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Wu et al. (2026) Widespread diverse impacts of climate change on the multi-attribute characteristics of flash drought in China: A three-dimensional dynamic assessment based on CMIP6 multimodel ensembles
This study proposes a comprehensive framework to project the spatiotemporal dynamic characteristics of flash drought (FD) in China under future climate change scenarios. It finds that climate change will exacerbate FD severity and intensity across most of China, prolong its duration, and extend the migration distance of long-duration events, while maintaining the overall migration direction.
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He et al. (2026) Explaining urban flood susceptibility under rainfall uncertainty through probabilistic modeling and interpretable machine learning
This study proposes a comprehensive framework integrating probabilistic hydrodynamic simulations with interpretable machine learning to systematically analyze urban and environmental factors influencing urban flood susceptibility at the grid scale, revealing that terrain features, drainage capacity, and urban form are key predictors with depth- and scale-dependent patterns.
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Li et al. (2026) Improving Ensemble Forecasts of Abnormally Deflecting Tropical Cyclones with Fused Atmosphere-Ocean-Terrain Data
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Wang et al. (2026) Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields
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Lin et al. (2026) Persistence of frozen-period soil moisture modulates late-spring surface thermal anomalies in the Tibetan Plateau
This study reveals that the persistence of frozen-period soil moisture (SM) in the Tibetan Plateau (TP) significantly modulates late-spring surface air temperature (SAT) anomalies. It demonstrates that January SM anomalies influence May SAT by altering evapotranspiration and the partitioning of surface sensible and latent heat fluxes, with a particularly strong impact on daily maximum SAT extremes.
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Rehbein et al. (2026) South American Mesoscale Convective Systems: Present and Future Climates
This study projects future changes in mesoscale convective systems (MCSs) over South America, finding a general intensification with higher maximum precipitation rates and larger precipitation volumes, alongside regional and seasonal shifts in their occurrence and precipitation contribution.
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Zhao et al. (2026) Climatic Effects of Ice Nucleation by Typical Natural and Anthropogenic Aerosols in East Asia Using a Developed Version of RegCM‐Chem
This study integrates an ice-nucleation parameterization into the RegCM-Chem model to investigate the regional climatic impacts of natural (dust) and anthropogenic (carbonaceous, sulfate) aerosols on ice clouds over East Asia. The research finds that aerosol ice nucleation leads to a radiative heating effect, predominantly driven by dust, which significantly alters regional temperature and precipitation patterns.
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Ellison et al. (2026) The Drivers and Impacts of Future Analogs of the 2011–2014 Drought in the Western and Central United States
This study assesses how anthropogenic climate change alters the large-scale drivers and impacts of droughts, focusing on simulated analogs of the 2011-2014 western and central US drought, finding fewer such droughts in the future but with altered hydrological and ecological impacts and potentially increased predictability of their drivers.
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Selkirk et al. (2026) Coherent interdecadal cycles in global rainfall, temperature, and cloud cover with alignment to Jovian orbital periods
This study identifies significant coherent 12.9- and 19.9-year cycles in global rainfall, surface temperature, and cloud cover, suggesting these interdecadal variabilities are externally paced by Jovian orbital dynamics rather than solely internal climate processes.
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Singh et al. (2026) Accounting for the bias in the median track of Indian Summer monsoon low-pressure systems in an Earth system model
This study investigates systematic biases in Monsoon Low-Pressure Systems (LPSs) and associated dry biases in the Community Earth System Model (CESM2.1.3) during the Indian Summer Monsoon. It finds a southward shift in LPS activity in CESM2.1.3, linked to biases in the low-level westerly jet and dry air intrusion, which are common across CMIP model generations.
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Ortenzi et al. (2026) Exploring groundwater-surface water interactions and recharge in fractured mountain systems: an integrated approach
This study developed an integrated approach to map groundwater-surface water interactions and quantify aquifer recharge in a fractured Mediterranean mountain catchment. By combining multi-source data, the research revealed significant groundwater contributions to streamflow, identified distinct hydrogeological sources, and quantified snowmelt's substantial role (approximately 18%) in aquifer recharge.
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Zelinka et al. (2026) Recent cloud trends and extremes reaffirm established bounds on cloud feedback and aerosol-cloud interactions
This study quantifies the meteorological and aerosol-related factors driving recent cloud-radiative anomalies and increased Earth's energy imbalance, reaffirming established bounds on cloud feedback and aerosol forcing, and supporting an equilibrium climate sensitivity near 3 °C.
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Zhou et al. (2026) Effects of the competing controlling factors of rainfall, landscape position and soil depth on moisture responses in the Mollisol region of China
This study quantified the interactive influence of rainfall patterns and landscape positions on event-scale soil moisture response metrics across various soil depths in the Mollisol region of China. It found that while midslope and downslope positions were critical for explaining response variance, rainfall type was the primary factor driving soil moisture dynamics across all depths, with heavy rainfall leading to greater and faster responses in shallow layers.
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Behera et al. (2026) Photosynthetic Recovery Dynamics Reveal Declining Vegetation Functional Resilience in Tropical Ecosystems
This study assesses global vegetation functional resilience from 2000 to 2019 using solar-induced chlorophyll fluorescence (SIF), revealing latitudinal contrasts in recovery rates and widespread resilience decline in tropical and Eurasian high-latitude ecosystems driven by climatic stressors.
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Guo et al. (2026) Divergent Changes in Oceanic and Terrestrial Surface Water Budgets Under Global Warming: Insights From ERA5 Reanalysis
This study develops a process-level decomposition framework to quantify the global surface water budget's sensitivity to climate warming using ERA5 reanalysis. It finds an intensified water cycle with strengthened ocean-to-land moisture redistribution, where circulation and transient eddies primarily shape the spatial response, while thermodynamic effects are secondary due to compensation.
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Ding et al. (2026) Global Urban‐Rural Differences in Precipitation: Cities See More Light Rain But Milder Extremes
This study investigates how urban areas differentially modulate precipitation intensity, revealing that cities generally increase light precipitation frequency while mitigating extreme precipitation magnitude. These effects, particularly for heavier precipitation, tend to shift to downwind rural areas.
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Fernández et al. (2026) Measurement of Soil Moisture Using Capacitance Measurements: Development of a Low-Cost Device for Environmental and Very-Low-Enthalpy Geothermal Energy Applications
This paper presents a novel low-cost capacitive soil moisture sensor with optimized interdigitated electrodes and a protective dielectric coating, demonstrating high sensitivity and precision for applications in very-low-enthalpy geothermal energy, agriculture, and environmental monitoring.
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Rateb et al. (2026) Freshwater Availability in the Mississippi River Basin and Adjacent Texas Aquifers Under Human and Climate Pressures
This study quantifies spatiotemporal water storage trajectories across the Mississippi River Basin and adjacent Texas aquifers (MRB-TX) by integrating diverse long-term datasets and climate projections. It reveals a significant longitudinal gradient in water storage, with substantial declines in arid western aquifers primarily due to irrigation and drought, contrasting with stable or gaining conditions in humid eastern regions.
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Liu et al. (2026) Cross-regional estimation of leaf chlorophyll and soil moisture content in drip-irrigated citrus orchards using UAV data and transfer learning
This study developed a transfer learning framework using UAV multispectral data to achieve cross-regional estimation of leaf chlorophyll content (LCC) and soil moisture content (SMC) in drip-irrigated citrus orchards. The Fine-tuning strategy, combined with the CNN-LSTM-Attention-XGBoost (CLA-X) model, significantly enhanced estimation accuracy in a new region, demonstrating a viable framework for precision water and fertilizer management.
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Liu et al. (2026) A Graph‐Based Deep Learning Approach for Daily Flash Flood Susceptibility Modeling in China
This study proposes a novel graph-based deep learning model (LTG) for daily-scale spatiotemporal flash flood susceptibility (FFS) simulation in China, addressing limitations of traditional models by integrating temporal dependencies, spatio-temporal interactions, and spatial dependencies between catchments. The LTG model significantly outperforms baseline models, achieving an AUC of 0.911 and CSI of 0.719, and effectively captures seasonal FFS variations and spatial hydrological dependencies.
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Bosc et al. (2026) Predicting thunderstorm risk probability at very short time range using deep learning
This study develops a deep learning methodology to predict thunderstorm risk probability at very short time ranges (every 5 minutes up to 1 hour ahead) for aviation safety. It utilizes an adapted Convolutional Neural Network with attention mechanisms, fed by satellite observations and Numerical Weather Prediction outputs, to generate well-calibrated lightning risk maps.
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Moghairib et al. (2026) A Model‐Agnostic Representation of Prairie Pothole Hydrology: Enhancing Generality and Implementation Across Hydrological Models
This paper introduces HDSv2, an open-source and model-agnostic module designed to improve streamflow modeling in pothole-dominated regions by representing dynamic contributing area and storage-discharge hysteresis. Integrating HDSv2 into various hydrological models significantly enhances numerical stability, process fidelity, and the reproduction of observed hydrographs and depressional storage relationships.
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Wu et al. (2026) Analysis on Multi-Factor Synergistic Hazards Mechanism of Wet Micro-Downburst: a Multi-Source Data Fusion Analysis Based on Passenger Vessel Capsizing Accident in Qianxi, Guizhou on 4 May 2025
This study investigates the multi-factor synergistic mechanism of a wet microburst that caused a passenger vessel capsizing accident in Qianxi, Guizhou, on 4 May 2025, by fusing multi-source data to reconstruct the event and elucidate the dynamic-microphysical coupling. It found that a rapidly descending strong radar echo core triggered an explosive near-surface divergent wind field (34.7 m/s), accompanied by significant temperature and pressure changes, and verified by lightning activity, surveillance videos, and on-site damage.
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Dey et al. (2026) Catchment’s Resilience to Flood Generation Mechanism: The Role of Soil Moisture
This study evaluates the drivers and mechanisms of catchment resilience to dominant flood-generating mechanisms under varying antecedent soil moisture conditions across 191 catchments in peninsular India. It reveals that soil properties, rainfall characteristics, land cover, and meteorological variables during dry spells systematically influence this resilience and the spatial variation of antecedent soil moisture.
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Hu et al. (2026) Multi‐Indicator Assessment to Assess the Increasing Impacts of Compound Dry and Hot Events on Global Wheat Yield
This study investigates the evolution of compound dry and hot events (CDHEs) during global wheat growing seasons from 1981 to 2020 and their impact on wheat yield. It finds significant increases in CDHE frequency, duration, and intensity globally, leading to negative yield anomalies, particularly in arid and semiarid regions, with specific multi-indicator combinations proving most effective for detection.
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Lannoy et al. (2026) Advancing crop modeling and data assimilation using AquaCrop v7.2 in NASA's Land Information System Framework v7.5
This paper integrates the AquaCrop v7.2 crop model into NASA's Land Information System Framework (LISF) v7.5, enabling high-performance, scalable crop modeling and satellite data assimilation. Through three showcases, it demonstrates improved canopy cover simulations with satellite-informed crop parameters, quantifies biomass uncertainty in relation to soil moisture, and explores the beneficial but limited impact of fractional vegetation cover (FCOVER) assimilation on yield estimates due to strong model constraints.
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Switanek et al. (2026) Leveraging normalized data to improve point-scale estimates of precipitation–temperature scaling rates
This study proposes and evaluates a methodology using normalized precipitation and dew point temperature data to improve point-scale estimates of precipitation-temperature scaling rates, demonstrating that normalization effectively accounts for spatio-temporal climatological variability and enhances prediction skill in the Upper Colorado River Basin.
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Kumar et al. (2026) A Framework for Selection of a Hydrological Model-SAWT: A Review
This review provides a detailed overview of the Soil and Water Assessment Tool (SWAT) model, including its development, structure, data requirements, and application strategies. It systematically compares SWAT with HEC-HMS, MIKE-SHE, and VIC based on eight key criteria, concluding that SWAT is a versatile and suitable model for watershed-scale hydrological research and management.
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Yu et al. (2026) Flood inundation monitoring with multi-source satellite imagery based on deep learning and explainable frameworks
This study introduces a U-Net deep learning model for flood inundation monitoring by integrating multi-source Sentinel-1 (SAR) and Sentinel-2 (Multispectral) satellite imagery, demonstrating enhanced accuracy and providing insights into model behavior using an explainable framework.
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Gómez et al. (2026) Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill
This study develops a storm-position–conditioned Quantitative Precipitation Forecast (QPF) displacement ensemble using HRRR forecasts to propagate precipitation perturbations through a 2D hydrodynamic model (SFINCS), quantifying their impacts on deterministic and probabilistic flood inundation mapping. The approach improves correlation with observations, reduces biases in predicted flood depths, and enhances the representation of flood impact variability in urban environments.
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Chen et al. (2026) Skillful Kilometer-Scale Regional Weather Forecasting via Global and Regional Coupling
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Antonio et al. (2026) ACE2-NEMO: Coupling an ML atmospheric emulator to a full-depth dynamical ocean model
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Azizi et al. (2026) SVH-BD : Synthetic Vegetation Hyperspectral Benchmark Dataset for Emulation of Remote Sensing Images
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Ren et al. (2026) Evaluating the Collision‐Coalescence Process in Idealized Cloud Convection Using Large‐Eddy Simulations With Lagrangian Microphysics
This paper employs large-eddy simulations with Lagrangian microphysics to evaluate the theoretical non-dimensional drizzle number (Dz) in a simulated convection-cloud chamber, confirming its utility in quantifying the impact of collisional growth on droplet size distribution and estimating collision rates.
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Bochow et al. (2026) Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature
This study introduces a novel physics-constrained generative machine learning framework, based on consistency models, to downscale Greenland's surface mass balance (SMB) and surface temperature (T_s) fields by a factor of up to 32 (from 160 km to 5 km grid spacing). The method ensures physical conservation during inference, enabling robust generalization to extreme climate states and providing realistic, high-resolution climate forcing for ice-sheet simulations with fast computational efficiency.
