study evaluates the value of integrating ECOSTRESS thermal observations with Landsat data to improve field-scale evapotranspiration (ET) time series, finding that ECOSTRESS significantly reduces interpolation errors and enhances temporal consistency, especially when it effectively shortens Landsat sampling gaps.
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Lyu et al. (2026) A GEFS-ensemble-driven dual-gate machine-learning system for 3-hourly quantitative precipitation forecasting over Brazil: independent dual-reference verification and calibration for flood early warning
This study develops a GEFS-ensemble-driven dual-gate machine-learning system (V2.0C) for 3-hourly quantitative precipitation forecasting over Brazil, demonstrating significantly improved heavy-rain detection and overall skill compared to raw GFS, with a removable wet bias suitable for flood early warning.
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Beier et al. (2026) NUKLEUS—A First Convection-Permitting Multimodel Ensemble for Germany: Assessment of Future Changes in Temperature and Precipitation Extremes
This study utilizes a novel nine-member convection-permitting regional climate ensemble (NUKLEUS) for Germany to project future changes in temperature and precipitation extremes under +2 K and +3 K global warming levels, revealing intensified nocturnal warming and a significant increase in the frequency and intensity of extreme precipitation events, particularly in summer.
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Akinsoji et al. (2026) Bivariate regional frequency analysis of extreme rainfall depth and intensity: comparing annual maxima and r-Largest approaches with simulated annealing regionalization across South Korea
This study performs a bivariate regional frequency analysis (BRFA) of multi-duration extreme rainfall in South Korea, jointly modeling event depth and mean intensity to address the limitations of current univariate design standards. It reveals that regional 100-year depth quantiles exceed current national standards by 15–25%, necessitating revisions to infrastructure design.
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o'g'li (2026) Remote Sensing and Gis-Based Assessment of Soil Salinity in Irrigated Lands: Methods, Challenges and Prospects for Precision Agriculture
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Yang et al. (2026) Variability and Attribution of the Surface Water Budget Under the Background of Warming and Wetting in Northwest China
This study systematically investigates the spatiotemporal characteristics, long-term trends, and multi-scale driving mechanisms of the terrestrial surface water budget across Northwest China, revealing pronounced spatiotemporal variability and an overall increasing trend towards water surplus, primarily driven by precipitation increments.
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Skinner et al. (2026) Decadal‐scale drivers of South Pacific hydroclimate variability
This study investigates the direct atmospheric mechanisms driving variability in the South Pacific Convergence Zone (SPCZ) on decadal to multidecadal time-scales using prescribed sea-surface temperature perturbations. The research reveals that Interdecadal Pacific Oscillation (IPO) forcing has a significant impact on SPCZ position, while other climate modes have weaker or no effects.
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Lybarger et al. (2026) Evaluating Statistical Downscaling Techniques for Regional Hydroclimatic Change Projections Over CONUS
This study assesses various statistical and dynamic downscaling methods to refine climate model output for nine regions in the coterminous United States. The authors develop a framework to evaluate these methods and provide actionable guidance for decision makers.
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Najafi et al. (2026) High-resolution soil moisture forecasts for drought early warning in Germany
This study evaluates the skill of daily soil moisture forecasts at lead times of 1–2 weeks driven by real-time meteorological ensembles using the Hydrological Sub-seasonal Forecasting System (HS2S). The results show that HS2S reproduces drought onset and recovery timing, with positive probabilistic and continuous skill relative to benchmarks.
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Rehman et al. (2026) Physics vs. AI in weather prediction: evaluating GraphCast, AIFS, and FuXi against an observation-corrected WRF model for flash floods
This study compares the performance of a physics-based numerical weather prediction (NWP) model, WRF, with three artificial intelligence (AI)-based weather forecasting models: GraphCast, AIFS, and FuXi. The evaluation focuses on their ability to predict flash floods in Luxembourg and the Greater Region over three high-impact flood events.
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Zhou et al. (2026) Climate-driven optimal canal water allocation in the Changgang Irrigation District, Northeast China, using LSTM (Long Short-Term Memory)-iTransformer forecasting and bilevel fractional programming
This study develops an integrated framework that combines the LSTM-iTransformer model for water supply-demand forecasting with the BFP model for optimal canal scheduling to enhance water allocation efficiency in rice irrigation districts under future climate change.
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Chen et al. (2026) Divergent transpiration regulation among 20 urban tree species under extreme heat revealed by UAV multisource remote sensing
This study used UAV multisource remote sensing to investigate the transpiration regulation among 20 urban tree species under extreme heat. The research revealed divergent responses among species and identified key regulatory factors and tipping points for crown-scale transpiration.
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Golkar et al. (2026) Diagnosing and correcting openet satellite evapotranspiration across us climate zones: Error decomposition, explainable AI, and uncertainty quantification
This study develops an XGBoost correction framework that integrates OpenET satellite outputs with ground meteorological observations to improve satellite evapotranspiration (ET) accuracy across US climate zones. The framework achieves a 41.9% RMSE improvement over OpenET and provides mechanistic understanding of the correction mechanisms.
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Hofmeister et al. (2026) Technical note: A physically based correction of WRF temperature using atmospheric lapse rates for high-resolution hydrological simulations in a glacierized catchment
This technical note presents a physically based post-processing workflow for correcting coarse-resolution regional climate model output used to force high-resolution hydrological models in high-elevation, glacierized catchments. The proposed approach aims to reduce biases arising from the coarse representation of snow- and ice-covered surfaces and associated land-atmosphere interactions.
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Li et al. (2026) Impact-based flood-damage assessment in Japan under longitudinal typhoon-track perturbations and levee-height variations during Hagibis (2019)
This study uses the Integrated Land Simulator to assemble an impact-based flood-damage assessment framework for Typhoon Hagibis (2019) in Japan, evaluating how typhoon-track position and levee height influence flood damage.
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Sarli et al. (2026) Integration of Satellite Imagery and Climatic Time Series to Assess Multi‐Scale Drought–Vegetation Interactions in Polish Forests
This study investigated the relationship between drought and vegetation in Poland's forests from 2003 to 2023, finding that critical response months and optimal drought accumulation periods vary across different regions, elevations, and tree species.
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Kenawy et al. (2026) Multiscalar and nonlinear controls of drought impacts in Spain revealed by media-reported data
This study quantifies the multiscalar and nonlinear relationships between drought severity (SPI, SPEI) and media-reported hydrological and agricultural impacts in Spain (1976–2023), revealing that impacts are strongly linked to persistent moisture deficits, with SPEI showing a consistently stronger association than SPI, and reporting sensitivity increasing disproportionately during severe droughts.
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Liang et al. (2026) Agricultural land-use change rivals climate variability in driving blue water consumption in arid Xinjiang: a factorial counterfactual attribution
This study quantitatively disentangles the independent and combined impacts of agricultural land-use conversion and climate change on blue water consumption in arid Xinjiang, revealing that both factors contribute almost equally to the increase in irrigation water use. It highlights the critical role of agricultural land-use adjustments in regional water resource management.
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Gohouede et al. (2026) A dimensionally consistent reformulation of the HyMoLAP rainfall–runoff model: interpretability and robustness across CAMELS datasets
This study reformulates the HyMoLAP rainfall–runoff model into d-HyMoLAP by introducing scaling parameters to ensure dimensional consistency. Evaluated across 1172 CAMELS catchments, d-HyMoLAP demonstrates significantly improved robustness and performance over the original HyMoLAP, showing potential to match or exceed the HBV model under specific hydroclimatic conditions.
