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Wang et al. (2026) Hysteretic response of water temperature and persistent thermal regime shift under extreme hydrological pulses
This study investigates the impact of extreme floods on river water temperature using a natural experiment approach. The results show that extreme floods can cause abrupt cooling, delayed recovery, and thermal over-recovery in rivers.
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Sanderson et al. (2026) METEORv1.6: Spatial climate variability and integrated impact emulation
METEORv1.6 extends the established METEOR spatial multi-timescale, multi-forcer climate emulation framework with two major capabilities: (1) a monthly climate variability model that generates realistic sub-annual climate sequences with seasonal cycles and inter-annual variability, enabling the generation of ensemble projections and (2) a modular impact assessment framework that translates climate projections into impact metrics.
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Su et al. (2026) Global Changes in Precipitation Concentration Through Consecutive Event Organization
This study quantifies global precipitation unevenness by analyzing consecutive wettest events and finds a significant decline in the duration of these events worldwide. The largest regional differences emerge in the contributions of precipitation amount, event intensity, and duration.
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Sharma et al. (2026) How Well Can the Surface Water and Ocean Topography (SWOT) Satellite Mission Observe Irrigation Canals?
This study assesses the capability of the Surface Water and Ocean Topography (SWOT) satellite to observe irrigation canal networks across 22 Asian countries, using a new confidence level metric. The results show that SWOT can meaningfully support irrigation canal water monitoring in many intensively irrigated regions.
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Zavadoff et al. (2026) How Predictable Was the Extreme Precipitation Preceding the May 2019 Arkansas River Floods?
This study investigates the role of atmospheric rivers (ARs) in extreme rainfall events over the central United States, highlighting their predictability on extended timescales.
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Mwakyusa et al. (2026) Automated System for Plant Health Monitoring and Stress Diagnosis Using IoT and Machine Learning
This study presents a decentralized system that combines IoT hardware with cloud-hosted predictive Machine Learning to automate irrigation decisions on smallholder farms, improving water use efficiency and crop yields.
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Yan et al. (2026) The interannual variability of the day-night compound hot days in the Yangtze River Basin: dynamic origins and seasonal predictability
This study investigates the interannual variability of day-night compound hot days (CHDs) in the Yangtze River Basin, China, and develops a physics-based empirical model to predict CHDs. The research reveals that CHDs are primarily associated with local anomalous barotropic high-pressure/anticyclone influenced by ocean-atmosphere precursors.
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Li et al. (2026) Divergent responses of streamflow reanalysis errors to precipitation reanalysis errors modulated by catchment heterogeneity
This paper investigates how streamflow reanalysis errors respond to precipitation reanalysis errors across different catchments. The results demonstrate that the responses are modulated by catchment heterogeneity, with streamflow errors being more directly associated with precipitation errors under saturation-excess conditions and soil moisture deficits dampening their effects.
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Feng et al. (2026) A Hybrid Bi‐ LSTM ‐ AM Framework for Improving Extreme Rainfall Prediction and Bias Correction
This study develops an attention-augmented, bidirectional long short-term memory framework (Bi-LSTM-AM) for reliable forecasting of extreme rainfall events. The Bi-LSTM-AM model outperforms other bias correction models in predicting extreme rain events.
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Balocchi (2026) A structurally constrained expo-linear rainfall–runoff model: enforcing physical admissibility in event-based hydrology
This study investigates the impact of climate change on water resources in the southwestern United States, focusing on the Colorado River Basin. The research reveals a significant decrease in water availability due to increased evaporation and altered precipitation patterns.
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Abdelrahman et al. (2026) Managed aquifer recharge in confined multi-layer aquifers: a scalable framework for drought resilience in central Europe
This study develops a scalable framework to assess the feasibility of Aquifer Storage, Transfer and Recovery (ASTR) in confined multi-layer aquifers for drought resilience in central Europe. It finds that 62.8% of the Berlin-Brandenburg region is viable for ASTR, with a mean annual recharge potential of 18.2–23.0 million cubic meters, offering significant cost savings compared to conventional water supply.
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Li et al. (2026) Investigating precipitation growth and decay due to orography using radar rainfall observations in Great Britain
This study investigates the effects of topography on precipitation growth and decay across Great Britain using a five-year weather radar dataset, providing insights into orographic effects and dynamic precipitation evolution.
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Mo et al. (2026) Runoff simulation in a typical karst basin based on a Robust Dynamic Merging Algorithm for multi-source satellite precipitation and an improved Xin'anjiang hydrological model
This study presents an integrated modeling framework combining the Robust Dynamic Merging Algorithm (RDMA) with the improved Xin'anjiang (KXAJ) model to simulate runoff in karst basins. The framework outperforms single-source satellite precipitation products and demonstrates strong structural robustness for data-scarce karst basins.
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Raji et al. (2026) Assessment of Drought Severity in Northern Nigeria Using Multi-Index Drought Indicators (1980–2023)
This study assessed the temporal, seasonal, and spatial characteristics of drought in Northern Nigeria from 1980 to 2023 using a multi-index approach, revealing pronounced variability in drought severity.
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Granata et al. (2026) Deep learning meets spectral analysis: reproducing long-term persistence in synthetic streamflow
This study evaluates a three-stage framework that combines latent-space generation with a temporal convolutional network (TCN), low-frequency spectral adjustment, and quantile-based rank reordering to reproduce long-term persistence in synthetic streamflow.
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Zhao et al. (2026) The impact of numerical error on hydrological model performance
This study investigates the impact of numerical error on hydrological model performance using the Xinanjiang model as a case study. The results show that controlling numerical error leads to systematic performance improvements, underscoring its non-negligible contribution to model uncertainty.
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Yu et al. (2026) A tri‑basin matching index improves Indian Summer Monsoon Rainfall predictability
This study proposes a Matching Index (MI) framework to improve Indian Summer Monsoon Rainfall predictability by identifying state-dependent ocean–monsoon coupling regimes. The MI framework improves ISMR reconstruction correlation from 0.73 using conventional multivariate linear regression to 0.83.
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Sun et al. (2026) China regional 3 km downscaling based on residual Corrective Diffusion model
This study presents a diffusion-based downscaling framework, Corrective Diffusion (CorrDiff), which is applied to the China region for producing 3 km resolution forecasts. The framework is enhanced with three key improvements: expanded domain size, extended target variables, and global residual connection.
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Unknown (2026) No Title
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Ranera et al. (2026) Adapting irrigated agriculture to water transfer reductions: Challenges and strategies in southeastern Spain
This study examines the effects of a 40% reduction in the Tagus-Segura water transfer on irrigated agriculture in southeastern Spain. The results indicate that alternative water sources, such as desalinated and reclaimed water, can partially mitigate these losses.
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Olsson et al. (2026) Characteristics of extreme snowfall–wind-gust events in Finland (1960–2024): frequency, duration, and intensity
This study analyzed the frequency, duration, and intensity of extreme snowfall-wind-gust events in Finland from 1960 to 2024. The results show that these compound events are more frequent along coastal regions and eastern Finland.
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Tu et al. (2026) Estimating hourly surface global and diffuse solar radiation across China using virtual radiation station networks
This study developed a two-stage strategy to estimate hourly surface global and diffuse solar radiation across China using virtual radiation station networks, achieving superior performance with RMSE (R2) of 82.55 W/m2 (0.91) for Rs and 64.11 W/m2 (0.73) for Rdif.
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Ablila et al. (2026) A multi-scale remote sensing approach for assessing drought impacts on rainfed and irrigated cereal yields in Lleida, Spain
This study evaluates the performance of agricultural drought indices at provincial and field scales in the Lleida region (northeastern Spain) from 2010 to 2024. The analysis reveals a strong dependence on crop type, phenological stage, spatial scale, and water management practices.
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Vangi et al. (2026) Investigating climate-phenology relationships among the most common Italian forest species using Sentinel-2-derived vegetation phenology and productivity products
This study investigates climate-phenology relationships among the most common Italian forest species using Sentinel-2-derived vegetation phenology and productivity products. The results indicate a general tendency toward lengthening of the growing season driven mainly by chilling accumulation and spring temperatures.
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Vrese et al. (2026) T-REX: the tile-based representation of lateral exchange processes in ICON-Land
This study presents a new scheme for representing lateral exchange processes in heterogeneous landscapes, called T-REX. The scheme is designed to capture the effects of subgrid-scale spatial heterogeneity on land-surface properties and includes five lateral exchange processes that can be categorized by transported quantity, scale, and mechanism.
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Fu et al. (2026) Effectively assimilate satellite land surface temperature into offline land surface models within ensemble-based assimilation frameworks
This study proposes a novel scheme to effectively assimilate satellite land surface temperature (LST) into offline land surface models by jointly updating soil temperature and soil moisture in upper layers. This approach significantly improves soil temperature, soil moisture, and snow variables, outperforming conventional temperature-only updates.
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Tosunoğlu et al. (2026) Physically informed non-stationary modelling of flood parameters in UK rivers using copulas
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Vishwakarma et al. (2026) Spatiotemporal shifts in composite weather stress across US croplands
This study introduces a multi-stressor framework to quantify compound weather stress across the Contiguous United States, linking it to county-scale crop yield anomalies. The framework reveals increasing weather stress divergence and declining similarity with historical baselines.
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Romanou et al. (2026) The NASA-GISS ModelE2.1-CC2 ESM: development and evaluation
This paper describes the development and evaluation of the NASA-GISS ModelE2.1-Carbon Cycle version 2 Earth System Model (NASA GISS E2.1-CC2 ESM), which includes an updated ocean and land carbon cycle and longer equilibrium simulations.
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Garg et al. (2026) Integrating AquaCrop-OSPy and machine learning to develop transferable surrogates for balancing wheat yield–irrigation trade-offs under deficit irrigation in semi-arid NW India
This study developed transferable surrogates for balancing wheat yield–irrigation trade-offs under deficit irrigation in semi-arid NW India using AquaCrop-OSPy and machine learning. The best-performing surrogates showed excellent generalizability and cross-district transferability, preserving the characteristic yield-irrigation response behavior.
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Badru et al. (2026) Integrating Vegetation Health Indices and Machine Learning for Early Prediction of Agricultural Drought
This study investigates the impact of climate change on water resources in the southwestern United States. The researchers used a combination of modeling and observational data to simulate future changes in precipitation and temperature patterns.
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Guo et al. (2026) Global hydrological drought diverges from atmospheric drying
This study provides a global observational assessment of hydrological drought evolution in approximately 19,000 catchments, comparing it with atmospheric drought trends over the past four decades. It reveals that while atmospheric drying is widespread, hydrological drought trends are highly heterogeneous and do not uniformly or proportionally transmit atmospheric drying signals into river flow.
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Zhu et al. (2026) Evaluation of the accuracy and response to recorded irrigation events of remote sensing and reanalysis soil moisture products in a typical irrigation district
This study comprehensively evaluated the accuracy and irrigation-associated response characteristics of seven microwave and reanalysis soil moisture products in the North China Plain using two in situ observation networks. It found that product suitability depends on the monitoring objective, with ERA5-Land performing best for continuous monitoring and ESA CCI Passive showing greater sensitivity to short-term irrigation-associated wetting.
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Moro et al. (2026) Proposal for using daily thermal amplitude to estimate reference evapotranspiration without relative humidity
This study proposes a new method to estimate dew point temperature (Tdew) using minimum temperature (Tmin) and daily thermal amplitude (TA), aiming to calculate actual vapor pressure (ea) and reference evapotranspiration (ETo) in data-limited scenarios.
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Adhikari et al. (2026) A 30‐Year Assessment of Temperature and Precipitation Indices Across Local Levels of Nepal
This study presents a nationwide assessment of climate extremes across all 753 Local Level Administrative Units (LLAUs) of Nepal during 1995–2024 using ERA5-Land data. The research reveals a pervasive warming signal and geographically differentiated risk profiles due to climate change.
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Luo et al. (2026) Characteristics and Mechanism of Water Vapour Resources in the Northern Slope of Kunlun Mountains
This study assesses the atmospheric water vapour and potential mechanisms in the northern slope of Kunlun Mountains using MERRA-2 data sets, revealing increasing trends in evapotranspiration, precipitation, and atmospheric water resources from 1980 to 2023.
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Secci et al. (2026) Hybrid physics–AI modeling in hydrogeology: a review of concepts, applications, and challenges
The paper reviews the integration of artificial intelligence (AI) and physics-based modeling in hydrogeology, highlighting the potential for hybrid approaches to reconcile data-driven and process-based models. The authors discuss various AI techniques, including theory-guided machine learning, physics-informed neural networks, and differentiable programming.
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Ding et al. (2026) Joint prediction of river discharge and suspended sediment load through a physics-informed capacity-supply decomposition framework
This study proposes a physics-informed capacity-supply decomposition (PCSD) framework for joint prediction of river discharge (RD) and suspended sediment load (SSL). The framework maintains high RD prediction accuracy while achieving the best point-estimate performance for SSL among evaluated models.
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Liang et al. (2026) Anthropogenic warming is projected to divert atmospheric rivers toward densely populated Asia
This study projects future changes in atmospheric rivers (ARs) over Asia using a high-resolution multi-model ensemble and multiple AR detection tools. The results reveal a dynamically inhibited yet thermodynamically enhanced activity under anthropogenic warming, with significant implications for population exposure to AR-induced extreme precipitation.
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Li et al. (2026) Scale-dependent responses of water use efficiency to precipitation variability in a rain-fed maize field of Northeast China
This study investigated the scale-dependent responses of ecosystem (EWUE), transpiration (TWUE), and intrinsic (IWUE) water use efficiency to precipitation variability in a rain-fed maize field in Northeast China over three contrasting hydrological years. It found that both increased and decreased precipitation reduced EWUE and TWUE, primarily due to disproportionate declines in gross primary productivity (GPP) relative to water loss, while IWUE exhibited contrasting responses, decreasing with increased precipitation but increasing with decreased precipitation.
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Zhang et al. (2026) Evaluating mobile drip irrigation as a water-saving alternative to center pivot irrigation for large-scale alfalfa production
This two-year field study compared mobile drip irrigation (MDI) and center pivot irrigation (CPI) for large-scale alfalfa production in water-limited conditions. MDI demonstrated potential as a water-saving alternative by reducing non-productive water losses, maintaining higher root-zone soil water content, and numerically increasing alfalfa yield and irrigation water productivity compared to CPI.
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Debsharma et al. (2026) Changing Antecedent Land‐Surface Conditions Can Amplify Future Heat Extremes Across New Zealand
This study investigates the contribution of land-surface moisture constraints to future heat-extreme intensification in New Zealand using dynamically downscaled CMIP6 projections. It finds that land-atmosphere feedbacks, specifically widespread reductions in evaporative fraction, substantially amplify extreme heat by approximately 1 °C to 2 °C across much of the North Island by the late 21st century, even in this maritime climate.
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Giardina et al. (2026) Groundwater regulates the spatial pattern of photosynthesis in the contiguous United States
This study investigates the role of groundwater in regulating photosynthesis in the contiguous United States, using satellite observations and model estimates. The results show that groundwater accounts for 48 to 101% of the effect attributed to aridity in modulating forest photosynthesis.
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Mammarella et al. (2026) Systematic review of thermal remote sensing for water status and evapotranspiration assessment in vineyard
This systematic review synthesizes the applications of thermal remote sensing in viticulture to assess vine water status and estimate Evapotranspiration (ET), highlighting its potential for efficient water management.
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Qiu et al. (2026) Physics-informed machine learning for cloud detection
This study introduces Fmask 5, a novel physics-informed machine learning (PIML) framework for cloud detection in Landsat and Sentinel-2 imagery, achieving overall accuracies of 93.46% for Landsats 8–9, 92.50% for Landsats 4–7, and 95.38% for Sentinel-2.
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Huang et al. (2026) An integrated vine-copula-based modeling framework for risk assessment and interval chance-constrained optimization of the water-energy-food nexus in a coastal watershed
This study develops a vine-copula-based framework to assess correlated resource risks in agricultural water-energy-food nexus (WEFN) systems and optimize decision-making for sustainable food production.
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Lutonsky et al. (2026) Deep learning with upstream-aware terrain representations and rainfall adapter for generalizable pluvial flood prediction
This study investigates the design of deep learning-based flood surrogates that can achieve robust spatial generalization across unseen terrains and modified topographies without retraining. The authors propose a two-stage training strategy with an attention-enhanced U-Net model and a rainfall adapter module to enable efficient adaptation to previously unseen rainfall events.
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Matin et al. (2026) Hydrologically constrained genetic programming for interpretable rainfall–runoff model discovery: A process–informed machine learning approach
This study introduces HydroXGP, a process-informed genetic programming framework for discovering basin-specific conceptual rainfall–runoff model structures. It automates structure discovery while keeping candidate hypotheses interpretable and grounded in hydrologic process components.
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Nandikanti et al. (2026) Unearthing dominant controls of precipitation – streamflow interaction using information theory and explainable AI in large-sample hydrology
This study uses information theory and explainable AI to quantify the strength of precipitation-streamflow interaction across the Conterminous United States. The results reveal that P-Q interactions vary systematically across hydroclimatic settings and are governed by region-specific combinations of climatic, hydrologic, and physiographic factors.
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Zhao et al. (2026) Global Characterization of Precipitation Peaks in the Seasonal Cycle
This study introduces a global framework to identify the number of precipitation peaks (NPP) during rainy periods using daily precipitation data and harmonic seasonal fits. It reveals that multimodal precipitation seasonality is widespread, covering approximately 60% of global land, establishing NPP as a complementary metric for diagnosing precipitation seasonality.
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Yang et al. (2026) Systematic Overestimation of Global Peak Runoff Synchronization in CMIP6 Models
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
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Sarkar et al. (2026) Temporal Inflow Dynamic Estimation Network (TideNet): A novel physics-informed graph neural network for daily reservoir inflow forecasting
This study introduces TideNet, a novel physics-informed graph neural network, to improve daily reservoir inflow forecasting by integrating physical laws and capturing hierarchical spatial dependencies. TideNet achieves a 6% improvement in forecasting accuracy over leading spatiotemporal GNNs and 13% over temporal baselines, demonstrating robustness under sparse and noisy conditions.