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Shelton et al. (2026) Impacts of subtropical pacific variability on snow precipitation fraction (SPF) in the upper Colorado river basin
This study investigates the spatiotemporal variability of the snowfall-to-total-precipitation fraction (SPF) in the Upper Colorado River Basin (UCRB) during the cold season (1980–2022), revealing a distinct north-south gradient and a strong connection between SPF variability and large-scale ocean-atmosphere interactions, particularly the Pacific Meridional Mode (PMM).
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Fallah-Mehdipour et al. (2026) Evaluation of agro-hydrological subseasonal-to-seasonal forecasts for tactical decision making in crop irrigation
This study investigates the utility of sub-seasonal to seasonal (S2S) forecasts (up to six weeks lead time) for estimating future crop irrigation at the field scale. It found that while forecast accuracy decreases with longer lead times, using a reliability-weighted median of S2S forecasts can potentially reduce irrigation water use and maintain or enhance crop water productivity in irrigated years.
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Montazeri et al. (2026) Spatial and temporal changes in wildfires and their attributes across the western United States
This study analyzed wildfire activity in the Western United States from 1992 to 2020, revealing a 31% decrease in the number of ignitions but a 40% increase in burned area, primarily due to environmental conditions promoting larger fires and earlier human-caused ignitions.
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Xu et al. (2026) Escalating Extreme River Discharge Events Driven by Precipitation Changes in the Yangtze River Basin
This study investigated the spatiotemporal characteristics and precipitation drivers of 3-hourly extreme river discharge events (ERDEs) across the Yangtze River Basin (YRB) from 2000 to 2019, revealing a significant increasing trend in ERDEs and distinct local/upstream precipitation patterns linked to specific atmospheric processes in different sub-regions.
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Tan et al. (2026) Holocene Climate Changes: Unraveling Processes, Mechanisms, and Impacts Across Spatiotemporal Scales
This special collection explores Holocene climate changes, focusing on reconstructing variability, elucidating multi-scale mechanisms, and understanding climate-human-environment interactions, revealing that regional climate processes often diverge from global trends, multi-sphere interactions are crucial, and natural and human impacts on regional environments are asynchronous.
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Zhu et al. (2026) Observed global increases in tropical cyclone rapid acceleration and deceleration events
This study empirically analyzes global tropical cyclone (TC) observations from 1988 to 2024 to identify temporal and spatial trends in TC translation speed changes. It finds that both rapid acceleration and strong deceleration of TCs have become increasingly prevalent globally, indicating a more variable pattern of storm motion.
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Wu et al. (2026) Phenology-Aware Collaborative Decision-Making and AG-PSTC Algorithm for Precision Irrigation in Smart Tea Gardens
This study develops a precision irrigation system for smart tea gardens, integrating a Phenology-Aware Collaborative Decision-Making (PACD) model and an Adaptive Gain Predictive Super-Twisting Sliding Mode Control (AG-PSTC) algorithm. The system effectively mitigates time delays and nonlinear interference while dynamically adjusting irrigation based on actual plant needs, significantly improving precision and suppressing false irrigation commands caused by biomass fluctuations from plucking.
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Haagmans et al. (2026) How montane forests shape snow cover dynamics across the central European Alps
This study comprehensively analyzed how montane forests influence snow cover dynamics across the central European Alps using a process-based snow model over eight hydrological years. It found that forests store 20–30% of midwinter snow, generally reducing peak snow water equivalent (SWE) but often delaying snow disappearance, with these effects varying significantly by elevation, aspect, region, and interannual weather variability.
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Zhang et al. (2026) Atmospheric Rivers Linking Tropical Forcing to Winter Precipitation over Southern China
This study identifies a specific tropical sea surface temperature pattern involving ENSO, IOD, and WNP SST anomalies that enhances atmospheric river intrusion into southern China, leading to significant winter precipitation anomalies that are reasonably predicted by seasonal forecast models.
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Tsypin et al. (2026) Influence of groundwater recharge projections on climate-driven subsurface warming: insights from numerical modeling
This study numerically models the coupled effects of rising surface temperatures and changing groundwater recharge on subsurface warming in Brandenburg, Germany, until 2100. It finds that while surface temperature is the primary driver of groundwater warming (up to 2.5 °C), groundwater flow dictates its spatial variability, and even increased winter recharge cannot counteract the overall warming trend.
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Lou et al. (2026) A highly generalizable data-driven model for spatiotemporal urban flood dynamics real-time forecasting based on coupled CNN and ConvLSTM
This study proposes a novel data-driven model, coupling CNN and ConvLSTM, for real-time spatiotemporal urban flood inundation depth forecasting. The model effectively captures inundation dynamics and demonstrates robust spatial generalization with significantly higher computational efficiency compared to physics-based models.
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Deng et al. (2026) Legacy effects of extreme precipitation sustain carbon sink of a subtropical forest
This study investigates the seasonal to interannual effects of extreme precipitation on net ecosystem productivity (NEP) in subtropical forests using a land model, revealing that while extreme events immediately reduce NEP, legacy effects from deep soil water recharge sustain the carbon sink in subsequent years.
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Du et al. (2026) Large-scale water depth reconstruction of Tibetan Plateau lakes using ICESat-2 photon data: Performance, limitations, and environmental drivers
This study reconstructs water depths for 350 Tibetan Plateau lakes using ICESat-2 photon data, achieving a root mean square error of 0.45 m, and identifies lake area, longitude span, and water clarity as key influencing factors.
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Zi et al. (2026) East Asian spring precipitation and its dry trend revealed by CMIP6 high-resolution coupled models
This study evaluates CMIP6 HighResMIP coupled models' ability to simulate East Asian spring precipitation climatology and its drying trend (1980–2014), finding that high-resolution models generally improve mean precipitation patterns but show varied and often limited success in reproducing the observed drying trend, with only a few models demonstrating significant improvement due to better representation of regional circulation drivers.
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Nikrou et al. (2026) Intercomparison of flood inundation models across land use types and hydrological flood stages
This study provides a context-stratified intercomparison of five flood inundation models across multiple hydrograph phases, land-use types, and benchmark datasets, revealing that model performance rankings systematically shift depending on the evaluation context. The findings offer guidance for model selection and the design of future intercomparison studies.
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Fu et al. (2026) Evapotranspiration-partitioned generalized complementary relationship model for heterogeneous vegetation
This study proposes an evapotranspiration-partitioned generalized complementary relationship (EPGCR) model to estimate total evapotranspiration (ET) and its components (transpiration, soil evaporation, and interception evaporation) using only meteorological and leaf area index data. Applied to heterogeneous vegetation, the EPGCR demonstrated high accuracy and consistent parameters, significantly expanding the applicability of the complementary relationship.
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Bah et al. (2026) A new comprehensive monitoring framework for global drought assessment
This study introduces a novel Comprehensive Drought Index (CoDI) for global drought assessment, integrating meteorological, agricultural, and hydrological drought dimensions using Principal Component Analysis. CoDI was validated across 60 major basins and 22 historical drought events, demonstrating superior capability in capturing diverse drought conditions and identifying global drought hotspots from 1982 to 2018.
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Lu et al. (2026) Transferring Physical Priors into Remote Sensing Segmentation via Large Language Models
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Simm et al. (2026) Calibrated Conformal Prediction Intervals for Microphysical Process Rates
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Kui et al. (2026) Detection of nine plateau lakes water level changes in Yunnan, China from ICESat-2 data
This study developed an outlier-detection framework for ICESat-2 ATL13 data to monitor water level changes in nine Yunnan plateau lakes from 2018 to 2024, revealing consistent declining trends for most lakes driven by seasonal natural factors modulated by anthropogenic influences.
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Ye et al. (2026) Agent-based intelligent real-time control for pluvial flood mitigation at urban scale
This study develops an Urban Flooding Control Model (UFCM) by integrating hydrological-hydrodynamic models with deep reinforcement learning (DRL) for real-time pluvial flood mitigation at the urban scale. Applied to Jinan, China, UFCM significantly reduced inundated areas compared to traditional methods, demonstrating efficient and accurate real-time decision-making for complex urban flood systems.
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Yao et al. (2026) First Triple‐Frequency Retrievals of Binned Raindrop Size Distributions
This study develops a two-step optimal estimation technique for retrieving binned raindrop size distributions (DSDs) aloft using a newly developed vertically-pointing triple-frequency radar system, demonstrating good agreement with ground-based disdrometer observations and providing the first radar-based view of DSD variations in Meiyu front rainfall.
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Wang et al. (2026) Unraveling uncertainty in compound flood modeling: sensitivity of simulations to forcings and model parameters
This study evaluates the sensitivity of the SFINCS model to forcing and parameter uncertainties during the Beryl compound flood event in Houston. It reveals strong spatial variability in forcing sensitivity and consistent parameter sensitivity, concluding that parameter-induced uncertainty generally outweighs forcing-induced uncertainty.
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Zhu et al. (2026) Integrated Analysis and Prediction of Drought in the Endorheic Basins of China
This study systematically analyzes historical (1960–2024) and future (2025–2100) drought trends and characteristics, including compound and extreme events, across the diverse Endorheic Basins of China, revealing a pronounced drying trend influenced by radiative forcing and oceanic teleconnections.
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Xie et al. (2026) Deep reinforcement learning for long-horizon reservoir operation: Temporal horizon, state representation, and hydrological data synthesis
This study develops a Deep Reinforcement Learning (DRL) framework for long-horizon reservoir operation, systematically evaluating the impact of episode length, state representation, and synthetic hydrological data. It finds that a 4-year episode length, two-dimensional periodic date encoding, and extreme-enhanced synthetic inflows significantly improve policy performance, stability, and robustness for the Three Gorges Reservoir.
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Zhang (2026) Spectral-Aware Text-to-Time Series Generation with Billion-Scale Multimodal Meteorological Data
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Feng (2026) StretchCast: Global-Regional AI Weather Forecasting on Stretched Cubed-Sphere Mesh
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Muhawenayo et al. (2026) PRUE: A Practical Recipe for Field Boundary Segmentation at Scale
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Mittermeier et al. (2026) Omega-blocks with spatially compounding extremes over Europe are highly sensitive to remote atmospheric drivers
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Yang et al. (2026) ReDF-net: a feature extraction and dynamic fusion framework based on residual networks for runoff forecasting
This paper introduces ReDF-Net, a residual network-based dynamic fusion framework for multimodal runoff forecasting that adaptively couples spatial feature extraction with temporal modeling and quantifies input contributions. It demonstrates significantly enhanced accuracy (NSE > 0.97) and improved interpretability across two Chinese basins, outperforming various conventional and state-of-the-art models.
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Subhadarsini et al. (2026) EXtreFormer: a general deep learning framework for forecasting compound extreme events: experience with dry-hot extremes and vegetation response
This study introduces EXtreFormer, a novel deep learning framework for long-horizon forecasting of compound dry-hot extreme events and vegetation response, demonstrating superior performance and interpretability in the Godavari River Basin. The framework effectively captures the complex interplay between temperature, soil moisture, and vegetation across diverse land use types, achieving high predictive accuracy for extreme events.
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Ding et al. (2026) HBiM2: A multi-angle hyperspectral soil radiative transfer model for simulating the reflectance of dry and wet soils
This paper develops HBiM2, a multi-angle hyperspectral soil radiative transfer model, to accurately simulate the spectral and directional reflectance of both dry and wet soils, demonstrating robust performance across diverse soil conditions and observational geometries.
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Shaddy et al. (2026) Probabilistic Forecasting of Localized Wildfire Spread Based on Conditional Flow Matching
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He et al. (2026) Enhancing runoff simulation in data-scarce mountainous regions: a coupled SWAT and transferable transformer approach
This study develops the SWAT-HydroTransformer, a physics–data hybrid framework that integrates SWAT-simulated hydrological processes as physical constraints into a multi-scale Transformer architecture. The framework aims to enhance runoff prediction in data-scarce mountainous regions, demonstrating superior predictive skill and robust transferability.
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Siqueira et al. (2026) Do our models capture the range of ENSO events?
This study employs a novel event-based space-time analog approach to evaluate whether state-of-the-art coupled climate models (CESM1 and E3SM1) capture the full range of observed ENSO variability. It reveals that low-resolution models generally outperform their high-resolution counterparts in reproducing observed ENSO events, and increasing resolution does not significantly reduce mean-state biases.
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Kuchinski et al. (2026) Causes, seasonality and climate variability of floods in southern Brazil
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Wang et al. (2026) Future Climate Projections of Hazardous Convective Weather Using an Ensemble of Environment‐Informed, Convection‐Permitting Dynamical Downscaling Simulations
This study introduces "environment-informed" convection-permitting dynamical downscaling (CPDD) to efficiently generate ensembles of hazardous convective weather (HCW) projections driven by multiple global climate models (GCMs). It demonstrates that future supercell occurrences, a key HCW indicator, vary significantly across GCMs, projecting an increased frequency over the Missouri Bootheel and an earlier start to the annual HCW risk cycle.
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Yan et al. (2026) Assessing the warming biases in CMIP6 models: the roles of fast response and cumulative effects to external forcings
This study introduces a novel method to assess warming biases in CMIP6 climate models using two indices, 𝑎 (fast response sensitivity) and 𝐻 (cumulative effects/long-term memory), derived from the climate system's scaling behavior. It finds that overestimated cumulative effects are a primary driver of warming biases in these models, offering an efficient framework for model evaluation and improvement.
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Gopalan et al. (2026) The water available for industrial sector (WAIS) framework for assessing the industrial water deficit risk while securing downstream availability
This study introduces the Water Available for Industrial Sector (WAIS) framework to quantify the double materiality of industrial water withdrawal (IWW) by jointly assessing its impacts on downstream water availability and operational risks. Applied to Thailand's Chao Phraya River Basin, the framework revealed localized water conflicts and substantial impacts, emphasizing the need for context-sensitive allocation strategies.