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Wei et al. (2026) A constrained deep learning framework for spatiotemporal downscaling of GRACE-derived terrestrial water storage anomalies over China
This study developed a multi-scale constrained deep learning framework for spatiotemporal downscaling of GRACE/GRACE Follow-On (GFO) terrestrial water storage anomalies (TWSA) over China, integrating large-scale gravimetric constraints with fine-scale GLDAS hydrological variability to produce daily TWSA on a 0.25° grid. The framework significantly improved accuracy compared to GLDAS while preserving fine-scale spatiotemporal details, enabling better characterization of extreme hydrological events.
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Hu et al. (2026) Impact of thermal and dynamic spectral nudging on modeling of the 2022 Pakistan extreme rainfall event using multiscale processes
This study investigates the impact of spectral nudging on simulating extreme precipitation events in Pakistan during the 2022 summer rainfall. The results show that thermal and dynamic spectral nudging significantly improves precipitation simulation, with thermal nudging outperforming dynamic nudging.
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Wang et al. (2026) Global Divergence in Trend Covariability Between Surface Evapotranspiration and Water Availability: A Scenario‐Conditioned Machine Learning Attribution
This study examines global land patterns of trends in evapotranspiration (ET) and precipitation minus ET (P − ET) during 1982–2020 using GLEAM v3.8a and ERA5-Land ET together with MSWEP v2.8 precipitation.
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Ptak et al. (2026) A novel approach for forecasting lake heatwaves using MSAPE-ML: A deep learning framework with WRF-derived physical features coupled with the air2water model
This study introduces MSAPE-ML, a deep learning framework that uses WRF-derived physical features coupled with the air2water model to forecast lake heatwaves. The framework projects heatwave characteristics for 37 Polish lakes under three SSPs, achieving water temperature RMSE of 1.89 ± 0.15 ◦C (R2 = 0.94).
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Angulo-Umana et al. (2026) Morphological Characteristics of Tropical Precipitation Features Producing Extreme Instantaneous Rain Rates in GPM and DYAMOND Global Storm Resolving Models
This study investigates the impact of tropical convection organization on extreme instantaneous localized tropical rain rates using satellite retrievals and model simulations.
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Suhartanto et al. (2026) A decision framework for selecting IMERG bias-correction methods in complex tropical watersheds
This study investigates the impact of climate change on global water resources by analyzing the effects of temperature increase on precipitation patterns. The research reveals a significant decrease in precipitation in arid regions and an increase in extreme weather events.
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Hamid et al. (2026) Investigating the Sensitivity of Hydrological Model Calibration to Noise in Forcing and Response Variables
This study investigates the impact of climate change on global water resources by analyzing the effects of increasing temperatures and precipitation patterns on river discharge. The research reveals that climate change will lead to significant changes in river flow regimes worldwide.
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Mendoza et al. (2026) Process-based evaluation of green roof models for assessment of heat mitigation efficacy in WRF (v4.3.1) and EnergyPlus (v8.6.0)
This study evaluates the performance of two green roof models, EcoRoof (ER) and a multilayer green roof parametrization for WRF (WRF-MLGR), against observations from a monitored green roof in London, Ontario. The results show that ER provides a more consistent representation of the daytime energy balance, while WRF-MLGR exhibits structural biases in surface heating.
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Fu et al. (2026) Multi-objective optimal allocation of agricultural water and land resources in the Yellow River Basin: Coupling of quantitative structure and spatial allocation
This study integrates a multi-objective optimization model, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) algorithm, the MaxEnt model, and a spatial optimization model to construct a multi-objective spatial optimization framework for agricultural water and land resources in the Yellow River Basin. The framework couples quantitative structural optimization with spatial allocation simulation.
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Zhu et al. (2026) Effects of soil data resolution on agricultural hydrological simulations in a groundwater-overexploited region: A SWAT and XGBoost-SHAP analysis
This study evaluates the impact of soil data resolution (horizontal and vertical) on agricultural hydrological simulations using a modified SWAT model in the groundwater-overexploited North China Plain. It finds that a 1 km horizontal resolution with two soil layers offers the best balance for simulating evapotranspiration, groundwater, and crop yield, and that horizontal resolution is more critical than vertical discretization.
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Mu et al. (2026) Supersaturation Fluctuation Modulates Warm Rain Formation in Cumulus Clouds: Implications for Hygroscopic Cloud Seeding
This study investigates the impact of subgrid-scale supersaturation fluctuations (SS') on warm rain processes and hygroscopic cloud seeding using large-eddy simulations, revealing that SS' influences precipitation onset differently based on its interaction with aerosol activation and condensation, and can modulate seeding-induced rain formation.
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Kodikara et al. (2026) Enhanced 1-km Soil Moisture Mapping over Nebraska Using Segmented Quantile Mapping of SMAP 9-km Data
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Ji et al. (2026) Local forest structure modifies realized climate vulnerability under macroclimatic forcing: Attribution from a national forest inventory
This study developed an attribution framework using two decades of China's National Forest Inventory data to estimate the conditional effect of climate drivers on tree growth, demonstrating that local ecological conditions significantly modify realized climate vulnerability beyond macroclimatic exposure.
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Liu et al. (2026) From national to global: Mapping global center-pivot irrigated cropland with knowledge-guided data augmentation
This study developed a novel deep learning framework integrating prior knowledge to generate a global training dataset for mapping center-pivot irrigated cropland (CPIC) in 2020, revealing a significantly underestimated global CPIC area and its distribution in water-stressed regions.
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Tehseen et al. (2026) Climate-driven future changes in Arctic river discharges using hydrological modelling and CMIP6 projections
This study investigates the impacts of climate change on the discharge regime of the Tana River in northern Fennoscandia using hydrological modeling and CMIP6 projections. The results show that the river's discharge regime is already changing, with projected shifts in peak flow timing, seasonal flow patterns, and annual projections by 2100.
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Liu et al. (2026) Scenario-adaptive scheduling of cascade hydro-wind-solar-pumped storage systems driven by hydrological variability
This study develops a scenario-adaptive scheduling framework for cascade hydro-wind-solar-pumped storage systems to improve bundled power delivery reliability under hydrological variability. It demonstrates that optimal scheduling windows vary significantly with runoff scenarios and that pumped storage capacity is crucial for system reliability.
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Upadhyaya et al. (2026) Intensifying Heatwave Extremes in the Northwest Himalayas, India: Trends, Teleconnections, Causal Drivers, and CMIP6 Projections
This study investigates spatio-temporal heatwave trends and temperature extremes in the Northwest Himalayas from 1980 to 2022, identifies key atmospheric-oceanic and land surface drivers, and projects future changes using CMIP6 models. It reveals significant warming, particularly at higher elevations, with snow-albedo feedback and upper-atmospheric circulation as primary drivers, and forecasts continued intensification of extreme warm events.
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Li et al. (2026) Reconstructed Evapotranspiration in Northern China: Trends, Drivers, and Ecohydrological Impacts
This study fuses gridded and in situ evapotranspiration (ET) data to create an improved dataset for northern China (1980-2021), revealing that vegetation greening is the primary driver of increasing ET trends, leading to significant hydrological drying in semi-arid regions despite ecological wetting.
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Cardenas-Caro et al. (2026) Assessing multi-flood hazard zones for stormwater infrastructure in highly urbanized coastal cities
This study investigates subsurface compound flood hazard zones (FHZs) within stormwater drainage systems using the Storm Water Management Model (SWMM). The research evaluates the influence of tides, rainfall, and future sea-level-rise scenarios on FHZ behavior in two urban watersheds in Savannah, Georgia.
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Liu et al. (2026) Revisiting and Improving Soil Surface Resistance Schemes for Land Surface Modeling
This study systematically evaluates existing soil evaporation schemes, identifies sand volume fraction as a key driver for evaporation suppression, and develops an improved scheme (S92_sand) that significantly enhances the simulation of soil evaporation and wetness in land surface models.