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Guo et al. (2026) Global patterns and trends in event-scale runoff generation reveal disproportionate declines in arid catchments
This study presents the first uncertainty-aware global synthesis of event-scale runoff generation, revealing that event runoff coefficients are substantially lower than long-term values and vary systematically with aridity, with significant declines in runoff occurrences and efficiencies disproportionately concentrated in arid regions.
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Haiyang et al. (2026) From ungauged to poorly gauged sites: A scalable deep learning framework for reference evapotranspiration forecasting through regional generalization and transfer learning
This study develops a scalable deep learning framework for reference evapotranspiration (ET0) forecasting through regional generalization and transfer learning. The framework adapts to local data availability and improves ET0 forecasting performance at ungauged or poorly gauged sites.
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Xin et al. (2026) Detecting irrigation signals from SMAP L3 and L4 soil moisture: A case study in California’s Central Valley
This study develops and demonstrates an approach to identify periodic irrigation signals using the differences between SMAP Level 3 Enhanced retrieval and Level 4 data assimilation soil moisture products, showing feasibility in California's Central Valley and validating the estimated signals.
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Mahmoodi et al. (2026) Integrating coupled surface–subsurface modelling and field measurements in a degraded fen: water-balance dynamics and a framework for evaluating rewetting measures
This study integrates coupled surface–subsurface modeling with field measurements in a degraded fen peatland to analyze water-balance dynamics. It provides a robust framework for evaluating rewetting measures by accurately quantifying hydrological fluxes and their sensitivity to climate and land use.
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Zhang et al. (2026) Freeze–thaw processes influence shallow groundwater recharge sources and pathways in the Qinghai Lake Basin: insights from water isotopes
This study quantifies the dynamic characteristics of shallow groundwater recharge sources and pathways in the Qinghai Lake Basin during freeze-thaw periods using water isotopes, revealing that soil water is the dominant recharge source and that freeze-thaw processes regulate water storage and transport.
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Doane‐Solomon et al. (2026) Moisture Budgets and Circulation Analogs: Diagnosing Dynamic and Thermodynamic Precipitation Change
This study compares moisture budget analysis and constructed circulation analogs to disentangle dynamic and thermodynamic drivers of precipitation trends in Central Chile, proposing a combined framework to reduce dynamic contamination in thermodynamic projections.
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PAULO MONTEIRO et al. (2026) Integrated Surface Water and Groundwater Management in the Algarve (southern Portugal) the Role of Numerical Modelling in Regional Water Resources Planning
This paper presents a regional numerical model for conjunctive surface water and groundwater management in the Algarve region, Portugal, as a decision-support tool for water resources planning. The study evaluates the effectiveness of this approach in optimizing water allocation and protecting coastal aquifers against seawater intrusion.
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Zhu et al. (2026) AnytimeHydroTwin: A deployment-valid inference-orchestration layer for AI-driven hydrological digital twins
This paper introduces AnytimeHydroTwin, an inference-orchestration layer for AI-driven hydrological digital twins. The layer combines a high-fidelity (HF) neural model with lag-1 persistence on a per-timestep basis and determines when to invoke the HF model or defer to a cheap persistence baseline.
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Al‐Yaqoubi et al. (2026) Bioengineering Method and Hydro-pedological Technique to Control Shallow Water Table Level in Urbanized Areas
This study investigates the impact of climate change on water resources in the southwestern United States. The research reveals that projected changes in precipitation and temperature will lead to significant alterations in streamflow patterns.
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Béland et al. (2026) Consideration of radiation absorption by stems in forests for microclimate modeling
This study modifies the radiative transfer component of the CanVeg2 multilayer canopy model to include radiation absorption by woody stems. The modified model is evaluated against a 3D ray tracing radiative transfer model and tower observations of albedo in four broadleaf forests.
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Kim et al. (2026) Deep learning in reduced-order space accelerates high-resolution multi-model climate downscaling
This study presents a lightweight deep-learning downscaling framework that generates high-resolution sea surface temperature (SST) projections within minutes on standard CPU-based systems. The framework operates in a reduced-dimensionality space by learning relationships between principal component (PC) time series derived from coarse-resolution climate model output and high-resolution dynamically downscaled SST.
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Guo et al. (2026) Heatwaves drive rapid snowmelt and disrupt streamflow in the western United States
This study investigates the impact of heatwaves on daily snowmelt rates and subsequent river discharge in the western United States. It finds that heatwaves significantly accelerate snowmelt rates, with lasting effects for 3 to 5 days after the event.
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Guo et al. (2026) Spatiotemporal dynamics and driving forces of water resource, socioeconomic, and ecosystem coupling in Xinjiang's oases
This study investigates the spatiotemporal dynamics and driving forces of water resource, socioeconomic, and ecosystem coupling in Xinjiang's oases from 1999 to 2023. The results show that the average coupling coordination degree is 0.542, indicating a barely coordinated state.
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Aker et al. (2026) Impact of climate forcing time step in an ice-sheet firn model
This study investigates the impact of climate forcing time step on firn models, specifically focusing on the IMAU-FDM model. The results show that increasing the climate forcing time step leads to more pore space in the firn layer, with a significant increase in areas with limited melt and high accumulation.
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Asfaw et al. (2026) Calibration using downscaled and bias-corrected satellite soil-moisture data can improve watershed model representation of soil-moisture variability
This research evaluated whether calibration using downscaled and bias-corrected satellite soil moisture improves prediction of field-scale soil moisture relative to conventional streamflow-based calibration. The study found that leveraging downscaled satellite soil moisture data substantially improved estimation of temporal soil moisture variability without affecting model streamflow performance, especially with multi-objective calibration.
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Khedhaouiria et al. (2026) The Canadian Surface Reanalysis (CaSR) v3.2 precipitation dataset: a 45-year high-resolution analysis for North America (1980–2024)
This study introduces and evaluates the Canadian Surface Reanalysis (CaSR) v3.2 precipitation dataset, a 45-year high-resolution product for North America that integrates surface observations with a numerical weather prediction background. The new version demonstrates substantial improvements in precipitation estimates, particularly in data-sparse regions, offering a reliable resource for hydrological and climatological studies.
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León‐FonFay et al. (2026) A country-level storyline attribution of global warming on heat-related extremes in South America
This study uses a storyline approach to attribute global warming to heat-related extremes in South America. The results show that under pre-industrial conditions, few countries would have experienced heat extremes, but with continued warming, many countries will experience increasingly persistent heat and drought conditions.
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Gupta et al. (2026) Balancing subsurface complexity and model simplicity: How much spatially distributed field data is needed to simulate streamflow in a subalpine critical zone
This study investigates the representation of subsurface heterogeneity in a critical zone hydrological model using geophysical data and evaluates its impact on streamflow simulation. The results show that incorporating geophysical measurements enhances subsurface characterization and water flux estimation, even when using a simplified subsurface model.
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Raghuvanshi et al. (2026) Improving NCMRWF Unified Model Rainfall Forecasts over India through IMERG-Calibrated IMDAA Reanalysis
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Vooda et al. (2026) A novel high-resolution satellite imagery approach for surface water detection in the United States
This study investigates the impact of climate change on water resources in the southwestern United States. The researchers used a combination of modeling techniques to simulate future changes in precipitation and temperature patterns.
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Stuivenvolt-Allen et al. (2026) The North American CORDEX-CMIP6 WRF evaluation run: comparing historical simulations from 25 km to convection-permitting scales
This study evaluates the performance of a high-resolution regional climate simulation for North America (NA) as part of the Coordinated Regional Downscaling Experiment (CORDEX). The new NA-CORDEX CMIP6 simulation at 12 km resolution is compared to an earlier NA-CORDEX CMIP5 simulation (25 km) and a convection-permitting CONUS-404 simulation (4 km).
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Asare-Ansah et al. (2026) Mapping agricultural dynamics in displacement-affected landscapes using satellite foundation model embeddings
This study presents a high-resolution assessment of cropland dynamics across refugee settlements in northern Uganda using satellite foundation model embeddings and machine learning, providing quantitative evidence for land-use planning and humanitarian interventions.
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Wang et al. (2026) Extending daily river discharge records across China using satellite-derived river widths
This study extends daily river discharge records across China using satellite-derived river widths, reconstructing and extending daily river discharge observations for 310 gauges from 1990 to 2024.
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Zhong et al. (2026) Spatially heterogeneous pathways linking atmospheric dryness to deep soil moisture
This study investigates the relationship between atmospheric vapor pressure deficit (VPD) and soil moisture across different depths using multiple global datasets. The research reveals spatially heterogeneous patterns of land-atmosphere coupling, with VPD influencing deep soil moisture independently of surface soil moisture in certain regions.
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Khan (2026) Emerging Hydroclimatic Risks in Pakistan: Regional Projections of Precipitation Characteristics Under CMIP6 Scenarios
This study projects future precipitation patterns in Baluchistan, Pakistan, using CMIP6 multi-model ensembles under SSP2-4.5 and SSP5-8.5 pathways, finding increased monsoon precipitation across all regions, heterogeneous spatial-temporal trends, and varying precipitation variability, with implications for water management and agriculture.
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Khoie et al. (2026) Characterising runoff processes for Australia: insights from a parsimonious rainfall-runoff event identification method
This study introduces the Robust Variance-based Event Identification Method (RVEIM), a parsimonious and robust approach for identifying rainfall-runoff events. It demonstrates RVEIM's superior performance in reducing uncertainty compared to existing methods and provides the first comprehensive characterization of rainfall-runoff event characteristics across 467 Australian catchments, revealing a strong climate gradient in runoff coefficients.
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Ruf et al. (2026) A fluvial flood risk model for quantifying the benefit of mitigation measures under uncertainty
This paper presents a dynamic probabilistic flood risk model designed for large-scale, uncertainty-aware flood risk assessments to support decision-making on mitigation measures. The model efficiently couples flood process components and distinguishes between aleatory and epistemic uncertainties, demonstrating its ability to quantify the risk-reduction potential of mitigation measures on the Bavarian Danube.
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Heldmyer et al. (2026) Predicting streamflow drought in the conterminous United States using machine learning and a donor-gage approach, 1982–2020
This study uses machine learning and a novel donor-gage approach to predict streamflow drought across the conterminous United States (CONUS) from 1982 to 2020. It identifies key meteorological drivers and physiographic characteristics influencing drought propagation, demonstrating that the donor-gage method provides comparable prediction performance to at-site models, particularly in ungaged locations.
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Zhuo et al. (2026) A physically constrained multivariate bias correction framework for projecting compound drought and heatwave risk in the Yangtze River Basin
This study developed a physically constrained, dependence-preserving multivariate bias-correction framework (integrating CycleGAN, BiLSTM, and QDM) to jointly correct daily precipitation and temperature from 13 CMIP6 models. The framework improved the representation of compound drought and heatwave (CDHW) dependence and historical spatial patterns, revealing that projected CDHW occurrence depends critically on the heatwave threshold definition.
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Semper et al. (2026) Sensitivity of Consecutive‐Dry‐Day Trends to Trace Loss in Matched Precipitation Archives
This study evaluates how different data provider versions of the same precipitation gauges affect the Consecutive Dry Days (CDD) index, revealing that the inconsistent handling of trace precipitation can create artificial trends in drought duration.
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Pierrat et al. (2026) OCO‐3 Meets ECOSTRESS: Insights Into Ecosystem Diurnal Water‐Use Efficiency From Co‐Located Solar‐Induced Fluorescence and Thermal Observations
The study evaluates the ability of the newly created ECOCO3 dataset, which harmonizes OCO-3 and ECOSTRESS satellite observations, to reproduce ecosystem water use efficiency (WUE) dynamics observed at FLUXNET sites.
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Doulgeris et al. (2026) Estimating rainfall–runoff model parameters for ungauged basins: application of NAM model to a Mediterranean water district
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Lee et al. (2026) Understanding the physical mechanism of record-breaking 2025 Korea-Japan heatwave
This study investigates the 2025 record-breaking heatwave in the Korea–Japan region, attributing its occurrence to a narrow midlatitude-confined thermal forcing. The findings highlight the importance of understanding regional air–sea coupling and thermal forcing structure for anticipating future heatwave risk.
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Trok et al. (2026) Global Emissions Since the Paris Agreement Have Intensified Europe's Recent Heatwaves
This study uses a generative machine-learning model to link recent greenhouse-gas emissions to changes in extreme weather events, specifically summer temperature extremes in Europe.
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Xu et al. (2026) Concurrent Eurasian Heatwaves Will Intensify Beyond the Mean Warming
This study investigates the intensification of concurrent heatwaves over Europe and North China, revealing that future events will be more severe than previously projected. The research highlights the importance of considering compound risks in climate projections.
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Zou et al. (2026) Linking agricultural drought to compound hot–dry risk: A process-based compound index for crop and water management in the North China Plain
This study develops a Copula-based Compound Hot-Dry Index (CHDI) to characterize compound hot-dry events in the North China Plain. The CHDI integrates meteorological drought, agricultural drought, and high-temperature anomalies into a unified probabilistic framework.
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Shahi (2026) Atmospheric drivers and modeling challenges of Mediterranean flood-inducing precipitation events: Insights from high-resolution ICON-NWP simulations
This study uses high-resolution ICON-NWP simulations to analyze the atmospheric drivers of two catastrophic Mediterranean heavy precipitation events (HPEs) in southern France, demonstrating the added value of convection-permitting models in representing localized precipitation while highlighting persistent challenges in accurately reproducing precipitation extremes.
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Zhang et al. (2026) Springtime Soil Moisture Teleconnections Drive Summertime Warming in the Western United States
This study uses an ensemble of soil moisture depletion experiments within CESM2 to demonstrate that reduced March surface soil moisture in the Southwest US causes positive May-June temperature anomalies across the Western US and precipitation anomalies in the Northwest, consistent with observations. It identifies land-atmosphere coupling and subsequent hemispheric-scale circulation changes as the mechanism for this seasonal teleconnection.
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Jia et al. (2026) Calibrating DSSAT–CROPGRO–Tomato to optimize stage-specific deficit irrigation for processing tomato across hydrological year types
This study calibrated and applied the DSSAT–CROPGRO–Tomato model for processing tomato under drip irrigation in Xinjiang, China, integrating field experiments, long-term hydrological year classification, and stage-specific deficit irrigation scenarios. It found that optimal deficit irrigation strategies are highly dependent on hydrological year type, recommending different regimes for wet/normal versus dry years to balance water saving and yield stability.
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Mohan et al. (2026) Evaluation of Temporal Fusion Transformers for Streamflow Forecasting: Effects of Input Configuration and Cross-Basin Performance
This study investigates the impact of climate change on global water resources using a comprehensive modeling framework. The research reveals that future changes in precipitation and evaporation patterns will significantly alter regional water availability, with some areas experiencing severe droughts while others face increased flooding.
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Cheng et al. (2026) Stand-density optimization could substantially increase aboveground biomass in China’s planted forests without land expansion
This study investigates the potential for increasing aboveground biomass in China's planted forests by optimizing stand density. The results show that a 8.84 Pg increase in biomass is possible through targeted thinning and enrichment planting, without expanding forest area.
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Dumitrescu (2026) A deep learning framework for gridding daily climate variables from a sparse station network
This study evaluates a deep learning framework, the Spatial Multi-Attention Conditional Neural Process (SMACNP), for gridding daily climate variables from a sparse station network in Romania. The SMACNP model outperforms Regression Kriging (RK) for both temperature and precipitation.
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Huang et al. (2026) Physically constrained machine learning improves radiation, clouds, and albedo forecasts globally
This study presents FuXi-RTM, a physics-guided machine learning model that improves radiation, clouds, and albedo forecasts globally by incorporating a differentiable radiative transfer model. The results show that FuXi-RTM outperforms state-of-the-art physics-based models for shortwave radiation and cloud-related variables.
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Wu (2026) Blauwgroene oplossingen ter vermindering van overstromingen en droogtegevolgen in urbane gebieden
This study evaluates the long-term hydrological performance of Blue-Green Infrastructure (BGI) measures in mitigating pluvial flooding and groundwater drought under future climate conditions. The research demonstrates that extensive BGI networks can effectively balance flood risks and groundwater retention.
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Zhang et al. (2026) Coupling root-zone osmotic monitoring with dynamic behavioral tariffs: A spatiotemporal eco-hydroeconomic allocation framework for salinity mitigation and groundwater resilience
This study presents a spatiotemporal eco-hydroeconomic allocation framework that couples root-zone osmotic monitoring with dynamic behavioral tariffs to mitigate salinity and groundwater resilience in arid irrigation systems. The framework reduces peak root zone salinity by 37–45% relative to static scheduling across contrasting soil textures, while maintaining farm gate revenue.
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Luan et al. (2026) Stomatal conductance dominates land surface hydrological responses to rising CO2 concentrations in the Yellow River Basin
This study investigates the impact of rising CO2 concentrations on land surface hydrology in the Yellow River Basin using a process-based ecohydrological model. The results show that stomatal conductance reductions dominate the effects of CO2-driven vegetation physiological and structural changes on evapotranspiration and runoff.
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Chen et al. (2026) Grid-based optimization for irrigation and water quality synergies
This study developed a grid-scale simulation-optimization framework integrating SWAT, DSSAT, and a cellular automata-based irrigation model to assess water-saving and pollution-reduction synergies in the Sanjiang and Songnen Plains under current and future climates, finding that optimized irrigation significantly reduces both pollutant emissions and water use.
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Dai et al. (2026) Global Downstream River Flows and Continental Runoff: An Update to 2024
This study updates and analyzes monthly streamflow data for the world's 936 largest rivers from 1950–2024 to quantify variations and long-term changes in continental discharge, finding that most rivers exhibit large interannual-decadal variations but insignificant long-term trends, with regional patterns consistent with precipitation trends influenced by climate variability and external forcing.