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Guan et al. (2026) Increasing Population and Cropland Exposure to Human‐Induced Sequential Heatwave‐Downpour Events
This study quantifies the anthropogenic influence on the increasing trend of compound sequential heatwave-downpour (SHD) events across the Northern Hemisphere and projects future population and cropland exposure. It finds that anthropogenic influences, primarily greenhouse gas emissions, account for approximately 82.2% of the increase in affected areas, with future exposure projected to increase nearly 8-fold under a high-emission scenario, predominantly driven by climate change.
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Wang et al. (2026) Interpretable hierarchical Bayesian modeling of monthly streamflow for heterogeneous basins: A comparative study of two basins
This study develops and compares two interpretable Bayesian hierarchical models (BHMs) for simulating monthly streamflow in two heterogeneous basins, demonstrating their ability to leverage shared information while capturing basin-specific storage dynamics and seasonal variations. The models provide an interpretable framework for understanding storage-driven responses and offer a robust pathway for operational streamflow forecasting in multi-basin settings.
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Pan et al. (2026) Ocean meridional heat transport estimated from energy budget constraint
This study quantifies global oceanic meridional heat transport (MHT) from 1985 to 2023 using an energy budget approach, revealing distinct MHT patterns and controls in the Atlantic and Indo-Pacific oceans, and providing improved estimates for climate research.
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Vishnu (2026) Contribution of Low Pressure Systems Rainfall to Interannual Variability of the Indian Summer Monsoon
This study re-examines the relationship between Monsoon Low-Pressure Systems (LPS) and Indian Summer Monsoon Rainfall (ISMR) variations, finding a strong correlation where increased LPS-generated rainfall, driven by higher rainfall rates from elevated humidity and stronger vertical motions, contributes substantially to excess ISMR, and reveals a coupling between LPS dynamical intensity and rainfall rates.
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Burns et al. (2026) Influence of Weather Fronts on Design Storm Profiles: Applied Event Partitioning and Comparative Analysis
This study objectively partitioned over 700 historic rainfall events by frontal storm signatures to develop synthetic storm profiles, revealing that frontal storms exhibit distinct temporal characteristics and shapes that significantly depart from standard distributions, which is crucial for hydrologic designs sensitive to temporal loading.
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Maruf et al. (2026) Soil moisture decorrelation timescales are sensitive to precipitation variability and land-atmosphere coupling
This study investigates the sensitivity of soil moisture decorrelation timescales to meteorological forcing autocorrelation and land-atmosphere coupling using the Community Land Model version 5 (CLM5). It finds that precipitation autocorrelation and soil moisture-precipitation feedback significantly enhance decorrelation timescales, with randomized forcing substantially reducing them, highlighting the influence of climate variability.
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Yong et al. (2026) Revealing the influence of topography and vegetation on hydrological processes using a stepwise modelling approach in cold alpine basins of the Mongolian Plateau
This study employed a stepwise modelling approach to systematically assess the influence of topography and vegetation on hydrological processes in cold alpine basins of the Mongolian Plateau, revealing that distributed and landscape-based models significantly improve runoff and snow water equivalent simulations, with high elevations driving sustained snowmelt runoff and low elevations generating rapid rainfall-driven runoff.
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Yuan et al. (2026) Baseflow in karst regions is significantly higher than the global average and exhibits spatial variability
This study quantifies baseflow characteristics in 1375 global karst basins, revealing that baseflow constitutes approximately 78 % of streamflow, significantly higher than the global average of 60 %. It also identifies significant spatial variability and an increasing temporal trend in karst baseflow, primarily influenced by vegetation factors.
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Zhang et al. (2026) Deceleration of water cycle for global semi-arid regions driven by climate warming
This study reveals a systematic deceleration of the water cycle in global semi-arid regions from 1982 to 2022, driven by climate warming, which contradicts the expected global intensification and is attributed to water-limited evapotranspiration responses and a positive feedback loop among water cycle components.
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Nunno et al. (2026) Teleconnection-informed clustering of temporally compound hydroclimatic extremes in Europe: post-drought extreme rainfall probability and long-term trends
This study analyzed compound drought-rainfall extremes across Europe (1976–2023) using teleconnection-informed clustering, revealing three distinct hydroclimatic regimes where post-drought extreme rainfall probabilities are heterogeneous and strongly modulated by large-scale atmospheric circulation. The findings indicate that extreme rainfall can occur during or after drought without necessarily ending the drought, emphasizing the critical role of atmospheric teleconnections.
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Sorando et al. (2026) Simulating mediterranean rice paddies’ water balance under climate change scenarios
This study applies the SWAT+ paddy rice module to simulate the water balance of a Mediterranean rice irrigation district in Albufera de Val`encia, Spain, under climate change scenarios. Projections indicate significant precipitation reductions (9–31%) and potential evapotranspiration increases (8–18%) by mid- and late-century, leading to higher irrigation requirements (4–10%) and modest rice yield declines (up to 8%).
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Gonçalves et al. (2026) Irrigated agriculture in the United States: Current status and future frontiers
This review assesses the current status and future frontiers of irrigated agriculture in the United States, analyzing regional trends, water sources, crop diversity, and management practices from 2003-2023, and identifies key challenges like groundwater depletion and an eastward shift in irrigation.
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Xie et al. (2026) Interactions Between Multiple Drought Types Across Temporal Scales
This study analyzed the complex interactions between meteorological, soil, vegetation, and groundwater droughts in the Heihe River Basin using an extended convergent cross mapping algorithm, revealing that temporal scale significantly influences interaction intensity and lag, with groundwater drought playing a crucial role in propagation.
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Huning et al. (2026) Cascading impacts of natural disasters in a connected world
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Pengxin et al. (2026) Multi-model integrated error correction for extreme precipitation: method and application
This study developed a novel multi-model integrated error correction framework for CMIP6 extreme precipitation projections, significantly improving simulation accuracy in the Hanjiang River Basin (HRB). The corrected data reveal pronounced upward trends in extreme precipitation in the HRB, particularly in its southwestern and downstream areas, under future moderate to high radiative forcing scenarios.
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Barahona et al. (2026) Deep learning representation of the aerosol size distribution
This study develops MAMnet, a deep learning model, to predict the aerosol size distribution (ASD) and mixing state for seven lognormal modes based on bulk aerosol mass and meteorological conditions. MAMnet accurately reproduces the output of a two-moment modal aerosol scheme and shows good agreement with field measurements when driven by reanalysis data, offering an efficient way to improve aerosol representation in atmospheric models.
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Faliagka et al. (2026) Adaptation of the VegSyst model to predict crop nutrient uptake and water needs for precise soilless crop fertigation in greenhouses
This study adapted and validated the VegSyst model for precise soilless crop fertigation in greenhouses, integrating climate forecasts to predict water and macronutrient needs for cucumber and tomato, demonstrating reduced nutrient leaching and increased agronomic efficiency without compromising yield.
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Mascaut et al. (2026) Ground-based atmospheric measurements at the Onsala Space Observatory (Sweden): data & trends (2009–2025)
This study presents and analyzes a comprehensive, long-term (2009-2025) dataset of ground-based atmospheric measurements from the Onsala Space Observatory, Sweden, revealing a statistically significant warming trend of approximately 0.15 kelvin per year, most pronounced in winter, and a significant decrease in rain rate intensity.
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Zeng et al. (2026) Synergistic Effects of Multi‐Timescale Atmospheric Teleconnections on Spring Monthly Droughts in Central‐Eastern China
This study investigates spring monthly drought variations in central-eastern China (CEC) and the synergistic effects of multi-timescale atmospheric teleconnections. It finds that in-phase alignments of high-frequency and low-frequency teleconnections (SCA, WP, NAO) amplify specific atmospheric circulation anomalies, leading to decreased precipitation and increased potential evapotranspiration, thus causing pronounced droughts in the CEC.
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Xu et al. (2026) Divergent latitude-specific urban humid heat risks are regulated by local climate types
This study systematically investigates the spatiotemporal evolution and drivers of urban wet-bulb temperature across 56 global cities from 2005-2024, revealing significant increases since 2020 with responses regulated by local climate types.
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Yang et al. (2026) DANRA: the kilometer-scale Danish regional atmospheric reanalysis
This paper introduces DANRA, a novel 2.5-kilometer resolution regional atmospheric reanalysis dataset covering Denmark and its surrounding regions from 1990 to 2023. DANRA demonstrates superior performance compared to global reanalyses like ERA5 in representing essential climate variables and extreme weather events, providing unprecedented detail for climate adaptation and impact modeling.
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Gensini (2026) Extreme events and the insurance industry in a changing climate
This letter argues for the formal integration of high-resolution downscaling, ensemble modeling, and catastrophe risk analysis to bridge the gap between coarse global climate models and the insurance industry's need for local, probabilistic risk assessment in a changing climate.
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Agbesi et al. (2026) Development and performance evaluation of a low-cost sensor-based automated drip irrigation system for small-scale farming
This study developed and evaluated a low-cost, sensor-based automated drip irrigation system for small-scale farming, demonstrating reliable performance in fine-textured soils (clay) compared to a commercial sensor, while identifying limitations in coarse-textured soils due to compaction and probe-soil contact issues.
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Xiao et al. (2026) Rainfall Regionalization for Mainland China Based on Storm Characteristics
This study develops a high-resolution rainfall regionalization for mainland China using sub-daily storm characteristics from hourly station data. It identifies five event types and clusters stations into six groups, aggregated into eight rainfall zones, revealing distinct spatial patterns of rainfall depth, duration, and intensity, and their correlation with topography and weather systems.
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Xu et al. (2026) A global all-weather PWV retrieval model integrating multi-band satellite observations considering land cover types and NDVI
This study developed an all-weather, high-resolution global model for retrieving precipitable water vapor (PWV) by integrating multi-band satellite observations (NIR, TIR, MW) with GNSS data, achieving a substantial reduction in global average RMSE from 15.19 mm to 5.37 mm compared to the MYD05 product.
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Baker et al. (2026) Reduced future North Atlantic eddy-driven jet variability in high-resolution, fully coupled global climate models
This study evaluates the impact of model resolution on North Atlantic winter jet streams under historical and future climate conditions, finding that higher resolution improves zonal wind representation and projects a strengthening and poleward shift of the mid-latitude jet by 2050, contrasting with low-resolution models.
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Millin et al. (2026) Analyzing Stratospheric and Tropical Contributions to the Subseasonal Forecasts of the December 2017 and January 2004 North American Cold Air Outbreaks
This study investigates the individual roles of stratospheric and tropical variability in driving subseasonal cold air outbreak (CAO) forecast skill in the central United States using targeted nudging experiments. It finds that the impact of these modes on CAO prediction skill is event-dependent, with stratospheric nudging significantly improving forecasts for one event while both modes had limited surface impact for another.
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Levin et al. (2026) Influence of sea surface temperature patterns and mean warming on past and future Atlantic tropical cyclone activity
This study investigates the relative contributions of large-scale thermodynamic and dynamic processes to decadal and multidecadal changes in Atlantic tropical cyclone (TC) activity from the late 19th century to 2100. It finds that TC frequency changes are primarily governed by potential intensity and moist entropy deficit, with regional sea surface temperature (SST) patterns, rather than global-mean warming, controlling both past variability and future changes.
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Liang et al. (2026) Vegetation in Central Asia is more sensitive to soil moisture stress than to precipitation and vapor pressure deficit stresses
This study investigated vegetation sensitivity to precipitation, soil moisture, and vapor pressure deficit stresses across Central Asia from 1982 to 2020, revealing that vegetation is most sensitive to soil moisture stress, a trend projected to continue increasing in the future.
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Banerjee et al. (2026) Sub-seasonal Modulation and Predictability of Indian monsoon hourly Rainfall Extremes
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Proistosescu et al. (2026) The forgotten role of wave dynamics in modulating the low cloud response to warm pool warming
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Wu et al. (2026) Basin-wide and regional low-frequency variability of Yangtze precipitation revealed by clustering and slow feature analysis
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Chrysanti et al. (2026) Large-scale drivers and ocean-land feedbacks contributing to extreme precipitation during the January 2021 South Kalimantan flood, Indonesia
This study investigated the meteorological drivers and ocean-land feedbacks contributing to the January 2021 South Kalimantan flood, finding that active cold surges and cross-equatorial northerly surges, modulated by the Madden-Julian Oscillation and Kelvin waves, were primary drivers, with land-ocean feedbacks playing a secondary, amplifying role. The research utilized both standalone and coupled atmospheric-hydrological models to delineate synoptic and mesoscale interactions.
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Zhou et al. (2026) Simulation of Extreme Weather Events in the Wanzhou Region of the Three Gorges Reservoir Area Using the WRF Model Coupled With Machine Learning Techniques
This study systematically evaluates the performance of the Weather Research and Forecasting (WRF) model with various physical parameterisation schemes for extreme precipitation and high-temperature events in the Wanzhou District of the Three Gorges Reservoir region. It identifies optimal WRF configurations and demonstrates that coupling these with machine learning models, particularly Random Forest, significantly enhances prediction accuracy and reliability.
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Salvi et al. (2026) Discrepancy in the sign of temperature trends in reanalysis datasets
This study evaluates the alignment of annual mean daily maximum and minimum temperature trends from three reanalysis datasets (ERA5, MERRA-2, NLDAS-2) against observed trends from 7,059 stations across the continental United States, revealing substantial trend misalignment (21-31%) that persists across various regions and record lengths.