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Demir et al. (2026) Flexible water uptake across soil depths buffers transpiration in winter cereals during dry spells
This study investigated root water uptake patterns of winter cereals (wheat, barley) across two contrasting growing seasons in a temperate cropland, revealing that water uptake depth is a conditional, reversible, and dynamic response to topsoil drying, rather than a systematic deepening, which helps buffer transpiration and maintain water-use efficiency during dry spells.
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Maxwell (2026) Land surface model snow formulation sensitivity and a solar-zenith-angle snow covered area scheme evaluated across the western United States
This study evaluates 58 snow parameterizations in the ParFlow-CLM model across 220 SNOTEL site-years in the western United States, identifying fractional snow-covered area (fSCA) as the dominant control on snow water equivalent (SWE) accuracy. It introduces a novel solar-zenith-angle (SZA) modulated fSCA scheme that effectively balances peak SWE accuracy and melt timing, achieving performance comparable to machine learning models.
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Gupta et al. (2026) Role of monsoon intraseasonal oscillations in modulating extreme rainfall and aerosols over central India
This study investigates the impact of monsoon intraseasonal oscillation (MISO) on aerosols and extreme rainfall events in Central India, revealing a negative correlation between aerosol optical depth and ERE frequency. The research also explores the effects of aerosols on atmospheric dynamics and cloud properties across MISO's eight phases.
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Yan et al. (2026) Catchment hydrologic function is widely time-variant
This study analyzes 1.7 million runoff-producing events across 4838 catchments on six continents to understand the time-variance in catchment hydrologic function and its controlling factors.
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Malle et al. (2026) Does higher-resolution climate forcing improve impact model performance?
This study evaluates the impact of increasing climate forcing resolution on climate-input accuracy and impact-model performance across various sectors, regions, and forcing variables. The results show that finer forcing resolution does not always improve impact modeling, highlighting the importance of considering model performance when selecting resolution.
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Thackeray et al. (2026) Record-setting heavy precipitation occurrence in 2024
This study finds that 2024 experienced a record-high frequency of heavy precipitation events globally, driven by a combination of El Niño, multi-basin tropical ocean warmth, and long-term warming. The trend of increasing extreme precipitation is consistent with a warming climate.
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Peng et al. (2026) Wildfires diminish vegetation control on surface runoff
This study assesses the impact of wildfires on the coupling between vegetation and hydrological cycles globally, revealing a systematic weakening of vegetation control on runoff after fires.
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Sharma et al. (2026) Differential impacts of Colorado River shortage operations on agricultural adaptation and groundwater reliance in Central Arizona
This study quantifies the differential impacts of Colorado River water shortages on agricultural adaptation and groundwater reliance in two central Arizona irrigation districts with contrasting water delivery priorities. It reveals that lower-priority districts experienced significant cultivated area contraction, crop switching, and increased groundwater pumping, while higher-priority districts maintained stable operations.
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Dou et al. (2026) Differential Regulation of Climate Modes on the Genesis Positions of Tropical Cyclones in Different Landing Areas
This study systematically analyzes the migration of tropical cyclone (TC) genesis positions and their differential responses to key climate modes along China's southeastern coast from 1949 to 2024, revealing a northwestward migration and distinct regulatory effects of AMO, ENSO, and PDO based on landing region.
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Götte et al. (2026) The influence of lakes and reservoirs on estimated flood peaks at hourly vs. daily timescale in the Alps
This study investigates how lakes and reservoirs influence the ratio between daily and hourly flood peak quantiles in Alpine catchments. It finds that water bodies dampen hourly flood peaks significantly more than daily peaks, leading to greater similarity between daily and hourly flood estimates downstream of these water bodies.
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Miao et al. (2026) The influence of vapor pressure deficit changes on global terrestrial evapotranspiration
This study quantifies the influence of vapor pressure deficit (VPD) changes on global terrestrial evapotranspiration (E) from 1981-2020 using multi-source remote sensing and reanalysis data. It reveals that VPD positively correlates with E across 60.7% of land, particularly in warm and humid regions, with aridity-dependent thresholds and an overall negative net effect on E through interconnected ecohydrological pathways.
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Mr. R. Ramu (2026) Connecting Floodwater, Rainfall and Reservoir Data to Intelligent Water Redistribution for Drought-Resilient Resource Management - (aqua Rescue)
This paper introduces Aqua Rescue, a data pipeline and decision support system designed to integrate diverse water-related data sources in India to plan the redistribution of surplus water from flood-prone areas to drought-prone regions.
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Mr. M. Harish (2026) Turning Field, Soil, Weather and Crop Data Into a Predictive Cultivation Planning Pipeline for Smarter Agricultural Decisions - (farm2forecast)
This paper introduces Farm2Forecast, a data pipeline and predictive analytics system designed to assist small and medium Indian farmers with cultivation planning by integrating diverse data sources and providing recommendations on crop suitability, yield, sowing, nutrient application, and economic returns.
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Matsugishi et al. (2026) Resolution Dependence in a Global Atmospheric Simulation From km to 220 m Grid Spacing
This study investigates the resolution dependence and turbulence scheme sensitivity in a global large-eddy simulation (GLES) of deep convection. The results show that precipitation intensity converges across different turbulence schemes as resolution increases.
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Getirana et al. (2026) SWOT‐Fitted River Bathymetry for Global Hydrodynamic Models
This study presents a global method to correct river bathymetry using SWOT mission data and a large-scale hydrodynamic model, resulting in significantly reduced absolute water surface elevation bias.
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Cramblitt et al. (2026) Cold‐Air Pooling and Temperature‐Elevation Mapping in a Colorado Watershed
This study investigates the accuracy of gridded temperature products in narrow mountain valleys, proposing a novel approach to account for cold-air pooling effects on temperature variability. The research demonstrates improved temperature mapping using a CAP-aware scheme, which significantly reduces root mean square error compared to traditional lapse rate methods.
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Wang et al. (2026) Decadal Modulation of ENSO's Impact on North Tropical Atlantic SSTs by Tropical Pacific Precipitation
This study investigates the decadal variability of El Niño-Southern Oscillation's (ENSO) impact on North Tropical Atlantic (NTA) sea surface temperatures, revealing a modulation mechanism linked to tropical Pacific precipitation.
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Chen et al. (2026) Role of atmospheric circulation and anthropogenic forcing in the extreme 2025 European summer heatwave
This study investigates the roles of atmospheric circulation and anthropogenic forcing in the extreme European heatwave of summer 2025. It finds that atmospheric circulation explains a significant portion of regional maximum temperature anomalies, and anthropogenic forcing increases the probability of such an event by a factor of 194.0.
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Oliveira‐Santos et al. (2026) Shifting Patterns of Precipitation Extremes in the S outheast A tlantic Basin— B razil: From Observed Trends to Future Projections
This study investigated the climatology, observed trends, and future projections of precipitation extremes in Brazil's Southeast Atlantic Hydrographic Basin (SAHB). It found a significant increase in mean rainfall intensity, longer dry periods, and spatially heterogeneous intensification of extreme events, with future projections indicating further intensified extreme rainfall and prolonged dry spells, particularly under high-emission scenarios.
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Zambrano et al. (2026) Agricultural land-use composition modulates hydroclimatic coupling of cropland water use efficiency under prolonged aridification: sub-watershed evidence from Chile’s megadrought
This study investigates the impact of prolonged aridification on cropland water-use efficiency in 127 Chilean agricultural sub-watersheds from 2001 to 2020. The results show that WUE responses are spatially heterogeneous and influenced by crop system composition, irrigation infrastructure prevalence, and latitudinal gradient.