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Jiang et al. (2026) Spatio-temporal evolution of late spring frost and associated yield reduction risk for winter wheat in China
This study developed a phenology-constrained framework to quantify the spatio-temporal evolution of late spring frost risk and associated yield reduction for winter wheat in China. It found that climate warming reshapes rather than removes late spring frost risk by altering phenology-climate overlap, shifting risk hotspots and increasing future losses for cold-sensitive cultivars, while cold-tolerant cultivars can significantly mitigate these losses.
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Fan et al. (2026) Bias-variance trade-off in radiative transfer model inversion drives uncertainty in leaf area index estimation
This study characterizes four common radiative transfer model inversion methods within a unified Bayesian framework, revealing that differences in their statistical principles lead to distinct bias-variance trade-offs, causing significant variations (30%–35%) in leaf area index (LAI) retrieval accuracy.
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Belfiore et al. (2026) Comparative assessment of two evapotranspiration models from earth observation data in arid agricultural regions
This study compares the thermal-optical fusion-based WaPORv3 ET product with the fully optical SW-S2 model, finding strong operational consistency for total evapotranspiration and canopy transpiration in semi-arid agricultural regions.
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Zheng et al. (2026) Propagation from precipitation extremes to root-zone soil moisture and attribute changes in dry and wet events: A global paired event analysis
This study investigates the propagation of precipitation extremes to root-zone soil moisture and attribute changes in dry and wet events globally. The research reveals that precipitation extremes do not always correspond to soil moisture extremes, and event attributes change depending on the sign of the anomaly.
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Chaturvedi et al. (2026) Harnessing Deep Learning Methods for Rainfall Downscaling and Projections Over India: A CMIP6 Based Assessment of Future Shifts
The study employs BiLSTM and GRU deep learning models to downscale CMIP6 precipitation data over India, demonstrating that BiLSTM significantly improves the spatial fidelity of rainfall projections and predicts a future shift of monsoon rainfall toward southern India.
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Olawoyin (2026) Long‐Term Evolution of Rainfall Extremes and Land–Atmosphere Interactions in the Volta River Basin Using Multi‐Source Satellite Observations
This study assesses rainfall extremes and land-atmosphere interactions in the Volta River Basin from 2000 to 2024, revealing pronounced spatial hydro-climatic contrasts but limited evidence for basin-wide temporal intensification of extremes.
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Ai et al. (2026) Physics-informed neural networks for hydrodynamic inversion and discharge estimation in river flows over complex bed topography
This study develops a physics-informed neural network (PINN)-based framework for hydrodynamic inversion and discharge estimation in river flows over complex bed topography under limited observations. The proposed approach can reliably estimate discharge and reconstruct hydraulic states even with sparse or no bed information available.
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Nayak et al. (2026) Projected Changes in Reference Evapotranspiration Across Indian Climate Regimes Using Bias‐Corrected CMIP6 Simulations
This study evaluates the historical accuracy and future projections of bias-corrected CMIP6-derived reference evapotranspiration ($\text{ET}_0$) across 10 benchmark stations in India, finding a general future increase in evaporative demand characterized by significant spatial and seasonal heterogeneity.
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刘全儒 et al. (2026) Depth-dependent predictive information in monitored soil moisture profiles: Contrasting roles of persistence, forcing, and HYDRUS-derived states
This study evaluates the incremental predictive value of HYDRUS-derived states in monitored soil moisture profiles. The results show that antecedent observed-state information (Persistence) is a strong benchmark for same-day SWC estimation, and HYDRUS-derived states do not provide a robust incremental accuracy gain in the tea-plantation profile.
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Ai et al. (2026) Stage-specific soil moisture regulation improves grain yield and water productivity of drip-irrigated winter wheat: Associations with stem carbohydrate dynamics and grain filling
This study investigated the effects of stage-specific soil moisture regulation on wheat growth, photosynthesis, yield formation, grain quality, and water productivity under drip irrigation in arid conditions. The results showed that using a higher irrigation-initiation threshold during reproductive growth was associated with sustained photosynthetic performance, more favorable stem carbohydrate dynamics, and improved grain accumulation.
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Wei et al. (2026) Antecedent soil moisture controls meteorological-to-agricultural drought triggering thresholds
This study developed an improved vine copula-based framework to quantify meteorological-to-agricultural drought triggering thresholds across mainland China by explicitly incorporating antecedent soil moisture. It found that drier antecedent conditions lead to higher triggering thresholds, making croplands more vulnerable, and identified land-atmosphere coupling as the dominant control on threshold spatial variability.
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Fernández‐Alvarez et al. (2026) The 2025–2026 Iberian Atmospheric Rivers Exhibited Moisture Transport Resembling the Early SSP5‐8.5 Climate Response
This study evaluates whether extreme landfalling atmospheric rivers (LARs) over the Iberian Peninsula between October 2025 and February 2026 exhibit characteristics consistent with early high-emission (SSP5-8.5) climate projections. The results indicate that these events produced moisture transport and precipitation levels that matched or exceeded those predicted under early high-emission scenarios.
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Li et al. (2026) Detecting diurnal dynamics of cotton leaf inclination angle under water-salt stress
The study developed a UAV-based analytical workflow called CAPLA to monitor the diurnal dynamics of cotton leaf inclination angle (LIA) under combined water and salinity stress. The results demonstrate that irrigation, salinity, and time significantly influence LIA, highlighting the necessity of dynamic structural monitoring over static canopy assumptions.
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Mobarrat et al. (2026) Beyond Traditional Hydrological Models: Transformer‐Based Deep Learning for Future Streamflow and Flood Risk Assessment in the Brahmaputra River Basin
This study presents an integrated framework combining process-based hydrological modelling with deep learning techniques to improve climate-driven streamflow prediction and flood risk assessment in the Brahmaputra River Basin. The Transformer model demonstrated superior predictive capability compared to traditional hydrological models.
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Brunner et al. (2026) The variability atlas: how internal climate variability affects the estimation of climate extreme indices
This study investigates the impact of internal climate variability on extreme temperature and precipitation events, using a 50-member ensemble from the MPI-ESM global climate model.
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Zhang et al. (2026) Influence of water network structure on regional water allocation under supply variability
This study investigates the impact of water network structure on regional water allocation under supply variability using a complex network-based framework. The results show that network structure strongly affects allocation performance and supply utilization under uncertainty.
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Chen et al. (2026) Contrasting Precipitation Responses to Ural Blocking over the Yangtze River Basin: Roles of Extratropical Evolution and Tropical Backgrounds
This study investigates the relationship between Ural blocking events and precipitation over the Yangtze River basin, revealing that wet and dry UB events have distinct circulation processes. The findings suggest that the joint configuration of extratropical evolution and tropical–subtropical circulation backgrounds determines the precipitation response.
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Houénafa et al. (2026) S-GRHyMoLAP: A stochastic extension of the GRHyMoLAP model with different diffusion mechanisms for probabilistic rainfall-runoff modeling
This study introduces a stochastic extension of the GRHyMoLAP model (S-GRHyMoLAP) with different diffusion mechanisms for probabilistic rainfall-runoff modeling. The proposed framework captures how perturbations in hydrological components contribute to flow dynamics and provides more robust uncertainty quantification.
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Zendrini et al. (2026) Air temperature partitioning of snow accumulation, erosion and melt: a regime shift occurring on Mt. Ortles (Eastern Italian Alps)
This study investigates the snow accumulation and erosion processes on Mt. Ortles (Eastern Alps) using a rare dataset of nivo-meteorological observations and the physics-based snow cover model SNOWPACK. The results show that wind erosion is the dominant ablation process at the study site, removing 21% of the snowfall.
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Jiang et al. (2026) Dampened Diurnal Cycles of Hot‐Wet Extremes in Urban Areas Contrast With Thermally Driven Daytime Stress in Rural Landscapes of Beijing‐Tianjin‐Hebei Region
This study investigates hot-wet extremes (HWEs) in the Beijing-Tianjin-Hebei region and reveals urban-rural contrasts in diurnal HWEs. The research highlights the importance of considering nighttime moisture conditions in heat-risk mitigation.
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Stebbins et al. (2026) Quantifying Projected Hydroclimate Changes per Degree of Global Warming over the Contiguous United States
This study uses thermodynamic global warming simulations to quantify projected changes in local hydroclimate characteristics across the contiguous United States as a function of degree of global warming. The results highlight significant changes in temperature, precipitation, and soil moisture.
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Kyriakidis et al. (2026) A new Earth Observation–based WRF configuration for urban regional climate simulations over Paris
This study presents a new Earth Observation–based WRF configuration for urban regional climate simulations over Paris. The results show that the use of EO-derived data to tailor urban canopy parameters improves model performance compared to default values.
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Akşan (2026) Climatic zoning of Türkiye under drought stress: a cluster analysis approach
This study investigates the impact of climate change on global water resources by analyzing satellite data and modeling simulations. The main finding is that climate change will lead to a significant decrease in freshwater availability in several regions, including Africa and South Asia.
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Li et al. (2026) Linking Extreme Precipitation to Pluvial Hydrometeorological Disasters in Southeast Asia Using a Long‐Term Event Database
This study develops a provincial-level data set integrating multi-source hazard reports with precipitation data to inform early-warning applications for pluvial hydrometeorological disasters in Southeast Asia. The research identifies empirical rainfall reference values associated with reported disaster occurrences.
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Wang et al. (2026) Warm-wet shift reshapes hydroclimatic synchronization networks across the Arid Area of Northwest China
This study investigates the impact of the warm-wet shift on hydroclimatic synchronization networks across the Arid Area of Northwest China (AANC). The results show that the transition period (1988-1996) exhibits extreme fragmentation of short-timescale hydroclimatic synchronization, while long-timescale networks remain relatively integrated.
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Li et al. (2026) A coupled flood forecasting framework of HYDRUS-1D and Xinanjiang models based on the transfer of physically-based soil moisture fields
This study develops a coupled flood forecasting framework (XAJ-H1D) by integrating the physically-based HYDRUS-1D model with the conceptual Xinanjiang model to improve initial soil moisture estimation. The framework demonstrates enhanced accuracy in event-based flood forecasting by transferring physically-based soil moisture fields, significantly reducing errors in flood volume and peak discharge.
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Kamnani et al. (2026) Co‐Occurring Weather Systems Vary by Atmospheric River Scale Across the United States
This study investigates the spatiotemporal overlap between Atmospheric Rivers (ARs) and other weather systems, such as fronts, low-pressure systems, and mesoscale convective systems. The research reveals that AR + Front combinations dominate in many regions of the conterminous U.S.
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Yoon et al. (2026) Assimilation of SWOT Water Surface Elevation Enhances Simulations of River Dynamics in the Ohio River Basin
This study investigates the impact of assimilating Surface Water and Ocean Topography (SWOT) water surface elevation data into a large-scale hydrodynamic model over the Ohio River Basin, demonstrating improved discharge estimates under real-world constraints.
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Kaur et al. (2026) Leveraging deep learning and kernel density representations for transformation-based regional flood frequency analysis
The study introduces a deep learning-based approach using kernel density representations to automate the selection of regional frequency distributions for flood frequency analysis, significantly reducing computational time while maintaining or improving estimation accuracy.
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Politti et al. (2026) Review and reframing of the operationalisation of the water planetary boundary for river basin planning
This study proposes a novel framework to operationalize the Water Planetary Boundary principles at the basin scale by integrating socio-ecological functions into water boundary assessment. The framework prioritizes Earth's stability and social equity.
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Mascolo et al. (2026) Rare events algorithm study of extreme double jet summers and their connection with heatwaves over the Northern Hemisphere
This study investigates the connection between extreme double jet summers and heatwaves over the Northern Hemisphere. The authors use a rare event algorithm to sample double jet structures in the CESM1.2 climate model, finding that persistent double jets are associated with long-lasting heatwaves.
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Maier et al. (2026) Undercatch corrected gridded precipitation data to improve hydrological modeling in high-alpine orography
This study proposes a method to correct undercatch errors in gridded precipitation data using Generalized Additive Models (GAMs) trained on undercatch-corrected station observations. The approach is applied to Austria and validated against hydrological modeling results.
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Fan (2026) A Physically Interpretable Spatial PDE Framework for El Niño Supports a Near-Critical Response-Mode Interpretation
This study recasts the recharge oscillator model as a spatially explicit strip model to better capture ENSO dynamics while maintaining physical interpretability. The results suggest that historical ENSO is more accurately described as a near-critical response mode rather than an intrinsic global instability.
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Bista et al. (2026) Understanding soil moisture dynamics and key drivers for single and mixed cover cropping systems using machine learning
This study investigates soil moisture dynamics in single-species and mixed cover cropping systems using machine learning. The results show that cover crop mixtures can improve water capture and utilization, and agronomy-guided machine learning can capture complex soil-plant-atmospheric interactions.
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Chen et al. (2026) Coupled drought–land use dynamics and agricultural resilience in mainland Southeast Asia
This study examines the coupled socio-ecological processes driving drought development and consequences in Mainland Southeast Asia, highlighting the need for coordinated investment in data integration, context-appropriate land and water interventions, and inclusive basin-scale governance.
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Bonan et al. (2026) Estimating the maximum mean precipitation in hothouse climates
This study presents an analytical model that estimates the maximum global-mean precipitation in climate models by approximating components of the surface energy budget using only temperature. The model reveals that the surface temperature at which precipitation peaks ranges from 300–360 K.
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Spiteri et al. (2026) Nonlinear economic damages from compound heat and drought events
This paper develops climate-augmented models to predict regional economic growth in Europe, combining standard economic indicators with high-frequency climate variables. The results show that climate predictors improve predictive accuracy in the agricultural sector, but have limited gains in other sectors.
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- et al. (2026) Enhancing resource efficiency and crop yields through stepwise progress in the Yellow River Basin
This study introduces a stepwise progress framework to improve water and fertilizer use efficiency while maintaining or increasing crop yields in the Yellow River Basin. The results reveal pronounced spatial differences in resource-use intensities among counties, indicating substantial potential for agricultural optimization.
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Su et al. (2026) An online spectral nudging-based correction system: improving physical model forecasts by incorporating large-scale circulations derived from machine learning models
This study presents an online spectral nudging-based correction system that improves physical model forecasts by incorporating large-scale circulations derived from machine learning models. The hybrid system combines the strengths of the FuXi model in forecasting circulation patterns with the advantages of the CMA-GFS in representing precipitation intensity and fine-scale details.
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OMI et al. (2026) A scalable physically based framework for large-scale GLOF simulation and downstream exposure assessment
This study presents a scalable, physically based framework for simulating glacial lake outburst floods (GLOFs) and their downstream impacts across large spatial domains. The framework is applied to Bhutan, revealing strong basin-scale heterogeneity in flood impacts.
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Li et al. (2026) Wildfire synchronicity across European regions is favored by specific weather regimes
This study analyzed synchronous wildfires across ten European regions from 2001 to 2020, attributing their seasonal occurrence and spatial extent to specific year-round weather regimes. It found that these regimes primarily modulate synchronous wildfires by driving warm and dry meteorological anomalies, suggesting an increased frequency of such events in a warming climate.
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Kerras et al. (2026) Economic performance of FAO-56 crop coefficient and Allen & Pereira approaches for irrigation scheduling in super-high-density olive orchards
This study compares the economic performance of two irrigation scheduling approaches: the FAO-56 crop coefficient approach and the Allen & Pereira approach. The results show that while the Allen & Pereira approach generates higher irrigation costs due to its greater estimated irrigation requirements, it does not significantly affect overall farm profitability.
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Li et al. (2026) Event-stage evidence reveals weakened vegetation recovery stability under multi-scale drought accumulation in the Yellow River Basin
This study evaluates how drought accumulation timescales reorganize vegetation response pathways in the Yellow River Basin during 2001–2022. The results show that long-duration drought events are associated with weaker post-loss recovery stability.
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Asfaw et al. (2026) Explainable AI as a diagnostic tool for analyzing spatiotemporal variability in simulated groundwater recharge: Application to a semi-arid river basin
This study develops machine learning (ML) models to predict groundwater recharge using explainable AI (XAI) as a diagnostic tool. The research focuses on the semi-arid Lower Arkansas River Basin in Colorado, USA.
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Leite et al. (2026) ENSO or the Atlantic: Which Actually Governs the Hydrological Vulnerability of the Caatinga?
This study challenges the traditional understanding of precipitation governance in the Caatinga biome, revealing that the Atlantic Ocean has a statistically dominant influence on regional water vulnerability modulation.
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Gelman et al. (2026) Downscaling of Temperatures Over the Eastern Mediterranean for the 21st Century
This study develops a statistical downscaling method to project daily temperature variability in Israel using global climate models, improving the accuracy of temperature projections at local scales.
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Prasain et al. (2026) Coupled climate–land use change impacts on the water–food nexus using the soil and water assessment tool plus (SWAT+): Application to the Condamine–Balonne River Basin, Australia
This study evaluates the impacts of climate-only (CC) and combined climate–land use change (CLUC) on water availability and agricultural productivity in the Condamine–Balonne River Basin (CBRB), Australia. The results show a progressive shift from blue-water availability towards greater atmospheric demand and green-water consumption, intensifying irrigation pressure, recharge constraints, and agricultural vulnerability.
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Aalstad et al. (2026) Evolving beyond collapse: an adaptive particle batch smoother for cryospheric data assimilation
This study presents an adaptive particle-based data assimilation scheme for cryospheric applications that leverages developments in importance sampling. The proposed approach combines the advantages of particle methods and iterative ensemble Kalman methods to improve resilience against ensemble collapse and enable early-stopping strategies.
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Liu et al. (2026) GloSVeT: a global 0.05° monthly mean surface soil and vegetation component temperature dataset (2003–2023)
This study presents GloSVeT, a global dataset of monthly mean surface soil and vegetation component temperatures from 2003 to 2023 at 0.05° spatial resolution. The dataset was generated using the FuSVeT framework, which integrates multi-temporal MODIS observations with ERA5-Land reanalysis data.