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Li et al. (2026) Fine‐Scale Characterization of Groundwater Recharge Efficacy Under Ecological Water Replenishment: An AI‐Enhanced Learning Framework Benchmarked Against Traditional Geostatistics
This study reconstructs high-resolution (250 m) groundwater level dynamics in the Yongding River basin using LightGBM and multi-source data, demonstrating that Ecological Water Replenishment (EWR) drives groundwater recovery but with diminishing marginal returns, while outperforming traditional interpolation methods.
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Jellis et al. (2026) Simulation of Water Vapor Transport to the Stratosphere by Overshooting Convection
## Identification - **Journal:** Journal of Geophysical Research Atmospheres - **Year:** 2026...
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Chen et al. (2026) Different Responses of the Madden‐Julian Oscillation to the Fast and Slow Decaying El Niño in Spring
This study investigates how El Niño's decay rate modulates the Madden-Julian Oscillation (MJO) during decaying springs, finding that fast-decaying El Niño weakens MJO activity over the central Pacific, while slow-decaying El Niño significantly enhances it through stronger moisture advection and lower-tropospheric moistening.
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Li et al. (2026) An accurate 10 m annual crop map product of maize and soybean across the United States
This study developed an openly available, annual, 10 m spatial resolution maize and soybean map product for the Contiguous United States (CONUS) from 2019 to 2022, achieving consistent overall accuracies greater than 95%. The research demonstrates that these higher-resolution maps significantly reduce mixed pixels compared to existing 30 m products, enhancing agricultural monitoring capabilities.
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Wan et al. (2026) Stable Isotope Record of Precipitation Dynamics in the Semi‐Arid Subtropics
This study investigated rainfall stable isotope variability in subtropical northwest Australia over 10 years to understand precipitation mechanisms and moisture sources, identifying a significant "amount effect," substantial sub-cloud evaporation, and significant land evapotranspiration recycling.
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Wang et al. (2026) Projected Earlier Australian Summer Monsoon Onset Associated With Faster Eastward MJO Propagation
This study projects a robust earlier onset of the Australian Summer Monsoon (AUSM) by approximately 5 days by the late 21st century under global warming, attributing this shift to the earlier arrival and accelerated eastward propagation of the first austral-spring Madden–Julian Oscillation (MJO) event.
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Yu et al. (2026) Identification of the Global Cloud‐Clear Sky Transition Zone and Its Shortwave Radiation Effects
This study developed a globally consistent method to detect the Cloud-Clear Sky Transition Zone (CCTZ) over both land and ocean using MODIS data and radiative transfer modeling. It found that the CCTZ has a cloud-type-dependent spatial scale (approximately 5 km from clouds) and significantly enhances global mean diffuse shortwave radiation by 16.3% while reducing direct shortwave radiation by 0.8% compared to pure clear-sky conditions.
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Eleftheratos et al. (2026) Evaluation of variability in GOME-2 total water vapor and nitrogen dioxide columns associated with natural oscillations
This study investigates the variability of total water vapor (H₂O) and nitrogen dioxide (NO₂) columns in association with natural oscillations (QBO, ENSO, NAO) using GOME-2 satellite data. It finds that GOME-2 data effectively captures these variabilities, with NO₂ showing QBO-type periodicity in the tropics and H₂O responding to ENSO in the tropics and NAO in northern mid-latitudes, demonstrating good agreement with reanalysis and model results.
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Wang et al. (2026) DAR-type model based on “long memory-threshold” structure: a competitor for daily streamflow prediction under changing environment
This study proposes a novel Fractional-differenced Dual-Threshold Double Autoregressive (FDTDAR) model to improve daily streamflow prediction accuracy under changing environments by capturing non-stationarity, non-linearity, and long-term memory. Applied to the Yellow River basin, the FDTDAR model, particularly with a Student's t-distribution for residuals, demonstrates superior predictive ability compared to AR-GARCH and LSTM models.
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Sylvestre et al. (2026) Decadal-scale droughts disrupted the African Humid Period in the Sahara
This study reconstructs the hydrological history of Lake Yoa, Chad, over the past 10.25 thousand years, revealing that the African Humid Period was interrupted by decadal-scale droughts, particularly a prominent 8.2 kyr bp event linked to Atlantic Meridional Overturning Circulation weakening.
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Matus et al. (2026) The sub-seasonal connection between the land surface and Great Plains low-level jet
This study investigates the modulation of Great Plains low-level jet (GPLLJ) intensity by sub-seasonal dry soil moisture anomalies. It finds that neglecting this land-surface interaction leads to significant errors in reconstructed GPLLJ wind speeds, highlighting the land surface's crucial role in GPLLJ variability.
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Zhou et al. (2026) Synergistic retrievals of leaf area index and leaf chlorophyll content in deciduous broadleaf forests from Sentinel-2 and Landsat
This study systematically evaluates synergistic Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) retrievals for deciduous broadleaf forests from Sentinel-2 and Landsat data. It identifies limitations in canopy structural representation as a primary driver of mutual error compensation and demonstrates that integrated parameterization strategies significantly improve retrieval accuracy and seasonal dynamics.
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Su et al. (2026) Precipitation observing network gaps limit climate change impact assessment
This study evaluates the global distribution of 221,483 precipitation gauges and identifies priority regions for network expansion under historical and future climate/socioeconomic scenarios. It finds that only 13.4% of the global land surface meets WMO monitoring requirements, with 25% currently needing urgent expansion, increasing to 32.1% under a high-emission scenario when socioeconomic vulnerabilities are considered.
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Ji et al. (2026) Robust hyperspectral reconstruction from satellite and airborne observations via a deep hierarchical fusion network across heterogeneous scenarios
This study develops a deep learning framework for robust high spatial resolution hyperspectral imagery (HR-HSI) reconstruction by fusing low-resolution hyperspectral (EMIT) and high-resolution multispectral (PlanetScope) satellite data. The framework consistently outperforms state-of-the-art models, demonstrating high spectral fidelity and reconstruction accuracy across diverse landscapes.
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Bulut et al. (2026) Toward early warning of drought impacts: a framework for predicting drought impacts in the UK
This study presents a data-driven framework to predict real-world drought impacts. Different modelling approaches were tested and evaluated in the United Kingdom using predictions at the time of occurrence, with the best-performing method selected for forecasting impacts months ahead. Both predictions and forecasts were validated using independent UK data and applied to Germany to test transferability, supporting early warning systems and improved drought risk planning.
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Jiang et al. (2026) Deep-learning full-waveform inversion of snowpack GPR: joint permittivity–resistivity imaging for snow–soil hydrological mapping
This study introduces a hybrid deep learning framework (ViT-BiLSTM) for dual-parameter full-waveform inversion of GPR data, enabling fast and accurate joint imaging of snowpack permittivity and resistivity for hydrological mapping. The framework demonstrates robust performance on synthetic and real-world data, providing spatially coherent snow liquid water content and soil moisture estimates.
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Martini et al. (2026) From near surface to root zone soil water losses: a new model validated with field TDR and remotely sensed data
This study introduces muSEC, a new multilayer soil moisture model based on the surface evaporative capacitor (SEC), designed to link surface and root zone soil moisture drydown dynamics. Validated against three other models using field TDR and satellite SMAP data across contrasting soils, muSEC demonstrates superior transferability from surface to root zone, supporting its potential for agricultural water management.
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Dong (2026) A Comparative Study of Machine Learning Models for Hourly Forecasting of Air Temperature and Relative Humidity
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Ku et al. (2026) Enhanced persistence of Ural blocking under strong positive AO: the role of North Atlantic storm tracks and potential vorticity dynamics
This study investigates how the magnitude of a positive Arctic Oscillation (AO) influences the persistence of Ural Blocking (UB) events. It finds that strong positive AO paradoxically enhances UB longevity by organizing North Atlantic storm tracks, leading to Arctic warming, sea ice loss, and a weakened meridional potential vorticity gradient that anchors the blocking system.
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Taylor et al. (2026) Assessing the Ability of Tree-Ring Derived Aridity Records to Detect Compound Drought and Heatwave Events
This study investigates whether extremes in the Palmer Drought Severity Index (PDSI), particularly those derived from tree-ring reconstructions, can identify past compound drought and heatwave events (CDHWs) in North America and Europe. It finds that in regions with strong land-atmosphere coupling, such as Central North America and Eastern Europe, negative summer PDSI values co-occur with precipitation deficits and high temperatures, allowing tree-ring PDSI reconstructions to identify CDHWs predating instrumental records.
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Nuriddinov et al. (2026) High Resolution Flood Extent Detection Using Deep Learning with Random Forest Derived Training Labels
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Kim et al. (2026) SIGMAformer: a spatiotemporal Gaussian mixture correlation transformer for global weather forecasting
This paper introduces SIGMAformer, a spatiotemporal Gaussian mixture correlation transformer for global multi-station weather forecasting, which integrates a dynamic spatiotemporal correlation (DSTC) mechanism with a Gaussian mixture pattern extractor (GMPE) to adaptively model nonlinear dependencies. The model consistently outperforms state-of-the-art forecasting models in global wind speed and temperature prediction, especially for extreme events, while providing interpretable insights into spatiotemporal patterns.
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Li et al. (2026) Improving seasonal prediction of global mean surface temperature by incorporating dynamic ENSO realistic forecasts
This study identifies an underrepresented ENSO-driven pantropical coupling mechanism as a major source of error in autumn-initialized global mean surface temperature (GMST) predictions. By incorporating skillful ENSO realistic forecasts into a new dynamic-statistical framework, the reliable GMST prediction lead-time is extended from two to four months, reducing hindcast errors by an average of 41% during 1980–2024.
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Liu (2026) Optimizing Multi-Agent Weather Captioning via Text Gradient Descent: A Training-Free Approach with Consensus-Aware Gradient Fusion
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Qin et al. (2026) Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors
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Stavrou et al. (2026) SmaAT-QMix-UNet: A Parameter-Efficient Vector-Quantized UNet for Precipitation Nowcasting
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Masquil et al. (2026) Deep S2P: Integrating Learning Based Stereo Matching Into the Satellite Stereo Pipeline
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Jurgens et al. (2026) Decadal Shifts in Groundwater Age Detected by Environmental Tracers Across California, USA
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Fu et al. (2026) Land-atmosphere feedbacks and anthropogenic greenhouse gas forcing intensify subseasonal drought-to-pluvial abrupt transitions
This study investigates subseasonal drought-to-pluvial abrupt transitions, revealing their global occurrence with an average probability of 45% and identifying land-atmosphere feedbacks as a key intensifying mechanism. Under high-emissions scenarios, both the frequency and probability of these transitions are projected to increase across over 75% of global land, primarily driven by anthropogenic greenhouse gas forcing.
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Nguyen et al. (2026) PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data
This study introduces PERSIANN-Unet (PUnet), a new quasi-global, high-resolution, near-real-time precipitation algorithm leveraging infrared (IR) data and a UNet architecture. PUnet provides half-hourly, 0.04° precipitation estimates, closely matching its training target (IMERG V07 Final) globally and demonstrating good performance against Stage IV over the Continental United States (CONUS).
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Dong et al. (2026) A fully automated OPTRAM (aOPTRAM) for soil moisture retrieval: Evaluating multiple fitting functions, vegetation indices, land-cover types, and scales
This study introduces a fully automated Optical Trapezoid Model (aOPTRAM) for high-resolution soil moisture retrieval, systematically evaluating its performance across diverse ecosystems using Sentinel-2 imagery and in-situ data. It demonstrates that aOPTRAM, without manual calibration, achieves performance comparable to optimal OPTRAM, providing a fast and robust framework for monitoring soil moisture in heterogeneous landscapes.
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Rooyen et al. (2026) Anthropogenic tritium as a continental-scale tracer in river-derived recharge
This study evaluates anthropogenic tritium (³H) and natural stable isotopes (δ¹⁸O, δ²H, deuterium excess) as tracers to quantify groundwater flow dynamics and travel times in an alluvial Managed Aquifer Recharge (MAR) system along the Rhine River in Switzerland, demonstrating their effectiveness for sustainable groundwater management.
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Shin et al. (2026) Negative CO2 emissions for long-term mitigation of extremes in land hydrological cycle
This study investigates terrestrial precipitation and vegetation feedbacks under idealized zero and negative CO2 emissions scenarios, finding that sustained negative emissions are crucial for long-term mitigation of hydrological extremes and enhanced water availability, primarily due to amplified transpiration.
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Ukkola et al. (2026) Future changes in seasonal drought in Australia
This study assesses future seasonal drought changes across Australia using an ensemble of 32 hydrological simulations, revealing widespread increases in meteorological, hydrological, and agricultural droughts, particularly in populated and agricultural regions, with Global Climate Models (GCMs) being the dominant source of uncertainty.
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Akbary et al. (2026) Projected changes in sub-daily extreme precipitation: comparing temperature-scaling approaches and convection-permitting models across an Alpine gradient
This study evaluates the reliability of temperature-scaling approaches for projecting sub-daily extreme precipitation changes by comparing them against convection-permitting model (CPM) outputs across a complex Alpine region. It finds that optimal scaling rates vary with duration and return period, and their reliability is modulated by local variability, seasonality, and elevation.
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Chen et al. (2026) An integrated framework for mapping agricultural water impoundments using Sentinel −2 and GEE in Northwest China
This study developed an integrated framework using Sentinel-2 and Google Earth Engine to accurately map and monitor small-scale agricultural water impoundments (AWIs) in arid regions, providing the first high-resolution, multi-year inventory for the Hexi Corridor.
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Ye et al. (2026) Anthropogenic climate change amplifies autumn heatwave risks for children during school reopening
This study attributes and projects the risk of autumn heatwaves for children during school reopening in China, revealing that anthropogenic climate change has significantly amplified the frequency and intensity of such heatwaves, increasing children's exposure risk by approximately 55% under the 2024 climate. Projections indicate continued increases in heatwave intensity, which will eventually outweigh declining child populations, leading to rising exposure risks by the end of the century under high emission scenarios.