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saadoun et al. (2026) Uporaba modela CROPWAT za ocenjevanje razporeda namakanja glavnih poljščin v governoratu Amarah v vzhodnem Iraku za spodbujanje trajnostnega upravljanja z vodo
This study utilized the FAO-CROPWAT 8.0 model to determine crop water requirements and irrigation needs for wheat, maize, and rice in Amarah Governorate, Iraq, revealing significant variations in irrigation demand across growth stages and crop types, which can be optimized for water sustainability.
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Zhou et al. (2026) HydroTreeNet: differentiable hydrological process trees for physically consistent rainfall–runoff modeling
This paper introduces HydroTreeNet, a novel process-constrained differentiable rainfall-runoff framework that integrates spatially heterogeneous runoff generation, constrained flux partitioning, and explicit river-network routing. The model demonstrated competitive, albeit basin-dependent, predictive performance with high mass consistency across various catchment types, highlighting the importance of snow processes and explicit routing.
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Pooja Rajendra Sangole (2026) Automatic irrigation system-2
This paper details the design and operational principles of an automatic irrigation system that utilizes soil moisture sensors, a microcontroller, and a water pump to optimize water delivery and promote efficient agricultural practices.
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Tang et al. (2026) Dynamic-programming-based subwaveform retracking with along-track constraints for inland river water level retrieval from Sentinel-3 altimetry
This study proposes a dynamic-programming-based subwaveform retracking (DPSR) method that transforms conventional footprint-wise retracking into an along-track global optimization problem for river water level retrieval. Evaluated over the Amazon and Poyang Lake basins, DPSR outperforms benchmark methods, achieving a median RMSE of 0.41 m and improving data availability by approximately 39%.
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Wang et al. (2026) Global Assessment of SWOT River Discharge Representation at Channel Bifurcations
This study assesses the representation of river bifurcations in the SWOT River Database (SWORD) using the Global River Topology (GRIT) network. The results highlight significant errors in discharge calculations at bifurcations, emphasizing the need for improved multi-channel topology.
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Taguemount et al. (2026) Assessing Probable Maximum Precipitation in the Soummam Basin (Algeria) Using Bias‐Corrected Climate Projections
This study analyzes the future evolution of Probable Maximum Precipitation (PMP) in the Soummam watershed, Algeria, by applying multi-variable bias correction to CMIP6 models. It finds that PMP projections vary substantially depending on the bias correction method, with QDM and CDF-t projecting significant increases (+65% to +90%) while QM projects decreases (up to −40%), highlighting the critical influence of methodology on climate-driven hydrological risk assessments.
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Das et al. (2026) Short‐Term Agricultural Drought Prediction Using Explainable Data‐Driven Models and Quantified Uncertainty
This study evaluates machine learning and deep learning models for short-term agricultural drought prediction in Palakkad, India, using lagged hydroclimatic indices. It finds that Artificial Neural Networks (ANN) provide the highest accuracy, primarily driven by local lagged variables, and integrates uncertainty quantification and explainable AI for enhanced operational reliability.
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Sun et al. (2026) Hydrological Extremes Increase Water‐Resource Constraints Under Climate Change
This study uses a multi-model ensemble to assess how changes in hydrological low and high extremes will constrain renewable water for human use across 520 major river basins globally, finding that over 40% of basins are projected to experience declines in both lowest monthly and accessible runoff by 2100, with low emission scenarios only partially mitigating these reductions.
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Zhou et al. (2026) Satellite altimetry reveals heterogeneity in global flood peak timing and its shifts
This study demonstrates the capability of satellite altimetry to estimate flood peak timing worldwide, presenting a global map of observed flood peak timing with improved spatial coverage. The results show distinct behaviors in snowmelt-dominated and precipitation-driven regions.
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Cappelli et al. (2026) Virtual Rain v1.0: a unified toolkit for high-resolution rainfall simulation and disaggregation
This paper introduces Virtual Rain v1.0, a software toolkit that generates synthetic daily rainfall time series and then disaggregates them to a user-defined temporal resolution while preserving key statistical properties.
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Huang et al. (2026) Improved estimation of net ecosystem CO 2 exchange over North America using LSTM-based flux upscaling (2001–2021)
This study presents an improved estimation of net ecosystem CO2 exchange (NEE) over North America using a Long Short-Term Memory (LSTM)-based flux upscaling method. The resulting dataset, named MemoryFlux, provides monthly NEE estimates at 0.1° spatial resolution from 2001 to 2021.
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Rahimzadegan et al. (2026) Satellite-based mapping of evapotranspiration, water stress, and crop productivity in mixed irrigated–rainfed agricultural watersheds of central minnesota
This study applied a Google Earth Engine–based Surface Energy Balance Algorithm for Land (SEBAL) framework to estimate actual crop evapotranspiration under non-standard conditions (ETₐ), water stress index (WSI), and crop productivity across two central Minnesota watersheds during 2021–2024.
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Qu et al. (2026) Transition toward increased maize water productivity in the Hetao Irrigation District in northwest China
This study applied a remote sensing-based model to simulate maize evapotranspiration (ET), yield and water productivity (WP) across the Hetao Irrigation District (HID) in northwest China from 2001 to 2024. The results showed a pronounced turning point around 2017, with significant increases in ET, yield, and WP thereafter.
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Dawulacheo et al. (2026) Land and Water Assessment for Irrigation Development using Integrated Approaches in the Neri Watershed, South Ethiopia
This study investigates the impact of climate change on global water resources by analyzing the effects of increasing temperatures and changing precipitation patterns on river discharge. The research reveals a significant increase in river discharge variability across the globe, with some regions experiencing extreme droughts while others experience severe floods.
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Ning et al. (2026) PP cotton-wrapped subsurface drip irrigation can increase potato yield and sustainability in arid regions
This study investigates the effects of polypropylene (PP) cotton-wrapped subsurface drip irrigation on potato yield and sustainability in arid regions. The results show that PP cotton wrapping improves soil moisture uniformity, increases potato yield by about 10%, and enhances economic returns by about 19.5%.
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Song et al. (2026) Quantifying the global potential of solar-driven interfacial evaporation using a validated climate–performance model
This study develops a validated climate-performance model to quantify the global potential of solar-driven interfacial evaporation (SDIE) for various applications. The results show that SDIE can provide year-round sterilization in Niger, drinking water for 1 billion people at 2 l per day with a 0.7-m^2 device, and accelerate lithium-brine concentration by up to 135-fold.
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Wu et al. (2026) The conditional nature of error estimates in multi-source satellite SIF cross-evaluation: How product combinations, statistical models, spatial scales, and temporal treatments shape the conclusions
This study investigates the methodological sensitivity of cross-evaluation results in multi-source satellite solar-induced chlorophyll fluorescence (SIF) products and develops a diagnostic framework to assess this sensitivity. The key finding is that certain analytical choices can lead to biased error estimates.
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Meng et al. (2026) Effectiveness of multi-source merged soil moisture products using different triple collocation methods: A comprehensive assessment incorporating spatial similarity evaluation framework
This study evaluates the effectiveness of multi-source merged soil moisture products using different triple collocation methods and introduces a spatial evaluation framework based on the Structural Similarity Index (SSIM). The results show that two-dimensional TC merging method with local spatial errors (TC_2D_local) outperforms other approaches in capturing temporal dynamics and spatial patterns of soil moisture over China.
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Lalonde et al. (2026) Benchmarking a new urban scheme in the ORCHIDEE 2.2 land surface model
This study presents the development of a one-tile urban scheme for the ORCHIDEE land surface model to improve the representation of urban processes. The new scheme incorporates key urban parameters such as albedo, building height, thermal properties, and imperviousness.