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Botetano et al. (2026) Large-scale climate drivers of extreme compound events in the Amazon from interannual to long-term timescales (1982–2024)
This study documents a coherent compound hydroclimatic mode dominating Amazonian interannual variability, introducing the Amazon Basin Compound Hydroclimatic Extremes Index (AB-CHEX) to identify dry–hot and wet–cold states. It reveals distinct seasonal ocean–atmosphere drivers—ENSO for the wet season and Caribbean–Tropical North Atlantic sea surface temperature for the dry season—and highlights the unprecedented nature of the 2024 dry–hot event.
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An et al. (2026) Signal-domain guided deep learning for gap-filling of XCO and XCH 4 : a masked spatio-temporal fusion of TROPOMI and GEOS-Chem (2019–2023)
This study proposes a novel signal-domain guided spatio-temporal fusion framework to generate daily global and regional continuous XCO and XCH4 products (2019–2023) at high resolution. The method effectively leverages complementary information from chemical transport modeling and frequency-domain representations.
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Reis et al. (2026) Improving flood forecasts: the combined impact of data assimilation and machine learning post-processing
This study assesses the combined impact of machine-learning-based post-processing, data assimilation, and calibration strategies on hourly streamflow forecasts for 687 catchments in metropolitan France. It finds that post-processing consistently improves forecast skill at short lead times, particularly for slow-response catchments, and can partially compensate for the absence of state updating, though it does not fully replace data assimilation.
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Dong et al. (2026) The weakest warming rate in spring over the mid-to-high latitude land in a warming climate
This study investigates the annual cycle of temperature changes in a warming climate over mid-to-high latitude land areas and finds that spring is the weakest-warming season. The weakened spring warming alleviates approximately one-quarter of the projected advance of spring phenology.
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Decoopman et al. (2026) Climatology and trends of extreme precipitation in France: evaluation of an explicit-convection regional climate model
This study evaluates the convection-permitting regional climate model AROME's ability to reproduce the climatology and trends of extreme daily and hourly precipitation in France (1959–2022). It finds the model valuable for daily extremes but highlights significant limitations in accurately representing the magnitude and spatial heterogeneity of convective hourly extremes and their trends.
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Seibert et al. (2026) Setting the bar: benchmarks for model performances in large-sample hydrology
This study develops and provides lower and upper benchmarks for hydrological model performance across 13 large-sample datasets, offering guidance on their computation and advocating for their broader use in assessing model performance relative to local hydroclimatic conditions.
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Kaur et al. (2026) Coupled surface-groundwater modeling of water stress-triggered irrigation and climate change effects on maize production in Southwestern Kansas
This study evaluates deficit irrigation as an adaptation strategy using the coupled surface–groundwater model SWAT + gwflow in the Sunset Memorial watershed of southwestern Kansas, to quantify the effects of different irrigation practices on maize yield and groundwater decline.
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Liu et al. (2026) Deep learning trained on high-fidelity climate model simulations extends the skillful prediction of Northern Tropical Atlantic SST anomalies
This study introduces a deep learning framework based on convolutional neural networks (CNNs) to predict Northern Tropical Atlantic SST anomalies. The CNN is trained on CMIP6 models that realistically capture the ENSO–NTA teleconnection, and it extends effective predictions to 8 lead months.
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Shao et al. (2026) A knowledge-guided machine learning framework for cross-scale wheat harvest monitoring via sample augmentation
This study proposes a Knowledge-Guided Machine Learning (KGML) framework that integrates multi-satellite Earth observation data to monitor wheat harvest from the field to regional scales with high accuracy. The framework combines spectral knowledge rules with machine learning models to generate high-confidence augmented samples and estimate harvest dates.
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Houssou et al. (2026) Spatial pattern regression for meteorological fields interpolation
This paper introduces Spatial Pattern Regression (SPR), a data-driven method that reconstructs gridded meteorological fields by combining spatial information extracted from high-resolution regional climate model simulations with station observations. SPR outperforms baseline interpolation methods, particularly under sparse network conditions.
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Peng et al. (2026) Linkage in the Diversity of Atmospheric Rivers: A Global Perspective on Multi‐Framework Classification
This study provides the first global quantification of statistical linkages among atmospheric river (AR) classifications, revealing a unified "wind-driven, cyclone-associated" archetype. The research integrates fragmented understandings of AR diversity, offering a consolidated reference for forecasting and climate modeling.
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Llasat-Botija et al. (2026) Storm Gloria (2020): coexisting types of compound flooding in the West Mediterranean Region
This study examines the coexisting types of compound flooding in the West Mediterranean Region during Storm Gloria (2020), a paradigmatic example of multivariate, spatially compound, and temporarily compound events. The analysis combines daily precipitation, maximum wind gusts, and significant wave height with municipality-level compensation data to quantify the probability of multivariate and spatially compound events.
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Lu et al. (2026) Water availability controls growing season length divergence between karst and non-karst forests
This study investigates the length of the growing season (LOS) in subtropical karst forests and non-karst forests across southwestern China. The results show that LOS has advanced significantly in both forest types, but at a faster rate in karst forests.
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Berthou et al. (2026) Km-scale regional coupled system in the Northwest European shelf for weather and climate applications: RCS-UKC4
This study presents an updated version of the Regional Coupled Suite – UK Coupled domain version four (RCS-UKC4), which brings corrections and enhancements relative to the UKC3 system. The RCS-UKC4 configuration includes implementation of the new Regional Atmosphere and Land configuration (RAL3.3) alongside updates to all model components relative to previous releases.
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Gong et al. (2026) Stage-specific water-heat mismatches and cumulative risk of extreme hydroclimatic events to double-season and mid-season rice yields in subtropical China
This study evaluates the impacts of extreme hydroclimatic events and stage-specific water-heat mismatches on double-season and mid-season rice systems in subtropical China. A novel Stage-specific Rice Water Deficit Index (SRWDI) was introduced to precisely capture the intensity of hydro-thermal imbalances during specific phenological windows.
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Yang et al. (2026) Compound Meteorological and Hydrological Hot‐Dry and Hot‐Wet Extremes Over Global Land Areas: Spatiotemporal Changes, Attribution, and Drivers
This study investigates the spatiotemporal variations and attribution of compound meteorological and hydrological hot-dry and hot-wet extremes across the globe during 1901-2019. The results show an increasing trend in the likelihood of compound extremes, with a higher frequency of hot-wet extremes.
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Li et al. (2026) Projecting spatial synchrony and socioeconomic risk of heatwaves and extreme precipitation in the future
This study projects the spatial synchrony and socioeconomic risk of heatwaves and extreme precipitation in the future under different climate scenarios. The results show that the proportion of land area affected by heatwaves and extreme precipitation events increases significantly under future warming, with stronger expansion under higher-emission scenarios.
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Kong et al. (2026) Combining AI and numerical models for improving short-medium range precipitation prediction
This study presents a hybrid approach to enhance short-to-medium-range precipitation forecasts by combining deep learning and numerical weather prediction (NWP) models. The Fast Precipitation Prediction (FPP) model outperforms the NCEP Global Forecast System (GFS) in predicting surface precipitation.
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Kim et al. (2026) Eurasian Summertime Intraseasonal Oscillation and its impact on compound hot-dry extremes
This study identifies the Eurasian Summertime Intraseasonal Oscillation (ESISO) as a distinct mid- to high-latitude contributor to Eurasian compound hot-dry extremes. The ESISO exerts phase-dependent control on the probability of compound hot-dry extremes across the Eurasian continent.
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Petit-Laurent et al. (2026) The Future of Atmospheric Heatwaves in Chile Projected by a Regional Climate Model
This study uses a regional climate model to project changes in heat wave characteristics in three Chilean cities under high-emission scenarios, highlighting the expected increase in frequency and duration of heat waves by 2080.
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Lee et al. (2026) Attribution and projection of February extreme rainfall events in Southeast Africa
This study investigates the impact of greenhouse gas (GHG) forcing on extreme rainfall events in Southeast Africa (SEAF), using multiple Earth System Model (ESM) ensembles and numerical weather prediction (NWP) experiments. The results indicate that GHG forcing significantly increases the risk of such events.
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Wang et al. (2026) Optimizing evapotranspiration products by merging multi-source datasets with three no-prior-knowledge methods over China
This study proposes a data fusion framework for optimizing evapotranspiration (ET) products by merging four ET datasets using three fusion methods without prior knowledge. The results show that merged ET datasets generally demonstrate better performances than the original ET datasets.
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Chen et al. (2026) Simulating the impacts of utility-scale photovoltaic installations with a physically based coupled WRF-PV model
This study presents a physically based and fully coupled PV-ground-atmosphere scheme implemented in the WRF model to simulate the impacts of utility-scale photovoltaic installations on regional climate. The model was applied to northwestern China for the summers of 2018-2024.
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Jin et al. (2026) TPHH: a long-term (1901–2023) high-resolution (1∕30°) near-surface humidity dataset for the Tibetan Plateau generated via spatial downscaling based on hybrid-structure deep learning
This study generates a long-term high-resolution near-surface humidity dataset (TPHH) for the Tibetan Plateau using a hybrid-structure deep learning framework. The dataset covers 1901–2023 at a spatial resolution of 1/30° × 1/30°.
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Nagamoto et al. (2026) Widespread drought-driven declines in streamflows and water quality in the Upper Colorado River Basin during 1998-2022
This study investigates the cascading impacts of meteorological droughts on streamflow, water temperature, and specific conductance in the Upper Colorado River Basin from 1998-2022 using a data-driven framework. It finds that severe droughts reduced streamflows by 15% while increasing water temperature by 9% and specific conductance by 5%, with catchment attributes significantly influencing spatial variability in vulnerability and resilience.
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Fan et al. (2026) First vegetation optical depth product retrieved from FengYun-3D X-band observations
This study presents the first vegetation optical depth product retrieved from FengYun-3D X-band observations, demonstrating strong consistency with commonly used vegetation-related variables. The new product exhibits improved correlation with temporal dynamics compared to existing approaches.
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Nie et al. (2026) Diagnosing Forecast Error Propagation and Large‐Scale Dynamics of Weather Extremes With an AI Weather Model
This study proposes a true-state constraint method to diagnose error propagation in artificial intelligence (AI) weather models and explores its application to predict regional extremes. The approach effectively captures critical atmospheric processes responsible for extreme event development.
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Haitao et al. (2026) Improvement and testing of the SWAT model for simulating hydrological processes and non-point source pollution in the paddy-dominated polder
This study developed a modified SWAT model (SWAT-PP) to simulate hydrological processes and non-point source pollution in paddy-dominated polders, achieving improved performance compared to the original SWAT and paddy-adapted SWAT-MD models.
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Jian et al. (2026) Short-term drought forecasting in Iran using multi-source machine learning: an assessment of autoregressive, teleconnection-driven, and hybrid paradigms
This study provides a comprehensive comparative assessment at the national scale of Iran to determine the relative superiority or synergy of three competing paradigms in short-term drought forecasting: autoregressive (based on temporal memory), teleconnection-driven (based on large-scale climate drivers), and hybrid. The results show that the optimal model structure is highly location-dependent, with the hybrid approach prevailing in arid and semi-arid regions.
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Stone et al. (2026) Vegetation groundwater use drives streamflow declines in Colorado River headwaters
This study reveals that vegetation in the Colorado River headwaters draws on shallow stream-bound groundwater to sustain evapotranspiration during dry periods, leading to reduced summer streamflow. The research demonstrates that growing-season temperature, not just winter snowpack, regulates streamflow through groundwater-supported ET.
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Ssembajwe et al. (2026) Seasonal precipitation characteristics and dynamics assessment over Uganda based on CHIRPS3
This study characterizes Uganda's rainfall seasonality using a modified Dual-phase Seasonal Search Scheme and CHIRPS version-3 data from 1990 to 2024. The results reveal significant spatial gradients in rainfall magnitude and duration across the country.
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Avanzi et al. (2026) Impacts of Mediterranean snow droughts on mountain socio-ecohydrology
This study comprehensively investigates the multi-sectoral socio-ecohydrological impacts of snow droughts in 38 Italian mountain catchments over 13 years, revealing that warm-dry snow droughts lead to shortened snow seasons, reduced summer runoff, increased vegetation Gross Primary Production at higher elevations, and widespread water-supply restrictions affecting both lowlands and high-elevation mountain communities.
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Soto et al. (2026) Deep learning-based forest disturbance detection for Europe using Landsat time series
This study presents a deep learning-based approach for detecting forest disturbances across all forests in continental Europe using Landsat time series from 1985 to 2024. The 1D U-Net model outperformed the TempCNN and Random Forest models, achieving an F1 score of 0.81 in spatial disturbance detection.
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Mehr et al. (2026) Improving Meteorological Drought Forecasting Through a CPO ‐Tuned VMD ‐Liquid Neural Network
The study develops a hybrid CPO-VMD-LNN model for one-month-ahead meteorological drought forecasting, which significantly outperforms SARIMA, LSTM, and standalone LNN models in the Urmia Lake Basin.
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Manikanta et al. (2026) Understanding data sufficiency and temporal informativeness for hydrological model calibration in data scarce regions
The study develops a predictive framework using Classification and Regression Tree (CART) and Symbolic Regression to estimate the minimum amount and optimal timing of calibration data required for hydrological models based on physical catchment characteristics.
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Balali et al. (2026) Dynamic adaptive capacity of coupled agriculture-water systems under Climate Change: Evidence from the Western United States
This study develops a dynamic framework for the Adaptive Capacity Index (ACI) to assess long-run adaptation in coupled agriculture-water systems across western U.S. states over the period 1997–2022. The findings reveal substantial variation in adaptive capacity across the region, with ACI values ranging from 0.197 to 0.793.
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Lihe et al. (2026) Reconstructing cloud-free Sentinel-2 time series under complex degradations with state-driven spatio-temporal modeling
This paper proposes a novel approach for reconstructing cloud-free Sentinel-2 time series under complex degradations using state-driven spatio-temporal modeling. The proposed method achieves robust reconstruction in global scenarios.
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Yan et al. (2026) Assessment of agricultural drought resilience in China based on coupled drought propagation and recovery processes
This study proposes a logistic regression-based framework for identifying agricultural drought recovery thresholds in China, incorporating initial soil moisture conditions to quantify the meteorological drought conditions required for agricultural drought to recover to normal conditions.
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Shakya et al. (2026) A generalized three-dimensional manifold for the Budyko hypothesis: analytical derivation and structural stability in open hydrological systems
This study presents a three-dimensional extension of the Budyko framework, known as the ShakyaQX (SQX) manifold, which represents hydro-climatic partitioning in open hydrological systems. The SQX formulation shows improved performance over the classical closed-system approach for monthly-scale predictions in the Brahmaputra Basin.
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Qian et al. (2026) A benchmark dataset for half-hourly evapotranspiration estimation in China from 2000 to 2024
This study presents a benchmark dataset for half-hourly evapotranspiration estimation in China from 2000 to 2024. The dataset is based on observations from 50 ChinaFlux sites and uses an automated machine learning approach (AutoML) to fill gaps and prolong time series.
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Mohammadiigder et al. (2026) Disentangling The Contribution of Precipitation and Epistemic Uncertainties on Streamflow Estimates from Hydrologic Models in The Canadian Prairies
This study investigates the impact of precipitation uncertainty on streamflow estimates in the Canadian Prairies using a combinatorial approach. The results show that precipitation-induced streamflow errors depend strongly on model parameterization.
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Xu et al. (2026) Global drought shows no detectable recent acceleration under climate warming
This study investigates the acceleration of global drought under climate warming using multiple potential evapotranspiration (PET) formulations. The results show that Penman-type PET methods overestimate drought trends, while energy-based PET methods provide a more physically consistent assessment of drought dynamics.
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Zhan et al. (2026) Integrating SWOT data and machine learning for high-resolution river surface velocity retrieval in data-scarce headwater regions
This study integrates SWOT data and machine learning to retrieve high-resolution river surface velocity in data-scarce headwater regions of the Tibetan Plateau. The developed framework achieved strong performance in retrieving spatially continuous river velocity fields.
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Hatami et al. (2026) Climate change both increases and decreases winter snowmelt across North America
This study investigates the impact of climate change on winter snowmelt across North America using a multivariate statistical framework. The results show that winter snowmelt is projected to increase by 60% in northern latitudes under warmer and wetter conditions, while southern regions are expected to experience a decline in winter snowmelt.
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Zhou et al. (2026) A dual-threshold framework for antecedent wetness controls on hydrological connectivity and nitrate source dynamics in a subtropical agricultural watershed
This study introduces a dual-threshold framework to control antecedent wetness effects on hydrological connectivity and nitrate source dynamics in a subtropical agricultural watershed. The framework identifies two operational thresholds that partition the hydrologic record into Baseflow, Transition, and Storm regimes.
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Rusjan et al. (2026) Investigating streamflow dynamics in heterogeneous catchments through sensitivity function analysis
This study investigates the streamflow recession behavior within the Vipava River catchment in southwestern Slovenia, comparing karst spring-fed upper Vipava river and torrential Branica stream draining flysch areas. The results highlight the information content of discharge recession data for identifying spatial heterogeneity and distinct flow mechanisms in catchments with different storage characteristics.
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Ouaadi et al. (2026) Combined effect of different irrigation and fertilization scenarios on wheat and barley development and their C-band radar response in a semi-arid region
This study investigates the combined effects of various irrigation and fertilization scenarios on wheat and barley development and their C-band radar response in a semi-arid region of Morocco. It reveals that irrigation is the primary factor determining crop development and yield, while C-band radar data shows promising potential for monitoring crop growth and water stress.
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Chen et al. (2026) SWOT Observations Reveal Basin‐Scale Reservoir Operating Patterns
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Guo et al. (2026) Monthly dynamics of global surface water from 2015 to 2023
This study presents the High precision Global Monthly Surface Water dataset (HGMSW), a 10 m monthly surface water record from 2015 to 2023. The framework combines Sentinel 1 SAR and Sentinel 2 multispectral observations to achieve high accuracy in surface water mapping.
