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Sorando et al. (2026) Simulating mediterranean rice paddies’ water balance under climate change scenarios
This study applies the SWAT+ paddy rice module to simulate the water balance of a Mediterranean rice irrigation district in Albufera de Val`encia, Spain, under climate change scenarios. Projections indicate significant precipitation reductions (9–31%) and potential evapotranspiration increases (8–18%) by mid- and late-century, leading to higher irrigation requirements (4–10%) and modest rice yield declines (up to 8%).
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Gonçalves et al. (2026) Irrigated agriculture in the United States: Current status and future frontiers
This review assesses the current status and future frontiers of irrigated agriculture in the United States, analyzing regional trends, water sources, crop diversity, and management practices from 2003-2023, and identifies key challenges like groundwater depletion and an eastward shift in irrigation.
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Schotte et al. (2026) Comparing absolute and standardized drought indices for modelling tree mortality of spruce, beech, pine, and oak based on the Crown Condition Survey in Germany
This study evaluated the association of absolute aridity and standardized drought indices with tree mortality for four major tree species in Germany (1990–2022). It found that standardized drought indices, particularly the Standardized Precipitation Evapotranspiration Index (SPEI), better explained mortality for Norway spruce, European beech, and Scots pine, with effects increasing over longer aggregation periods of up to five years.
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Huning et al. (2026) Cascading impacts of natural disasters in a connected world
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
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Wu et al. (2026) Prediction of Forest Fire Occurrence Risk in Heilongjiang Province Under Future Climate Change
This study quantified the multi-source drivers of forest fire occurrence in Heilongjiang Province and developed a long-term fire risk forecast using a Deep Neural Network with Residual Connections (ResDNN), which achieved 85.6% accuracy and was applied with CMIP6 projections to map future fire probability from 2030 to 2070.
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Shakeel et al. (2026) Optimizing GCM ensemble selection and weighted MME development for improved drought projection under global climate models simulations
This study proposes a novel framework for selecting optimal Global Climate Model (GCM) subsets and developing weighted Multi-Model Ensembles (MMEs) to improve drought projection accuracy. It introduces the Multi-Location Multimodel Standardized Drought Index (MLMSDI), demonstrating its effectiveness in assessing future drought across various Shared Socioeconomic Pathways (SSPs) and timescales in Punjab Province, Pakistan.
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Li et al. (2026) A three stage ‘Matching-Retrieval-Optimization’ method for radar–rainfall retrieval: a case study in the Yiluo River Basin, China
## Identification - **Journal:** Geomatics Natural Hazards and Risk - **Year:** 2026...
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Pengxin et al. (2026) Multi-model integrated error correction for extreme precipitation: method and application
This study developed a novel multi-model integrated error correction framework for CMIP6 extreme precipitation projections, significantly improving simulation accuracy in the Hanjiang River Basin (HRB). The corrected data reveal pronounced upward trends in extreme precipitation in the HRB, particularly in its southwestern and downstream areas, under future moderate to high radiative forcing scenarios.
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Sujono et al. (2026) Remote sensing-based early warning for agricultural flood damage mitigation in recurrent flood-prone areas
This study develops a remote sensing-based early warning system for agricultural flood damage mitigation in recurrent flood-prone areas, using Sentinel-1 SAR data and a localized change detection approach in Demak Regency, Indonesia, to identify high-risk zones and estimate potential crop losses.
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Barahona et al. (2026) Deep learning representation of the aerosol size distribution
This study develops MAMnet, a deep learning model, to predict the aerosol size distribution (ASD) and mixing state for seven lognormal modes based on bulk aerosol mass and meteorological conditions. MAMnet accurately reproduces the output of a two-moment modal aerosol scheme and shows good agreement with field measurements when driven by reanalysis data, offering an efficient way to improve aerosol representation in atmospheric models.
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Adhikari et al. (2026) Identifying ENSO events and their nexus with precipitation and flood dynamics in the Karnali River Basin, Nepal
This study investigates the influence of El Niño Southern Oscillation (ENSO) events on precipitation and flood dynamics in Nepal's transboundary Karnali River Basin (KRB) from 1964 to 2020, revealing a strong positive correlation between basin mean precipitation and discharge, and significant river channel shifts during strong ENSO events.
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Faliagka et al. (2026) Adaptation of the VegSyst model to predict crop nutrient uptake and water needs for precise soilless crop fertigation in greenhouses
This study adapted and validated the VegSyst model for precise soilless crop fertigation in greenhouses, integrating climate forecasts to predict water and macronutrient needs for cucumber and tomato, demonstrating reduced nutrient leaching and increased agronomic efficiency without compromising yield.
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Wang et al. (2026) Cumulative and lagged effects of climate factors on vegetation end of the growing season in the Yangtze River Basin
This study quantifies the cumulative and lagged effects of temperature, solar radiation, and precipitation on the end of the growing season (EOS) in the Yangtze River Basin from 2001-2023. It reveals that incorporating these temporal effects significantly improves the explanation of EOS variability and prediction accuracy, highlighting their critical role in phenology modeling.
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Singh et al. (2026) START: A Hybrid Spatio-Temporal Attention ResNet Transformer for Explainable Multivariable Meteorological Bias-correction
This study introduces START, a hybrid deep learning framework for multivariable meteorological bias correction over the contiguous United States, integrating heterogeneous data streams to achieve substantial improvements in forecast accuracy and provide explainable, calibrated uncertainty estimates.
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Mascaut et al. (2026) Ground-based atmospheric measurements at the Onsala Space Observatory (Sweden): data & trends (2009–2025)
This study presents and analyzes a comprehensive, long-term (2009-2025) dataset of ground-based atmospheric measurements from the Onsala Space Observatory, Sweden, revealing a statistically significant warming trend of approximately 0.15 kelvin per year, most pronounced in winter, and a significant decrease in rain rate intensity.
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Zeng et al. (2026) Synergistic Effects of Multi‐Timescale Atmospheric Teleconnections on Spring Monthly Droughts in Central‐Eastern China
This study investigates spring monthly drought variations in central-eastern China (CEC) and the synergistic effects of multi-timescale atmospheric teleconnections. It finds that in-phase alignments of high-frequency and low-frequency teleconnections (SCA, WP, NAO) amplify specific atmospheric circulation anomalies, leading to decreased precipitation and increased potential evapotranspiration, thus causing pronounced droughts in the CEC.
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Guo et al. (2026) Monitoring glacier-fed river width dynamics in High Mountain Asia from Sentinel-2 time series using a deformable UNet and skeleton evolution framework
This study developed a novel framework integrating a deformable UNet (DUNet) deep learning model and a discrete, shape-preserving skeleton evolution algorithm to accurately monitor glacier-fed river width dynamics in High Mountain Asia using Sentinel-2 time series. The proposed method demonstrated superior performance over conventional deep learning models and existing global datasets, revealing significant seasonal variations in river width.
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Campomanes et al. (2026) Assessment of Flood-Prone Areas in the Lacramarca River Basin in the Santa Clemencia and Pampadura Region, Peru, Under Climate Change Effects
This study assesses flood-prone areas in the Lacramarca River basin, Peru, under historical and 2050 climate change scenarios, revealing a significant increase in flood extent due to projected climate variability and highlighting the inadequacy of current protection infrastructure.
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Xu et al. (2026) Divergent latitude-specific urban humid heat risks are regulated by local climate types
This study systematically investigates the spatiotemporal evolution and drivers of urban wet-bulb temperature across 56 global cities from 2005-2024, revealing significant increases since 2020 with responses regulated by local climate types.
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Yang et al. (2026) DANRA: the kilometer-scale Danish regional atmospheric reanalysis
This paper introduces DANRA, a novel 2.5-kilometer resolution regional atmospheric reanalysis dataset covering Denmark and its surrounding regions from 1990 to 2023. DANRA demonstrates superior performance compared to global reanalyses like ERA5 in representing essential climate variables and extreme weather events, providing unprecedented detail for climate adaptation and impact modeling.
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Gensini (2026) Extreme events and the insurance industry in a changing climate
This letter argues for the formal integration of high-resolution downscaling, ensemble modeling, and catastrophe risk analysis to bridge the gap between coarse global climate models and the insurance industry's need for local, probabilistic risk assessment in a changing climate.
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Zhang et al. (2026) Mountain front recharge of a karst aquifer in the Denver Basin, southeastern Wyoming (USA): Recharge mechanism and multiyear drought impacts
This study investigated mountain-front stream recharge mechanisms to a karst aquifer in the Denver Basin, southeastern Wyoming, and the impacts of a multiyear drought (2017–2022) on aquifer water levels. It found that snowmelt-driven streamflow is the primary recharge source via fractures and conduits, and drought significantly reduced aquifer recharge, highlighting the critical reliance on mountain snowmelt.
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Agbesi et al. (2026) Development and performance evaluation of a low-cost sensor-based automated drip irrigation system for small-scale farming
This study developed and evaluated a low-cost, sensor-based automated drip irrigation system for small-scale farming, demonstrating reliable performance in fine-textured soils (clay) compared to a commercial sensor, while identifying limitations in coarse-textured soils due to compaction and probe-soil contact issues.
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SINGH et al. (2026) Subsurface fertigation modifies soil–plant–water interactions to improve productivity of cotton–wheat systems under reduced tillage
This study investigated how subsurface drip fertigation (SDF) influences soil physical properties, plant physiological functioning, and system productivity in a low-tilled cotton-wheat rotation over two growing seasons. It found that SDF significantly improves soil-plant interactions and resource-use efficiency, enhancing productivity while mitigating pressure on groundwater resources compared to conventional surface flood irrigation.
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Makhambetov et al. (2026) Assessment of the Spatial Structure and Condition of Urban Green Infrastructure in Aktau (Kazakhstan) Under Arid Climate Conditions Using NDVI and SAVI
This study assesses the spatial structure and condition of urban green infrastructure in Aktau, Kazakhstan, under arid climate conditions from 2015 to 2025 using satellite imagery and inventory data. It found a moderate overall increase in vegetation but with persistent spatial fragmentation and center-periphery asymmetry, emphasizing the critical role of irrigation and targeted greening strategies.
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Xiao et al. (2026) Rainfall Regionalization for Mainland China Based on Storm Characteristics
This study develops a high-resolution rainfall regionalization for mainland China using sub-daily storm characteristics from hourly station data. It identifies five event types and clusters stations into six groups, aggregated into eight rainfall zones, revealing distinct spatial patterns of rainfall depth, duration, and intensity, and their correlation with topography and weather systems.
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Düzenli et al. (2026) Assessing the utility of statistical downscaling for subseasonal temperature forecasts
This study benchmarks 27 statistical downscaling methods for subseasonal temperature forecasts, demonstrating that while most methods successfully transfer skill from coarse (~100 km) to local (~5 km) resolution, method choice is critical, with some enhancing and others degrading skill, and incorporating atmospheric patterns or using weekly predictors showing benefits.