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Xv et al. (2026) Biophysical regulation mechanisms of land surface temperature driven by the spatiotemporal evolution of cropland
This study systematically investigated the biophysical mechanisms linking cropland evolution to land surface temperature (LST) variations across China from 2000 to 2020, revealing spatially heterogeneous effects where cropland expansion caused warming in arid northwestern regions and cooling in northeastern regions, primarily driven by ground heat flux, surface emissivity, and albedo.
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Lv et al. (2026) An adaptive decomposition-denoising and temporal context fusion framework for multi-station water-level forecasting
This study constructs a high-resolution hydrological dataset for the Mengjiang River Basin and proposes a hybrid deep learning framework, CEEMDANVF-WD-TCF-LSTM, for multi-station water-level forecasting. The framework demonstrates superior accuracy and stability, particularly in mitigating multi-step error accumulation for both short-term and medium-term predictions.
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Wang et al. (2026) Seasonal variation patterns and drivers of baseflow recession dynamics across Australia
This study quantifies the event-scale seasonal variation of the baseflow recession parameter 'a' across 596 Australian catchments and identifies vegetation, temperature, and evaporative demand as key drivers using machine learning.
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Niu et al. (2026) Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction
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Yin et al. (2026) Exploring a process-aware spatiotemporal graph-based surrogate for integrated urban drainage simulation
This study proposes PAST, a Process-Aware Spatio-Temporal graph-neural-networks-based surrogate model, to efficiently simulate integrated urban drainage processes by holistically representing rainfall–runoff-routing and incorporating regulation effects. PAST achieves high performance and physical explainability, significantly outperforming baseline models, especially under regulated and extreme rainfall conditions.
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Fronzi et al. (2026) Toward quantitative DNA tracer tests: development and validation of a novel capture device for groundwater flow characterization in karst and carbonate aquifers
This study develops and validates a novel passive device for selectively capturing biotinylated synthetic DNA tracers, enabling time-integrated, semi-quantitative assessment of groundwater flow. Laboratory and field tests demonstrate that the device produces breakthrough curves consistent with conventional tracers, facilitating broader application of DNA tracers in complex hydrogeological systems.
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Prayag et al. (2026) Assessing infiltration dynamics using integrated hydrogeophysical monitoring in a managed aquifer recharge pond
This study investigated infiltration dynamics in a Managed Aquifer Recharge (MAR) pond over eight months using an integrated hydrogeophysical monitoring system (automated Direct Current Resistivity and Induced Polarisation (DCIP), Ground Penetrating Radar (GPR), and hydrological data). It revealed subsurface heterogeneity and time-dependent infiltration pathways, including a high-permeability westward-dipping layer, and identified signs of clogging affecting lateral water spreading.
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Yang et al. (2026) Fusing dynamic physical constraints with PINN-xLSTM to enhance accuracy and physical consistency in runoff prediction under extreme hydrological events
This study introduces a novel PINN-xLSTM model with a dynamic physical constraint weighting mechanism to enhance runoff prediction accuracy and physical consistency, particularly during extreme hydrological events. The model demonstrates superior performance in accuracy, flood peak characterization, and adherence to hydrological principles compared to existing models.
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Feldman et al. (2026) Widespread Co‐Location of Less Frequent and More Intense Daily Precipitation Over Land
This study investigates the global co-location of trends towards more intense and less frequent daily precipitation events, finding that fewer, larger events are common and distributed across terrestrial ecosystems, often counteracting increases in annual precipitation totals due to simultaneous decreases in small-to-moderate events.
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Chen et al. (2026) A novel soil moisture retrieval method via combining radiative transfer model and machine learning
This study introduces a novel, interpretable soil moisture retrieval framework by integrating a radiative transfer model (RTM) with a Kolmogorov–Arnold Network (KAN) to derive explicit mathematical expressions from satellite observations, achieving global soil moisture estimates comparable in accuracy to the SMAP Level-3 product.
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Khorami et al. (2026) Estimation of root zone water storage capacity (S) in natural ecosystems subject to high interannual climate variability
This study evaluates two cumulative water deficit (CWD) methods for estimating root zone water storage capacity (Sr) in 105 Australian forested catchments, finding that a multi-year CWD approach yields significantly higher Sr estimates (median 25% higher) in regions with high interannual climate variability compared to the conventional single-year method.
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Zhang et al. (2026) Dynamic conversion coefficients improve alpine lake daily evaporation estimation based on multi-evaporator observations
This study developed a refined pan conversion method with dynamic coefficients, based on multi-evaporator observations, to estimate daily lake surface evaporation (LSE) for alpine lakes, including ice-covered periods, and quantified the meteorological controls on LSE variability.
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Wu et al. (2026) EcoTWIN 1.0: a fully distributed tracer-aided ecohydrological model tracking water, isotopes, and nutrients
This paper introduces EcoTWIN 1.0, a fully distributed tracer-aided ecohydrological model that simultaneously tracks water, isotope, and nutrient fluxes. The model demonstrated good performance in reproducing calibrated in-stream targets and uncalibrated internal fluxes across 17 large European catchments, proving its flexibility and transferability for prediction and process inference in diverse terrestrial ecosystems.
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Jasechko (2026) Global cases of groundwater recovery after interventions
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Li et al. (2026) Assessing the global performance of a parsimonious soil temperature model for frozen ground prediction
This study globally evaluates a simplified soil temperature model for predicting frozen/unfrozen ground states using only air temperature and snow cover data. The model demonstrates robust global performance (average true frozen rate of 0.90, false frozen rate of 0.06), offering a computationally efficient solution for hydrological models, though it shows limitations in mountainous regions.
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Wang et al. (2026) A novel adaptive soil moisture retrieval method via stacked ensemble learning and a local Bayesian dynamic algorithm
This study introduces a novel local Bayesian dynamically weighted stacking ensemble learning model (Stacking-BO) and a high-resolution spatiotemporal multilayer soil moisture simulation framework to enhance the accuracy and stability of soil moisture retrieval, demonstrating superior performance over existing methods.
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Jin et al. (2026) Spatiotemporal evolution and hyetograph changes of global extreme precipitation
This study systematically analyzes the global spatiotemporal evolution of annual maximum 3-hour precipitation events and their hyetograph changes, revealing a global decline in peak intensity but an increase in total event precipitation due to more temporally distributed rainfall, which is likely to exacerbate flood risk.
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Redaelli et al. (2026) Changes in irrigation practices may deplete aquifers faster and more severely than meteorological droughts: A numerical modeling approach
This study quantitatively assessed the drivers of aquifer depletion in an intensively irrigated system, finding that changes to more efficient irrigation practices, which reduce irrigation return flow, have a more severe impact on groundwater resources than meteorological droughts.
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Aftab et al. (2026) A Daily Soil Moisture–Temperature Compound Index for Characterising Dry–Hot Extremes
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Holzschuh et al. (2026) Remote Sensing of Soil Moisture in Restored Wetlands
This study examines the correlation between remote sensing indices and soil infiltration dynamics of treated wastewater in a restored wetland to optimize irrigation and monitor recycled water fate.
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Galli et al. (2026) Integrating biophysical models and remote sensing to evaluate irrigation practices in four global hubs
This study compares irrigation demand from an agro-hydrological model (WATNEEDS) with irrigation water use from five satellite products across four global irrigation hubs, finding significant correlations (above 0.6 for 3/4 cases) and using discrepancies to identify hydroclimatic and anthropogenic irrigation drivers.
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Tiwari et al. (2026) Examining the Changes in Precipitation Patterns Across the Western Himalayan Region During the Winter Season
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Saini et al. (2026) Development of a Virga Detection Tool and Associated Study of Arctic Virga and Precipitation
## Identification - **Journal:** Journal of Geophysical Research Atmospheres - **Year:** 2026...
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Yan et al. (2026) Attribution of runoff changes in the semi-arid Xiliao River Basin – A Budyko-elasticity approach to deconstructing compound climate and human impacts
This study quantified the contributions of climate change and human activities to runoff reduction in the semi-arid Xiliao River Basin from 1980 to 2022 using a Budyko-elasticity framework, revealing an abrupt runoff decline in 2002 with significant spatial heterogeneity: upstream areas are climate-driven, while middle-downstream areas are human-driven, primarily due to agricultural irrigation.
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SHEN et al. (2026) Estimation of water savings from farmland fallowing in the tarim river basin under food security and ecological security constraints and threshold effects of driving factors
This study developed a framework to estimate water savings from farmland fallowing (FLWC) in the Tarim River Basin under food and ecological security constraints. It found that suitable fallow areas and FLWC peaked in 2015, with cotton exhibiting the highest water-saving potential, and identified mean air temperature and mean relative humidity as primary climatic drivers with threshold effects on FLWC.
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Li et al. (2026) The critical role of soil moisture in compound hazards
This review synthesizes current understanding of soil moisture's critical role in the evolution and onset of diverse compound hazards, highlighting its mechanisms in amplifying drought-heatwave-wildfire events, promoting clustered storms, and driving vegetation die-offs, landslides, and flooding. It also identifies persistent challenges and a roadmap for integrating soil moisture into hazard prediction and early warning systems.
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Ackroyd et al. (2026) Spatial assessment of snow grain size from airborne lidar reflectance against coincident imaging spectroscopy retrievals
This study evaluates three methods for deriving snow grain size and albedo from 1064 nm airborne lidar reflectance against coincident imaging spectroscopy retrievals over a snow-covered glacier. It demonstrates that incorporating incidence angle corrections is crucial for accurate lidar-derived snow properties in mountainous terrain, highlighting lidar's potential for high-resolution snow property mapping.
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Rivoire et al. (2026) The future is in the past? A flexible resampling approach to generate multivariate time series
This paper introduces a straightforward method for generating synthetic climate time series by constrained sampling of observations, demonstrating its ability to preserve physical consistency and multivariate dependencies while simulating multi-day extremes under future climate scenarios.
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Chen et al. (2026) Impact of deep-inland typhoon track uncertainty on the 2023 record-breaking rainfall over North China: an ensemble-based analysis
This study applied ensemble sensitivity analysis to ECMWF ensemble data to diagnose the large-scale circulation controls on the unprecedented Beijing–Tianjin–Hebei rainfall event in July-August 2023, finding that rainfall extremes were tightly governed by the precise trajectory of Typhoon Doksuri's remnants, which modulated the critical spatial alignment of moisture flux, typhoon intensity, and terrain-enhanced convergence.
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Cao et al. (2026) Effect of regional marine cloud brightening on land climate
This study uses the CESM Earth system model to investigate the land climate consequences of regional Marine Cloud Brightening (MCB), finding that while MCB stabilizes global temperature and offers benefits like reduced drought stress and increased GPP, its abrupt termination leads to severe and rapid land warming with significant ecological risks.
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Jong et al. (2026) Reversal of extreme precipitation trends over the Northeast US in response to aggressive climate mitigation in GFDL SPEAR
This paper assesses projected changes in extreme precipitation over the Northeast US under an aggressive overshoot mitigation pathway, finding that while warm-season extremes decline quickly after greenhouse gas reductions, cold-season extremes exhibit a delayed response and hysteresis, returning to mid-century levels by 2100.
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Du et al. (2026) A recent significant increase in tropical cyclone-induced precipitation in the North China Plain
This study reveals a significant increasing trend in tropical cyclone (TC) precipitation over the North China Plain from 1981 to 2024, driven by a remarkable surge since 2018 due to elongated TC tracks with deeper inland penetration, linked to an anomalous easterly steering flow.
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Wei et al. (2026) Critical role of intraseasonal oscillations in shaping extreme rainfall from tropical cyclones over the South China Sea
This study quantifies the critical role of intraseasonal oscillations (ISOs), including the quasi-biweekly oscillation (QBWO) and Madden–Julian oscillation (MJO), in shaping extreme accumulated rainfall (EAR) from tropical cyclones (TCs) over the South China Sea (SCS), finding that ISOs contribute significantly to the occurrence and characteristics of these extreme events.
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Zhou et al. (2026) Synchronized Heat Extremes in the Northern Hemisphere Based on a Complex Network
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Fu et al. (2026) Global distribution of the terrestrial moisture dynamics response to the meteorological dry-wet abrupt alternation
This study quantifies the global patterns and trends in the coincidence between meteorological dry-wet abrupt alternation (M-DWAA) and terrestrial dry-wet abrupt alternation (T-DWAA) events. It finds that approximately 17.4% of M-DWAA events globally coincide with a T-DWAA within the subsequent season, with the M-DWAA alternation velocity being the dominant influencing factor.
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Çelik et al. (2026) Solar Radiation–Driven Machine Learning for Modelling Reference Evapotranspiration Using ERA5 ‐Land in a Semi‐Arid Microclimatic Basin in Türkiye
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Putri et al. (2026) Groundwater recharge from intense rainfall in Indonesia: evidence from East Kalimantan
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Dong et al. (2026) How Lateral Flow Impacts Heavy Rainfall in Complex Terrain: A Composite Analysis Over the Southern Anhui Mountainous Region
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Tang et al. (2026) Elevation‐Dependence of Different Precipitation Phases Concentrations in the Asian Water Tower: Differences in Rainfall and Snowfall Concentration Changes
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Dadaser‐Celik et al. (2026) Future Hydrological Trajectories of Burdur Lake Under Climate Change and Basin‐Scale Human Interventions
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Hiraga et al. (2026) Numerical experiments of cloud seeding for mitigating localization of heavy rainfall: a case study of Mesoscale Convective System in Japan
This study numerically investigated the potential of cloud overseeding to mitigate localized heavy rainfall from a mesoscale convective system in Japan, finding that an optimal seeding configuration could reduce area-averaged 3-hour accumulated rainfall by 11.5% and maximum rainfall by 32% in the heavy rainfall region.