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Nevarez et al. (2026) Moist Thermodynamic Processes Leading to the Onset of Moist Tropical Heat Waves
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Du et al. (2026) Event-specific gross primary productivity anomaly responses to abrupt dry–wet hydrological transitions
This study examines the relationship between abrupt dry-wet hydrological transitions and gross primary productivity (GPP) anomalies in the Yangtze River Basin from 1982 to 2018. The results show that GPP responses depend on the combined effects of hydrological change and atmospheric constraints.
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Cooley et al. (2026) Lakes Modify the Magnitude and Timing of the Northern Hemisphere Terrestrial Cryosphere Radiative Effect
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Unknown (2026) Multilayer soil moisture deficit amplifies drought impacts on global ecosystems
The study finds that droughts are significantly more damaging when moisture deficits occur simultaneously across multiple soil layers, as this eliminates vertical hydrological buffering and threatens global carbon uptake.
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Cloutier-Gervais et al. (2026) Evaluation of the Canadian Regional Climate Model in Simulating Extratropical Cyclones Over Northeastern North America
This study evaluates the Canadian regional climate model (CRCM) v6 in simulating extratropical cyclones (ETCs) over northeastern North America, demonstrating that a 2.5-km convection-permitting configuration improves the representation of extreme precipitation and wind speed biases compared to a 12-km configuration.
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Llompart et al. (2026) Effect of water temperature on the performance of emitters in drip irrigation
This study investigates the impact of water temperature on pressure-compensating (PC) and turbulent-flow (T) drip emitters under controlled laboratory conditions. The findings show that water temperature significantly affects emitter discharge and hydraulic performance.
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Brown et al. (2026) Historical evolution of snowpack capacity to buffer rain-on-snow runoff in a large Columbia River headwaters basin
This study investigates the historical evolution of snowpack capacity to buffer rain-on-snow runoff in a large Columbia River headwaters basin. The results show that the snowpack's ability to buffer runoff has declined significantly over the past 72 years, with the largest declines occurring during the spring period.
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Adombi (2026) Ensembling or not? On the value of boosting and bagging for physics-aware machine learning in hydrology
This study investigates the value of ensemble machine learning strategies when applied to physics-aware machine learning models for hydrological prediction. The results show that ensemble strategies do not yield substantial improvements over a properly configured standalone PaML model.
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Jiang et al. (2026) A crucial role of land-atmosphere interactions in shaping subseasonal convective variability over the Maritime Continent in idealized model simulations
This study investigates the role of land-atmosphere interactions in shaping subseasonal convective variability over the Maritime Continent using idealized model simulations. The findings suggest that the coupling between land and atmosphere plays a critical role in reducing Madden-Julian Oscillation (MJO) convection over the region.
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Leng et al. (2026) Well-balanced lattice Boltzmann modelling of the depth-averaged age equation and its correlations with environmental indicators in a typical urban flood retention area
This study develops a well-balanced lattice Boltzmann method to simulate water age distribution in urban flood retention areas and analyzes its correlations with environmental indicators. Significant nonlinear correlations are found between water age and NH3-N, TP, and DO.
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Muccioli et al. (2026) Compound atmospheric phenomena driving Antarctic mean and extreme precipitation
This study assesses the combined effects of atmospheric phenomena on Antarctica's hydroclimate by applying a novel analysis framework to ERA5 reanalysis data. The results reveal that extreme precipitation events are rarely driven by single phenomena, but rather involve co-occurring systems.
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Petrova et al. (2026) ENSO-driven climatic and vegetation changes in Amazonia by the end of the century
This study investigates future climate and vegetation changes in the Amazon driven by El Niño-Southern oscillation (ENSO) using Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations. The results suggest that the southwestern region will become highly vulnerable to temperature and precipitation impacts linked to ENSO.
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Mouallem et al. (2026) Development of a high-resolution coupled SHiELD-MOM6-LM4 model – Part 2: Model overview, coupling technique, and evaluation of hydrological extremes during Hurricane Helene
This paper describes the development of a high-resolution coupled SHiELD-MOM6-LM4 model, which integrates the land model LM4 with the atmospheric model SHiELD. The new system is evaluated through a global simulation of Hurricane Helene's landfall in 2024.
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Ledari et al. (2026) Central Asian Future Dynamics of Coldwave and Cold Spell Under Changing Climate
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Jing et al. (2026) Atmospheric aridity drives functional reorganisation and resilience loss in global forests
A global analysis reveals a widespread divergence between forest productivity (GPP) and resilience, where rising GPP co-occurs with declining resilience in approximately 34% of global forests. Rising vapour pressure deficit (VPD) is the primary driver of this resilience loss.
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Zou et al. (2026) Spatio-temporal characteristics and driving factor identification of precipitation use efficiency: Based on machine learning and SHAP analysis
This study investigates the spatiotemporal characteristics and driving factors of precipitation use efficiency (PUE) in the cold regions of Northeast China using machine learning and SHAP analysis. The results show that PUE exhibits a gradient pattern with higher values in the northwest and lower values in the southeast, and is influenced by multiple individual factors and their interactions.
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Dai et al. (2026) Synergistic regulation of soil water and salt through mud blanket mulching and raised fields improves rainfed winter wheat yield on coastal saline-alkali land
This study investigates the synergistic effects of mud blanket mulching (MBM) and raised fields on soil water-salt dynamics, crop physiology, and grain yield in rainfed winter wheat under coastal saline-alkali conditions. The results demonstrate that MBM and raised fields jointly regulate the soil water-salt environment, leading to improved leaf water potential, enhanced photosynthesis, and promoted dry matter accumulation.
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Barlow et al. (2026) Rapid increase of climate extremes reveals new areas of concern in Amazonia
This study assesses climate change in Amazonia over 43 years (1981-2023), revealing rapid increases in extreme temperature and water deficit, particularly in the central-north region. The findings highlight the urgent need for adaptation measures to mitigate the impacts of climate extremes on socio-ecological systems.
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Qian et al. (2026) Asymmetric Relationship Between the East China September Precipitation and El Niño‐Southern Oscillation in the Decaying Phases Over the Past Three Decades
This study examines the asymmetric impact of decaying El Niño and La Niña events on September precipitation in East China, finding that La Niña leads to significant precipitation deficits while El Niño results in weak positive anomalies.
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Liu et al. (2026) The origins and physical drivers of moisture transport anomalies during monsoon season droughts in the Yangtze River Valley
This study investigates the origins and physical drivers of moisture transport anomalies during monsoon season droughts in the Yangtze River Valley. The research found that atmospheric moisture deficits over the YRV during MSDs stem from reduced precipitation and elevated evapotranspiration.
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Li et al. (2026) Spatiotemporal patterns of crop water productivity under changing water–salinity conditions in an arid irrigation district of the upper Yellow River basin
This study integrated remote sensing, multi-source datasets, trend analysis, and the GeoDetector model to investigate long-term crop-specific patterns of yield and water productivity (CWP) in a large arid irrigation district. The results showed that maize CWP increased markedly, reaching 3.44 kg m⁻³ in 2021, while sunflower CWP increased from 0.76 to 1.08 kg m⁻³ and wheat CWP remained largely stable.
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Maina et al. (2026) Retrospective and Near–Real-Time Surface Meteorology in the North American Land Data Assimilation System Phase 3 (NLDAS-3)
This study presents the North American Land Data Assimilation System Phase 3 (NLDAS-3), a high-resolution surface meteorology dataset available in both retrospective and near-real-time modes. NLDAS-3 integrates satellite-based precipitation products and reanalysis fields from NASA and partner agencies, providing finer spatial resolution and broader geographic coverage than previous phases.
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Fichtner et al. (2026) Effects of spatial soil moisture variability in forest plots on model parametrization and simulated groundwater recharge estimates
This study explores the effects of spatial soil moisture variability on model parametrization and simulated groundwater recharge estimates in forest plots. The results show that soil moisture variability at the plot with heterogeneous soil was greater, both horizontally and in depth, throughout the study period.
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Xue et al. (2026) Extraordinary Tibetan Plateau early-winter heating drove record-breaking winter precipitation in California and adjacent regions
This study reveals that anomalous early-winter heating over the Tibetan Plateau played a crucial role in driving extreme precipitation events in California during La Niña conditions. The results suggest that high-elevation terrestrial temperature anomalies can be used as a predictive source for subseasonal to seasonal winter extreme hydroclimate events.
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Castaño (2026) Aplicació d'algorismes evolutius per a optimitzar la distribució de l'aigua de reg sota condicions agroclimàtiques
This study proposes a Real-Coded Genetic Algorithm optimization model for irrigation planning and optimal crop area allocation under agroclimatic constraints. The model outperforms existing methods in certain scenarios.
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Samakinwa et al. (2026) Mean-state warming loads Germany’s extreme summer heat burden
This study develops an observation-based framework to quantify how global warming increases Germany's extreme summer heat burden and explores the scaling of this risk with global-warming level.
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Zhenghao et al. (2026) Integrated remote sensing retrieval of surface and root-zone soil moisture through physical mechanisms-guided machine learning
This study proposes an integrated retrieval framework for surface and root-zone soil moisture that combines physical mechanisms with machine learning models, demonstrating strong generalization and spatial extrapolation capabilities.
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Bechtold et al. (2026) A global hourly ISIMIP3 climate forcing dataset for impact modeling
This study generates a global hourly climate dataset by temporally disaggregating the Inter-Sectoral Impact Model Intercomparison Project Phase 3 (ISIMIP3) daily climate archives using the Temporal Disaggregation Tool (Teddy). The resulting dataset provides a harmonized foundation for more realistic sub-daily climate-impact modeling across sectors.
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Tumushabe et al. (2026) Enhancing river flow prediction accuracy in data scarce catchments using a time varying ensemble deep learning approach
This study evaluates the performance of a Time-Varying Dynamic Model Averaging (TV-DMA) ensemble for river flow prediction in two data-scarce tropical catchments. The TV-DMA approach achieved superior performance compared to Bayesian Model Averaging (BMA) and process-based HEC-HMS model.
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Liu et al. (2026) EGO: a global 0.05° hourly GPP dataset for monitoring diurnal photosynthesis dynamics
This study presents a new global hourly GPP dataset (EGO) developed using a causal-constrained machine-learning framework based on eddy-covariance site observations. EGO achieves high accuracy in reproducing diurnal photosynthetic dynamics and captures vegetation responses to extreme climatic events.
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Chen et al. (2026) A trend toward shorter duration and higher intensity of grassland degradation in Chinese oases
This study examines the evolution, ecological impacts, and potential drivers of degradation events in China's oasis grasslands during 1990–2021. The results show that degradation events affected 68.16% of oasis grasslands, with a shift towards shorter duration and higher intensity events.
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Faal et al. (2026) Accelerating retreat of northern hemisphere snow regimes
This study introduces a phenology-based framework to characterize the annual snow cover cycle and examines its changes over 1950–2025 in the Northern Hemisphere. The research reveals strong seasonal asymmetry in snow cover change, with accelerating degradation of six physically coherent snow regimes.
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Snir et al. (2026) Real-time control of rainwater harvesting in an urban catchment: a model predictive control approach under forecasting uncertainty
This study investigates the effects of favourable control policies on an urban catchment retrofitted with rainwater harvesting systems. A simulation-optimisation procedure is developed to generate flexible control policies that adapt to changing rainfall forecasts using Model Predictive Control (MPC) approach.
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Massari et al. (2026) Detecting Irrigation From Spectral Differences Between Satellite and Modeled Soil Moisture Across the Contiguous United States
The study presents a wavelet-based method to detect irrigation by analyzing spectral differences between Noah-MP model simulations and SMOS satellite soil-moisture observations across the contiguous United States.
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Justino et al. (2026) Hydroclimatic influence of atmospheric rivers on vegetation and wildfire susceptibility across South America
This study investigates the role of atmospheric rivers (ARs) in modulating precipitation and wildfire activity across South America. The results reveal widespread declines in AR frequency and AR-associated precipitation across much of the continent, particularly during March–May and June–August.
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Liu et al. (2026) Resolving the Physical Ambiguity of Passive Infrared Cloud Optical Thickness Retrievals With Microwave Observations
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Shalini (2026) Apparent recovery asymmetry across global reservoir records can be reproduced by index thresholds and persistence
This study investigates the duration of below-normal conditions in reservoir storage after the minimum is reached and proposes a new approach to treat this duration as an index diagnostic rather than physical refill time. The research uses satellite storage estimates for 6,819 reservoirs from 1999-2018.
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Lazzarin et al. (2026) P-FLOOD: a probabilistic framework for flood hazard mapping in levee-protected floodplains
This paper presents a probabilistic framework (P-FLOOD) for flood hazard mapping in levee-protected floodplains. The approach combines the probability of hydrological forcing and multiple levee failure scenarios to estimate the exceedance probability of flooding scenarios.
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Jover et al. (2026) SWOT Discharge Accuracy Benchmarked in South America
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Erfani et al. (2026) Isolating the contribution of local and non-local shortwave flux biases towards climatological sea surface temperature and precipitation biases within a coupled climate model
This study investigates the impact of replacing net shortwave flux biases in a coupled climate model on climatological sea surface temperature (SST) and precipitation biases. The results show that this approach reduces global mean SST bias but does not eliminate biases in all regions.
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Chin et al. (2026) Machine learning-enhanced satellite precipitation products: a global review of advances, biases and future directions
This study investigates the impact of climate change on global water resources by analyzing the effects of temperature and precipitation changes on river discharge. The results show that, under a high-emission scenario, global river discharge is expected to decrease by 20% by the end of the century.
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Guzinski et al. (2026) Towards a global actual evapotranspiration product for the Copernicus Land Monitoring Service
This paper describes the development and evaluation of a global actual evapotranspiration product for the Copernicus Land Monitoring Service. The product is based on two ET models: TSEB-PT and ETLook, which are driven by input data from Sentinel-3 satellites and other Copernicus products.
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Bernhardt et al. (2026) AIDspat: Introduction of a model framework to estimate spatio-temporal agricultural irrigation demands – A study area in Germany
This study introduces AIDspat, a new framework for estimating agricultural irrigation demand (AID) that combines a soil water balance model with irrigation scheduling. The model enables scenario-based calculations at low computational cost across scales, from individual field to national assessments.
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Jang et al. (2026) Integrating MODIS and SMAP data for spatially calibrated SWAT+ modeling in a forested watershed of South Korea
This study integrates MODIS and SMAP data for spatially calibrated SWAT+ modeling in a forested watershed of South Korea. The results show that satellite-guided calibration improves the representation of hydrograph dynamics and reduces parameter equifinality.
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Xu et al. (2026) A new adaptive approach for detecting crop sowing and harvesting dates from Sentinel-1 time series
This study proposes an adaptive Sentinel-1 Indicator Selection (ASIS) framework for detecting crop sowing and harvesting dates from Sentinel-1 time series. The framework integrates VH and CR signatures to reliably delineate crop growing seasons and detect CADs with high accuracy.
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Sheshadrivasan et al. (2026) GWSWEX v1.0: a dual-solver 1D unsaturated zone model for mass-conservative groundwater recharge and runoff computation in distributed hydrological modelling
This paper introduces GWSWEX (Groundwater–Surface Water EXchange), a vertically resolved, process-based UZ coupler designed to address the gap between integrated 3-D models and bucket-style conceptual models. The model is tested against HYDRUS-1D as a representative iterative implicit Richards-equation reference for vertical 1-D variably saturated flow at the column scale.
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Li et al. (2026) Self-similarity criticality of the world’s large river systems
This study investigates the self-similar criticality of global large river systems using a unified approach combining high-resolution geospatial analytics, energy-slope coupling models, and nonlinear dynamics theory. The research reveals that river networks converge at low-to-medium stream orders but diverge at higher orders, with a dominant range of 0.57–0.67 for the self-similar validity ratio (SVR) distribution aligning with the golden-ratio conjugate.
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Jaarsveld et al. (2026) Global hyper-resolution modeling of historical and future groundwater dynamics
This study presents a global hyper-resolution model (GLOBGM v1.1) that simulates past and future groundwater dynamics at 30âarcsec (~1 km) resolution on a monthly time step. The model is used to estimate historical trends in groundwater storage and project future changes under different climate and socioeconomic scenarios.
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Park et al. (2026) Development and testing of ensemble-variational data assimilation capabilities for radar data within JEDI coupled with FV3-LAM model
This study presents the development and testing of ensemble-variational data assimilation capabilities for radar data within the Joint Effort for Data assimilation Integration (JEDI) framework coupled with the FV3-LAM model. The refined reflectivity observation operator improves consistency with the Thompson microphysics scheme, leading to better analyses and short-range forecasts.
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Ma et al. (2026) Differences in compound drought–heatwave events between the atmosphere and soil during the growing season in the Huai River Basin: Insights from atmospheric evapotranspiration demand and surface energy distribution
This study investigates the differences in compound drought–heatwave events between the atmosphere and soil during the growing season in the Huai River Basin. The results show that both atmospheric and soil compound dry-hot trends intensified significantly from 1981 to 2025, with the soil exhibiting a more severe intensification trend.
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Wang et al. (2026) Sub-seasonal variability of extreme heatwaves in eastern China and its structural linkage to the Tibetan Plateau atmospheric heat source
This study examines the sub-seasonal variability of extreme heatwaves in eastern China and its structural linkage to the Tibetan Plateau atmospheric heat source. The research reveals that the TP AHS exhibits a month-dependent configuration structurally coupled with sub-seasonal heatwaves across eastern China.
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Wang et al. (2026) Spatiotemporal dynamics of reference evapotranspiration in China (1981–2020): Insights into its teleconnection with sea surface temperature patterns
This study investigates the spatiotemporal dynamics of reference evapotranspiration (ET0) in China from 1981 to 2020, revealing its teleconnection with sea surface temperature (SST) patterns.
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Gu et al. (2026) MEDAL: a mechanism-embedded dual-physics attention learning framework for snowmelt-driven runoff prediction in cold-region basins
This study proposes a Mechanism-Embedded Dual-Physics Attention Learning (MEDAL) framework for daily runoff prediction in snowmelt-driven cold-region basins, achieving improved performance compared to existing physics-guided hybrid modeling approaches.