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Xu et al. (2026) A global all-weather PWV retrieval model integrating multi-band satellite observations considering land cover types and NDVI
This study developed an all-weather, high-resolution global model for retrieving precipitable water vapor (PWV) by integrating multi-band satellite observations (NIR, TIR, MW) with GNSS data, achieving a substantial reduction in global average RMSE from 15.19 mm to 5.37 mm compared to the MYD05 product.
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Baker et al. (2026) Reduced future North Atlantic eddy-driven jet variability in high-resolution, fully coupled global climate models
This study evaluates the impact of model resolution on North Atlantic winter jet streams under historical and future climate conditions, finding that higher resolution improves zonal wind representation and projects a strengthening and poleward shift of the mid-latitude jet by 2050, contrasting with low-resolution models.
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Millin et al. (2026) Analyzing Stratospheric and Tropical Contributions to the Subseasonal Forecasts of the December 2017 and January 2004 North American Cold Air Outbreaks
This study investigates the individual roles of stratospheric and tropical variability in driving subseasonal cold air outbreak (CAO) forecast skill in the central United States using targeted nudging experiments. It finds that the impact of these modes on CAO prediction skill is event-dependent, with stratospheric nudging significantly improving forecasts for one event while both modes had limited surface impact for another.
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Zhang et al. (2026) Lake bathymetric reconstruction and water storage estimation method based on terrain feature similarity
This study proposes a novel method for lake bathymetric reconstruction and water storage estimation by extrapolating surrounding topographic parameters, demonstrating its applicability and accuracy for lakes on the Qinghai–Tibet Plateau, particularly for those lacking measured underwater data.
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Irfan et al. (2026) Forecasting of global water usage in agriculture and total global consumption by using the Bi-GRU model
This study develops and applies a Bidirectional Gated Recurrent Unit (Bi-GRU) model to forecast global Total Water Consumption (TWC) and Agricultural Water Use (AWU), demonstrating its superior accuracy compared to other deep learning models for effective water resource management.
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Levin et al. (2026) Influence of sea surface temperature patterns and mean warming on past and future Atlantic tropical cyclone activity
This study investigates the relative contributions of large-scale thermodynamic and dynamic processes to decadal and multidecadal changes in Atlantic tropical cyclone (TC) activity from the late 19th century to 2100. It finds that TC frequency changes are primarily governed by potential intensity and moist entropy deficit, with regional sea surface temperature (SST) patterns, rather than global-mean warming, controlling both past variability and future changes.
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Pérez-Campo et al. (2026) Streamflow Simulation Based on a Hybrid Morphometric–Satellite Methodological Framework
This study investigated the relationships between GR4J hydrological model parameters and watershed characteristics in the Caquetá River Basin, finding strong correlations (R² 0.80-0.98) that support parameter regionalization based on physiographic and environmental descriptors.
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Naresh et al. (2026) Deep Learning-Based Agricultural Drought Monitoring and Prediction Using Vegetation Health Index in the Papagni River Basin, India
This study developed and validated a deep learning-based framework using the Vegetation Health Index (VHI) to monitor historical agricultural drought (2001–2022) and predict future conditions (2025–2040) in India's semi-arid Papagni River Basin, revealing chronic mild drought and achieving high predictive accuracy with an LSTM model.
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Liu et al. (2026) Sensing Vegetation Resistance and Recovery Along Urban–Rural Gradients
This study investigates how vegetation resistance and recovery to extreme heat events vary along urban-rural gradients in the North Tianshan Slope Urban Agglomeration, China. It finds that rural vegetation provides a strong cooling effect and exhibits higher resistance and recovery than urban vegetation, with driving factors varying by spatial scale.
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Sonny et al. (2026) Hydrological drought projections across Europe under climate change
This study comprehensively assesses future hydrological drought dynamics across Europe using the Standardized Runoff Index (SRI) under two climate change scenarios, revealing an intensification of drought conditions, particularly in southern Europe, with spring identified as the most drought-prone season. The findings project significant increases in drought frequency, severity, and spatial extent, necessitating urgent, seasonally adaptive water management strategies.
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Zhang et al. (2026) Divergent impacts of flash drought drivers on alpine ecosystem resilience
This study reveals divergent impacts of flash drought drivers on alpine ecosystem resilience in the Yarlung Tsangpo River Basin, finding that while soil moisture deficits consistently weaken resilience, temperature-driven events can temporarily enhance it due to meltwater subsidies, masking underlying vulnerabilities.
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Liang et al. (2026) Vegetation in Central Asia is more sensitive to soil moisture stress than to precipitation and vapor pressure deficit stresses
This study investigated vegetation sensitivity to precipitation, soil moisture, and vapor pressure deficit stresses across Central Asia from 1982 to 2020, revealing that vegetation is most sensitive to soil moisture stress, a trend projected to continue increasing in the future.
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Bounajra et al. (2026) Reference evapotranspiration variability and trends in relation to irrigation demand in a semi-arid agricultural region of Morocco
This study investigates the spatio-temporal variability and long-term trends of reference evapotranspiration (ET₀) in the semi-arid Chichaoua agricultural province of Morocco over 45 years, explicitly linking ET₀ dynamics to operational drip irrigation design practices. It reveals a significant upward trend in ET₀ driven by thermo-radiative factors, which directly translates into increased irrigation water requirements based on current engineering assumptions.
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Wu et al. (2026) Basin-wide and regional low-frequency variability of Yangtze precipitation revealed by clustering and slow feature analysis
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Chrysanti et al. (2026) Large-scale drivers and ocean-land feedbacks contributing to extreme precipitation during the January 2021 South Kalimantan flood, Indonesia
This study investigated the meteorological drivers and ocean-land feedbacks contributing to the January 2021 South Kalimantan flood, finding that active cold surges and cross-equatorial northerly surges, modulated by the Madden-Julian Oscillation and Kelvin waves, were primary drivers, with land-ocean feedbacks playing a secondary, amplifying role. The research utilized both standalone and coupled atmospheric-hydrological models to delineate synoptic and mesoscale interactions.
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Zou (2026) training_samples_zarr_v32_final
## Identification - **Journal:** ScienceDB - **Year:** 2026...
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Zhou et al. (2026) Simulation of Extreme Weather Events in the Wanzhou Region of the Three Gorges Reservoir Area Using the WRF Model Coupled With Machine Learning Techniques
This study systematically evaluates the performance of the Weather Research and Forecasting (WRF) model with various physical parameterisation schemes for extreme precipitation and high-temperature events in the Wanzhou District of the Three Gorges Reservoir region. It identifies optimal WRF configurations and demonstrates that coupling these with machine learning models, particularly Random Forest, significantly enhances prediction accuracy and reliability.
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Duan (2026) Dataset for hybrid streamflow simulation in a semi-arid grassland catchment
This dataset supports a study on hybrid streamflow simulation in a semi-arid grassland catchment, utilizing an enhanced distributed hydrological model and deep learning for residual correction. It provides processed hydro-meteorological forcing data, model input files, parameter-related variables, and simulation outputs for model calibration and validation.
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Daliakopoulos (2026) Monitoring reservoir storage using remote sensing and large language models
This study presents an innovative framework for monitoring reservoir storage using Sentinel-1 Synthetic-aperture radar (SAR) imagery, validated against quantitative storage values extracted from online media with the assistance of large language models (LLMs), demonstrating a scalable solution for data-scarce regions.
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Corbetta et al. (2026) Modeling Spatio-Temporal Surface Elevation Changes in Argentino and Viedma Lakes, Patagonia, Employing ICESat-2
This study develops and validates operational models for spatio-temporal surface elevation changes in Argentino and Viedma Lakes, Patagonia, using ICESat-2 laser altimetry data. The models accurately separate water volume changes, atmospheric forcing effects (wind and air pressure), and geoid contributions, significantly reducing elevation variability and demonstrating ICESat-2's capability for high-precision water resource monitoring in data-sparse regions.
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Salvi et al. (2026) Discrepancy in the sign of temperature trends in reanalysis datasets
This study evaluates the alignment of annual mean daily maximum and minimum temperature trends from three reanalysis datasets (ERA5, MERRA-2, NLDAS-2) against observed trends from 7,059 stations across the continental United States, revealing substantial trend misalignment (21-31%) that persists across various regions and record lengths.
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Li et al. (2026) Fine‐Scale Characterization of Groundwater Recharge Efficacy Under Ecological Water Replenishment: An AI‐Enhanced Learning Framework Benchmarked Against Traditional Geostatistics
This study reconstructs high-resolution (250 m) groundwater level dynamics in the Yongding River basin using LightGBM and multi-source data, demonstrating that Ecological Water Replenishment (EWR) drives groundwater recovery but with diminishing marginal returns, while outperforming traditional interpolation methods.
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Jellis et al. (2026) Simulation of Water Vapor Transport to the Stratosphere by Overshooting Convection
## Identification - **Journal:** Journal of Geophysical Research Atmospheres - **Year:** 2026...
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Chen et al. (2026) Different Responses of the Madden‐Julian Oscillation to the Fast and Slow Decaying El Niño in Spring
This study investigates how El Niño's decay rate modulates the Madden-Julian Oscillation (MJO) during decaying springs, finding that fast-decaying El Niño weakens MJO activity over the central Pacific, while slow-decaying El Niño significantly enhances it through stronger moisture advection and lower-tropospheric moistening.
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Peng et al. (2026) A Multi-Source Radar Data Complementary Enhancement Generation Method Based on Diffusion Model
This paper proposes the Multi-source Radar Reflectivity Complementary Enhancement (MSR-CE) method, utilizing a conditional diffusion model and a Radar-Physics-Aware Loss, to fuse S-band Doppler radar and X-band phased-array radar data, generating high-resolution pseudo X-band reflectivity fields that overcome the individual limitations of each radar type.
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Feng et al. (2026) Spatiotemporal response of depth‑to‑water table (ZWT) to the Three Gorges Reservoir impoundment across hydrological year types
This study quantifies the spatiotemporal response of depth-to-water table (ZWT) to the Three Gorges Reservoir (TGR) impoundment across hydrological year types in the Jianghan Plain. It finds that TGR impoundment leads to a general deepening of ZWT, with the strongest and most coherent response observed in wet years, concentrating in a narrow corridor near the Yangtze River.
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Li et al. (2026) An accurate 10 m annual crop map product of maize and soybean across the United States
This study developed an openly available, annual, 10 m spatial resolution maize and soybean map product for the Contiguous United States (CONUS) from 2019 to 2022, achieving consistent overall accuracies greater than 95%. The research demonstrates that these higher-resolution maps significantly reduce mixed pixels compared to existing 30 m products, enhancing agricultural monitoring capabilities.