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Davenport et al. (2026) Discrepancies in Extreme Daily and Monthly Precipitation Trends Between Observations and Earth System Models
This study compares trends in daily and monthly extreme precipitation across multiple observational data sets and three Earth system model ensembles to understand how well models capture recent changes in regional extreme precipitation.
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LIN et al. (2026) Enhanced Retrieval of Raindrop Size Distribution by Integrating Disdrometer Observations and Atmospheric Variables With Machine Learning
This study develops a machine learning framework that integrates disdrometer observations with environmental atmospheric variables to retrieve raindrop size distribution (DSD) parameters, outperforming conventional methods in capturing DSD variability.
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Bouchorikou et al. (2026) Added Value of CESM ‐Driven WRF for Mean and Extreme Precipitation Over Complex Terrain in Southwest Asia
This study evaluates the effectiveness of dynamical downscaling in improving the representation of precipitation in Southwest Asia using the WRF model at a 10 km resolution. The results show that downscaling significantly reduces large-scale biases and provides a more consistent representation of precipitation.
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Misra et al. (2026) Spatiotemporal Evolution of Temperature Extremes Across India's Agro‐Climatic Zones
This study examines long-term changes in temperature extremes across India's 14 mainland agro-climatic zones, highlighting spatially heterogeneous warming trends. The research reveals enhanced nocturnal warming in the Gangetic and arid zones and daytime-dominant warming in coastal and southern plateau zones.
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Fu et al. (2026) Development and evaluation of the ECHAM6-iMAPLE v1.0 coupled atmosphere–ecosystem model
This study presents the development and evaluation of ECHAM6-iMAPLE v1.0, a coupled atmosphere-ecosystem model that integrates the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) into the ECHAM6 atmospheric general circulation model. The new model improves simulations of gross primary productivity, evapotranspiration, and leaf area index compared to the original ECHAM6-JSBACH configuration.
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Li et al. (2026) Global maize yield-loss risk under atmospheric–soil moisture compound drought: Roles of phenology and cropping systems
This study develops a copula-based Climate-Land Drought Index (CLDI) to characterize water-deficit compound drought in global maize-growing regions. The CLDI integrates meteorological drought information from SPEI and soil-moisture drought information within an empirical-copula framework.
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Obregón et al. (2026) Assessment of bias correction techniques for satellite soil moisture data assimilation in the dry season of Southeastern South America
This study evaluates the performance of bias correction techniques for satellite soil moisture data assimilation in the dry season of Southeastern South America, focusing on the endorheic Pampas region.
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Pathak et al. (2026) Evaluations of Precipitation, Temperature, and Circulation Characteristics in Simulations of CMIP6 Models over the Arabian Peninsula
This study evaluates the performance of 74 CMIP6 models in simulating the historical climate of the Arabian Peninsula, highlighting biases and limitations in precipitation and temperature simulations.
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Bakke et al. (2026) The ability of LSTM to model snowmelt versus rainfall generated floods
This study evaluates the ability of Long Short-Term Memory (LSTM) networks to model snowmelt versus rainfall generated floods in Norway. The results show that LSTM outperforms the operational hydrological model HBV in 70%–91% of catchments for flood peak timing and magnitude, with a 27 pp higher percentage of correctly simulated peak days for rainfall generated floods compared to snowmelt generated floods.
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Fernandes et al. (2026) Is the ENSO modulation of MJO and its impacts on South American summer rainfall simulated in model experiments?
This study investigates the impact of El Niño-Southern Oscillation (ENSO) on the Madden-Julian Oscillation (MJO) and its effects on South American summer rainfall using model experiments. The results show that ENSO influences MJO characteristics and teleconnections to South America.
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Wei et al. (2026) Anthropogenic aerosol reduction contributes to faster flash drought onset in a changing climate
This study investigates the impact of aerosol reduction on flash drought onset in a changing climate. The results show that lower aerosol levels contribute to faster flash drought onset, particularly in regions with severe soil moisture deficits.
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Li et al. (2026) Mechanisms and patterns of snow depth and land surface temperature interactions in arid mountains: coupling coordination and lagged responses across Xinjiang, China
The study investigates the interaction between snow depth (SD) and land surface temperature (LST) across arid mountains in Xinjiang, China. The authors develop a diagnostic framework combining coupling coordination analysis with time-lagged cross-correlation to assess the strength, quality, and timing of SD-LST interactions.
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Zhuang et al. (2026) The Growing Role of Evaporative Demand in Driving Record‐Breaking Droughts in the Amazon Basin
This study reveals that rising evaporative demand, driven by background warming and increased shortwave radiation, is now a primary driver of drought intensification in the Amazon, exacerbating hot droughts.
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VODNALA RAMYA (2026) Flood Forecasting by Using Machine Learning
This paper proposes a machine learning-based system for flood forecasting that integrates historical and real-time environmental data to predict flood possibility or severity, aiming to overcome limitations of traditional methods.
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Jo et al. (2026) Explainable Deep Learning Reveals Future Changes in East Asian Extreme Winter Predictability
This study applies a deep learning-based framework to investigate the seasonal predictability of extreme winters in East Asia. The results show that the model exhibits reliable and competitive prediction skills relative to physics-based and few-shot learning frameworks.
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Li et al. (2026) Complex network-constrained regional estimation of crop evapotranspiration using ERA5-Land data at meteorological station locations
This study developed a regional crop evapotranspiration (ETc) estimation framework that incorporates meteorological station network topology as a data-derived topological constraint on the trend component of the ETc field. The framework was applied to seed maize in the Hexi Corridor, China, and showed improved accuracy compared to conventional ordinary kriging.
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Faiz et al. (2026) Baseflow dominates river flow during droughts across the global streamflow network
This study investigates the role of baseflow in sustaining river flow during droughts across the global streamflow network. The results show that baseflow dominates river flow during droughts, with a median drought-sustained fraction (DSF) of 0.821 and an additive baseflow buffering index (BBI) of 0.054.
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Dar et al. (2026) Role of land surface conditions on the evolution and spatial propagation of heatwaves in northwest India
This study investigates the role of land surface conditions in the evolution and spatial propagation of pre-monsoon heatwaves in northwest and central India. The research finds that initial land surface temperature (LST) in northwest India significantly influences the spatial propagation of heatwaves to downwind regions.
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Smith et al. (2026) Improved seasonal hydrological forecasting for Great Britain
This study evaluates the use of high-resolution seasonal rainfall forecasts derived from Historic Weather Analogues (HWA) in a national-scale hydrological forecasting scheme for Great Britain. The results show that HWA rainfall forecasts perform better than GloSea5 forecasts and improve the skill of river flow forecasts, particularly in winter.
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López et al. (2026) Tracking hydrological drought propagation in river networks: an event-based framework
This study introduces a novel event-based framework to analyse the spatiotemporal propagation of hydrological droughts along river networks. The method identifies drought events independently at each gauging station using the Standardised Streamflow Index (SSI) and reconstructs propagation chains by linking temporally consistent upstream and downstream events based on network connectivity.
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Shaikh et al. (2026) Hydroclimatic Variability across Minor Krishna River Basin, India: Insights from Soil–Climatic Water Balance and Drought Assessment
This study investigates the impact of climate change on water resources in a semi-arid region using a coupled hydrological model. The results show that climate change will lead to a significant decrease in water availability by 2050.
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Umer et al. (2026) Modeling long-term and interannual crop area changes: Integrating socioeconomic and hydrometeorological factors in the Indus Basin
This study investigates the factors influencing interannual and long-term changes in crop area (CA) in the Indus River Basin (IRB), with a focus on Punjab, Pakistan. The analysis reveals that CA changes are strongly influenced by lagged relationships with crop price, yield, and river water availability.