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Shin et al. (2026) Incomplete recovery in compound extreme events under a CO 2 removal scenario
This study investigates the response of compound climate extremes to carbon dioxide removal (CDR) pathways using a CO 2 ramp-up and ramp-down experiment with CESM1. The results show that CDR does not lead to complete recovery of global mean temperature, precipitation, and wind speed by 2300.
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Wu et al. (2026) High-spatiotemporal-resolution GRACE(-FO)-constrained terrestrial water storage reconstruction captures the hydrological footprints of successive typhoons in southern mainland China
This study proposes a cascading framework that combines empirical-statistical reconstruction with machine-learning downscaling to generate high-spatiotemporal-resolution terrestrial water storage anomaly (TWSA) reconstructions. The framework is applied in the Pearl River Basin during four successive typhoons, revealing distinct patterns in reconstructed TWSA responses.
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Nash et al. (2026) Annual and Seasonal Rainfall Variability in Mozambique During the Nineteenth Century
This study presents the first annually and seasonally resolved documentary rainfall reconstructions for Mozambique from 1817 to 1900, revealing significant spatial-temporal variability and non-stationary relationships with ENSO.
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Kim et al. (2026) Sensitivity of Snow at Western US Ski Resorts to Climate Change
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Du et al. (2026) Projected intensification of compound river flooding and urban waterlogging in riverside cities of the Yangtze river basin
This study projects the intensification of compound river flooding and urban waterlogging in riverside cities of the Yangtze River Basin under future climate scenarios. The results indicate significant increases in flood frequency and severity by 2050.
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Chen et al. (2026) A revised Köppen climate classification reveals hydrothermal controls on eco-climate over the Qinghai–Tibet Plateau
This study introduces a revised Köppen–Geiger climate classification that incorporates hydrothermal constraints into the framework. The revised classification is tailored to the Qinghai-Tibet Plateau (QTP) and addresses limitations of classical climate classifications in complex high-elevation environments.
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Gai et al. (2026) Water-yield supply–demand relationships in Northwest China three water lines region: An InVEST–glacier assessment
This study developed an InVEST–Glacier framework to quantify water yield service supply and demand in Northwest China Three Water Lines region (TWL region) and identify their dominant driving factors. The results showed that glacier meltwater contributed more than 70% of water yield in most inland basins of the Tarim Basin.
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Lim et al. (2026) UrbanFloodBench: Bridging AI and hydrology through benchmarking of coupled 1D–2D urban flood surrogate models
This paper introduces UrbanFloodBench, a new open benchmark for coupled 1D–2D urban flood surrogate modelling. The benchmark combines a multi-catchment dataset, a variance-aware evaluation framework, and a global AI challenge to enable large-scale comparison of AI approaches.
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Pei et al. (2026) Modified daily standardized precipitation evapotranspiration index based on influence attenuation of water surplus and deficit
This study proposes a modified daily standardized precipitation evapotranspiration index (MDSPEI) that accounts for the attenuation of water surplus and deficit impacts on subsequent days. The MDSPEI outperforms the traditional DSPEI in capturing drought characteristics.
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Gan et al. (2026) Asymmetric Response of Land Precipitation Variability Under Carbon Dioxide Removal
This study investigates how precipitation variability responds to symmetric carbon dioxide removal experiments using CMIP6 models, revealing an asymmetric response across time scales.
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Adebayo et al. (2026) Sub-seasonal forecasting of cropland productivity anomalies using satellite soil moisture in water-limited environments
This study explores the potential of satellite root-zone soil moisture to provide skillful sub-seasonal forecasts of crop productivity anomalies in drought-prone croplands across Eastern and Southern Africa. The research demonstrates that incorporating SMAP RZSM into a ConvLSTM model can improve forecast accuracy, with the added value of RZSM concentrated in water-limited environments.
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Huang et al. (2026) The Role of Vegetation Clumping in Regulating Global Heat Fluxes Accounting for Land‐Atmosphere Coupling
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Shen et al. (2026) SMFS-RF: a knowledge-guided machine-learning method for crop phenology extraction from fine-resolution vegetation index data
The study introduces SMFS-RF, a knowledge-guided machine learning framework that combines shape model fitting with random forest regression to accurately extract eight key rice phenological stages from fine-resolution NDVI data.
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Wang et al. (2026) Modulation of High‐Temperature Events in the Pearl River Delta by Tropical Cyclones With Different Track Types
This study examines how different tropical cyclone (TC) track types influence temperature events in the Pearl River Delta (PRD) and finds that global warming will likely weaken both TC-induced warming and cooling effects due to a decrease in TC frequency.
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Deng et al. (2026) Effects of aboveground biomass and soil moisture drought on VOD-based global isohydricity estimates
This study evaluates the robustness of two VOD-based isohydricity metrics ($\sigma$ and $R_{slope}$), concluding that $R_{slope}$ is significantly more stable and less influenced by biomass dynamics and dataset variations than $\sigma$.
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Wu et al. (2026) Analysis of urban-scale typhoon precipitation characteristics and spatiotemporal patterns: a case study of Ningbo, China
This study analyzed urban-scale typhoon precipitation characteristics and spatiotemporal patterns in Ningbo, China. The results show that the impacts of typhoon-related rainfall in Ningbo are dominated by long-duration extreme rainfall, with current published IDF curves underestimating the extremes for such events.
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Zeraati et al. (2026) Rapid soil moisture depletion during flash droughts accelerates wildfire spread in the United States
This study investigates the relationship between flash droughts (FDs) and wildfire spread dynamics across the conterminous United States. The results show that rapid soil-moisture depletion during FD events accelerates wildfire spread, with a 1.3-1.8-fold faster early burning rate compared to non-FD conditions.
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PENG et al. (2026) A Global Surface Turbulence Heat Flux Dataset resolving tropical cyclones
This study presents a new global surface turbulence heat flux dataset (GHFD) that resolves tropical cyclones with improved accuracy. The GHFD is generated using satellite and reanalysis data, combined with the International Best Track Archive for Climate Stewardship (IBTrACS) best track data and TC-wind merged CCMP product.
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Zhang et al. (2026) From precipitation forecasts to optimal reservoir operation: an integrated downscaling-forecasting-operation framework for reservoir floodwater utilization
This study presents an integrated framework that combines downscaling, forecasting, and operation to optimize reservoir floodwater utilization. The framework was successfully applied in a case study of the Three Gorges Reservoir.
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Zhenyuan et al. (2026) Impacts of climate change on rain-on-snow events and spring agricultural drought in Northeast China
This study investigates the impacts of climate change on rain-on-snow events and spring agricultural drought in Northeast China. The results show that climate change has significantly increased the frequency and intensity of rain-on-snow events, leading to severe spring agricultural droughts.
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Yang et al. (2026) Synchronized declines in global Landsat cloud occurrence probability and area.
This study investigates the synchronized declines in global Landsat cloud occurrence probability and area using a 39-year dataset. The results show a significant decrease in cloud cover over land and ocean areas.
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Chalmers et al. (2026) Evaporative demand delays soil moisture accumulation timing during the dry-to-wet season transition in the Sahel
This study investigates the joint influence of precipitation and atmospheric demand on soil moisture dynamics during the dry-to-wet season transition in the Sahel. The results show that increased evaporative demand can offset increases in precipitation, delaying the emergence of plant-available water.
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Cai et al. (2026) Features of Regional Compound Dry‐Hot Events Over the Yangtze River Basin in Warm Season and Related Mechanisms
This study explores the characteristics and mechanisms of regional compound dry-hot events over the Yangtze River Basin during warm seasons from 1961 to 2024. The results show significant increasing trends in the coverage rate, intensity, and occurrence days of these events.
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Sela et al. (2026) Sensitivity of cloud structure and precipitation to cloud microphysics schemes in ICON and implications for global km-scale simulations
This study examines the sensitivity of cloud structure and precipitation to microphysics schemes in ICON (Icosahedral Non-hydrostatic modelling framework) and its implications for global km-scale simulations. The results show that cloud hydrometeor characteristics are sensitive to the microphysics scheme, with differences between single- and double-moment schemes affecting simulated clouds and precipitation.
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Huang et al. (2026) Multi‐scale flow analysis for scale‐aware urban canopy models
This study applies a multi-scale coarse-graining framework to investigate the impact of urban morphology on flow heterogeneity and assess the applicability of urban parameterisations at high-resolution numerical weather prediction (NWP) models. The results highlight the importance of considering the characteristic urban length-scale of morphology-dependent flow heterogeneity when developing scale-aware urban canopy models.
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Quan et al. (2026) Integrated crop residue return, film mulching, and irrigation enhance climate resilience of maize production and soil carbon storage in cold regions
This study evaluates the effects of climate change on maize production and soil carbon storage in cold regions. The authors used the SPACSYS model to simulate the responses of maize yield and SOC dynamics to projected climatic conditions during 2021–2060 (near future) and 2061–2100 (far future) under Shared Socioeconomic Pathway 2–4.5 (SSP245) and Shared Socioeconomic Pathway 5–8.5 (SSP585). The results show that maize yield under local management increased by 11.71% and 15.96% in the near future relative to the baseline period (1981–2020) under the SSP245 and SSP585 scenarios, respectively.
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Lang et al. (2026) A multi‐scale loss formulation for learning a probabilistic model with proper score optimisation
This study explores the impact of a multi-scale loss formulation on training probabilistic machine-learned weather forecasting models, specifically AIFS-CRPS, and finds that it improves the representation of variability without negatively impacting forecast skill.
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Chen et al. (2026) Understanding hydrological drought recovery patterns in a changing environment: Insights from a non-stationary multivariate analysis
This study investigates the recovery patterns of hydrological droughts in a changing environment using non-stationary multivariate analysis. The results show that climate change affects the recovery rates of hydrological droughts.
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Dong et al. (2026) Unraveling meteorological contributions and lagged effects on multi-depth soil moisture prediction using Transformer-SHAP framework
This study employs the Transformer-SHAP framework to predict soil moisture across multiple depths and investigates the meteorological contributions and lagged effects on these predictions.
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Otmani et al. (2026) When runoff no longer follows rainfall: a new index for diagnosing hydrological decoupling in semi-arid catchments
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Cho et al. (2026) Toward transferable flash flood oriented high flow prediction in ungauged basins using a decoupled physics–DL framework
This study proposes a decoupled physics–DL framework to predict flash floods in ungauged basins by combining physics-based surface runoff routing with deep-learning based rainfall loss estimation.
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Liu et al. (2026) How Much Water Can Be Seasonally Buffered by China's Largest Floodplain Lake? A Refined Estimate From SWOT
This study presents a novel framework for estimating the seasonal buffering capacity of floodplain lakes using satellite data from the SWOT mission. The framework reveals that Poyang Lake has a significant role in regional hydrology by mitigating floods and sustaining wetland ecosystems.
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Gao et al. (2026) Systematic Underestimation of Nonlinear and Synergistic Soil Moisture‐Precipitation Coupling in Convection‐Permitting Models
This study applies the High-Dimensional Model Representation (HDMR) framework to observation-based data sets over the contiguous United States to diagnose temporal soil moisture-precipitation coupling. The results show that traditional linear approaches underestimate coupling strength and highlight the importance of nonlinear diagnostics in improving the representation of sub-daily land-atmosphere coupling.
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Qian et al. (2026) Contrasting Terrestrial Moisture Controls on Precipitation and Interannual Variability across the Southern and Northern Tibetan Plateau
This study employs the Eulerian water tracer technique to quantify moisture sources of summer and winter precipitation over the Tibetan Plateau. The results show that terrestrial sources dominate both climatological mean and interannual variability of TP precipitation, with stronger terrestrial control over the northern TP.
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Masten et al. (2026) Climate change impacts on alpine springs in Austria: shifts in discharge seasonality and water availability
This study assesses climate change impacts on 70 alpine springs across the Austrian Alps, covering a wide range of climatic and hydrogeological settings. The results indicate decreasing spring discharge in western Austria under all scenarios and periods.
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Wei et al. (2026) Diagnosing gridded relative food-provision shortfall under compound drought signals and cropland-background conditions in a humid-subtropical drought corridor
This study developed a mass-constrained framework to diagnose county-statistics-constrained relative food-provision shortfall at 1-km resolution in the Heng-Shao drought corridor, China. The results showed that compound drought signals were strongly associated with gridded relative food-provision shortfall under heterogeneous cropland and environmental conditions.
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Ding et al. (2026) Drought development characteristics modulate global dry–wet abrupt alternation through distinct land–atmosphere feedbacks
This study investigates the global distribution and characteristics of dry-wet abrupt alternation (DWAA) events, revealing that antecedent drought development modulates subsequent precipitation through distinct land-atmosphere feedbacks. The research highlights the importance of considering drought development characteristics in predicting extreme precipitation events.
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Sageengrana et al. (2026) Smart Irrigation Using Transfer Learning in Iot-Based Agricultural Networks
This study proposes a transfer learning-based smart irrigation system that optimizes water management in agricultural IoT-based networks by adapting pre-trained deep learning models to make precise irrigation decisions with limited data.
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Conesa et al. (2026) Dry snow initialization and densification over the Greenland and Antarctic ice sheets in the ORCHIDEE land surface model
This study aims to improve the representation of snow densification processes over Greenland and Antarctica in the ORCHIDEE land surface model. The authors develop a new initialization procedure for snowpacks, a wind-based surface snow density parameterization, and recalibrate the dry-snow densification scheme using observations and offline simulations.
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Wirthensohn et al. (2026) Learning water diversion behaviour in regulated river networks using graph neural networks and neural decision trees
This study investigates how graph-based deep learning models can represent water diversion behaviour in regulated river networks under varying degrees of prior operational knowledge. The results show that different levels of operational knowledge affect the ability of graph-based models to represent water diversion behaviour.
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Toride et al. (2026) The significance of water vapor isotopes in improving weather prediction
This study demonstrates that assimilating mid-tropospheric water vapor isotopes (δD) improves atmospheric analysis and short-range forecasts, including precipitation. The results indicate that δD assimilation has the largest gains in the midlatitudes.
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Xie et al. (2026) Predicting root-zone soil moisture using a small sample deep learning approach
This study proposes a small sample deep learning approach to predict root-zone soil moisture, achieving high accuracy with limited data. The method demonstrates potential for practical applications in hydrology.
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Islam et al. (2026) Future-proofing raised bed irrigation for rabi maize using sensor-based soil moisture monitoring, AquaCrop modeling, and CMIP6-based climate projections in Bangladesh
This study integrates multi-season field experimentation, sensor-based soil moisture monitoring, FAO AquaCrop calibration and validation, and a ten-model CMIP6 ensemble to evaluate raised bed furrow irrigation as a climate adaptation strategy for rabi maize production in north-central Bangladesh. The results show that medium-raised beds (65 cm) deliver the highest grain yield, water productivity, and harvest index while reducing seasonal water use by 19-21% relative to conventional flood irrigation.
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Blain et al. (2026) SPEIChanges: A framework for assessing climate-change-driven shifts in drought frequency using the standardized precipitation evapotranspiration index
This study presents SPEIChanges, a framework for assessing climate-change-driven shifts in drought frequency using the Standardized Precipitation Evapotranspiration Index. The method quantifies how changes in precipitation patterns affect drought occurrence and can detect and quantify changes in drought frequency even when traditional methods fail.
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Asl et al. (2026) Global evidence on scaling nature-based solutions for compound hazards
This systematic review assesses the effectiveness of nature-based solutions in reducing climate-related hazards, highlighting a scarcity of direct evidence and emphasizing research priorities.
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Zhang et al. (2026) Weather Typing and Associated Moisture Source Distinctions for Extreme Precipitation over the Yangtze River Basin
This study investigates the relationship between weather typing and associated moisture source distinctions for extreme precipitation over the Yangtze River Basin. The research identifies four typical circulation patterns that contribute to extreme precipitation events in the region.
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Tian et al. (2026) Environmental drivers and machine-learning analysis of grapevine sap flow on hilly slopes in Southwest China
This study investigated grapevine sap flow dynamics on hilly slopes in Southwest China using machine learning analysis. The results showed that photosynthetically active radiation was the key factor affecting sap flow, and Random Forest and XGBoost models accurately predicted daily grapevine sap flow.
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Malik et al. (2026) Enhanced prediction of monthly reference evapotranspiration by optimizing SVR with the bagging algorithm in Indian Punjab
This study investigates the impact of climate change on global precipitation patterns using a high-resolution climate model. The main finding is that the model predicts a significant increase in extreme precipitation events worldwide.
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Chai et al. (2026) A differentiable Xin’anjiang model incorporating transfer learning for cross-basin flood prediction under data scarcity
This paper presents a differentiable Xin'anjiang model (dXAJ) that integrates transfer learning to enhance regional adaptability for cross-basin flood prediction under data scarcity. The dXAJ achieves robust temporal generalization and strong spatial transferability.
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Xiang et al. (2026) Dual physical pathways shape subseasonal-to-seasonal predictability of North American summer heat extremes
This study identifies two dominant sources of subseasonal-to-seasonal predictability for weekly hot days across North America: an ocean-forced mode associated with a Pacific Decadal Oscillation-like sea surface temperature pattern and an internally generated atmospheric mode linked to the summer Arctic Oscillation and summer North Atlantic Oscillation. The findings provide a scientific basis for improving extended-range early warnings of heat extremes.
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Cofie et al. (2026) Why water matters for Africa's food systems: A Call to Action for irrigation investment
This study highlights the importance of irrigation in Africa's agriculture, emphasizing its potential to enhance food security, economic growth, and livelihoods. Despite considerable irrigation potential, realizing it sustainably requires aligning national investment decisions with underlying basin and aquifer conditions.
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Jo et al. (2026) Implementation of the Generalized Double-Moment scaling Normalization method for raindrop size distribution in a WRF 4.3.1 bulk-type cloud microphysics scheme: a case study over the Korean Peninsula
This study presents the first implementation of the Generalized Double-Moment scaling Normalization method for raindrop size distribution in a WRF 4.3.1 bulk-type cloud microphysics scheme and evaluates its impacts on an isolated summer convection event over the Korean Peninsula.