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Mahamadou et al. (2026) Statistical Assessment of the Extreme Rainfall Event of 2024 in Maradi and Zinder of South-Eastern Niger
This study analyzed the 2024 rainy season in south-eastern Niger, determining it to be an exceptional climatic anomaly with record rainfall, surpassing previous extremes and indicating a profound climate mutation in the region.
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Yang et al. (2026) Multi-Channel Super-Resolution Reconstruction Model Based on Dual-Band Weather Radar Fusion
This study proposes a deep neural network-based super-resolution method for S-band reflectivity, fusing dual-frequency (S-band and X-band) radar observations to address resolution mismatch and enhance the spatial resolution of S-band data, demonstrating improved detail recovery and structural reconstruction under severe weather conditions.
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Khan et al. (2026) Physics-informed Bayesian Neural Network for groundwater recharge estimation in data-scarce arid regions
This study developed a Physics-Informed Bayesian Neural Network (PI-BNN) to estimate groundwater recharge and its uncertainty in the data-scarce South Al Batinah (SAB) Basin, northern Oman. The PI-BNN significantly reduced uncertainty bounds by approximately 50% compared to Latin Hypercube Sampling (LHS) while maintaining physically consistent and realistic recharge estimates.
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Wan et al. (2026) Stable Isotope Record of Precipitation Dynamics in the Semi‐Arid Subtropics
This study investigated rainfall stable isotope variability in subtropical northwest Australia over 10 years to understand precipitation mechanisms and moisture sources, identifying a significant "amount effect," substantial sub-cloud evaporation, and significant land evapotranspiration recycling.
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Keefe et al. (2026) Projections of Temperature-Driven Changes in Seasonal Ice Coverage Around Prince Edward Island, Canada
This study assesses the influence of climate change on seasonal ice coverage along Prince Edward Island's coast, projecting a substantial decline in freezing degree days, seasonal ice indices, and ice season length by the 2090s under various emission scenarios.
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Abebe et al. (2026) Assimilating leaf area index and soil moisture into the WOFOST model for improved maize (Zea mays L.) yield estimation in Ethiopia
This study developed a data assimilation framework using the Ensemble Kalman Filter to jointly assimilate satellite-derived Leaf Area Index (LAI) and Soil Moisture (SM) into the WOFOST crop model, significantly improving maize yield estimation accuracy in Ethiopia compared to univariate assimilation or open-loop simulations.
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Teng et al. (2026) Coupling GSFLOW with a river hydrodynamic model for flow simulation in a mountain river basin
This study developed a numerical algorithm to dynamically couple the GSFLOW hydrological model with a river hydrodynamic model (RHM-SG) to improve streamflow and flood simulation accuracy in mountain rivers. The integrated GSFLOW–RHM-SG model significantly enhanced predictions of extreme flows during flood events and better represented stream–groundwater interactions in the Zamask–Yingluoxia subbasin of the Heihe River Basin, China.
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Wang et al. (2026) Projected Earlier Australian Summer Monsoon Onset Associated With Faster Eastward MJO Propagation
This study projects a robust earlier onset of the Australian Summer Monsoon (AUSM) by approximately 5 days by the late 21st century under global warming, attributing this shift to the earlier arrival and accelerated eastward propagation of the first austral-spring Madden–Julian Oscillation (MJO) event.
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Seidov (2026) Self-Organization of Ocean Circulation: A Synergetic Perspective on Ocean and Climate Dynamics
This study reinterprets large-scale ocean circulation, particularly the Atlantic Meridional Overturning Circulation (AMOC), using self-organization theory and synergetics, demonstrating how a simplified nonlinear model (Brusselator) can capture key bifurcation behaviors relevant to AMOC instability and regime transitions.
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Yu et al. (2026) Identification of the Global Cloud‐Clear Sky Transition Zone and Its Shortwave Radiation Effects
This study developed a globally consistent method to detect the Cloud-Clear Sky Transition Zone (CCTZ) over both land and ocean using MODIS data and radiative transfer modeling. It found that the CCTZ has a cloud-type-dependent spatial scale (approximately 5 km from clouds) and significantly enhances global mean diffuse shortwave radiation by 16.3% while reducing direct shortwave radiation by 0.8% compared to pure clear-sky conditions.
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Li et al. (2026) Interpretable Cotton Mapping Across Phenological Stages: Receptive-Field Enhancement and Cross-Domain Stability
This study developed an interpretable semantic segmentation framework for cotton mapping in arid irrigated agroecosystems using multi-source remote sensing data, achieving high classification accuracy and robust generalization while explicitly quantifying the importance of different predictors across phenological stages.
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Gowera et al. (2026) Spatial prediction and mapping of soil salinity using machine learning and remote sensing covariates
This study evaluated remote sensing models for mapping soil salinity in irrigated agroecosystems with predominantly low electrical conductivity values, finding that Support Vector Machine models outperformed Random Forest using Landsat and LiDAR data.
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Wang et al. (2026) Drivers of Shrinkage in Daihai Lake Based on Influence of Climate Change, Vegetation Variation and Agricultural Water Saving on ET
This study investigated the impact of vegetation dynamics on Daihai Lake shrinkage, finding that forest expansion and its associated evapotranspiration, alongside climate change, are significant drivers, and recommends shrub-grass combined restoration for sustainability.
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Hussain et al. (2026) Development of high-resolution land surface temperature and paddy area estimation technique using multi-source satellite image-based downscaling
This research developed a technique to downscale Land Surface Temperature (LST) from 30 meters to 3 meters using multi-source satellite imagery and multispectral indices, enabling accurate estimation and monitoring of paddy field areas and their temporal changes for precision agriculture. The study successfully revealed fine-scale LST variations crucial for understanding agricultural heterogeneity and resource management.
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Eleftheratos et al. (2026) Evaluation of variability in GOME-2 total water vapor and nitrogen dioxide columns associated with natural oscillations
This study investigates the variability of total water vapor (H₂O) and nitrogen dioxide (NO₂) columns in association with natural oscillations (QBO, ENSO, NAO) using GOME-2 satellite data. It finds that GOME-2 data effectively captures these variabilities, with NO₂ showing QBO-type periodicity in the tropics and H₂O responding to ENSO in the tropics and NAO in northern mid-latitudes, demonstrating good agreement with reanalysis and model results.
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Kim et al. (2026) ENSO phase transition enables prediction of winter North Atlantic Oscillation one year ahead
This study demonstrates that the predictability of the winter North Atlantic Oscillation (NAO) one year ahead significantly improves during El Niño–Southern Oscillation (ENSO) phase transition years, a phenomenon linked to the northward propagation of atmospheric angular momentum anomalies.
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Wang et al. (2026) DAR-type model based on “long memory-threshold” structure: a competitor for daily streamflow prediction under changing environment
This study proposes a novel Fractional-differenced Dual-Threshold Double Autoregressive (FDTDAR) model to improve daily streamflow prediction accuracy under changing environments by capturing non-stationarity, non-linearity, and long-term memory. Applied to the Yellow River basin, the FDTDAR model, particularly with a Student's t-distribution for residuals, demonstrates superior predictive ability compared to AR-GARCH and LSTM models.
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Sylvestre et al. (2026) Decadal-scale droughts disrupted the African Humid Period in the Sahara
This study reconstructs the hydrological history of Lake Yoa, Chad, over the past 10.25 thousand years, revealing that the African Humid Period was interrupted by decadal-scale droughts, particularly a prominent 8.2 kyr bp event linked to Atlantic Meridional Overturning Circulation weakening.
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Warren (2026) Extreme climate outcomes could still occur with just 2 °C of global warming
This News & Views article highlights that even if global warming is limited to 2 °C above pre-industrial levels, extreme climate impacts, such as drought and flooding, could still occur at levels often predicted for much higher warming, emphasizing the need for policy to consider worst-case scenarios.
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Matus et al. (2026) The sub-seasonal connection between the land surface and Great Plains low-level jet
This study investigates the modulation of Great Plains low-level jet (GPLLJ) intensity by sub-seasonal dry soil moisture anomalies. It finds that neglecting this land-surface interaction leads to significant errors in reconstructed GPLLJ wind speeds, highlighting the land surface's crucial role in GPLLJ variability.
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Zhou et al. (2026) Synergistic retrievals of leaf area index and leaf chlorophyll content in deciduous broadleaf forests from Sentinel-2 and Landsat
This study systematically evaluates synergistic Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) retrievals for deciduous broadleaf forests from Sentinel-2 and Landsat data. It identifies limitations in canopy structural representation as a primary driver of mutual error compensation and demonstrates that integrated parameterization strategies significantly improve retrieval accuracy and seasonal dynamics.
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Su et al. (2026) Precipitation observing network gaps limit climate change impact assessment
This study evaluates the global distribution of 221,483 precipitation gauges and identifies priority regions for network expansion under historical and future climate/socioeconomic scenarios. It finds that only 13.4% of the global land surface meets WMO monitoring requirements, with 25% currently needing urgent expansion, increasing to 32.1% under a high-emission scenario when socioeconomic vulnerabilities are considered.
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Ji et al. (2026) Robust hyperspectral reconstruction from satellite and airborne observations via a deep hierarchical fusion network across heterogeneous scenarios
This study develops a deep learning framework for robust high spatial resolution hyperspectral imagery (HR-HSI) reconstruction by fusing low-resolution hyperspectral (EMIT) and high-resolution multispectral (PlanetScope) satellite data. The framework consistently outperforms state-of-the-art models, demonstrating high spectral fidelity and reconstruction accuracy across diverse landscapes.
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Bevacqua et al. (2026) Moderate global warming does not rule out extreme global climate outcomes
This study reveals that extreme global climate outcomes for several sectors (e.g., droughts, precipitation, fire weather) may occur even under a moderate 2 °C global warming, often exceeding model-averaged projections for 3 °C or 4 °C warming, primarily due to large uncertainties in climate model projections.
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Pellicone et al. (2026) Understanding Trends in Near-Surface Air Temperature Lapse Rates in a Southern Mediterranean Region
This study investigated the spatiotemporal variability of near-surface air temperature lapse rates in Calabria, identifying altitude as the dominant driver of temperature distribution and revealing a significant long-term decline in lapse rates, indicating accelerated warming at higher elevations.
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Azizi et al. (2026) Comparative machine learning and deep learning approaches for agricultural drought monitoring: Dual-index modeling in Iran
This study develops a dual-index machine learning framework for agricultural drought monitoring in Iran, integrating the Soil Moisture Deficit Index (SMDI) and the 3-month Standardized Precipitation–Evapotranspiration Index (SPEI-3) using multi-source predictors. It demonstrates that SMDI is estimated more reliably (best RMSE = 0.80, R² = 0.82) than SPEI-3 (best RMSE = 0.96, R² = 0.55) and proposes an operational classification system with uncertainty quantification.