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Santos (2026) Projected Intensification of Hydroclimatic Extremes in Rio Grande do Norte, Brazil, Under CMIP6 Scenarios
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Qin et al. (2026) Gain–phase characteristics of groundwater responses to barometric pressure: interpreting subsurface confinement in layered systems
This study applies frequency-domain barometric response functions (BRF) to analyze groundwater response to atmospheric pressure in a layered aquifer. It demonstrates the utility of BRF analysis for interpreting pressure transmission and confinement in vertically heterogeneous aquifers, revealing distinct gain-phase relationships for different confinement types.
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Hassan et al. (2026) Climate adaptation-aware flood prediction for coastal cities using Deep Learning
This study develops a novel, lightweight Convolutional Neural Network (CNN)-based model, CASPIAN-v2, for rapid and accurate prediction of high-resolution coastal flooding in urban areas under various sea-level rise scenarios and shoreline adaptation strategies. The model significantly outperforms state-of-the-art methods, reducing mean absolute error by nearly 20%, and offers a scalable tool for climate adaptation planning.
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Guo et al. (2026) Wind-water energy characteristics and sediment transport prediction in sandy coarse sand basin
This study developed an energy-based wind-water composite watershed erosion and sediment transport model for the sandy coarse sand area of the Yellow River, demonstrating its high accuracy in predicting sediment yield across different soil types and revealing significant spatiotemporal differentiation of erosion energies.
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Chen et al. (2026) An explainable correction and fusion framework for global bare-earth DTM generation in mountain areas
This study developed an explainable correction and fusion framework to generate high-accuracy global bare-earth Digital Terrain Models (DTMs) in mountainous regions, addressing height biases in existing Digital Surface Models (DSMs). The proposed framework, combining AutoML-SHAP, a CNN-Transformer, and a fusion model, achieved significant vertical accuracy improvements (43.13%–76.86%) over current GDEMs and correction methods.
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Rahmani et al. (2026) Wetlands set the pace of annual runoff in the northern Great Plains
This study reveals that in North America's Prairie Pothole Region, annual wetland inundation extent, rather than climate drivers, is the dominant factor explaining interannual variability in runoff and high-flow in 69% of 109 studied catchments over 38 years, with most catchments exhibiting threshold-like buffering behavior linked to geographically isolated wetlands.
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Khosravi et al. (2026) A geographically weighted XGBoost framework for Pixel-Level modeling of vegetation responses using Multi-Source Earth Observation data
This study introduces Geographically Weighted XGBoost (GW-XGBoost), a hybrid and interpretable framework, to model pixel-level vegetation responses to climate extremes in the Middle East. The model, calibrated with 30 years of multi-source Earth Observation data, outperforms baseline models and reveals a significant ecological transition where vegetation sensitivity has shifted from cold/precipitation constraints to warm temperatures and episodic moisture pulses.
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Uysal et al. (2026) A data-assimilated SEAS5 forecasting framework for seasonal hydropower inflows in a snow-dominated basin
This study developed a seasonal hydropower inflow forecasting framework for a snow-dominated basin by integrating a variational data assimilation (VarDA) scheme into the HBV hydrological model, demonstrating significant improvements in inflow and snow water equivalent predictions, especially at short lead times.
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Li et al. (2026) Weakening vegetation control on global terrestrial evapotranspiration in a warmer world
This study quantifies future changes in the sensitivity of terrestrial evapotranspiration (ET) to leaf area index (LAI) under projected warming scenarios, finding that vegetation control on ET will weaken globally by 2100 due to reduced stomatal conductance outweighing CO₂ fertilization. This weakening will diminish LAI-driven evaporative cooling, leading to enhanced water conservation.
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Rehbein (2026) Reconstructing nineteenth-century Danube river water levels with transformer-based computer vision
This study developed a semi-automated workflow using transformer-based computer vision to convert nineteenth-century hand-drawn Bavarian Danube gauge charts into daily water-level series. The method achieved high accuracy (mean composite score 0.979) across three representative gauges while reducing manual effort by an order of magnitude, providing openly available, transparently documented historical hydrological data.
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Lin et al. (2026) Is the recently enhanced mesoscale convective systems in East Asia due to global warming or decadal variability?
This study investigates the drivers of increased mesoscale convective system (MCS) precipitation in the Yangtze River Basin, finding that decadal variability, primarily through increased MCS frequency, is the main cause, with anthropogenic warming playing a smaller, reinforcing role.
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Bidabadi et al. (2026) Spatial risk assessment of drought-induced operational failures in interconnected irrigation canals: application to the Mahyar–Jarghooye district, Iran
This study develops a stakeholder-scale, map-based risk assessment framework to evaluate drought-induced operational failures in interconnected irrigation canals under water shortages (WS) and inflow fluctuations (IF). Applied to the Mahyar–Jarghooye district in Iran, it generates spatial vulnerability, consequence, and risk maps to identify hotspots and inform management.
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Chen et al. (2026) Rapid recovery from permafrost thaw subsidence after extreme warmth inferred from InSAR
This study examines the response of ice-rich permafrost in Northwest Alaska to the 2019 extreme warmth, revealing a short-lived subsidence of approximately 6 cm followed by a partial recovery of about 3 cm over three years, suggesting substantial resilience rather than widespread sustained degradation.
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Leopardi et al. (2026) A satellite-based approach for estimating runoff and river discharge in the Pan-Arctic region from 2003 to 2022
This study presents daily, long-term (2003–2022) satellite-based estimates of river discharge and gridded runoff at 0.25° × 0.25° spatial resolution across the continental Pan-Arctic region. Integrating various satellite observations into the adapted STREAM hydrological model, it demonstrates high performance (median Kling-Gupta Efficiency of 0.83) and quantifies freshwater fluxes to the Arctic Ocean at 4760 ± 619 km³ yr⁻¹.
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Nana et al. (2026) Assessing the ability of the ECMWF seasonal prediction model to forecast extreme September–November rainfall events over Equatorial Africa
This study assesses the European Centre for Medium-Range Weather Forecasts seasonal prediction system 5.1 (ECMWF-SEAS5.1) in forecasting extreme September–November (SON) rainfall events over Equatorial Africa (EA). It finds that the model generally reproduces observed rainfall patterns and teleconnections with tropical sea surface temperatures well, with better skill for September initial conditions, but tends to underestimate the magnitude of extreme events and shows limitations in representing certain atmospheric features at longer lead-times.
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Dixit et al. (2026) Integrating SMART principles in flood early warning system design in the Himalayas
This study integrates SMART principles with low-cost, real-time hydrometeorological monitoring to design an urban flood early warning system (EWS) in the data-scarce Lesser Himalayas. It demonstrates how community-engaged monitoring captures crucial spatiotemporal rainfall variability and watershed dynamics, which are poorly represented by secondary datasets, providing foundational insights for effective, community-centered EWS implementation.
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Leistert et al. (2026) Accumulation-based Runoff and Pluvial Flood Estimation Tool (AccRo v.1.0)
This paper introduces AccRo (Accumulation-based Runoff and Flooding), a computationally efficient model designed to estimate maximum inundation depth, flow velocity, and specific discharge for pluvial flood events at larger spatial scales. The model demonstrates high accuracy in reproducing analytical solutions for design cases and closely matches the results of state-of-the-art 2D hydrodynamic models for real-world scenarios, offering a robust alternative for flood hazard assessment.
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HONG et al. (2026) Characteristics and influencing factors of soil moisture memory across mainland China
This study utilized 2,218 daily in situ soil moisture observations across mainland China to characterize the spatial patterns and influencing factors of soil moisture memory (SMM) using an exponential drydown model, highlighting its heterogeneity and discrepancies with satellite/reanalysis products.
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You et al. (2026) Quantifying climate-induced cascading effects on runoff in a cold region using a glacier-enhanced Budyko framework
This study introduces a novel Budyko-based attribution framework, integrating glacier mass balance and ridge regression, to comprehensively separate the impacts of direct climate change, cascading climate effects, and human activities on runoff in cold regions. Applied to an alpine watershed, the framework robustly quantified these drivers, revealing an antagonistic effect where direct climate increased runoff while cascading effects and human activities suppressed it.
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Pérez et al. (2026) Regional synchronization patterns between climate indices and colombian hydroclimatic variables
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Li et al. (2026) Spectral albedo, vegetation greenness, and radiative forcing responses of the Amazon to drought and wet conditions from 2005 to 2016
This study investigates the responses of spectral albedo, vegetation greenness, and albedo-driven radiative forcing to drought and wet conditions in the Amazon (2005-2016) across evergreen broadleaf forest, grassland, and savannas. It finds that visible and shortwave albedo negatively correlate with wetness over grasslands and savannas, while evergreen broadleaf forests show less pronounced and more complex responses, with significant implications for surface radiative forcing.
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Hacker et al. (2026) Multidecadal reconstruction of terrestrial water storage changes by combining pre-GRACE satellite observations and climate data
This study reconstructs multidecadal terrestrial water storage anomalies (TWSA) for global land from 1984 to 2020 by optimally combining pre-GRACE geodetic satellite observations (SLR and DORIS) with climate data-driven regression models, providing a long-term consistent dataset (TWSTORE) for climate change attribution and hydrological studies.
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Li et al. (2026) Evaporation, surface energy balance, and water-heat-salt transport under saline shallow groundwater: Lysimeter and modeling insights across soil textures
This study investigated the coupled water, heat, and salt transport in two soil textures (silt loam and sand) under shallow saline groundwater and natural conditions using field lysimeters and numerical modeling, revealing texture-dependent salt precipitation patterns that influence evaporation resistance and soil temperature.
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Ye et al. (2026) Leveraging water vapor to extend forecast horizons for forecast-informed reservoir operations: a vapor-precipitation-streamflow three-line defense
This study proposes a vapor-precipitation-streamflow (VPS) Forecast-Informed Reservoir Operations (FIRO) scheme that leverages precipitable water vapor (PWV) to extend forecast horizons. The VPS-FIRO scheme enables earlier prerelease operations, reducing spilled water volume by 5.6% and decreasing the duration of excessive outflow from 1% to 0.3% compared to traditional precipitation-streamflow (PS) FIRO.
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Harley et al. (2026) California Temperature Since 1520 CE Shows Interactions in Extremes of Heat, Drought, and Fire
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Rivoire et al. (2026) Identification of hydro-meteorological drivers for forest low greenness events in Europe
This study identifies hydro-meteorological drivers of forest low greenness events across Europe using a random forest model and satellite Normalized Difference Vegetation Index (NDVI) data. It reveals that warm and dry conditions in spring and early summer, along with multi-year influences, are critical predictors for forest browning, with regional and forest-type specific variations.
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Hancock et al. (2026) 21st century hydrological trends in the Mississippi River basin intensify the east to west moisture gradient
This study validates 19 CMIP6 models against historical observations to project future monthly hydroclimate changes in the Mississippi River system under the SSP3-7.0 pathway. It finds consistent increases in precipitation but decreases in soil moisture due to enhanced evaporative demand, with highly divergent and regionally varied trends for runoff and discharge driven by large-scale atmospheric and oceanic patterns.
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Martí et al. (2026) Implementation of a dry surface layer soil resistance in two contrasting semi-arid sites with SURFEX-ISBA V9.0
This study evaluates and improves the SURFEX-ISBA V9.0 land surface model's estimation of latent heat fluxes in semi-arid environments by implementing a dry surface layer (DSL) soil resistance. The DSL resistance successfully reduced the overestimation of bare soil evaporation, leading to a 29% to 32% reduction in the daily Root Mean Square Error of latent heat flux at two contrasting sites.
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Li et al. (2026) A tailored deep learning method to improve spatial rainfall downscaling
This study developed a tailored deep learning model, RM-ResNet, incorporating a spatial correction algorithm to downscale satellite rainfall data from 8 km to 1 km resolution. The method successfully improved the representation of rainfall spatial patterns, including extreme events and storm centers, demonstrating consistency with observations.
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Shu et al. (2026) High-resolution urban flooding inundation forecasting through hydrodynamic interaction and multimodal deep learning
This research proposes a multimodal deep learning model (SMDFN) tightly coupled with a hydrological-hydrodynamic model to improve high-resolution urban pluvial flooding inundation forecasting by capturing hydrodynamic interactions and enabling multimodal feature extraction. The model demonstrates superior performance, reducing RMSE by 13.8%, and offers a multi-region collaborative forecasting solution.
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Wang et al. (2026) Beyond Annual Averages: Multi‐Scale Rainfall Variability, Drought Indicators, and Seasonal Shifts Under a Changing Climate
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Arbai et al. (2026) Projected Annual and Monsoonal Precipitation Trends of CMIP 6 Over P eninsular M alaysia
This study investigated historical (1973–2022) and projected (2051–2100) precipitation trends over Peninsular Malaysia using ground-based records and CMIP6 GCMs, revealing spatially heterogeneous patterns influenced by monsoons, with future projections indicating modest increases under SSP2-4.5 but widespread declines under SSP5-8.5.
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Liang et al. (2026) WetFramework: A deep learning framework for coastal wetland boundary extraction and inundation frequency estimation
This paper introduces WetFramework, a novel deep learning framework that integrates Transformer, Mamba, and wavelet transforms to accurately extract coastal wetland boundaries and quantitatively estimate inundation frequency at microscales, demonstrating superior performance and generalization across diverse coastal regions.
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Tsiros et al. (2026) Variability of the Climate in Athens‐Greece Over the Last 165 Years of the Period 1858–2023: An Assessment Based on Thornthwaite's Climate Classification and Relevant Indices
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Miao et al. (2026) SM2RAIN–dual: a global rainfall fusion product derived from multi-source satellite soil moisture observations
This study addresses regional disparities in SM2RAIN-derived rainfall estimates by developing a rainfall data fusion scheme using multi-source satellite soil moisture products. The research generated a global, more reliable rainfall product called SM2RAIN–Dual, which combines SMAP and ASCAT data.