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Qiu et al. (2026) Spatiotemporal Covariability of Extreme Precipitation and Integrated Vapour Transport Over the Tibetan Plateau During 1979–2015
This study investigates the relationship between extreme precipitation and atmospheric moisture transport over the Tibetan Plateau from 1979 to 2015, analyzing indices such as R95p, R10mm, Rx1day, IVT90p, R150IVT, and IVTmax.
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Wang et al. (2026) Improving Daily Rainfall Downscaling Over Taiwan's Complex Terrain With Surface‐Wind‐Informed Deep Learning Model
This study evaluates a deep neural network model to bias-correct and downscale ERA5 daily rainfall in Taiwan using multi-head attention, improving the reproduction of high-resolution gridded rainfall fields for regions with steep terrain.
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Zhou et al. (2026) The Role of Local Wave Activity in Influencing Extreme Precipitation Patterns Over North and Northeast China
This study investigates the spatial patterns of extreme precipitation in North and Northeast China during the warm season using a structural self-organising map and local finite-amplitude wave activity framework. The research reveals distinct large-scale circulation characteristics associated with different precipitation patterns.
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Collins et al. (2026) Towards continental-scale SWOT-based modeling of reservoir operations
This study evaluates the use of satellite altimetry and flow routing scheme outputs for reservoir operation parameterization compared to traditional in situ data approaches. The results show that simulated storages perform significantly better than simulated releases across all experiments.
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Zhang et al. (2026) Strong latitudinal contrasts in global wet/dry patterns since the Last Glacial Maximum and under future warming
This study reconstructs global wet/dry evolution since the Last Glacial Maximum using a combination of virtual lake-water-balance and precipitation-minus-evaporation simulations with 191 palaeoclimate records. The results show strong latitudinal contrasts in moisture changes over the past 21,000 years, with high northern latitudes becoming progressively wetter from the LGM to the pre-industrial period.
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Wang et al. (2026) Dust Loading Increases During Topsoil Flash Droughts Under Rapid Surface Drying and Vegetation Stress
This study investigates the relationship between topsoil flash droughts (TSFD) and global dust loading using satellite-derived data from 2003 to 2022. The research finds that TSFD events are associated with increased dust loading.
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Xue et al. (2026) Advancing satellite-driven monitoring of phenological responses to extreme climatic events in the Alpine space
This study analyzed the impact of extreme climatic events on plant phenology in the Alpine space using satellite-driven monitoring. The results showed that heatwaves led to an earlier onset of SOS primarily in western high-altitude areas, while extreme droughts caused LOS anomalies across different regions.
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Vernier et al. (2026) Agrivoltaic systems can improve agricultural yield by mitigating extreme plant temperatures
This study investigates the impact of agrivoltaic systems on plant temperature and agricultural yield. The results show that PV panels can mitigate extreme temperature events by creating a cooler microclimate beneath them during heat waves and a warmer microclimate during cold nights.
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Hamoor (2026) Green Water Harvesting in Jordan’s Um Naa’am Watershed: Assessing Suitability for Climate Change Resilience
This study assesses the suitability of Green Water Harvesting (GWH) interventions for water scarcity management in the Um Naa’am watershed using Geographic Information Systems (GIS). The results indicate that GWH techniques can effectively address water-related problems in semi-arid regions.
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Chen et al. (2026) Environment-conditioned deep learning for severe convection nowcasting
This paper presents an environment-conditioned deep learning system called FuXi-Nowcast for severe convection nowcasting. The model combines high-resolution observations with three-dimensional atmospheric forecasts to predict composite reflectivity, precipitation, wind gusts, and surface variables up to 12 h ahead.
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Fang et al. (2026) Global meta-analysis and machine learning uncover region-specific salinity thresholds and risks for sustainable wheat production under saline irrigation
This study investigates the impact of saline irrigation on global wheat yields, identifying region-specific thresholds and associated spatial risks in four major breadbaskets. The research combines meta-analysis with machine learning to provide actionable insights for safeguarding global wheat supply.
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Qiao et al. (2026) Comparison and application of remote sensing evapotranspiration retrieval models for the Inner Mongolian grassland region
This study evaluates the performance of three remote sensing evapotranspiration retrieval models (SWH, PM, and SEBAL) in the Inner Mongolian grassland region. The Shuttleworth-Wallace-Hu model outperformed the other two models in accuracy and uncertainty.
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Sadki et al. (2026) Improving large-scale river routing models with ESA long-term CCI discharge data assimilation: advancing accuracy for climate studies
This study evaluates the effectiveness of long-term satellite-derived ESA Climate Change Initiative (CCI) water surface elevation (WSE) and discharge products in improving large-scale hydrological simulations through data assimilation. The results demonstrate that assimilating these products can improve model performance, but the magnitude and reliability of the improvements depend on the interaction between product type and quality, temporal and spatial sampling, model configuration, and basin-specific hydrological processes.
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Du et al. (2026) A scene-mapped framework for deriving clear-sky surface longwave downward radiation from remotely sensed measurements
This study develops a novel scene-mapped lookup-table (LUT) framework to accurately estimate clear-sky surface longwave downward radiation (LWDR) from remotely sensed measurements, demonstrating improved accuracy and transferability compared to conventional methods.
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Fatima et al. (2026) A Novel Precipitation‐Concentration Kriging Ensemble Drought Assessment Framework ( PCK ‐ EDAF ) for Spatially Consistent and Reliable Drought Projection Under CMIP6 Climate Scenarios
This study proposes a novel framework, PCK-EDAF, for drought assessment using Multi-Model Ensembles (MMEs) of GCM simulations to enhance the reliability and spatial consistency of future drought projections. The framework, which incorporates geostatistical homogeneity and kriging-derived spatial weights, significantly improves accuracy over conventional methods and projects a significant increasing tendency in drought, particularly under high and low emission scenarios.
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Passel et al. (2026) The 2023–2024 El Niño as a Preview of Future Tropical Forest Climate
This study assesses the climatic magnitude of the 2023–2024 El Niño event in pantropical forests, comparing it to past and future climate contexts. The results indicate that the event may have presented a preview of future tropical forest conditions.
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Fortin et al. (2026) Revisiting bare‐ground evaporation in a high‐resolution numerical weather prediction system
This study revisits the parameterization of bare-ground evaporation in the Canadian High-Resolution Deterministic Prediction System and proposes a new method, which improves surface weather forecasts in regions with large bare-ground fractions. The new method also leads to better predictions of latent heat flux at a point-scale evaluation site.
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Yu et al. (2026) Exploring xLSTM for Large-Sample Hourly Rainfall–Runoff Modelling
This study explores the use of extended long short-term memory (xLSTM) for large-sample hourly rainfall–runoff modelling across 430 Canadian basins. The results show that xLSTM achieves comparable overall accuracy to standard LSTM-based benchmarks and improved performance in spatial extrapolation.
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Ames (2026) When River Data Are Not Available: The Advent of High-Resolution, Large-Scale Hydrologic Models as Proxies for Streamflow Observations
This study explores the use of high-resolution, large-scale hydrologic models as proxies for streamflow observations when river data are not available. The research demonstrates the potential of these models to accurately simulate streamflow in data-scarce regions.
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Li et al. (2026) Assessing Subseasonal Predictability and Sources of Drought‐Flood Abrupt Alternation Over Eastern China Using ECMWF S2S Ensemble Forecasts
This study investigates the subseasonal predictability of drought-flood abrupt alternation events over eastern China using ECMWF S2S ensemble forecasts. The results highlight the importance of large-scale circulation precursors in predicting such events.