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Bulut et al. (2026) Toward early warning of drought impacts: a framework for predicting drought impacts in the UK
This study presents a data-driven framework to predict real-world drought impacts. Different modelling approaches were tested and evaluated in the United Kingdom using predictions at the time of occurrence, with the best-performing method selected for forecasting impacts months ahead. Both predictions and forecasts were validated using independent UK data and applied to Germany to test transferability, supporting early warning systems and improved drought risk planning.
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Abeysingha et al. (2026) Drought pattern under climate change in Harris County, Texas, USA based on CMIP6 projections
This study assessed future drought conditions in Harris County, Texas, using CMIP6 GCMs under various SSP scenarios for 2026–2085, revealing a substantial projected increase in drought frequency, intensity, and severity, especially in the far-future (2056–2085).
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Keoagile et al. (2026) Assessing Crop Yield Variability Using Meteorological Drought Indices for Agricultural Drought Monitoring in Botswana
This study assesses drought impact on Botswana's agricultural sector by evaluating the predictive power of various drought indices on crop yields and integrating local knowledge, revealing the sector's high vulnerability and the need for integrated early warning systems.
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Ouassanouan et al. (2026) Crop and irrigation types ground-truth dataset for Moroccan agricultural regions
This paper presents a comprehensive, open-access ground-truth dataset comprising 10,000 geolocated agricultural parcels in Morocco, detailing 45 crop types and 6 irrigation systems, to serve as a high-quality reference for calibrating and validating Earth Observation-based agricultural monitoring products.
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Liu et al. (2026) Global assessment of drought risk to expanded urban land from 2020 to 2100
This study assesses global drought risk for expanded urban land from 2020 to 2100 by integrating future urban land cover projections with climate, socioeconomic, and demographic data, finding a continuous increase in drought risk across all climate change scenarios, particularly in developing countries.
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Dong (2026) A Comparative Study of Machine Learning Models for Hourly Forecasting of Air Temperature and Relative Humidity
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Ku et al. (2026) Enhanced persistence of Ural blocking under strong positive AO: the role of North Atlantic storm tracks and potential vorticity dynamics
This study investigates how the magnitude of a positive Arctic Oscillation (AO) influences the persistence of Ural Blocking (UB) events. It finds that strong positive AO paradoxically enhances UB longevity by organizing North Atlantic storm tracks, leading to Arctic warming, sea ice loss, and a weakened meridional potential vorticity gradient that anchors the blocking system.
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MICHALKE et al. (2026) Field Trial of a Low-Cost Sensor Network for Hydrometeorological Monitoring of Water Pans and Small Dams in Kenya
This paper describes the development and field testing of a low-cost monitoring station network designed to measure water level, precipitation, and air temperature/humidity for small, decentralized water pans in rural areas. The system, costing approximately 93 USD per station, demonstrated potential for addressing data scarcity, with water level measurements proving accurate, despite inaccuracies in precipitation data and biases in air temperature.
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Wang et al. (2026) Anomaly Detection and Correction for High-Spatiotemporal-Resolution Land Surface Temperature Data: Integrating Spatiotemporal Physical Constraints and Consistency Verification
This paper proposes a two-stage anomaly detection and correction framework for high-spatiotemporal-resolution Land Surface Temperature (LST) data, integrating temporal physical constraints and spatial consistency verification. The method significantly enhances LST data quality by effectively distinguishing physically plausible weather changes from data errors, outperforming conventional statistical methods with substantial improvements in accuracy and correlation.
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Peng et al. (2026) Forecasting a Hailstorm in Western China Plateau by Assimilating XPAR Radar Network Data with WRF-FDDA-HLHN
This study evaluates the assimilation of high spatiotemporal resolution X-band phased-array radar (XPAR) data into the WRF model, combined with a humidity adjustment scheme, to improve hailstorm prediction over the Yun-Gui Plateau. It demonstrates that XPAR data assimilation significantly reduces model error and enhances the representation of rapid hail cloud evolution, especially when coupled with humidity adjustments.
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Liu et al. (2026) Deciphering divergent atmospheric river environments in extreme and non-extreme precipitation over the lower reach of Yangtze River Basin
This study investigates the distinct atmospheric environments of atmospheric rivers (ARs) that lead to extreme precipitation (EP) versus those that do not, focusing on the lower Yangtze River Basin (LYRB) over 32 summer seasons. It reveals that AR&EP events are characterized by enhanced moisture from mid-to-high latitudes, specific large-scale circulation anomalies, and the crucial role of the Mei-yu front, providing key insights for improved forecasting.
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Bo (2026) High Resolution Agricultural Irrigation Water Use Dataset In China
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Yu et al. (2026) Investigating the Dry–Wet Differentiation of the Yellow River Basin Driven by Climate Change and Anthropogenic Activities
This study investigates the long-term evolution and driving mechanisms of dry-wet patterns in the Yellow River Basin, constructing a TWSA-DSI for historical analysis (1995–2014) and projecting future changes (2026–2100) under SSP scenarios, finding a historical shift from aridification to humidification and projecting continued humidification driven primarily by precipitation.
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Zhang et al. (2026) Daily Snow Depth Fusion Products for Arid Regions of Central Asia
This study developed a high-precision daily snow depth fusion product for Central Asia (1980–2023) by integrating multiple existing snow depth products and in-situ observations using an XGBoost machine learning model, achieving significantly improved accuracy.
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Gogineni et al. (2026) An integrated machine learning and decomposition framework for enhanced drought prediction
This study introduces a novel integration-prediction framework combining multiple signal decomposition algorithms with machine learning models for enhanced drought prediction. It found that hybrid decomposition models significantly improved accuracy over standalone models, with the VMD-SVR model consistently demonstrating superior performance across the studied drought-prone regions.
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Horzum et al. (2026) Investigation of base flow changes in Konya Closed Basin
This study analyzed daily flow data from 1987 to 2022 at six sites in the Konya Closed Basin to determine base flow trends. It found a statistically significant decreasing trend in base flow, indicating increasing water scarcity and drought risk due to climate change.
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Demir (2026) Multi-Depth Soil Moisture Prediction Using Machine Learning Across Türkiye's Diverse Environments
This study developed a machine learning framework to predict soil moisture at multiple depths using environmental variables in Türkiye. The Extreme Gradient Boosting (XGBoost) model achieved strong accuracy (R² up to 0.74) and revealed depth-dependent and spatially varying controls on soil moisture dynamics.
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Mohajer et al. (2026) Key natural influences on groundwater storage changes in Central and Southern Arizona
This study quantifies the natural hydroclimatic controls on groundwater storage variability in Central and Southern Arizona using GRACE/FO data, revealing that natural factors account for approximately 16% of spatial variance, primarily driven by evapotranspiration, precipitation, and subsurface runoff. The research identifies distinct subbasin clusters based on their hydroclimatic responses, offering a transferable framework for groundwater sustainability assessments.
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Taylor et al. (2026) Assessing the Ability of Tree-Ring Derived Aridity Records to Detect Compound Drought and Heatwave Events
This study investigates whether extremes in the Palmer Drought Severity Index (PDSI), particularly those derived from tree-ring reconstructions, can identify past compound drought and heatwave events (CDHWs) in North America and Europe. It finds that in regions with strong land-atmosphere coupling, such as Central North America and Eastern Europe, negative summer PDSI values co-occur with precipitation deficits and high temperatures, allowing tree-ring PDSI reconstructions to identify CDHWs predating instrumental records.
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Boussalim et al. (2026) Integrated RUSLE-machine learning modeling for water erosion risk assessment under climate change in a Mediterranean semi-arid region: a comparison of LR, SVM, and RF models
This study integrates the RUSLE model with machine learning (LR, SVM, RF) to predict future water erosion risk in the Ksob watershed, Morocco, under climate change scenarios (SSP2-4.5, SSP5-8.5), demonstrating that Random Forest best models rainfall erosivity (R) and vegetation cover (C) factors, leading to a projected dominant downward trend in erosion risk by the 2030s and 2050s due to decreased rainfall erosivity and improved vegetation.
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Castelli et al. (2026) Editorial: Sociohydrology in drylands
This editorial synthesizes the contributions of a Research Topic on "Sociohydrology in drylands," highlighting the critical need for interdisciplinary approaches, integration of local knowledge, and consideration of socio-political dynamics to address water scarcity and foster sustainable human-water co-evolution in these vulnerable regions. It advocates for broadening sociohydrological research beyond flood-centric studies to encompass long-term water scarcity and justice issues prevalent in drylands.
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Xu et al. (2026) Semi-empirical model of soil organic matter and soil moisture content with bayesian joint inversion
This study developed a novel semi-empirical radiative transfer model (SW-ETM) and a Bayesian joint inversion framework to simultaneously estimate soil organic matter (SOM) and soil moisture content (SMC) from spectral reflectance, effectively addressing their mutual interference and significantly improving prediction accuracy.
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Nuriddinov et al. (2026) High Resolution Flood Extent Detection Using Deep Learning with Random Forest Derived Training Labels
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Kim et al. (2026) SIGMAformer: a spatiotemporal Gaussian mixture correlation transformer for global weather forecasting
This paper introduces SIGMAformer, a spatiotemporal Gaussian mixture correlation transformer for global multi-station weather forecasting, which integrates a dynamic spatiotemporal correlation (DSTC) mechanism with a Gaussian mixture pattern extractor (GMPE) to adaptively model nonlinear dependencies. The model consistently outperforms state-of-the-art forecasting models in global wind speed and temperature prediction, especially for extreme events, while providing interpretable insights into spatiotemporal patterns.
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Li et al. (2026) Improving seasonal prediction of global mean surface temperature by incorporating dynamic ENSO realistic forecasts
This study identifies an underrepresented ENSO-driven pantropical coupling mechanism as a major source of error in autumn-initialized global mean surface temperature (GMST) predictions. By incorporating skillful ENSO realistic forecasts into a new dynamic-statistical framework, the reliable GMST prediction lead-time is extended from two to four months, reducing hindcast errors by an average of 41% during 1980–2024.
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Liu (2026) Optimizing Multi-Agent Weather Captioning via Text Gradient Descent: A Training-Free Approach with Consensus-Aware Gradient Fusion
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Qin et al. (2026) Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors
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Stavrou et al. (2026) SmaAT-QMix-UNet: A Parameter-Efficient Vector-Quantized UNet for Precipitation Nowcasting
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Masquil et al. (2026) Deep S2P: Integrating Learning Based Stereo Matching Into the Satellite Stereo Pipeline
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He et al. (2026) Unraveling future hydrological and sediment dynamics through an integrated GCMs-PLUS-SWAT coupling framework
This study developed an integrated GCMs-PLUS-SWAT framework to project future hydrological and sediment dynamics in the Yangtze River Basin under SSP245 and SSP585 scenarios, revealing significant increases in precipitation and temperature, distinct intra-annual streamflow redistribution, and spatiotemporal divergence in sediment transport.