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Bagheri et al. (2026) RetroSight and ForeSight ensemble model (ReForM) for improved time series prediction: A case study on river temperature prediction
This study introduces ReForM, a novel data-driven and physics-informed ensemble model that leverages both historical data and future physics-based simulations to significantly improve time series predictions. Applied to river temperature forecasting, ReForM demonstrates superior accuracy, especially for long-term predictions, outperforming state-of-the-art machine learning benchmarks.
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Chen et al. (2026) Combined effect of tides, irregular waves and beach recovery on groundwater flow and marine-sourced salt transport in coastal unconfined aquifers
This study numerically investigates the combined impact of tides and irregular waves on groundwater dynamics and marine-sourced salt transport during beach recovery. It reveals that wave action, particularly overtopping waves, significantly amplifies subsurface mixing and solute transport, leading to substantial increases in intertidal saline infiltration and submarine groundwater discharge.
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Wang et al. (2026) Projected Future Changes of Atmospheric Rivers by a High- and Low-resolution CESM
This study evaluates atmospheric river (AR) simulations and projections using high-resolution (HR) and low-resolution (LR) Community Earth System Model (CESM), finding that LR CESM systematically underestimates AR frequency, intensity, and precipitation, particularly for extreme events, while HR CESM significantly improves historical simulations and provides more robust projections of increased extreme ARs under warming.
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Wen et al. (2026) Preferential flow reduces overland flow on slopes: insights from a field experiment on the Chinese Loess Plateau
This study investigated how preferential flow influences slope runoff under various vegetation restoration conditions on the Chinese Loess Plateau, revealing that vegetation restoration significantly increases preferential flow, thereby reducing overland flow.
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Goodman et al. (2026) Modeling cumulative hydrologic effects of multiple floodplain restoration projects in a 4th-order river channel network
This study used HEC-RAS to evaluate the cumulative hydrologic effects of floodplain restoration projects on flood propagation in a generic 4th-order river channel network. It found that flood attenuation generally increased with restored channel length, but project location and existing restoration significantly influenced benefits, sometimes even exacerbating flooding due to peak flow synchronization.
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Adhikari et al. (2026) Design of stormwater bioretention systems for improved volume and peak runoff reduction
This study investigated 54 bioretention system design combinations using a calibrated SWMM model to optimize hydrologic performance for both common and intense rainfall events. It found that a storage connection consistently improved performance, while higher filter media fractions enhanced volume reduction during common events, and lower fractions were more beneficial for reducing overflows during high-intensity rainfall.
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Dobrovolný et al. (2026) Spatiotemporal Changes in Precipitation Concentration in the Atlantic‐European Region
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Esmond et al. (2026) A multi-tracer approach to constraining water sources of culturally and ecologically significant natural springs: Combining environmental isotopes and environmental DNA
This study developed an eco-hydrogeological approach, integrating geochemical tracers (hydrochemistry, stable and radio-isotopes) with environmental DNA (eDNA), to create a robust conceptual model of groundwater flow paths and water sources for Great Artesian Basin springs in Carnarvon Gorge, Australia. The findings revealed that vertical inter-aquifer flow and multiple recharge zones control spring dynamics, with eDNA proving more sensitive than isotopes in distinguishing hydraulically separated flow paths and recharge areas.
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ADOMBI (2026) DeepDiscover: towards autonomous discovery of bucket-type conceptual models – a proof of concept applied to hydrology
This study introduces DeepDiscover, a physics-embedded machine learning framework designed to autonomously infer bucket-type conceptual hydrological models from data. It demonstrates the feasibility and superior predictive performance of this approach compared to traditional benchmarks, reducing reliance on expert-defined model formulations.
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Wu et al. (2026) A two-level attribution method for water resource changes based on water budget balance and distributed simulation
This study develops and applies a novel two-level attribution method for water resource changes, integrating water balance principles and a distributed human-water relationship model. The method effectively clarifies the driving mechanisms of water resource changes across scales, revealing that climatic factors dominated runoff changes in the Qin River Basin (2001–2022), while human activities had complex effects on natural and actual runoff.
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Yang et al. (2026) Quantitative assessment impact of anthropogenic heat flux on global urban evapotranspiration retrieval at multiple temporal scales
This study quantified the global impact of anthropogenic heat flux (AHF) on urban evapotranspiration (ET) estimation by comparing remote sensing-derived ET with and without AHF across 668 cities worldwide. It found that neglecting AHF leads to significant ET underestimation, particularly in cold regions and megacities, with discrepancies peaking in summer.
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Haberlandt et al. (2026) Assessing the maximization potential for historical floods by spatio-temporal simulation of precipitation
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Mhanna et al. (2026) Hydrological and ecological consequences of the Kakhovka dam collapse
This study assesses the cascading hydrological impacts of the Kakhovka dam destruction in June 2023, revealing significant decreases in total water storage, amplified river variability, episodic flooding, and long-term challenges for re-emerging wetlands due to groundwater decline.
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Yang et al. (2026) 20 years of trials and insights: bridging legacy and next generation in ParFlow and Land Surface Model Coupling
This study reviews two decades of ParFlow-land/atmosphere coupled modeling, presents a renewed recoupling of ParFlow with the updated Common Land Model (CoLM) demonstrating improved performance, and proposes a sustainable coupling framework and a community-led model intercomparison project (PLCMIP) for future development.
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Taylor et al. (2026) Wind shear enhances soil moisture influence on rapid thunderstorm growth
This study reveals that wind shear significantly enhances the influence of soil moisture (SM) contrasts on the rapid growth of thunderstorms, particularly for extreme events, by modulating mesoscale circulations that promote deep convection. Analyzing 2.2 million afternoon events across sub-Saharan Africa, the authors found 68% more extreme initiations under favorable soil conditions when wind shear was strong.
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Merlo et al. (2026) Tracking shifts in European drought hotspots
This study develops novel impact-based Combined Drought Indices (iCDIs) using a machine learning framework to directly link hydroclimatic drivers to remotely sensed vegetation stress across Europe. The iCDIs outperform traditional indices and project a significant northward shift in future drought impacts, identifying Central Europe as an emerging hotspot, contrary to conventional views.
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Sun et al. (2026) Land use and land cover change intensified soil moisture drought: evidence from CMIP6-LUMIP
This study quantifies the long-term impacts of historical land use and land cover change (LULCC) on global soil moisture drought (SMD) characteristics from 1901-2014, finding that LULCC significantly intensifies SMD over more than half of the global land area by altering surface energy partitioning and depleting soil water storage.
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Kwon et al. (2026) Synergistic impact of simultaneously assimilating radar- and radiometer-based soil moisture retrievals on the performance of numerical weather prediction systems
This study evaluates the synergistic impact of simultaneously assimilating radar-based (ASCAT) and radiometer-based (SMAP) soil moisture retrievals into the Korean Integrated Model (KIM) using a weakly coupled data assimilation system. The findings demonstrate that multi-sensor soil moisture assimilation leads to more balanced and improved analyses and forecasts of specific humidity, air temperature, and precipitation compared to single-sensor assimilation.
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Zhang et al. (2026) Long-Term Evolution of Permafrost across the Qinghai-Tibet Plateau: Perspectives from Multi-Model Ensembles and Machine Learning
This study combined CMIP6 data with machine learning models to project permafrost extent and maximum seasonal soil freeze depth (SFD) across the Qinghai-Tibet Plateau (QTP) from 2025 to 2100 under various SSP scenarios. Results indicate continuous permafrost degradation into seasonally frozen ground, with SFD declining significantly, and specific high-risk zones identified, with the Deep Neural Network (DNN) model demonstrating superior performance.
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Ling et al. (2026) An improved Hydrology-Informed attention LSTM(HIA-LSTM) model for runoff simulation with seasonal snowmelt
This study proposes a Hydrology-Informed Attention LSTM (HIA-LSTM) that embeds physical inductive biases into its neural architecture to improve runoff simulation in alpine basins with complex cryospheric processes. The HIA-LSTM significantly outperforms conventional deep learning models, achieving superior accuracy and interpretability, especially in melt-driven runoff scenarios.
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Chen et al. (2026) Contrary effects of soil moisture-atmosphere feedback on dry and humid heatwaves
This study investigates the distinct impacts of soil moisture-atmosphere feedback (SAF) on dry and humid heatwaves globally, revealing that SAF amplifies dry heatwaves but has spatially divergent effects on humid heatwaves, reducing their severity in low-to-mid latitudes while intensifying them in high latitudes. These contrary effects are primarily driven by the competition between SAF-induced thermal warming and moisture depletion, which modulate mean wet-bulb temperatures.
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Miazza et al. (2026) Technical note: Transit times of reactive tracers under time-variable hydrologic conditions
This study derives and explores novel analytical solutions for the transit time distributions (TTDs) of reactive tracers in randomly sampled hydrological systems, demonstrating how processes like sorption, degradation, and evapotranspiration, along with input patterns, cause tracer TTDs to differ significantly from water TTDs.
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Liu et al. (2026) Mapping Synchronous Heatwaves in the Northern Hemisphere: Insights from Climate Network Analysis
This study identifies hotspot regions and dominant synchronization patterns of summertime synchronous extreme heatwaves across the Northern Hemisphere using a climate network method. It reveals connections to large-scale atmospheric circulation patterns, including Rossby waves and zonal wave trains, and highlights the role of positive soil moisture feedback in intensifying these events.
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Sellami (2026) Spatiotemporal Droughts Propagation and Direct Driving Variables Under Climate Change Projections: A Case Study of Tunisia
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Aich et al. (2026) Conditional diffusion models for downscaling and bias correction of Earth system model precipitation
This paper introduces a machine learning framework utilizing conditional diffusion models for simultaneous bias correction and downscaling of Earth System Model (ESM) precipitation. The approach outperforms existing statistical and deep learning methods, particularly for extreme events, by improving spatial structure and statistical fidelity while preserving climate change signals.
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Athukoralalage et al. (2026) The impact of a mega-flood event on the water quality of the southern Murray-Darling Basin, Australia
This study investigated the impact of a 2022–2023 mega-flood and five other major flow events on Total Nitrogen, Total Phosphorus, and Dissolved Organic Carbon dynamics in the southern Murray-Darling Basin, Australia. It found that the mega-flood significantly increased nutrient loads and prolonged water quality degradation, particularly in downstream areas due to extensive floodplain inundation and delayed nutrient release.
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Premier et al. (2026) Assessing the impact of Earth Observation data-driven calibration of the melting coefficient on the LISFLOOD snow module
This study evaluates the LISFLOOD hydrological model's snow module and the impact of calibrating its snowmelt coefficient using Earth Observation (EO) snow cover fraction (SCF) data across nine European basins. It demonstrates that while EO-based calibration significantly improves snow cover representation, its impact on basin-level discharge simulations is minimal, suggesting that standard discharge-based calibration adequately captures snow dynamics for streamflow.
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Wang et al. (2026) A wetland partitioning method based on the hydrological connectivity and the underlying causes of their occurrence
This study developed a wetland partitioning method based on hydrological connectivity using hydrodynamic modeling and clustering, demonstrating its effectiveness in delineating subareas in the Zhalong Wetland and revealing how human activities and topography influence connectivity across different hydrological years.
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Poschlod et al. (2026) Climate change effects on river droughts in Bavaria using a hydrological large ensemble
This study investigates the impact of climate change on rare and extreme river droughts in two Bavarian catchments using a unique hydrological large ensemble. It projects a drastic increase in the frequency and intensity of summer droughts, with historical 100-year events becoming significantly more common by the far future (2070–2099) under a high-emission scenario.
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Mliyeh et al. (2026) Advancing hydrological modeling in the Mediterranean: Multi-objective calibration of the SWAT+ model using open-source data and tools
This study evaluated multi-variable calibration strategies for the SWAT+ model in the Upper Oum Er Rbia watershed, Morocco, integrating streamflow and remote sensing evapotranspiration data. The multi-variable approach achieved satisfactory and balanced performance for both streamflow (NSE = 0.75, KGE = 0.77) and evapotranspiration (NSE = 0.51, KGE = 0.64), highlighting the value of open-access remote sensing ET data in refining hydrological model parameters for data-scarce regions.
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Wei et al. (2026) A framework for long-term vegetation latent heat estimation and forecasting combining ERA5-land and Landsat data
This study developed a globally applicable framework integrating ERA5-Land reanalysis and Landsat data with machine learning to estimate and forecast monthly vegetation latent heat (LE) at 30 m resolution from 1984 to the present. It found Random Forest performed best for estimation and proposed two forecasting frameworks, LE-ML and LE-Direct, with varying performance based on training data availability.
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TIAN et al. (2026) Intensifying droughts, heatwaves, and compound drought–heatwave events and their spatiotemporal patterns in Africa (1979–2024)
This study systematically evaluates the spatiotemporal patterns of heatwaves, droughts, and compound drought–heatwave (CDHW) events across Africa from 1979 to 2024, revealing significant intensification of all three, with CDHWs accelerating since the 2000s, particularly in Eastern and Southern Africa.
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Häberli et al. (2026) Unprecedented extreme meteorological droughts simulated in Fenno-Scandinavia with high-resolution climate models
This study assesses future meteorological drought probabilities in Fenno-Scandinavia using high-resolution convection-permitting regional climate models (CPRCMs) and a novel multi-threshold Standardized Precipitation Index (SPI) method. It projects a decrease in moderate droughts but a significant increase in unprecedented extreme droughts, particularly during the critical growing season, highlighting the added value of CPRCMs.
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Adekilae et al. (2026) Pseudo‐diffusivity characteristic curves for surface–rootzone soil hydrologic connectivity
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Martin et al. (2026) Corrigendum to “Estimating irrigation consumptive use for the conterminous United States: coupling satellite-sourced estimates of actual evapotranspiration with a national hydrologic model” [J. Hydrol. 662 (2025) 133909]
This document is a corrigendum to the original paper "Estimating irrigation consumptive use for the conterminous United States: coupling satellite-sourced estimates of actual evapotranspiration with a national hydrologic model," providing necessary corrections to author affiliations, definitions, references, numerical values, and figure/table captions.