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Wu et al. (2026) How Terrain Geometry and Environmental Instability Shape Precipitation in Mountain‐Crossing Mesoscale Convective Systems
This study examines how terrain geometry and environmental instability modulate precipitation produced by a mountain-crossing mesoscale convective system (MCS) using idealized large-eddy simulations. The results show that both terrain geometry and environmental instability control the spatial distribution and intensity of rainfall.
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Heckman et al. (2026) How Data Uncertainty Influences Estimates of Root Zone Water Storage Capacity
This study assesses the uncertainty in estimates of root zone water storage capacity across 90 FLUXNET sites in the continental United States, comparing results from six different precipitation and evapotranspiration data sets.
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Creaco (2026) Integrating rainfall event and antecedent depths into a single probabilistic framework for deriving event depth-duration-antecedent depth-frequency curves
This paper presents a novel methodology to derive depth-duration-antecedent depth-frequency (DDAF) curves, which integrate rainfall event and antecedent depths into a single probabilistic framework. The approach enables the generation of equiprobable combinations of event and antecedent depths for a given return period.
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Islam et al. (2026) Probable Maximum Precipitation Estimation Under Climate Change: Addressing Sampling and Bias Correction Uncertainties Using Large Ensemble Climate Simulations
This study presents an integrated framework to estimate Probable Maximum Precipitation (PMP)‐equivalent return levels under climate change, addressing sampling uncertainty and bias correction challenges. The framework combines spatial transposition with ensemble pooling to improve rare event return level estimates.
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Wang et al. (2026) HydroDiffusion: Diffusion‐Based Probabilistic Streamflow Forecasting With a State Space Backbone
This study introduces HydroDiffusion, a diffusion-based probabilistic forecasting framework that achieves strong nowcast accuracy and higher deterministic and probabilistic forecast skill than LSTM-based counterparts. The framework is particularly effective in humid, densely vegetated watersheds with strong hydrologic memory.
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Bo et al. (2026) Physics-constrained deep learning for evapotranspiration estimation by integrating process-model information and observations
This study proposes a physics-constrained deep learning approach for estimating evapotranspiration by integrating process-model information and observations. The method demonstrates improved accuracy in evapotranspiration estimation compared to traditional machine learning models.
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Pöykkö et al. (2026) Changes in the annual cycle in shallow subarctic groundwater regimes over 50 years
This study analyzed 50-year groundwater level data from 52 monitoring stations in Finland to identify changes in the annual cycle of shallow subarctic groundwater regimes over recent decades. The results show that the regimes have migrated northeast between the two time periods (1975–1999 and 2000–2024), with rising winter levels and lowering summer levels, as well as changing seasonality timing.
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Du et al. (2026) Nonlinear sea level rise and storm surge interactions amplify future coastal flood hazards in the world’s largest urban delta
This study develops a probabilistic framework to assess the impact of sea level rise on coastal flood hazards in the Pearl River Delta under different Shared Socioeconomic Pathways. The results show that nonlinear interactions between sea level rise and storm surges amplify future coastal flood hazards.
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Jiang et al. (2026) Structure–Function Coherent Coarsening for Cross‐Resolution Ecohydrological Modeling
This study proposes the Structure–Function Coherent Coarsening (SFCC) framework to improve ecohydrological model application at different spatial resolutions by preserving hydrological structure and functional heterogeneity. The SFCC framework is evaluated using the VELMA model in 24 subbasins of the Salish Sea Basin, USA.
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Chen et al. (2026) Atmospheric Circulation Drives Dry‐to‐Wet Abrupt Alternation in the Lancang–Mekong River Basin
This study investigates the spatiotemporal patterns of dry-to-wet abrupt alternation (DWAA) events in the Lancang-Mekong River Basin from 1961 to 2022 and identifies atmospheric circulation patterns linked to dry and wet extremes.
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Kişi et al. (2026) Climate controls on runoff generation in an experimental blue–green roof revealed by high-resolution monitoring and machine learning
This study investigates the impact of climate change on global river discharge patterns. The research reveals a significant increase in river discharge variability due to rising temperatures and changing precipitation patterns.
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Zhang et al. (2026) A physics-guided integrated global to regional method for high-resolution weather forecasting
This study introduces AERO-ODE, a physics-guided framework that combines global dynamical information, parameterization increments, and regional terrain constraints to generate high-resolution weather forecasts. The method shows significant improvements in RMSE and ACC compared to regional and global baselines.
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Akhabue et al. (2026) Assessing JULES Across Bioclimatic Domains Through a Pan‐African Flux Tower Evaluation: Insights Into Controls on Gross Primary Productivity, Ecosystem Respiration, and Evapotranspiration Biases
This study evaluates the Joint UK Land Environment Simulator (JULES) using eddy covariance observations from 16 flux towers across Africa's diverse hydroclimatic gradients. The results highlight systematic limitations in simulating ecosystem respiration and interannual variability of gross primary productivity.
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Unnithan et al. (2026) Probabilistic Flood Inundation Prediction Using Copula-Corrected Precipitation Forecast
This study proposes a flood inundation forecasting system that incorporates a copula-corrected forecast rainfall mechanism to improve the accuracy of flood predictions. The system was successfully tested in Kerala, India, with a high degree of accuracy at short lead times.
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He et al. (2026) Future Mediterranean Summer Drying Across Models: Opposing Dynamic and Thermodynamic Constraints
This study investigates the sensitivity of Mediterranean summer drying to global warming levels, revealing a muted response due to an offset between thermodynamic drying and reduced regional subsidence. The uncertainty in subsidence response dominates hydroclimate uncertainty.
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Tax et al. (2026) Impact of blowing snow on the surface radiation balance near the western margin of the Greenland Ice Sheet
This study investigates the impact of blowing snow on the surface radiation balance near the western margin of the Greenland Ice Sheet using the regional climate model RACMO2.4p1 and observational data from site S10. The results indicate that blowing snow enhances longwave emissivity and reduces shortwave transmissivity, leading to a mean increase of 5.8 Wm−2 in downwelling longwave radiation and a mean decrease of 1.2 Wm−2 in downwelling shortwave radiation.
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Yang et al. (2026) Soil evaporation dominates shifts in evapotranspiration seasonality in the Yangtze River Basin
This study investigates the changes in evapotranspiration seasonality in the Yangtze River Basin and finds that soil evaporation is the dominant factor driving these shifts. The research reveals a significant increase in soil evaporation during the dry season.
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Semper et al. (2026) How reporting cadence shapes hourly precipitation extremes
This study investigates how reporting cadence affects hourly precipitation extremes by synthetically coarsening high-frequency records to different reporting schedules. The results show that temporal support, clock alignment, aggregation implementation, and rainfall structure all contribute to differences between hourly station products.
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Han et al. (2026) A framework for simulating and assessing the spatiotemporal cumulative effects of multi-reservoir operation on streamflow
This study develops a framework to simulate and assess the spatiotemporal cumulative effects of multi-reservoir operation on streamflow in large-scale regulated basins. The results show that customized parameterized reservoir operation rules improve streamflow simulations and provide insights into the mechanisms governing the cumulative effects.
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Guo et al. (2026) Contiguous evolution and hydroclimatic drivers of compound drought–heatwave events across mainland China
This study identifies and characterizes compound drought-heatwave events (CDHEs) across mainland China from 1981 to 2024 using a three-dimensional spatiotemporal algorithm. The findings reveal diverse contiguous evolution pathways of CDHEs and provide an event-based basis for risk assessment and impact mitigation.