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Al-Yaqoubi et al. (2026) Modeling the fresh–saline water interface dynamics in coastal aquifers under managed aquifer recharge (MAR)
This study utilized the SEAWAT numerical model, calibrated against sand-tank experiments, to simulate and analyze saline water dynamics in a coastal unconfined aquifer under Managed Aquifer Recharge (MAR) conditions, demonstrating that MAR effectiveness is highly dependent on aquifer hydraulic conductivity, saline water density, and injection rate.
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Cai et al. (2026) Fusion-Based Regional ZTD Modeling Using ERA5 and GNSS via Residual Correction Kriging
This study proposes a Residual Correction Kriging (RK ZTD) method to fuse sparse Global Navigation Satellite System (GNSS) Zenith Tropospheric Delay (ZTD) data with continuous but biased ERA5 ZTD grids, significantly improving the precision and mitigating systematic biases of regional ZTD products in the Netherlands.
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Lu et al. (2026) Application and Comparison of Two Transformer-Based Deep Learning Models in Short-Term Precipitation Nowcasting
This study systematically compares Earthformer and LLMDiff, two Transformer-based deep learning models, for short-term extreme precipitation nowcasting using the SEVIR dataset, finding Earthformer excels for rapid early warning of light precipitation at shorter lead times (0-30 minutes) while LLMDiff is better for high-accuracy nowcasting of heavy precipitation at longer lead times (up to 60 minutes).
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Zhou et al. (2026) Slope-Controlled Partitioning of Vertical and Lateral Solute Transport Pathways Revealed by Inclined Leaching Experiments
This study investigated how slope influences the partitioning of vertical and lateral transport pathways for a highly mobile solute (PFOA) using laboratory-scale experiments. It found that solute transport shifts from vertical-dominated under flat conditions to lateral-dominated at moderate slopes, a shift well described by an exponential partitioning model with a critical crossover at approximately 4° slope.
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Ma et al. (2026) SGCAD: A SAR-Guided Confidence-Gated Distillation Framework of Optical and SAR Images for Water-Enhanced Land-Cover Semantic Segmentation
This paper introduces SAR-guided class-aware knowledge distillation (SGCAD) to resolve fusion conflicts in multimodal SAR and optical semantic segmentation, particularly for critical categories like water bodies, by leveraging SAR as a water-expert teacher and enhancing boundary continuity.
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Jurgens et al. (2026) Decadal Shifts in Groundwater Age Detected by Environmental Tracers Across California, USA
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Fu et al. (2026) Land-atmosphere feedbacks and anthropogenic greenhouse gas forcing intensify subseasonal drought-to-pluvial abrupt transitions
This study investigates subseasonal drought-to-pluvial abrupt transitions, revealing their global occurrence with an average probability of 45% and identifying land-atmosphere feedbacks as a key intensifying mechanism. Under high-emissions scenarios, both the frequency and probability of these transitions are projected to increase across over 75% of global land, primarily driven by anthropogenic greenhouse gas forcing.
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Pepelnik et al. (2026) Isotopic Composition of Precipitation and Its Role in Forest Hydrology Under Climate Change: Insights from Slovenian Lowland Forests
This study systematically analyzed 65 years of air temperature and precipitation changes in two Slovenian lowland forests, combining it with throughfall isotopic composition. It found that rising temperatures and altered precipitation patterns are reflected in throughfall isotopes, confirming extreme events and aiding in estimating groundwater residence time and tree water origin.
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Belarbi et al. (2026) Machine learning estimation of reference evapotranspiration using MODIS-Derived and limited ground variables across Moroccan agro-climatic zones
This study evaluates machine learning models for estimating daily reference evapotranspiration (ETo) in data-scarce Moroccan agro-climatic zones, demonstrating that MODIS remote sensing and limited ground variables can achieve high accuracy and support water management, despite challenges in inter-regional transferability.
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Nguyen et al. (2026) PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data
This study introduces PERSIANN-Unet (PUnet), a new quasi-global, high-resolution, near-real-time precipitation algorithm leveraging infrared (IR) data and a UNet architecture. PUnet provides half-hourly, 0.04° precipitation estimates, closely matching its training target (IMERG V07 Final) globally and demonstrating good performance against Stage IV over the Continental United States (CONUS).
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St-Pierre et al. (2026) Emergence time of CO2-forced European summer climate trends
This study quantifies the Time of Emergence (ToE) for European summer climate trends, including near-surface temperature, soil moisture, and the hydrological cycle, using a large ensemble climate model. It reveals rapid emergence for near-surface temperature (20-70 years) but delayed or absent emergence for precipitation, while demonstrating that extreme summer climate distributions are significantly altered even when mean trends do not formally emerge from natural variability.
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Dong et al. (2026) A fully automated OPTRAM (aOPTRAM) for soil moisture retrieval: Evaluating multiple fitting functions, vegetation indices, land-cover types, and scales
This study introduces a fully automated Optical Trapezoid Model (aOPTRAM) for high-resolution soil moisture retrieval, systematically evaluating its performance across diverse ecosystems using Sentinel-2 imagery and in-situ data. It demonstrates that aOPTRAM, without manual calibration, achieves performance comparable to optimal OPTRAM, providing a fast and robust framework for monitoring soil moisture in heterogeneous landscapes.
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Rooyen et al. (2026) Anthropogenic tritium as a continental-scale tracer in river-derived recharge
This study evaluates anthropogenic tritium (³H) and natural stable isotopes (δ¹⁸O, δ²H, deuterium excess) as tracers to quantify groundwater flow dynamics and travel times in an alluvial Managed Aquifer Recharge (MAR) system along the Rhine River in Switzerland, demonstrating their effectiveness for sustainable groundwater management.
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Shin et al. (2026) Negative CO2 emissions for long-term mitigation of extremes in land hydrological cycle
This study investigates terrestrial precipitation and vegetation feedbacks under idealized zero and negative CO2 emissions scenarios, finding that sustained negative emissions are crucial for long-term mitigation of hydrological extremes and enhanced water availability, primarily due to amplified transpiration.
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Ukkola et al. (2026) Future changes in seasonal drought in Australia
This study assesses future seasonal drought changes across Australia using an ensemble of 32 hydrological simulations, revealing widespread increases in meteorological, hydrological, and agricultural droughts, particularly in populated and agricultural regions, with Global Climate Models (GCMs) being the dominant source of uncertainty.
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Thompson et al. (2026) Simulation of the Hydro-ecological Impacts of Climate Change on an Upland Peatland in the Massif Central
This study assesses the hydro-ecological impacts of 60 climate change scenarios on peat ecosystems in the Dauges National Nature Reserve using high-resolution hydrological modeling. Results project increased hydrological seasonality, with wetter winters and drier summers, leading to declining summer peat groundwater levels and a reduction in the area suitable for mire vegetation, particularly at peatland margins.
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Irifi et al. (2026) Landscape Dynamics of Marly Slopes in the Lower Valley of Wadi Tamri (Morocco): An Integrated Approach Using Geomorphometry, Toposequences, and Remote Sensing
This study investigates the landscape dynamics of marly slopes in the lower Wadi Tamri valley, Morocco, using an integrated approach of geomorphometry, remote sensing, and field observations. It reveals significant landscape degradation, characterized by a substantial loss of Argan forest cover and an increase in exposed marly substrate, driven by recurrent droughts and human activities, leading to accelerated gully erosion and slope instability.
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Akbary et al. (2026) Projected changes in sub-daily extreme precipitation: comparing temperature-scaling approaches and convection-permitting models across an Alpine gradient
This study evaluates the reliability of temperature-scaling approaches for projecting sub-daily extreme precipitation changes by comparing them against convection-permitting model (CPM) outputs across a complex Alpine region. It finds that optimal scaling rates vary with duration and return period, and their reliability is modulated by local variability, seasonality, and elevation.
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Chen et al. (2026) An integrated framework for mapping agricultural water impoundments using Sentinel −2 and GEE in Northwest China
This study developed an integrated framework using Sentinel-2 and Google Earth Engine to accurately map and monitor small-scale agricultural water impoundments (AWIs) in arid regions, providing the first high-resolution, multi-year inventory for the Hexi Corridor.
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Ye et al. (2026) Anthropogenic climate change amplifies autumn heatwave risks for children during school reopening
This study attributes and projects the risk of autumn heatwaves for children during school reopening in China, revealing that anthropogenic climate change has significantly amplified the frequency and intensity of such heatwaves, increasing children's exposure risk by approximately 55% under the 2024 climate. Projections indicate continued increases in heatwave intensity, which will eventually outweigh declining child populations, leading to rising exposure risks by the end of the century under high emission scenarios.
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Wang et al. (2026) Interpretable WTConv1D-BiLSTM monthly-scale precipitation prediction model based on novel multilevel and multi-scale decomposition
This study proposes an interpretable deep-learning framework, WTConv1D-BiLSTM, for accurate monthly precipitation prediction by integrating novel multilevel and multi-scale decomposition techniques to address nonstationarity and scale mixing. The model demonstrates superior performance and interpretability in predicting monthly precipitation across 30 provinces in mainland China.
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Akiner et al. (2026) Is runoff the key input of evapotranspiration?: AI-based hydro-climatic assessment in Southeastern Türkiye’s Dams Region
This study assesses hydro-climatic variability and evapotranspiration dynamics in Southeastern Türkiye's Dams Region using AI models and long-term reanalysis data. It identifies runoff as the most critical, non-linearly influential factor in evapotranspiration, highlighting its importance for water resource management in semi-arid, dam-regulated environments.
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Westfall (2026) Shifts in Transport Pathways Before, During, and After Drought
This thesis investigates the cause of a long-term shift in Victoria's water balance, finding that changes in stream salinity suggest reduced water and salt transport to streams due to long-term shifts in the vertical groundwater gradient.
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Xv et al. (2026) Biophysical regulation mechanisms of land surface temperature driven by the spatiotemporal evolution of cropland
This study systematically investigated the biophysical mechanisms linking cropland evolution to land surface temperature (LST) variations across China from 2000 to 2020, revealing spatially heterogeneous effects where cropland expansion caused warming in arid northwestern regions and cooling in northeastern regions, primarily driven by ground heat flux, surface emissivity, and albedo.