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Fu et al. (2026) Climate change enhances the propagation from meteorological to lake drought
This study quantified the propagation time and probability from meteorological to lake droughts for 153,643 global lakes from 1985 to 2018, revealing that climate change is enhancing this propagation, particularly in arid regions and North America due to rising temperatures and vapor pressure deficit.
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Gianotti et al. (2026) Meteorological to Agricultural Drought Transitions Compounded by Heat Waves in Historical and Future Climates
## Identification - **Journal:** Water Resources Research - **Year:** 2026...
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Guo et al. (2026) Simulation and Rapid Prediction of Water Quantity and Quality Processes Based on Numerical Models and Deep Learning
This study develops a coupled 1D-2D numerical model (GAST-SWMM) to simulate urban water quantity and quality processes, generating a training database for a Long Short-Term Memory (LSTM) deep learning model. The LSTM model provides rapid and accurate predictions of pollutant concentrations on urban surfaces and within sewer networks, outperforming other machine learning models and significantly reducing computational time.
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Finkel et al. (2026) Rare Event Sampling for Moving Targets: Extremes of Temperature and Daily Precipitation in a General Circulation Model
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Risser et al. (2026) Correction: A framework for detection and attribution of regional precipitation change: application to the United States historical record
This paper develops and applies a framework for the detection and attribution of regional precipitation changes, specifically using the historical record of the United States.
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Risser et al. (2026) Correction: Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation
This correction notice addresses and rectifies a misspelling of an author's name, Christina M. Patricola-DiRosario, in a previously published article.
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Ma et al. (2026) Monitoring Reservoir Storage Using SWOT Satellite Observations and a Reservoir Operation Model
This study evaluates the accuracy of reservoir storage estimates derived from the new Surface Water and Ocean Topography (SWOT) satellite mission against in situ observations for 12 Western U.S. reservoirs, finding that SWOT provides highly accurate water surface elevation and storage data that can effectively constrain hydrological models to fill temporal gaps.
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Tajima et al. (2026) Climate Change Alters Post‐Surge Recovery of Coastal Aquifers
This study quantifies the combined effects of increasing storm-surge intensity and decreasing frequency on coastal aquifers using integrated numerical simulations. It reveals two distinct long-term regimes—full recovery or shifted equilibrium with persistent salt accumulation—determined by critical thresholds of storm intensity and frequency, which can be predicted by a dimensionless number.
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Riche et al. (2026) Predicting LULC Changes and Assessing their Impact on Surface Runoff with Machine Learning and Remote Sensing Data
This study developed an approach integrating remote sensing and machine learning to predict future land use and land cover (LULC) changes and assess their impact on surface runoff in a semi-arid Mediterranean watershed. It found that urbanization significantly increases runoff, while forests mitigate it, with land factors having limited influence during intense rainfall events.
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Neri et al. (2026) On the Choice of Optimal Reservoir Operating Rules in a Changing Climate for the Sustainable Management of Drinking Water Sources
This study develops a multi-objective optimization framework to define optimal reservoir withdrawal rules for a multi-basin drinking water supply system in Northern Italy, assessing their adaptation to climate change under historical and future meteorological forcings to maximize production and minimize deficits. The research quantifies future expected relative changes in optimal operating rules and outlines corresponding patterns of withdrawal volumes and potential water system failures throughout the century.
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Wu et al. (2026) Evaluating Evapotranspiration Simulation Performance in 30 Conceptual Hydrological Models: Insights Into ET Representation Across Diverse Climates
This study investigates 30 conceptual hydrological models to assess their evapotranspiration (ET) representations and ability to reproduce state-of-the-art ET products across 507 diverse CAMELS-US catchments, providing guidance for appropriate ET modeling based on climate.
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Sánchez‐Gómez et al. (2026) Climate Change in the Upper Tagus River Basin: Impacts on Climate Variables and Hydrological Processes
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Azghandi et al. (2026) Machine Learning–Based Characterization of Groundwater Recharge in Semi-Arid Drylands
This study characterized groundwater recharge dynamics in the semi-arid Karkheh Plain (Iran) from 2001–2024 using satellite-based water balance and machine learning, finding that ΔSoil Moisture is the dominant driver and that positive recharge peaks have significantly declined, indicating increasing groundwater vulnerability.
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Yang et al. (2026) A hybrid method coupling physical process-driven model with generative deep learning for probabilistic flood forecasting
This paper proposes a novel hybrid method that integrates a physical process-driven model with generative deep learning to enhance the accuracy and reliability of probabilistic flood forecasting.
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Hanumantha et al. (2026) A Spatially Explicit Water Balance Model for Assessing Recharge Sensitivity to Climate and Land Cover Change in Central Mexico
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Singh et al. (2026) Global shifts in rainfall drought relationship: weakening association in tropics
This study examines global meteorological drought dynamics from 1951 to 2016, revealing a sixfold increase in global drought frequency, particularly in tropical and subtropical regions. It finds that increased rainfall variability, rather than just rainfall deficit, is increasingly driving these droughts, leading to a 60 % rise in drought likelihood even during surplus rainfall years.
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Eatesam et al. (2026) Quantifying the attribution of ecohydrological degradation: a comparative deep learning approach in a changing environment
This paper aims to quantify the attribution of ecohydrological degradation in a changing environment using a comparative deep learning approach.
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Calvi et al. (2026) Origin, Age, and Flow Path of Groundwater Associated With High‐Mountain Springs in Arid Andean Regions
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Demirbaş et al. (2026) Modelling the Impact of Climate Change on the Reservoir Filling Rates of Dams Used for Drinking Water Supply Through Artificial Neural Networks
This study models the impact of climate change on the reservoir filling rates of drinking water supply dams in Ankara, Istanbul, and Izmir, Türkiye, using Artificial Neural Networks (ANNs). It quantifies the divergence between expected and observed precipitation, revealing significant water losses and the susceptibility of these urban water systems to climate change.
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Deger (2026) Exploring Monthly, Seasonal and Annual Spatio-Temporal Variability of Temperature and Precipitation Series by Classical and Innovative Techniques
This study comprehensively analyzed the spatio-temporal variability and trends of temperature and precipitation across 12 stations in Southeastern Anatolia, Türkiye, revealing a dominant significant increasing trend in temperature and a prevalence of non-significant decreasing trends in precipitation.
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Zhang et al. (2026) Can Conceptual Rainfall‐Runoff Models Capture Multi‐Annual Storage Dynamics?
This study investigated if specific structural components enable conceptual rainfall-runoff models to capture multi-annual storage dynamics during droughts. It found that models incorporating a long-term store, its disconnection from direct streamflow, and a water loss mechanism from it were significantly more successful in representing long-term hydrological memory and drought response.
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Pan et al. (2026) A Study on Rapid Dynamic Flood Forecasting in Small Watersheds Using a GNN-Transformer Approach Integrated with Spatial Physical Information
This study develops a novel GNN-Transformer deep learning model for rapid flood forecasting in small watersheds, integrating static physical information and dynamic rainfall data. The model achieves high accuracy (NSE > 0.99, RMAE < 7%) and significantly improved computational efficiency (100-200 times faster) compared to traditional hydrodynamic models.
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Bril et al. (2026) Assessing the Effectiveness of Nature‐Based Solutions and Building‐Level Flood Risk Reduction Measures: An Open‐Source Coupled Model
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Man et al. (2026) Multi-Target Water Demand Forecasting with Graph Neural Networks: A Comparative Study
This study systematically evaluates Graph Neural Networks (GNNs) for multi-target water demand forecasting (MTF), demonstrating their superior accuracy and robustness compared to traditional sequence-based models. Self-learning GNNs, specifically MTGNN and MTGODE, achieved enhanced accuracy and stability, particularly under data irregularities and for multi-step predictions.
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Negro et al. (2026) Integrating Satellite Data Into Meso‐Scale Habitat Modeling for Non‐Perennial Rivers and Streams
This study introduces a novel methodology using the MesoHABSIM model and satellite imagery to assess aquatic habitat dynamics in a non-perennial river, revealing species-specific vulnerabilities of fish and macroinvertebrates to flow intermittency and informing ecological flow strategies.
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Özel et al. (2026) Multi-dimensional Assessment of the Water-Food Nexus in a Semi-Arid Watershed
This study holistically assesses the water-food nexus in the semi-arid Upper Sakarya Watershed, Türkiye, by integrating hydrological modeling, economic analysis, and stakeholder perspectives to evaluate agricultural water management scenarios. It finds that effective scenarios, particularly crop pattern changes, can significantly reduce irrigation water use (up to 60 million m³ per year) while increasing farmers' net income per cubic meter of water, but implementation faces technical, practical, and political constraints.
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Bernal‐Mujica et al. (2026) The Impact of Deciduous Forest and Topography on Snowpack Dynamics in a Headwater Catchment in the Southern Andes Cordillera
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Ma et al. (2026) Divergence or Convergence? A Comparison of InVEST and SWAT in Simulating Water Conservation Patterns and Drivers
This study quantitatively compares the InVEST and SWAT models in simulating water conservation patterns and drivers in the Liupan Mountain region from 2003 to 2022, finding both models show an increasing trend and consistent spatial heterogeneity, but with complementary strengths for different application contexts.
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Zhang et al. (2026) A high-order Model-free Dynamic Framework for Accurate Daily Streamflow Prediction
This paper introduces a high-order lightweight dynamic framework (HoLDF) for daily streamflow forecasting, which integrates high-order structural information identified by an improved Granger causality inference approach into a reservoir computing paradigm. HoLDF significantly outperforms baseline deep learning models in accuracy, robustness, and computational efficiency, making it suitable for operational deployment.
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Wolkeba et al. (2026) Water scarcity indicator based on GRACE derived total water storage for fast water scarcity monitoring
This study introduces a novel water scarcity indicator derived from GRACE total water storage anomaly, offering a robust and efficient alternative to traditional Global Hydrological Model (GHM)-based assessments. The new indicator demonstrates strong alignment with established blue water scarcity metrics and provides comparable estimates of populations and land areas under scarcity.
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Zhang et al. (2026) Longitudinal Mean Velocity and Turbulent Kinetic Energy Within an Emergent Canopy in Nonuniform Flows
This study investigates the longitudinal velocity and turbulent kinetic energy (TKE) dynamics in emergent canopies under streamwise varying flow conditions using laboratory flume experiments. It found that both time-mean longitudinal velocity and TKE significantly enhance downstream, and developed an analytical model revealing an effective power-law exponent of 2/3 between longitudinal TKE and mean velocity due to flow nonuniformity.
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Baioni et al. (2026) A regionally based method to identify lithology-specific hydraulic conductivity distributions in shallow aquifers using catchment-scale effective values
This paper introduces a novel method (HCDM) to infer lithology-specific hydraulic conductivity distributions in shallow aquifers using catchment-scale effective values. Validated with synthetic data and applied to 113 catchments in the Armorican Massif, the method demonstrates high predictive accuracy, with 85% of modeled conductivities falling within a 90% confidence interval of observed values.
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Yang et al. (2026) A Global Assessment of Climate Change and Anthropogenic Effects on Changes in Streamflow
This study globally assessed spatiotemporal streamflow changes in 2264 catchments from 1961–2014, quantifying the contributions of precipitation, potential evapotranspiration, and landscape characteristics using the Budyko hypothesis. It found that precipitation was the dominant factor for streamflow changes in most catchments, with significant regional variations in trends and sensitivities to climate and anthropogenic factors.
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Jiang et al. (2026) Integrating socio-hydrological modeling and climate change projections for sustainable water resource management in agricultural systems
This paper integrates socio-hydrological modeling with climate change projections to develop sustainable water resource management strategies specifically for agricultural systems.
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Lapides et al. (2026) Potential Impacts of Groundwater Pumping on Stream Temperature Are Greatest in Streams With Substantial Cold Groundwater Inflows
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Lechner et al. (2026) Hydrological drivers of surface runoff during high intensity rainfall experiments in Alpine ski regions
This study investigates surface runoff behavior in 12 Eastern Alpine ski regions using 74 rainfall simulation experiments, revealing significantly higher surface runoff coefficients on ski slopes (median 0.57) compared to reference areas (median 0.07). A random forest model identified geological factors as the strongest predictors on ski slopes, while soil and land use variables were more influential on reference areas.
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Wang et al. (2026) A DeepONet surrogate for accelerating distributed hydrological model simulations
This paper introduces a DeepONet surrogate model designed to significantly accelerate the simulation speed of distributed hydrological models.
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Mohomi et al. (2026) Projections of extreme rainfall in South Africa using CMIP6 ISIMIP models
This study projects extreme rainfall in South Africa using CMIP6 ISIMIP global climate models under SSP1-2.6 and SSP5-8.5 scenarios, finding an overall increase in extreme rainfall events, with a trend towards a drier west and a wetter east, posing significant risks to water resources and infrastructure.
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Wang et al. (2026) Advancing Physical Realism in Hydrological Modelling: Selection and Integration — A Review and Synthesis
This review synthesizes 30 hydrological models and 186 peer-reviewed studies to propose a decision-oriented framework for model selection and integration, aiming to enhance physical realism, address data scarcity, and improve long-term and sub-daily simulations for resilient water resources management.
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Yaldiz et al. (2026) A Step Toward Rainfall Erosivity Mapping Over Türkiye Using Kriging With External Drift
This study mapped the rainfall erosivity (R) factor over Türkiye using Kriging with External Drift (KED) and satellite-derived Modified Fournier Index (MFI). The KED model significantly outperformed linear regression, ordinary kriging, and a global erosivity product, achieving a Kling-Gupta Efficiency (KGE) of 0.68.