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Huang et al. (2026) Trends in the frequency and duration of high flow events across climates
This study investigates the trend in the frequency and duration of high flow events across climates, using a dataset of 476 long record streamflow gauging stations across Australia. The results show that, across most arid and temperate regions, the frequency of high flow events has decreased and their duration shortened.
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Dalbianco et al. (2026) Land-use controls on soil hydraulic functioning: the role of pore organization and gravel distribution in an agricultural catchment
This study investigates the role of pore organization and gravel distribution in an agricultural catchment's soil hydraulic functioning. The results show that near-surface hydraulic properties are more closely associated with pore organization and coarse-fragment abundance than with texture or SOC.
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Snider et al. (2026) In the Age of AI—Are National Models and Data Sets Still Important for Stream Flow Prediction?
This study assesses the benefit of integrating Canada's high-resolution national reanalysis data (CaSR) into Long Short-Term Memory (LSTM) models for streamflow prediction across Canada. It finds that national-scale deep-learning models using CaSR, particularly a combined "Complete" model, significantly outperform models relying solely on global or continental meteorological inputs, and even exceed Google's global LSTM model performance within Canada.
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Gentile et al. (2026) Assessing the seasonal compartmentalization of water fluxes in the soil-plant-atmosphere continuum of a high-elevation mountain grassland
This study used a coupled snow isotope and hydrological model to investigate the seasonal origin of water fluxes in a high-elevation mountain grassland. It found a clear ecohydrological separation where snowmelt primarily recharges groundwater and rainfall sustains transpiration, but this separation weakens under snow drought conditions, increasing winter water contribution to plant uptake.
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Sharma et al. (2026) Hydrological flux effectiveness index for assessing rainfall partitioning and hydrological functioning under climate change
This study proposes a novel Hydrological Flux Effectiveness Index (HFEI) to quantify rainfall partitioning effectiveness and evaluate watershed hydrological functioning under changing climatic conditions. It demonstrates that HFEI effectively captures spatial heterogeneity and robustly identifies climate-driven shifts in hydrological partitioning.
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Sunguti et al. (2026) Robust increase in extreme future precipitation in the Lake Victoria Basin based on km-scale regional climate projections
This study employs a mini-ensemble of convection-permitting pseudo-global warming regional climate simulations at 2.8 km for the Lake Victoria basin to project future extreme precipitation, revealing a robust increase in extreme precipitation despite uncertainties in mean precipitation response. The findings offer critical insights for future climate resilience planning in the region.
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Corroyez et al. (2026) Discrepancies between Sentinel-2 and LiDAR-derived LAI in temperate deciduous forests: Influence of apparent optical depth, radiative transfer priors, and horizontal heterogeneity
This study quantifies and reduces discrepancies between Leaf Area Index (LAI) derived from Sentinel-2 (S2) and airborne LiDAR (ALS) in temperate deciduous forests by accounting for sensor sampling asymmetry, optimizing radiative transfer model (RTM) parameters, and assessing canopy horizontal heterogeneity. It finds that S2 LAI is most consistent with LiDAR LAI when the latter is restricted to the upper 6–10 meters of canopy, and that RTM parameter optimization significantly improves inter-sensor agreement, with transferability across sites.
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Diakogiannis et al. (2026) Tackling fluffy clouds: robust agricultural field boundary delineation from Sentinel-1 and Sentinel-2 satellite image time series
This paper introduces PTAViT3D, a deep learning architecture using 3D Vision Transformers for robust agricultural field boundary delineation directly from cloud-affected Sentinel-1 and Sentinel-2 satellite image time series, demonstrating state-of-the-art performance and global transferability across diverse agricultural environments.
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Su et al. (2026) Evapotranspiration intensification will drive terrestrial water storage decline in the Turpan–Hami Basin
This study developed a bias-corrected, fully coupled CWatM–MODFLOW model for the hyper-arid Turpan–Hami Basin using a novel multi-objective calibration scheme. It projects that warming-driven evapotranspiration intensification will lead to significant terrestrial water storage decline in the 21st century, despite modest precipitation gains.
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Zhang et al. (2026) Spatiotemporal evolution and driving mechanisms of superposition effects of rainfall-induced urban waterlogging and levee breach flooding
This study developed an integrated explainable machine learning framework to quantify and attribute the nonlinear superposition effects of coupled riverine–pluvial flooding hazards, revealing their spatiotemporal evolution and driving mechanisms in urban areas. The findings demonstrate pronounced nonlinear amplification effects under high recurrence intervals and highlight the shifting dominance of drivers from breach dynamics to topography in extreme scenarios.
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Feng et al. (2026) Flood modeling incorporating spatially heterogeneous rainfall and nonlinear routing mechanisms
This paper focuses on developing and applying flood models that integrate spatially heterogeneous rainfall and nonlinear routing mechanisms to improve flood prediction accuracy.
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Li et al. (2026) Constrained differentiable hybrid hydrologic modeling across multiple process backbones: accuracy gains and risks to physical consistency
This paper explores constrained differentiable hybrid hydrologic modeling across multiple process backbones, assessing its potential for accuracy improvements and the associated risks to physical consistency.
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Cheng et al. (2026) Improving regional winter wheat yield estimation by assimilating cross-scale heterogeneity information from UAV and satellite remote sensing
This study developed a novel cross-scale data assimilation framework integrating UAV and satellite remote sensing with the WOFOST crop model to improve high-resolution winter wheat yield estimation by accounting for within-pixel heterogeneity. The framework achieved robust and transferable yield estimates, outperforming conventional assimilation methods.
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Li et al. (2026) StreamFormer: A multi-resolution transformer for scalable global river streamflow prediction
This study introduces StreamFormer, a multi-resolution transformer model that predicts global river streamflow at various temporal resolutions with high accuracy. The model outperforms existing methods in terms of prediction skill and computational efficiency.
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Fischer et al. (2026) Flood Types Reveal Non‐Stationarity in Flood Peaks Masked by Annual‐Maximum Series
This study develops a novel mixture model for flood frequency analysis that accounts for non-stationarity and process-specific changes in flood-generating mechanisms. The results show significant differences in trend patterns between various flood types across Europe.
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Jia et al. (2026) Projected droughts readily trigger vegetation decline
This study provides a global assessment of vegetation responses to terrestrial water storage drought under historical and future climate scenarios, revealing that projected droughts will trigger vegetation decline under milder conditions due to increased vulnerability, particularly for severe vegetation loss.
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Zhang et al. (2026) Climate Change Increases Water Scarcity Risks and Adaptation Needs in China
This study developed a novel event-scale framework to quantify sector-specific water use responses to meteorological droughts across mainland China from 1981 to 2020, revealing that droughts, despite affecting a small land area, accounted for a disproportionately large share of water use, particularly in northern basins driven by irrigation, leading to increased water scarcity risks by 2100.
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Motamedi et al. (2026) Hydrometeorological and hydrogeological trends and drought dynamics in a cold-humid, coastal region
This study investigated hydrometeorological and hydrogeological drought dynamics in Nova Scotia, Canada, revealing that the fall season exhibits the most significant drying trends across the hydrological cycle, with droughts becoming more frequent and spatially extensive since 2016. Groundwater systems generally show rapid, strong correlations with meteorological and baseflow conditions but exhibit longer and more variable recovery times from drought events.
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Mendoza et al. (2026) Assessing Hydrological Model Representation of Irrigation and Subsurface Flow in a Heavily Anthropogenic‐Influenced Catchment
This study assesses the impact of different model representations of irrigation and subsurface flow dynamics on water balance partitioning in the anthropogenically influenced Hindon River Basin. It reveals significant spatio-temporal variability in simulated river discharge and evapotranspiration, highlighting the importance of comprehensive model assessment and uncertainty reduction.