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Lv et al. (2026) An adaptive decomposition-denoising and temporal context fusion framework for multi-station water-level forecasting
This study constructs a high-resolution hydrological dataset for the Mengjiang River Basin and proposes a hybrid deep learning framework, CEEMDANVF-WD-TCF-LSTM, for multi-station water-level forecasting. The framework demonstrates superior accuracy and stability, particularly in mitigating multi-step error accumulation for both short-term and medium-term predictions.
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Fathi et al. (2026) Toward accelerating fluvial morphodynamic simulations through a speed accuracy trade-off assessment
This study evaluates the combined application of morphological acceleration factor (morfac) and condensed hydrograph inputs to accelerate physics-based fluvial morphodynamic simulations. The integration of these two techniques achieved a theoretical computational efficiency exceeding a 98.8% reduction in total runtime, enabling more feasible long-term simulations.
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Wang et al. (2026) Seasonal variation patterns and drivers of baseflow recession dynamics across Australia
This study quantifies the event-scale seasonal variation of the baseflow recession parameter 'a' across 596 Australian catchments and identifies vegetation, temperature, and evaporative demand as key drivers using machine learning.
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Choukri et al. (2026) Comparative analysis of CHIRPS and ERA5-Land for precipitation and drought assessment in Morocco (1987–2016)
This study comprehensively compared CHIRPS and ERA5-Land precipitation products against 114 ground stations in Morocco (1987–2016) to evaluate their performance in precipitation estimation and drought detection across various temporal scales and altitudes. It found ERA5-Land generally more accurate for precipitation variability and long-term drought, while CHIRPS showed limitations, especially in mountainous regions and for drought severity.
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El-Haddad et al. (2026) Flood and heavy metal risks from wastewater site in Sohag Governorate, Egypt: integrating hydrological modeling and mapping
This study evaluates flood inundation hazards and heavy metal contamination from untreated wastewater disposal sites in the Al-Kola Basins, Sohag, Egypt, by integrating hydrological models and geochemical analysis. It found that increased rainfall significantly exacerbates flood hazards and can mobilize high concentrations of anthropogenic heavy metals into critical surface water systems like irrigation canals and the River Nile.
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Hamed et al. (2026) How the carbon emission reduction scenarios affect drought patterns in the Middle East and North Africa region
This study assesses how carbon emission reduction scenarios, aligned with the Paris Agreement's 1.5 °C and 2.0 °C warming targets, affect drought patterns in the Middle East and North Africa (MENA) region. The findings project a significant increase in drought frequency, intensity, and duration across MENA, with extreme droughts becoming dominant, primarily driven by increased potential evapotranspiration variability, even under these mitigation efforts.
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Wang et al. (2026) Spatiotemporal dynamics of global surface and rootzone soil moisture: a comprehensive assessment from dominant factors, impact pathways, and deficit probability
This study comprehensively assessed global surface and root-zone soil moisture dynamics from 2001 to 2021, identifying atmospheric water demand as the primary driver of aridity and revealing vegetation's mediating role in climate-soil moisture interactions, with precipitation, SPEI, and vegetation dynamics as key deficit risk factors.
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Laassilia et al. (2026) Integrated Multi-scale Assessment of CHIRPS and PERSIANN-CDR for Meteorological, Agricultural, and Hydrological Drought Monitoring in Semi-arid Environments
This paper evaluates two satellite precipitation products (CHIRPS and PERSIANN-CDR) for multi-scale meteorological, agricultural, and hydrological drought monitoring in the semi-arid Moulouya Basin, Morocco. The study found that CHIRPS generally outperforms PERSIANN in accuracy and event-scale detection, while both products effectively capture observed drought patterns and reveal a progressive aridification trend in the region.
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Niu et al. (2026) Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction
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Roundy (2026) Zonal Propagation of the Indian Basin MJO Across Varying Background Wind and Seasonal Background Wind States
This paper investigates the seasonal variability of the Madden-Julian Oscillation's (MJO) eastward propagation and its relationship with equatorial upper tropospheric background wind patterns, finding that propagation speed is strongly modulated by the strength of these background winds and that upper tropospheric signals are often stronger than lower tropospheric ones.
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Fiorillo et al. (2026) Magnitude of hydrological events and extremes using the Z value
This study introduces a statistical method using the dimensionless Z value, derived from standardizing hydrological time series, to quantify event magnitude and define extremes. It demonstrates that the Z value offers a more stable and context-invariant measure of hydrological event magnitude than the traditional return period, particularly for extreme events.
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Ahmed et al. (2026) Application of Precision Agriculture and IoT-Based Smart Irrigation in Greenhouse Vegetable Production
This review synthesizes the application of Precision Agriculture and IoT-based smart irrigation systems for optimizing greenhouse vegetable production, demonstrating significant reductions in water use and energy consumption while enhancing crop yield and nutrient efficiency.
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Yin et al. (2026) Exploring a process-aware spatiotemporal graph-based surrogate for integrated urban drainage simulation
This study proposes PAST, a Process-Aware Spatio-Temporal graph-neural-networks-based surrogate model, to efficiently simulate integrated urban drainage processes by holistically representing rainfall–runoff-routing and incorporating regulation effects. PAST achieves high performance and physical explainability, significantly outperforming baseline models, especially under regulated and extreme rainfall conditions.
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Belghit et al. (2026) Applying AdaBoost algorithm on multiclass OvA-SVM for the delineation of rainy clouds using multispectral MSG-SEVIRI data
This study implements and evaluates an AdaBoost-enhanced multiclass One-versus-All Support Vector Machine (AdaOvA-SVM) model for classifying and delineating precipitating clouds in northern Algeria using satellite and radar data, demonstrating its superior performance compared to existing techniques.
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Khattaoui et al. (2026) Runoff–Based Streamflow Modeling at a Catchment Outlet Using HEC-HMS
The study evaluated the HEC-HMS model for ungauged basins, revealing significant variability in initial abstraction (IA) across three catchments, thus emphasizing the need for site-specific IA assessment for accurate hydrological modeling.
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Fronzi et al. (2026) Toward quantitative DNA tracer tests: development and validation of a novel capture device for groundwater flow characterization in karst and carbonate aquifers
This study develops and validates a novel passive device for selectively capturing biotinylated synthetic DNA tracers, enabling time-integrated, semi-quantitative assessment of groundwater flow. Laboratory and field tests demonstrate that the device produces breakthrough curves consistent with conventional tracers, facilitating broader application of DNA tracers in complex hydrogeological systems.
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Prayag et al. (2026) Assessing infiltration dynamics using integrated hydrogeophysical monitoring in a managed aquifer recharge pond
This study investigated infiltration dynamics in a Managed Aquifer Recharge (MAR) pond over eight months using an integrated hydrogeophysical monitoring system (automated Direct Current Resistivity and Induced Polarisation (DCIP), Ground Penetrating Radar (GPR), and hydrological data). It revealed subsurface heterogeneity and time-dependent infiltration pathways, including a high-permeability westward-dipping layer, and identified signs of clogging affecting lateral water spreading.
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Yang et al. (2026) Fusing dynamic physical constraints with PINN-xLSTM to enhance accuracy and physical consistency in runoff prediction under extreme hydrological events
This study introduces a novel PINN-xLSTM model with a dynamic physical constraint weighting mechanism to enhance runoff prediction accuracy and physical consistency, particularly during extreme hydrological events. The model demonstrates superior performance in accuracy, flood peak characterization, and adherence to hydrological principles compared to existing models.
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Jaeger et al. (2026) Lost in translation: Reconciling different streamflow permanence data products
This study develops a framework to reconcile and evaluate two streamflow permanence datasets (NHDPlus HR and PROSPER model output) for the Pacific Northwest, finding 68% agreement regionally and identifying reliability patterns to inform land and water management decisions.
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Feldman et al. (2026) Widespread Co‐Location of Less Frequent and More Intense Daily Precipitation Over Land
This study investigates the global co-location of trends towards more intense and less frequent daily precipitation events, finding that fewer, larger events are common and distributed across terrestrial ecosystems, often counteracting increases in annual precipitation totals due to simultaneous decreases in small-to-moderate events.
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Huang et al. (2026) Impacts of timescales on the relationship between compound drought-hot extremes based on precipitation and groundwater
This study investigates the spatial distribution and differences between compound groundwater droughts and hot extremes (CGDHEs) and compound meteorological droughts and hot extremes (CMDHEs), attributing these differences to hydrological lags. It identifies optimal precipitation timescales to reduce these discrepancies, thereby improving the potential for near-real-time monitoring of CGDHEs.
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Li et al. (2026) Global Agricultural Drought Crisis: Synergistic Impacts of Climate Change and Human Activities and Their Feedback Mechanisms
This review synthesizes the synergistic impacts of climate change and human activities on global agricultural drought, revealing how their interactions form amplifying feedback loops that intensify drought frequency, intensity, duration, and spatial extent, leading to ecological degradation, crop yield loss, and socioeconomic inequality. It proposes a three-dimensional framework integrating mitigation, adaptation, and collaborative governance to address this escalating crisis.
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Dastjerdi et al. (2026) Comparing novel backward hydrological models for watershed-scale precipitation estimation: an evaluation of inverted PDM and Kirchner-hybrid structures
This study developed and evaluated two novel backward hydrological models, an inverted Probability Distributed Model (PDM) and a hybrid Soil Moisture to Rain (SM2RAIN)-Kirchner model, for daily watershed-scale precipitation estimation. The locally calibrated backward models significantly outperformed established Global Gridded Precipitation Products (GGPPs), with the Kirchner model achieving the highest performance (KGE = 0.62) and the inverted PDM proving robust (KGE = 0.55).
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Chen et al. (2026) A novel soil moisture retrieval method via combining radiative transfer model and machine learning
This study introduces a novel, interpretable soil moisture retrieval framework by integrating a radiative transfer model (RTM) with a Kolmogorov–Arnold Network (KAN) to derive explicit mathematical expressions from satellite observations, achieving global soil moisture estimates comparable in accuracy to the SMAP Level-3 product.
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Zarei (2026) Medium -term monitoring and machine learning-based forecasting of drought dynamics in Iran
This study comprehensively assesses historical drought conditions in Iran from 1967 to 2024 and forecasts decadal drought dynamics for 2025–2036 using climate observations and machine learning, revealing a projected significant increase in drier conditions and the disappearance of extreme wet periods.
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Kuntiyawichai et al. (2026) Combined drought index for drought monitoring and severity assessment under future climate and land use changes
This study developed a Combined Drought Index (CDI) for the Prom-Choen-Upper Phong River Basin using Principal Component Analysis (PCA) of SPEI, SSFI, and SSDI to assess future drought risk under climate and land use changes. It found that while increased future rainfall may reduce overall drought risk, the SSP585 scenario significantly expands very high-risk drought areas, underscoring the need for the CDI in mitigation strategies.