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Phuong et al. (2026) Land use change and climate variability: key drivers of hydrological and sediment shifts in Vietnam’s Upper Ma River basin
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Ali et al. (2026) Optimizing soil moisture-triggered automated irrigation and nitrogen management in wheat–maize systems using RZWQM2
This study optimized soil moisture-triggered automated irrigation and nitrogen management in wheat–maize systems using the RZWQM2 model. The results showed that moderate crop-specific root-zone depletion offers a climate-resilient pathway for sustainable water and nitrogen management.
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Masoodi et al. (2026) Evaluating different roughness approaches and infiltration parameters for vegetation-influenced overland flow in hydrological model
This study evaluates the impact of vegetation on overland flow in hydrological models by comparing various roughness approaches and infiltration parameters. The results show that incorporating water depth-dependent roughness into the model improves its performance in simulating overland flow.
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Wu et al. (2026) Global Transpiration Changes and Its Drivers in the Future
The study develops a quantitative first-order attribution framework to determine how atmospheric $\text{CO}_2$, air temperature, and leaf area index (LAI) jointly influence projected changes in global plant transpiration across different Shared Socioeconomic Pathways (SSPs).
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Li et al. (2026) Mapping Groundwater‐Dependent Ecosystems for Contiguous China With Selected Hydroclimatic Windows
The study developed a mapping framework integrating remote sensing, soil, and modeled groundwater depth to identify Groundwater-dependent ecosystems (GDEs) across contiguous China, identifying varying levels of GDE potential across the region.
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Fan et al. (2026) Three‐Dimensional Simulation of Tile Drainage System for Farmland: Quantifying Tile Drainage Impacts on Hydrological Cycle
This study investigates the impact of tile drainage design—specifically depth and spacing—on evapotranspiration (ET) and water budgeting in an Iowa corn field using an enhanced PFLOTRAN model. The results indicate that tile depth significantly influences ET and surface runoff, whereas tile spacing has a minimal effect.
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Schneider (2026) The satellite revolution for monitoring forest ecosystems needs knowledge integration
The paper argues that the primary challenge in forest monitoring has shifted from a lack of data to the need for integrating satellite observations with field data and models across scales to address biodiversity and climate crises.
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Jung et al. (2026) Development of a New Generic AI Model for Spatio‐Temporal Prediction of Soil Moisture and Soil Water Isotopes
The study implements a sequential AI approach combining LSTM and Random Forest models to simulate daily soil moisture and soil water isotopes in a mixed land use catchment, demonstrating superior performance over traditional process-based models.
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Cao et al. (2026) Anthropogenic dominance of water storage variability in the Yellow River Basin: A machine learning synthesis of multi-source data (1981–2031)
This study utilizes a machine learning synthesis of multi-source data to analyze water storage variability in the Yellow River Basin from 1981 to 2031, concluding that anthropogenic factors are the dominant driver.
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Feng et al. (2026) Reconstructing terrestrial water storage and quantifying drought evolution in Australia using LSTM networks
The study implements a Long Short-Term Memory (LSTM) framework to reconstruct high-resolution Terrestrial Water Storage (TWS) in Australia by fusing GNSS and GRACE data with meteorological inputs, effectively filling GRACE mission gaps and improving drought monitoring.
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Tao et al. (2026) Drought Propagates Asynchronously Across Hydrological Compartments: Stage Differences in Propagation Time and Implications for Drought Monitoring
This study examines the timing and characteristics of drought propagation across meteorological, soil, and groundwater compartments in China, revealing that the transition from soil to groundwater is significantly slower than the transition from meteorological to soil conditions.
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Çavuş et al. (2026) Budyko Curve Conformance Unravelling Hydrological Change in a Semi‐Arid Mediterranean‐Climate River Basin
This study utilizes a Budyko-based water balance framework to analyze long-term streamflow changes in the Gediz River Basin, concluding that climate variability—specifically declining precipitation—is a primary driver of streamflow reduction since a regime shift in 1984.
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Diaz et al. (2026) Predicting Minnesota Lake Ice Phenology With Deep Learning, Explainable Methods, and a Physically Based Benchmark, 1980–2018
This paper explores the use of machine learning (ML) approaches to hindcast lake ice time series for 625 Minnesota lakes, demonstrating that ML can accurately estimate historical lake ice formation and breakup dates.
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Cheng et al. (2026) Identifying Irrigated Areas With a kNDMI – NDVI Framework and Assessing Distance‐Related Runoff Responses in the Upper Dahei River Basin, 2000–2020
This study aims to improve the understanding of irrigation-related hydrological responses in semi-arid regions by developing a soil-moisture-sensitive fusion of kNDMI and NDVI to identify irrigated croplands. The findings suggest that hydraulic connectivity between irrigation water sources and the river network may provide a plausible process-based explanation for spatial variation in irrigation-related runoff responses.
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Ji et al. (2026) Impacts of Small‐Scale Terrain on the Organization of an Extreme‐Rainfall‐Producing MCS Over the Southeastern Coast of China
This study investigates the impact of small-scale terrain on mesoscale convective systems (MCSs) over the southeastern coast of China during a record-breaking warm-sector heavy rainfall event. The results show that small-scale terrain significantly enhances coastal convergence, moisture accumulation, and upscale growth of convective cells.
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Mena-Rivera et al. (2026) Direct Rapid Recharge Controls the Hydrological Response in a Peri‐Urban Mountainous Tropical Catchment
This study investigates the hydrological responses in a mountainous region of Costa Rica using stable isotopes and other tracers to characterize groundwater-to-surface water connectivity. The research reveals that rainfall inputs significantly alter this connectivity, with direct rapid recharge regulating it during rainy periods.
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Eswara et al. (2026) The Application of Artificial Intelligence in Streamflow Forecasting: A Review
This paper reviews the application of Artificial Intelligence (AI) methods in streamflow forecasting and identifies key trends and challenges associated with this field. The analysis highlights the potential for improving forecast accuracy by exploring alternative input parameters and extending the forecast window time frame.
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Ghezzehei (2026) Interpretable Soil Water Retention Prediction Using Hierarchical Attention Networks With Uncertainty Quantification
This study introduces Hierarchical Attention-Based Pedotransfer Function (HABIT) to improve soil water retention predictions by directly predicting soil moisture and quantifying property importance across different soil moisture regimes.
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Woodhouse et al. (2026) Upper Rio Grande Streamflow, Climate, and Recent Increases in Runoff Efficiency
This study investigates the hydroclimatic trends in the Rio Grande headwaters, finding no significant trend in annual flow despite declining snowpack and earlier melt-off. The increase in runoff efficiency is attributed to a shift in timing of runoff and high-elevation tree mortality due to a spruce beetle epidemic.
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Hasan et al. (2026) Assessment of Seasonal Streamflow Controls Using SWAT +: Roles of Wetland Connectivity and Storage
This study investigates the relative influence of wetland connectivity and storage capacity on seasonal flow regimes in a forested coastal-plain watershed using controlled numerical experiments with SWAT+.
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Liu et al. (2026) Novel Parameterization of Building Storage Heat Flux for Urban Climate Modeling
This study develops a physically informed parameterization for the Objective Hysteresis Model (OHM) coefficients, linking them to key construction properties and weather forcing. The new scheme enables rapid inference of OHM coefficients from readily available building and meteorological data.
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Tao et al. (2026) Increased Arctic winter Rain-on-snow-induced melt events detected by spaceborne passive microwave remote sensing
This study uses spaceborne passive microwave remote sensing to detect increased Arctic winter rain-on-snow-induced melt events. The results show a significant increase in the frequency and duration of these events, which is linked to changes in atmospheric circulation patterns.
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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, Part B (2025) 133909]
This corrigendum corrects errors in the original paper "Estimating irrigation consumptive use for the conterminous United States" to improve its accuracy.
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XUE et al. (2026) Drivers of Runoff Sensitivity in the Rivers of Great Britain
This study investigates the runoff sensitivity of 547 watersheds in Great Britain using a generalized additive model approach, revealing strong spatial and temporal variability in response to changes in driving factors.
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Bakels et al. (2026) Enhanced Dry Season Moisture Recycling in the Congo and Amazon Rainforests
This study investigates the role of rainforests in maintaining rainfall during dry periods by analyzing moisture recycling statistics over 43 years in the Amazon and Congo regions. The findings highlight a significant increase in dry-season rainfall reliance on rainforest moisture.
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Yang et al. (2026) Characteristics of Extratropical Cyclones Under Uniform SST Warming and Increased CO 2 Forcing in a Global Storm‐Resolving Model
This study examines extratropical cyclone responses under three scenarios: 4 K sea surface temperature warming, increased CO2 concentration, and their combination using the X-SHIELD global storm-resolving atmosphere model. The main finding is that both SST warming and CO2 forcing have distinct effects on ETC characteristics.
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Eshetie et al. (2026) A hybrid AquaCrop–XGBoost framework for climate-smart irrigation and crop yield resilience under CMIP6 scenarios in the Lake Tana Basin, Ethiopia
This study integrates field experiments, the AquaCrop model, and machine learning to evaluate drip and furrow irrigation systems for tomato production in the Lake Tana Basin, Ethiopia. The results show that drip irrigation substantially improved tomato productivity and maintained greater yield stability under future climate scenarios.
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Xu et al. (2026) Global warming enhances salinity gradient energy potential through hydroclimatic change
This study investigates the impact of global warming on salinity gradient energy (SGE) potential. Using 19 climate models from CMIP6, the authors find that SGE generally increases under global warming, with regional heterogeneity emerging.
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Zhang et al. (2026) Drought Propagation in Mainland China Is More Strongly Associated With Precipitation Partitioning Than With Bulk Precipitation: Historical Patterns and Future Projections
This study investigates the role of precipitation frequency and intensity in regulating meteorological drought propagation to hydrological drought under natural conditions across mainland China. The results highlight the critical importance of precipitation partitioning in regulating drought propagation.
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Hong et al. (2026) Remote Sensing Hydrology Revisited: Advances, Persistent Challenges, and a Science‐Driven Agenda for the Next Decade
This Commentary revisits the integration of satellite remote sensing into hydrologic science, arguing for a more accountable approach to quantify water storage, movement, and cycle changes.
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Fan et al. (2026) Responses of crop yield and water productivity to stage-specific moisture anomalies under different precipitation scenarios
This study investigates the responses of crop yield and water productivity to stage-specific moisture anomalies under different precipitation scenarios in rainfed winter wheat systems. The results show that both crop yield and water productivity exhibit clear deviation under different precipitation anomaly scenarios, with varying response intensity depending on moisture background and scenario type.
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Birkel et al. (2026) Hydrological Insights From Combined Long‐Term Monitoring (Hydrometric and Tracer) and Modelling of Headwater Catchments in Costa Rica
This study integrates hydrometric and tracer monitoring with ecohydrological modelling to understand water partitioning in tropical headwater systems under climatic variability and land-use pressures. The research reveals dynamic flow paths driven by climate regimes and highlights the importance of long-term monitoring for water resource management.
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Kiałka et al. (2026) Subgrid parametrizations mediate the large‐scale effects of soil macroporosity on the water cycle in the ORCHIDEE land surface model
This study investigates the impact of soil macroporosity on large-scale land surface processes and finds that accounting for subgrid variabilities of soil hydraulic properties or precipitation can counteract scale-induced suppression of soil-structural effects.
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Shan et al. (2026) How can the Lateral Profile of Velocity Be Predicted on a Sloping Riverbank With Vegetation?
This study investigates the impact of vegetated sloping banks on the velocity profile in a trapezoidal channel using laboratory experiments and develops a new depth-averaged dimensionless equation for predicting velocity profiles.
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Beylat et al. (2026) Assimilation of Atmospheric CO 2 Concentration in the ORCHIDEE Land Surface Model: Exploring the Added Value of Explicit Forest Regrowth Representation
This study uses atmospheric measurements to constrain a land surface model and evaluate its performance in simulating terrestrial carbon fluxes, with a focus on the spatial distribution of the carbon sink.
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Li et al. (2026) Daily Pan Evaporation Reconstruction Reveals a Climate‐Gradient‐Dependent Evaporation Paradox Across China
This study presents an observation-aware framework to reconstruct daily pan evaporation records across China's climate gradients, addressing instrument transitions and missing data issues. The framework achieved high performance in reconstructing daily pan evaporation values.
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Li et al. (2026) Model–Observation Discrepancies in the Coupling of Vegetation Greening and Transpiration Under Rising Vapor Pressure Deficit
This study investigates the relationship between vapor pressure deficit (VPD) and plant transpiration (Tran) on a global scale using satellite-based observations, reanalysis data sets, and simulations from CMIP6 models. The results show that VPD has a broadly positive influence on Tran over most land areas.
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Wang et al. (2026) Integrating Physical-Based Xinanjiang Model and Deep Learning for Interpretable Streamflow Simulation: A Multi-Source Data Fusion Approach across Diverse Chinese Basins
This study presents a hybrid streamflow simulation model that combines the physical-based Xinanjiang (XAJ) model with the data-driven Temporal Convolutional Network (TCN)-Gated Recurrent Unit (GRU) model through nonlinear ensemble. The XAJ-TCN-GRU model exhibits promising results in simulating streamflow in four different basins in China, achieving high Nash-Sutcliffe Efficiency (NSE) values.
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Huang et al. (2026) Stage‐Specific Associations Between Extreme Climate Events and Crop Failures Across China
This study develops an event-level diagnostic framework to quantify the association between crop failures and climate variability in China from 1981 to 2018. The findings support phenology-aware diagnostics for resolving crop-specific climate risks.
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Yu et al. (2026) A Multidimensional Critical Regulation Approach to Water Resources Optimization
This study develops an integrated prediction-simulation-optimization framework to optimize water allocation in the Yellow River Basin, incorporating multidimensional water-system order into the optimization objective. The improved model reduces expected and worst-case water shortages under uncertainty.
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Arnold et al. (2026) A Simplified Higher Order Closure + Mass Flux Boundary‐Layer Parameterization in the Goddard Earth Observing System
This paper presents a novel implementation of the Simplified Higher Order Closure (SHOC) scheme coupled with a multi-plume eddy diffusivity mass flux (MF) scheme in the GEOS model, improving subgrid scale variances and shallow clouds representation.
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Sivaprasad et al. (2026) The Role of Assimilating Deep Learning‐Enhanced AMSR‐E/2 Observations in European Soil Moisture Reanalysis
This study presents a European daily soil moisture reanalysis that addresses limitations in satellite-derived observations by pre-processing them prior to assimilation, resulting in improved reanalysis skill and physically consistent soil moisture dynamics.
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Baker et al. (2026) Natural Drought Mitigation: Hydrological Impacts of Beavers in Internationally Important Peatlands
This study evaluates the hydrological impacts of beaver reintroduction on a peatland system in Northwest Midlands Meres and Mosses, demonstrating that beaver-induced modifications increase peatland water retention and resilience to drought.
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Zhu et al. (2026) Rising Aerosol Burdens in the Warm Desert City of Phoenix, AZ: Hydroclimatic–LULC Drivers and Exposure Disparities in a Climate–Land Surface–Society Framework
This study examines the relationship between aerosol burdens, land-surface changes, and racial/ethnic exposure dynamics in Phoenix, AZ, revealing disproportionate air pollution impacts on minority communities.
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Jalilvand et al. (2026) Beyond SMAP: Can GNSS‐R Platforms Ensure Continuity of Global Soil Moisture Observations?
This study compares and contrasts the soil moisture retrieval capabilities of GNSS-R and SMAP missions, highlighting their complementarity strengths in a resilient global observing system.
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Qu et al. (2026) Seasonal River Infiltration: Processes, Controls, and Quantitative Approaches
This review synthesizes current knowledge on seasonal river infiltration mechanisms, highlighting dynamic variations, influencing factors, and observational/quantitative assessment approaches. The study emphasizes the need for improved representation of time-varying boundary conditions and riverbed properties in models.
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Pan et al. (2026) Interannual Variability of Compound Hot‐Dry Events Over the Tibetan Plateau
This study investigates the interannual variability of summer compound hot-dry events (CHDEs) in the Tibetan Plateau and identifies key large-scale climate drivers. The research reveals that El Niño-Southern Oscillation (ENSO) is a primary driver of CHDEs, with different responses to Eastern Pacific (EP) El Niño and Central Pacific (CP) La Niña events.
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Galli et al. (2026) Combined Agro‐Hydrological and Flood Modeling Highlights Seasonal and Crop‐Specific Flood Regulating Ecosystem Services
This study presents a novel approach to assess the seasonality of crop-specific Flood Regulating Agro-Ecosystem Services (FLORAES) in small agricultural catchments. The results show that different crops generate distinct initial soil moisture conditions, affecting flood peak discharges.
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Augas et al. (2026) Hydrological Models Must Accurately Represent Processes in High‐Storage Watersheds
This study evaluates the hypothesis that hydrological model performance improves with more accurate representation of watershed processes by comparing 36 models against their capacities to simulate observed processes in 424 US watersheds.
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Natali et al. (2026) Field-scale irrigation estimation using Sentinel-1 and Sentinel-2 data within a soil water balance model coupled to the Water Cloud Model
This study presents a novel method to estimate irrigation at the field scale by integrating Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter data and Sentinel-2 NDVI data within a modeling framework. The proposed approach involves jointly calibrating the Water Cloud Model (WCM) and a two-layer Soil Water Balance (SWB) model to simultaneously simulate backscatter, irrigation, surface and root-zone soil moisture, and other water balance components.
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Lu et al. (2026) Toward fine-scale microwave monitoring of dryland vegetation: an alternative high spatial resolution vegetation optical depth retrieval algorithm from Sentinel-1 over the Sahel region
This study develops a 1 km vegetation optical depth (VOD) product over the Sahel region from Sentinel-1 observations for biomass estimation and monitoring of vegetation dynamics. The VOD retrievals show high spatial and temporal correlation with vegetation indices.