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Khorami et al. (2026) Estimation of root zone water storage capacity (S) in natural ecosystems subject to high interannual climate variability
This study evaluates two cumulative water deficit (CWD) methods for estimating root zone water storage capacity (Sr) in 105 Australian forested catchments, finding that a multi-year CWD approach yields significantly higher Sr estimates (median 25% higher) in regions with high interannual climate variability compared to the conventional single-year method.
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Zhang et al. (2026) Dynamic conversion coefficients improve alpine lake daily evaporation estimation based on multi-evaporator observations
This study developed a refined pan conversion method with dynamic coefficients, based on multi-evaporator observations, to estimate daily lake surface evaporation (LSE) for alpine lakes, including ice-covered periods, and quantified the meteorological controls on LSE variability.
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Wu et al. (2026) EcoTWIN 1.0: a fully distributed tracer-aided ecohydrological model tracking water, isotopes, and nutrients
This paper introduces EcoTWIN 1.0, a fully distributed tracer-aided ecohydrological model that simultaneously tracks water, isotope, and nutrient fluxes. The model demonstrated good performance in reproducing calibrated in-stream targets and uncalibrated internal fluxes across 17 large European catchments, proving its flexibility and transferability for prediction and process inference in diverse terrestrial ecosystems.
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Zowam et al. (2026) Climate Variability and Groundwater Levels: A Correlation and Causation Analysis
This study investigated the dynamic relationship between terrestrial water cycle intensity (WCI) and groundwater level (GWL) anomalies in arid Arizona, USA, using statistical correlation and causation analyses. It found a dominantly negative, lagged relationship where GWL changes typically precede WCI responses by 1–2 months, implying that an intensified water cycle may signal already depleting groundwater resources.
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Simantiris et al. (2026) AIFloodSense: A Global Aerial Imagery Dataset for Semantic Segmentation and Understanding of Flooded Environments
This paper introduces AIFloodSense, a comprehensive and globally diverse evaluation benchmark designed to advance domain-generalized Artificial Intelligence for climate resilience and flood detection. It demonstrates that rigorous dataset diversity, rather than sheer scale, is more effective for training robust flood detection models, leading to superior generalization capabilities.
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Jasechko (2026) Global cases of groundwater recovery after interventions
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Szanyi et al. (2026) Assessment of Changes in Groundwater Resources Due to Climate Change for the Purpose of Sustainable Water Management in Hungary
This study assessed climate and pumping impacts on the Nyírség groundwater system using monitoring and modeling, finding that climate-driven recharge reductions will dominate basin-scale declines by 2050, with managed aquifer recharge offering localized benefits.
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Maragkaki et al. (2026) Hydrological and Geochemical Modeling of Water Availability and Quality in the Jordan Valley Under Climate Change
This study applied an integrated hydrological and hydrogeochemical modeling framework to quantify water availability and quality and assess climate change impacts in the Jordan Valley, revealing it is evapotranspiration-dominated, highly dependent on imported irrigation, and faces exacerbated water scarcity under future climate change.
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Li et al. (2026) Assessing the global performance of a parsimonious soil temperature model for frozen ground prediction
This study globally evaluates a simplified soil temperature model for predicting frozen/unfrozen ground states using only air temperature and snow cover data. The model demonstrates robust global performance (average true frozen rate of 0.90, false frozen rate of 0.06), offering a computationally efficient solution for hydrological models, though it shows limitations in mountainous regions.
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Liu et al. (2026) A snow-fire bridge mechanism for the 2025 Southern California winter wildfire
This study identifies a "snow-fire bridge" mechanism where reduced western Eurasian snow cover triggers an atmospheric teleconnection, leading to weather conditions favorable for winter wildfires in Southern California, as observed during the January 2025 event.
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Wang et al. (2026) A novel adaptive soil moisture retrieval method via stacked ensemble learning and a local Bayesian dynamic algorithm
This study introduces a novel local Bayesian dynamically weighted stacking ensemble learning model (Stacking-BO) and a high-resolution spatiotemporal multilayer soil moisture simulation framework to enhance the accuracy and stability of soil moisture retrieval, demonstrating superior performance over existing methods.
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Mesallam et al. (2026) Strategic dam site selection and hazard mapping using remote sensing: insights from Wadi Araba, Egypt
This study developed an integrated remote sensing, GIS, and Analytical Hierarchy Process (AHP) framework to map flash flood susceptibility and identify optimal dam sites for flood mitigation and groundwater recharge in Wadi Araba, Egypt. The framework successfully delineated flood-prone areas and prioritized dam locations, demonstrating its utility for water resource management in hyper-arid regions.
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Jin et al. (2026) Spatiotemporal evolution and hyetograph changes of global extreme precipitation
This study systematically analyzes the global spatiotemporal evolution of annual maximum 3-hour precipitation events and their hyetograph changes, revealing a global decline in peak intensity but an increase in total event precipitation due to more temporally distributed rainfall, which is likely to exacerbate flood risk.
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Redaelli et al. (2026) Changes in irrigation practices may deplete aquifers faster and more severely than meteorological droughts: A numerical modeling approach
This study quantitatively assessed the drivers of aquifer depletion in an intensively irrigated system, finding that changes to more efficient irrigation practices, which reduce irrigation return flow, have a more severe impact on groundwater resources than meteorological droughts.
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Alhathloul (2026) Spatiotemporal Trends and Abrupt Changes in Annual Potential Evapotranspiration and Water Balance over Saudi Arabia
This study investigates the interannual variability, long-term trends, and abrupt regime shifts in potential evapotranspiration (PET) and water balance (WB) across Saudi Arabia from 1985 to 2022. It reveals a widespread increase in atmospheric evaporative demand and declining WB, indicating an intensifying water deficit, with a significant hydroclimatic regime shift identified in the late 1990s.
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Garrido et al. (2026) A Scalable Method to Delineate Active River Channels and Quantify Cross-Sectional Morphology from Multi-Sensor Imagery in Google Earth Engine Using the Photo Intensive System for Channel Observation (PISCOb)
This study developed and validated an automated Google Earth Engine workflow using multispectral indices from Landsat and Sentinel-2 to delineate active channel width, finding Sentinel-2 with MNDWI-EVI provided the highest accuracy and highlighting the importance of local geomorphic and ecological conditions for threshold selection.
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Nie et al. (2026) Soil Moisture Retrieval Using Multi-Satellite Dual-Frequency GNSS-IR Considering Environmental Factors
This study developed a dual-frequency GNSS-IR framework for soil moisture retrieval, integrating multi-satellite observations and environmental factors. It found that retrieval performance converges with 5-6 satellites per constellation, and that dual-frequency fusion and environmentally informed nonlinear models significantly enhance accuracy and stability.
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Zhang et al. (2026) Integrating GLASS LAI into the SWAT Model for Improved Hydrological Simulation in Semi-Arid Regions
This study modified the SWAT model by integrating high-resolution Global Land Surface Satellite (GLASS) Leaf Area Index (LAI) data to improve hydrological simulations in the semi-arid Wuding River Basin, significantly enhancing runoff and evapotranspiration accuracy by correcting unrealistic vegetation dynamics.
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Kumar et al. (2026) Estimation of location-specific precipitation using Deep Neural Networks
This study introduces two Deep Neural Network (DNN) architectures for location-specific precipitation estimation, demonstrating their superior accuracy and computational efficiency compared to traditional Kriging methods across various meteorological conditions in India. The DNN models, especially one incorporating additional meteorological variables, consistently outperform Kriging in capturing spatial precipitation patterns and extreme events.
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Mo et al. (2026) Self-Supervised Reservoir Water Area Detection Across Multi-Source Optical Imagery
This paper develops a geo-spectral feature-guided Self-Supervised Water Detection (SWD) framework for automated, multi-source optical imagery to monitor reservoir water extent. The SWD framework outperforms supervised methods, demonstrating high consistency and stable generalization across scales and regions, and accurately captures water-level fluctuations without manual labels or model training.
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Mondal et al. (2026) Advancements in Spatio-temporal agricultural drought monitoring and modeling: a comprehensive review on multi-source remote sensing and machine learning techniques
This comprehensive review synthesizes advancements in spatio-temporal agricultural drought monitoring and modeling, focusing on the integration of multi-source remote sensing data with machine learning (ML) and deep learning (DL) techniques. It highlights the effectiveness, cost-efficiency, and transferability of these advanced geospatial methods for assessing and predicting agricultural drought conditions across various scales.
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Johnston et al. (2026) The snow meteorology and phenology classification (SnowMAP): global snow cover observations enhance snow’s representation
This study introduces SnowMAP, a novel global snow classification system that integrates meteorological controls (snowfall, temperature, wind) with snow phenology (seasonal presence, melt timing), providing a more complete and decision-relevant view of global snow conditions. The system identifies 18 distinct snow classes that reflect variations in snow depth, geography, land cover, and infrastructure, enhancing the understanding of snowpack formation and evolution.
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Fengour et al. (2026) A taxonomy-based benchmark of parametric and non-parametric machine learning models for data-driven precipitation prediction in Morocco
This study introduces a taxonomy-based benchmark of parametric and non-parametric machine learning models for data-driven precipitation prediction in Morocco. It demonstrates that non-parametric models consistently outperform parametric models, effectively capturing the complex, non-linear relationships inherent in highly intermittent and zero-inflated rainfall data.
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Aftab et al. (2026) A Daily Soil Moisture–Temperature Compound Index for Characterising Dry–Hot Extremes
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Giuseppe et al. (2026) Climate in a Mediterranean nature reserve: patterns and trends in the Castelporziano Presidential Estate (Italy)
This study analyzes seasonal and annual patterns and trends of temperature and precipitation, including extreme events, in the Castelporziano Nature Reserve (Italy) from 1980, revealing a significant warming trend, particularly for maximum temperatures and extreme heat indices, influenced by coastal proximity, while precipitation shows no significant trend.
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Hadjipetrou (2026) A review of statistical methods for climate downscaling: the underexplored potential of geostatistical simulation
This review synthesizes developments in statistical and stochastic climate downscaling, critically assessing various methods including regression, weather generators, analogs, and machine learning. It highlights the significant, yet underexplored, potential of geostatistical simulation, particularly Multiple-Point Statistics, to provide spatially coherent and uncertainty-aware fine-scale climate information.
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Galli et al. (2026) Integrating biophysical models and remote sensing to evaluate irrigation practices in four global hubs
This study compares irrigation demand from an agro-hydrological model (WATNEEDS) with irrigation water use from five satellite products across four global irrigation hubs, finding significant correlations (above 0.6 for 3/4 cases) and using discrepancies to identify hydroclimatic and anthropogenic irrigation drivers.