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Moghanjoghi et al. (2026) Reconstruction of a Flash Flood in a Semi‐Arid Ungauged Catchment Using Integrated Surface–Subsurface Modelling and Non‐Stationary Extreme Value Analysis
This study reconstructs the hydrological response of a semi-arid ungauged catchment in Iran and identifies key factors contributing to a deadly flash flood. The findings highlight the importance of infiltration, transient subsurface storage, and channel conveyance in attenuating flash flood peaks.
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Zhao et al. (2026) Meteorological Triggers of Regionally Extreme Atmospheric Evaporative Demand Across Global Land: Attribution With Adversarial Random Forests
This study develops a physically constrained counterfactual attribution framework combining the FAO56 Penman–Monteith equation with adversarial random forests to quantify the meteorological drivers of regionally extreme atmospheric evaporative demand (AED) globally, revealing that dominant trigger mechanisms vary systematically across hydroclimatic regimes.
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Moore et al. (2026) Camp Creek Revisited, One More Time: Evaluating Multiple Approaches to Quantify Peak Flow Response to Forestry for a Medium‐Size Snow‐Dominated Catchment
This study re-examines the effect of forest disturbance on peak flows in a snow-dominated catchment using five approaches, highlighting the limitations of existing methods and the need for alternative indices. The design-life-level (DLL) approach provides a conceptually meaningful probabilistic analysis under non-stationary conditions.
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Kallon et al. (2026) Estimating Baseflow Dynamics and Influencing Factors Using WHAT in the Wei River Basin, China
This study estimates annual baseflow in the Wei River Basin using the Web-based Hydrograph Analysis Tool (WHAT) and analyzes its relationship with groundwater levels and land use/land cover changes.
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Hou et al. (2026) Evaporation Dynamics From Flowing Water Surfaces
This study experimentally investigates the effects of flow characteristics and boundary conditions on water evaporation from turbulent surfaces of streams, providing novel insights into evaporation dynamics from riverine networks.
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Zhang et al. (2026) Revealing hidden structural regimes in fluvial stage–discharge relationships using time-series imaging
This study reveals that annual stage–discharge dynamics in the lower Yellow River exhibit a limited set of identifiable structural regimes, rather than varying as a smooth continuum. A physics-guided time-series imaging framework is developed to classify these regimes.
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Esenther et al. (2026) Climate Models Accurately Predict Ocean‐Going Meltwater Runoff From the Northwest Greenland Ice Sheet
This study compares in situ proglacial discharge observations from Northwest and Southwest Greenland with SMB model estimates to evaluate the accuracy of climate models in estimating Greenland Ice Sheet runoff contributions.
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Madakumbura et al. (2026) Anthropogenic Warming Weakens Mild Offshore Winds in Southern California but Not the Extreme Fire‐Weather Winds
This study investigates the impact of anthropogenic climate change on extreme wind events in southern California, focusing on the mesoscale katabatic pressure gradient driving these winds. The research finds that while ACC weakens mean pressure gradients, it has little effect on extreme wind events.
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Li et al. (2026) Hydrological Controls and Climate–Land Surface Interactions Regulating Long‐Term Lake Area Dynamics Across the Mongolian Plateau
This study reconstructed lake area changes across the Mongolian Plateau from 1990 to 2025 and examined associations with hydroclimatic, ecological, and human factors using machine learning models. The results show that lake dynamics reflect interacting influences that vary spatially and with lake size.
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Nguyen et al. (2026) A New Global Hypsometric Data Set Derived From Satellite and Geometric Methods for Reservoir Storage Estimation
This study introduces a new approach to estimate reservoir hypsometric relationships using satellite and geometry-based methods, improving the accuracy of storage dynamics for global hydrological applications.
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Xiong et al. (2026) Spatiotemporal Patterns of the Vegetation‐Atmosphere Decoupling Coefficient Across the Contiguous United States
This study quantified the decoupling coefficient (Ω) across 52 AmeriFlux sites in the contiguous U.S., revealing a significant increasing trend in continental-mean Ω due to progressively weaker vegetation-atmosphere coupling.
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Huang et al. (2026) Global Estimate of Reservoir Hydropower Storage Potential Based on SWOT Observations
This study leverages the Surface Water and Ocean Topography (SWOT) satellite to construct a global, observation-based inventory of energy storage potential for major reservoirs, quantifying the global energy storage potential at 638.14 ± 218.52 TWh.
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Bunya et al. (2026) Resolving Spatiotemporal Interactions of Compound Flood Drivers through Explicit Multi-Scale Channel Network Delineation in a Unified 1D/2D Model
This study presents a hydrologic-hydrodynamic application of the ADCIRC model to resolve spatiotemporal interactions of compound flood drivers in eastern North Carolina during Hurricane Florence (2018). The unified 1D/2D model captures multi-scale compound flood interactions, with enhanced bathymetry and event-agnostic calibration improving model accuracy.
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Renaud et al. (2026) Controls of bulk density and grain size on Stage 1 evaporation from packed sand columns
This study investigates the impact of bulk density on Stage 1 evaporation in sandy systems using controlled laboratory experiments and numerical simulations. The results show that evaporation dynamics are influenced by the interplay between capillary flow, water storage, and hydraulic connectivity across layers.
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Cheng et al. (2026) Observation‐Masked Localized Assimilation of Satellite‐Derived Snow Cover Data for Enhancing Runoff Prediction in the Rhône River Basin, France
This study presents an Observation-Masked Localized Ensemble Kalman Filter (OML-EnKF) framework for assimilating satellite data into a distributed hydrological model, improving snowpack characterization and discharge prediction in the Rhône River basin.
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Abeshu et al. (2026) Coupling water balance and vegetation carbon dynamics in a parsimonious global hydrologic model
This study demonstrates the extension of a parsimonious global hydrologic model to diagnose coupled water-carbon dynamics while retaining interpretable structure and computational efficiency. The model captures gross primary productivity (GPP) robustly and produces independently evaluated evapotranspiration (ET) skill despite ET not being used in calibration.
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Wang et al. (2026) Self‐Organization Shapes Divergent Water Use Strategies of Shrubs for Drought Resilience in Drylands
This study investigates the water use strategies of self-organized shrubs in dryland ecosystems during drought periods, revealing distinct ecohydrological processes between scattered and clumped vegetation configurations.
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Qian et al. (2026) An Interpretable and Lightweight Dynamic Framework for Streamflow–Meteorology Networks to Enhance Daily Streamflow Forecasting
This study proposes an improved reservoir computing framework that enhances the interpretability and accuracy of streamflow forecasting by integrating structural inference and dynamic weight updates. The framework achieves significant improvements in prediction accuracy compared to benchmark models.
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Luo et al. (2026) Declining Runoff Efficiency and Storage Reorganisation in the Source Region of the Yellow River Under Climate Warming, 1974–2025
This study investigates the hydroclimatic changes in the Yellow River source region over 52 years, revealing a reorganization of runoff generation despite increased precipitation. The findings suggest that annual runoff alone underrepresents changes in storage dependence and climate sensitivity.
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Xu et al. (2026) Investigating Climate Change Impacts on the 2020 Extreme Meiyu Through Global Variable‐Resolution Ensemble Subseasonal Hindcasts
This study investigates the impact of climate change on extreme Meiyu rainfall over the middle and lower reaches of the Yangtze River using global variable-resolution ensemble subseasonal hindcasts. The results show that post-1980 climate change enhanced 2020 extreme Meiyu rainfall by approximately 14.67%.
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Muste et al. (2026) A Hysteresis‐Based Framework for Evaluating and Selecting Streamflow Monitoring Approaches Under Unsteady Gradually Varied Flow Conditions
This paper introduces a hysteresis-centric performance metric to evaluate departures between traditional stage-discharge rating curves and alternative methods under unsteady gradually varied flow conditions. The framework is applied to three USGS gaging stations, providing practical guidance for monitoring-system selection.
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HedayatiAram et al. (2026) Polygon‐Based Spectral Reflectance Sampling for Real‐Time River Discharge Estimation
This study proposes a machine learning approach to estimate river discharge from satellite imagery, overcoming traditional limitations in remote sensing-based methods, and provides a robust estimation framework with uncertainty quantification.
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Gu et al. (2026) Multilayer soil moisture depletion intensifies drought impacts on global ecosystems
This study identifies vertically compound droughts, a high-risk form of drought where every soil layer suffers simultaneous water deficits. The research reveals that these events have increased globally over the past four decades and are projected to intensify under anthropogenic forcings.
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Charbonneau et al. (2026) Ice jam formation at river confluences: comprehensive field investigation and comparison to laboratory-derived predictive equations
This study investigates ice jam formation at river confluences through a comprehensive field investigation and comparison to laboratory-derived predictive equations. The research characterizes the interaction between tributary and main river ice during breakup, quantifies hydro-environmental factors controlling breakup sequences and jam formation, and evaluates the applicability of laboratory-derived ice jam prediction equations.
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Jiang et al. (2026) Observation‐Based Constraints on Future Runoff Projections for Large Reservoirs
This study investigates the future evolution of runoff within reservoir drainage areas in China, combining georeferenced reservoir data with historical simulations and future projections from an ensemble of Global Hydrological Models. The results highlight the dominant drivers of runoff-related variations and provide insights for effective reservoir management.
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1Dr. Y. Chitti Babu (2026) Ai-Powered Monitoring of Crop Health, Soil Condition, and Pest Risks Using Multispectral/hyperspectral Imaging and Sensor Data
This paper presents an AI-powered framework that integrates multispectral/hyperspectral imaging with IoT sensor data to monitor crop health, soil condition, and pest risk. The system provides comprehensive field assessments and generates timely recommendations for precision agriculture, demonstrating improved reliability and support for sustainable farming practices.
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An et al. (2026) Reversal of Temperature Sensitivity in Vegetation Greenness Rates Along Aridity Gradients Across Global Ecoregions
This study investigates the impact of aridity on the climatic sensitivities of satellite-derived green-up rate (GR) and senescence rate (SR) in natural ecosystems at a global scale. The research reveals that aridity fundamentally reshapes vegetation-climate interactions, with important implications for ecosystem carbon uptake under climate change.
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Li et al. (2026) Dynamic Bayesian modeling and optimization of agricultural water–energy–food–carbon systems under resource constraints: Evidence from the North China Plain
This study developed a Dynamic Bayesian Network–Bayesian Neural Network (DBN–BNN) framework to assess the water–energy–food–carbon (WEFC) system across 492 counties from 2001 to 2020 in the North China Plain. The DBN represented temporal dependencies, while an approximate Bayesian neural network based on Monte Carlo dropout captured nonlinear responses and predictive uncertainty.
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Bellemin-Laponnaz et al. (2026) A Variational Method for Reconstructing and Separating Balanced Motions and Internal Tides From Wide‐Swath Altimetric Sea Surface Height Observations
This study proposes a variational data assimilation method to dynamically reconstruct and separate balanced motions and internal tides from altimeter sea surface height observations, including SWOT. The approach successfully separates the two dynamical contributions and reconstructs their variability.
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Sadana et al. (2026) Validation of the open-source hydrodynamic model SFINCS on historical river floods at the global scale
This study evaluates the performance of the Super-Fast INundation of CoastS (SFINCS) hydrodynamic model in simulating riverine floods at a global scale. The results show that SFINCS performance improves with upstream basin size, with a global mean Critical Success Index (CSI) of 0.42 for basins larger than 1000 km2 and a CSI of 0.29 for smaller basins.
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Xu et al. (2026) Projecting Compound Wind‐Rainfall Hazards From Tropical Cyclones in China Under Climate Change
This study investigates the evolution of compound hazards from tropical cyclones (TCs) in coastal China under a changing climate, projecting significant increases in wind and rainfall extremes by 2100. The analysis reveals a northward shift in TC track density, intensification of wind speeds, and amplification of rainfall accumulations.
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Kang et al. (2026) High‐Resolution Dynamical Downscaling Refines the Time of Emergence of Sea Level Rise in the Northwestern Pacific
This study estimates the time of emergence (ToE) of sea-level rise in Northwestern Pacific marginal seas using high-resolution dynamical downscaling, highlighting the importance of resolving mesoscale and boundary-current variability.
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Tebaldi et al. (2026) Changes in the Seasonal Cycle of Precipitation and Heat Extremes Over Tropical Land Regions: An Amazon Story
This study analyzes super-ensemble simulations of seven Earth System Models to investigate delays in the onset of the rainy season and changes in heat extremes over tropical land regions. The Amazon region shows a significant trend towards delayed precipitation onset and more intense hot days during the dry season.
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Liu et al. (2026) Corrigendum to “PINN framework for urban flood depth prediction: integrating data-driven insights with physical constraints” [J. Hydrol. 665 (2026) 134693]
This corrigendum corrects a typographical error in the annotation of evaluation metrics within subplots of Figure 10 from the original article. The correction does not affect any data, results, analyses, or conclusions presented.
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Dong et al. (2026) The Urban Building Community Model for Earth System Modeling: Data and Comprehensive Evaluation
This study develops a comprehensive global 500 m data set for urban canopy models and evaluates the Common Land Model (CoLM‐UBCM) across multiple scales, demonstrating its suitability for high-resolution urban simulations.
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Ge et al. (2026) Soil Moisture Regulates Hydrologic Niche Overlap in Mixed Plant Ecosystems: A Global Synthesis
This study investigates the impact of plant mixture on hydrologic niche overlap in mixed plant ecosystems (MPEs) using stable water isotope techniques. The results show that soil moisture is the primary factor regulating hydrologic niche differentiation across different plant mixtures.
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Liu et al. (2026) Contrasting Impacts of Climate and Vegetation Changes on Water Yield Across Global and Regional Scales
This study conducted a global spatiotemporal analysis of water yield (WY) using multi-source remote sensing and meteorological data from 1982 to 2020, highlighting nonlinear and regional responses of WY to climate and vegetation changes. The results show that climate change contributed more to WY at the global scale and in humid regions.
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Shoup et al. (2026) Sensitivity Analysis of Evaporation Determined by the Isotope Mass Balance Method: A Case Study From a High‐Elevation Reservoir, Jackson Lake, Wyoming
This study employed an isotope mass balance to estimate evaporation rates from Jackson Lake, a reservoir in the Rocky Mountains, and assessed the impact of various factors on these estimates. The results highlight the applicability of this method for complex terrain.
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Zhang et al. (2026) Cascade Reservoirs Reshape Environmental Constraints on Vegetation Phenology in Alpine Riparian Ecosystems
This study investigates the impact of cascade reservoirs on riparian vegetation phenology within the Lancang-Mekong River, decoupling their effects from global warming. The research reveals that reservoir regulation counteracts climate-driven changes in growing season timing.
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Patel et al. (2026) Small Reservoirs Offer a New Perspective on Flood Reduction in Large Basins
This study investigates the flood reduction potential of small reservoirs in a large river network continuum, revealing that their influence is often underestimated due to the importance of network properties over traditional reservoir characteristics.
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Benson et al. (2026) Estimating Snowmelt Infiltration by Numerical Integration of Soil Moisture Data
This study estimates snowmelt infiltration into soils using a first-order diagnostic process, with results showing 12%–36% of snow water equivalent infiltrating to soil moisture.
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Tarricone et al. (2026) Interferometric Synthetic Aperture Radar (InSAR) for Monitoring Seasonal Snow
This review synthesizes the use of Interferometric Synthetic Aperture Radar (InSAR) for measuring seasonal snow water equivalent (SWE) and snow depth at basin-scale hydrology. InSAR can provide high-resolution, spatially distributed information on snow accumulation and ablation under dry-snow conditions.
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Cai et al. (2026) A Probabilistic Pathway Framework for River–Sea Connectivity in Distributary Networks: A Case Study in the Guangdong–Hong Kong–Macao Greater Bay Area
This study develops a probabilistic framework to quantify river-sea hydrological connectivity in distributary networks by accounting for all feasible pathways and cumulative low-head barrier effects. The results show that despite network expansion, functional river-sea connectivity declined by approximately 40% across maximum, average, and minimum connectivity metrics.
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Li et al. (2026) From Lumped to Spatially Distributed Hydrologic Modeling: A Data‐Driven Framework Evaluated Across North American Catchments
This study develops a deep learning framework that ingests spatially distributed meteorological forcings to improve large-scale hydrologic modeling. The approach demonstrates significant performance gains in streamflow prediction compared to traditional lumped models.
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Fu et al. (2026) Corrigendum to “SCARF: A new algorithm for continuous prediction of biomass dynamics using machine learning and Landsat time series” [Remote Sens. Environ. 314 (2024), 114348]
This corrigendum corrects the base maps used in Fig. 1 of a previous paper on biomass dynamics prediction using machine learning and Landsat time series data, ensuring compliance with official Chinese cartographic regulations.
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Pilbala et al. (2026) Dune Evanescence at Flood Stage in Lowland Sand‐Bed Rivers: A 2D Modeling Approach With Dynamic Resistance
This study enhances a 2D hydrodynamic model to account for bedform evolution and applies it to the Po River in Italy to analyze conveyance shifts caused by dune evanescence during floods, improving flood prediction and flow capacity assessment.
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Talha et al. (2026) Watershed-agnostic modeling: An explainable, distributed, data-driven hydrologic model
This study introduces a data-driven framework that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers to estimate next-day discharge across a dense network of Michigan gauges. The method achieves predominantly 'good' to 'very good' Nash-Sutcliffe and Kling-Gupta efficiencies at most stations.
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Justino et al. (2026) Contrasting Atmospheric River detection frameworks reveal distinct thermodynamic responses at high northern latitudes
This study assesses the relationships between Atmospheric Rivers (ARs) and extratropical cyclones with relative thermal degree day (𝑇𝐷𝐷rel) variability and precipitation across the Northern Hemisphere high latitudes. The results identify AR frequency as an important statistical predictor of high-latitude thermodynamic variability.
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Onen et al. (2026) Strategic Deep‐Water Observations Enhance Probabilistic Parameter Estimation of Lake Hydrodynamic Models
This study investigates the impact of observation depth on lake water temperature prediction accuracy and parameter equifinality using a physics-based model. The results show that single-depth deep-water observations can outperform shallower depths and full-profile observations in achieving accurate predictions.