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Asadzadeh et al. (2026) Water detention structures as a flood mitigation strategy: A case study of the Elgin Creek Basin
This study developed a computationally efficient, system-scale modeling framework to assess flood risks and evaluate water detention structures for mitigating road washouts in data-scarce Prairie basins. It identified five strategically located detention dams that collectively eliminate road washout risk for floods up to the 100-year return period in the Elgin Creek Basin.
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Al-Timimi et al. (2026) Effects of climate change on temperature and precipitation in the Euphrates-Tigris Basin
This study comprehensively assesses projected climate change impacts on temperature and precipitation across the entire Euphrates-Tigris Basin under four RCP scenarios (2.6, 4.5, 6.0, 8.5) for three future periods, revealing a consistent warming trend and significant spatial redistribution of precipitation, with severe implications for downstream water resources.
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Yan et al. (2026) Attribution of runoff changes in the semi-arid Xiliao River Basin – A Budyko-elasticity approach to deconstructing compound climate and human impacts
This study quantified the contributions of climate change and human activities to runoff reduction in the semi-arid Xiliao River Basin from 1980 to 2022 using a Budyko-elasticity framework, revealing an abrupt runoff decline in 2002 with significant spatial heterogeneity: upstream areas are climate-driven, while middle-downstream areas are human-driven, primarily due to agricultural irrigation.
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Saini et al. (2026) Development of a Virga Detection Tool and Associated Study of Arctic Virga and Precipitation
## Identification - **Journal:** Journal of Geophysical Research Atmospheres - **Year:** 2026...
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Zhang et al. (2026) Impact of spatial scale on the sensitivity of the water supply-demand balance to driving factors
This study develops an integrated water footprint accounting framework to diagnose water stress and its drivers across grid (~1 km²), municipal, and sub-basin scales in the Yellow River Basin from 2000 to 2024. It reveals a widening upper-to-lower reach divergence in water stress, driven by coupled socio-hydrological interactions that are often masked by aggregated analyses, providing scale-differentiated management recommendations.
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Wang et al. (2026) Quantitative identification of the impact of human activities and climate change on sediment load in the Yellow River Basin of China
This study quantitatively identified the contributions of climate change and human activities to sediment load variations across the Yellow River Basin from 1961 to 2022, revealing a progressive shift from climate-dominated to human-dominated controls, particularly in midstream and downstream reaches.
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Han et al. (2026) SWOT performance in monitoring water level of high-mountain lakes on the Tibetan Plateau
This study introduces a novel Gaussian kernel density estimation approach to retrieve water levels of high-mountain lakes from SWOT observations, demonstrating that SWOT reliably captures variations in water level (average r = 0.72, RMSE = 0.29 m) and has transformative potential for monitoring global small water bodies.
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Wu et al. (2026) Comparative analysis of SAR-based soil moisture inversion methods for crop-covered under cloudy, rainy, and irrigation conditions
This study developed a scenario-adaptive framework for soil moisture inversion in crop-covered areas, integrating Radarsat-2 SAR and HJ-2A/B optical data with Random Forest (RF) and the Water-Cloud Model (WCM). It found that direct optical-SAR fusion via RF achieved the highest accuracy (R² = 0.90) under clear conditions, while the VWC-coupled WCM was optimal (R² = 0.61) for cloudy, rainy, or irrigation scenarios.
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Kavi (2026) Climate change and variability: an analysis of trends in rainfall and temperature in the Volta and Northern Regions of Ghana
This study analyzed climate change and variability in the Volta and Northern Regions of Ghana over a 39-year period (1984–2023), focusing on temperature and rainfall trends and their implications for agriculture. Findings reveal a statistically significant decreasing rainfall trend in the Northern Region and significant upward trends in both maximum and minimum temperatures across both regions, indicating a warming climate with risks for agricultural productivity.
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Ducco et al. (2026) The role of precipitation and irrigation on groundwater droughts in the Piemonte Plain, Italy
This study investigates the relationship between meteorological and groundwater droughts in the shallow aquifers of the Piemonte Plain, Italy, focusing on the impact of widespread irrigation. It finds that irrigation significantly weakens the correlation between precipitation and groundwater levels, delays groundwater response, and mitigates the propagation of meteorological drought into severe groundwater drought, particularly in rice-cultivated areas.
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An et al. (2026) Scenario-dependent responses of soil conservation service flow to climate change across karst development gradients in the Pearl River Basin
This study quantifies the scenario-dependent responses of soil conservation service flow (SCSF) to climate change across different karst development degrees (KDDs) in the Pearl River Basin using a coupled Global Climate Model (GCM)-Soil and Water Assessment Tool (SWAT) framework, revealing that SCSF dynamics are significantly influenced by both climate scenarios and karst geomorphology.
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SHEN et al. (2026) Estimation of water savings from farmland fallowing in the tarim river basin under food security and ecological security constraints and threshold effects of driving factors
This study developed a framework to estimate water savings from farmland fallowing (FLWC) in the Tarim River Basin under food and ecological security constraints. It found that suitable fallow areas and FLWC peaked in 2015, with cotton exhibiting the highest water-saving potential, and identified mean air temperature and mean relative humidity as primary climatic drivers with threshold effects on FLWC.
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Bhuiyan et al. (2026) Improving coastal water level estimation by merging nadir-only satellite altimetry data into a hydrodynamic model
This study evaluates a novel method to improve coastal water level (CWL) predictions by assimilating nadir-only satellite altimetry data from four missions into the ADCIRC hydrodynamic model along the U.S. East Coast, finding that combined assimilation significantly enhances model performance at over 80% of gauge locations.
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Tiwari et al. (2026) Examining the Changes in Precipitation Patterns Across the Western Himalayan Region During the Winter Season
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Yadav et al. (2026) Cooling Effects of Wetlands in a Tropical Megacity: Evidence from the East Kolkata Wetlands, India
This study assesses the complex cooling role of peri-urban wetlands in tropical megacities using a geospatial framework and Landsat imagery, revealing that wetlands create significant thermal gradients with waterbodies as the coolest surfaces and dumping grounds as hotspots. The cooling effect exhibits non-linear distance-decay and directional asymmetry, governed by hydrological connectivity and landscape permeability.
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Arebu et al. (2026) Hydrological Modeling of Reservoir Sedimentation and Evolution of Elevation–Capacity Curve of the Dam Reservoir
This study introduces a novel hydrological approach, integrating the sediment rating curve (SRC) and the dam reservoir elevation-capacity curve (ECC), to accurately estimate reservoir sedimentation and its impact on capacity, demonstrating its effectiveness at the Wadi Fatimah Dam.
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Wei et al. (2026) Critical role of intraseasonal oscillations in shaping extreme rainfall from tropical cyclones over the South China Sea
This study quantifies the critical role of intraseasonal oscillations (ISOs), including the quasi-biweekly oscillation (QBWO) and Madden–Julian oscillation (MJO), in shaping extreme accumulated rainfall (EAR) from tropical cyclones (TCs) over the South China Sea (SCS), finding that ISOs contribute significantly to the occurrence and characteristics of these extreme events.
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Li et al. (2026) The critical role of soil moisture in compound hazards
This review synthesizes current understanding of soil moisture's critical role in the evolution and onset of diverse compound hazards, highlighting its mechanisms in amplifying drought-heatwave-wildfire events, promoting clustered storms, and driving vegetation die-offs, landslides, and flooding. It also identifies persistent challenges and a roadmap for integrating soil moisture into hazard prediction and early warning systems.
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Wang et al. (2026) Numerical simulation-based study on the response of urban drainage networks to flooding and road risk in typical plain city
This study developed an integrated hydrodynamic model to simulate urban pluvial flooding under various rainfall scenarios in Taocheng District, China, demonstrating that drainage systems significantly reduce surface and road inundation and mitigate flood risk.
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Sharifi et al. (2026) Introducing Resiliency as a Novel Metric for Enhanced Drought Monitoring
This study introduces resiliency as a novel metric for dynamic drought monitoring, demonstrating its effectiveness in assessing recovery potential across different drought types (meteorological, hydrological, groundwater, and combined) in the Aleshtar subbasin, Iran, using 30 years of hydroclimatic data.
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Armanuos et al. (2026) Assessing the impact of groundwater abstraction and concrete dam fractures on saltwater intrusion using numerical modeling and interpretable machine learning
This study develops and validates machine learning models to predict the relative saltwater intrusion (SWI) wedge length (L/H) in coastal aquifers, considering groundwater abstraction and fractured underground dams. The XGBoost model demonstrated superior accuracy (R²=0.9978, RMSE=0.216) and identified the relative recharge well rate as the dominant predictor, offering a robust tool for SWI management.
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Che et al. (2026) Temperature sensitivity and rainfall heat flux drive rapid mass loss of low-latitude glaciers in the Southeastern Qinghai–Tibet Plateau
This study investigates the impact of air temperature and rainfall heat flux on the mass balance of low-latitude temperate glaciers in the southeastern Qinghai–Tibet Plateau (QTP), finding that these glaciers exhibit a nearly linear sensitivity to temperature and that rainfall heat flux contributes significantly to their rapid mass loss.
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Ackroyd et al. (2026) Spatial assessment of snow grain size from airborne lidar reflectance against coincident imaging spectroscopy retrievals
This study evaluates three methods for deriving snow grain size and albedo from 1064 nm airborne lidar reflectance against coincident imaging spectroscopy retrievals over a snow-covered glacier. It demonstrates that incorporating incidence angle corrections is crucial for accurate lidar-derived snow properties in mountainous terrain, highlighting lidar's potential for high-resolution snow property mapping.
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Su et al. (2026) SynxFlow-based urban pluvial flood simulation and sensitivity evaluation in the central urban area of Shenzhen
This study assesses the efficacy of SynxFlow, a newly developed open-source hydrodynamic model, for urban pluvial flood simulation and sensitivity evaluation in the central urban area of Shenzhen. It demonstrates SynxFlow's robust performance and highlights the critical role of integrated drainage modules for enhancing flood resilience planning in mega-cities.
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Du et al. (2026) A recent significant increase in tropical cyclone-induced precipitation in the North China Plain
This study reveals a significant increasing trend in tropical cyclone (TC) precipitation over the North China Plain from 1981 to 2024, driven by a remarkable surge since 2018 due to elongated TC tracks with deeper inland penetration, linked to an anomalous easterly steering flow.
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Pham-Thanh et al. (2026) Seasonal precipitation prediction over Vietnam: evaluation of RegCM dynamical downscaling and statistical bias correction of NCEP CFS forecasts
This study evaluates the performance of dynamically downscaled seasonal precipitation forecasts over Vietnam using RegCM-NH driven by NCEP CFS, and the improvements obtained through statistical bias correction. It finds that multiple linear regression (MLR) significantly enhances forecast accuracy, reducing systematic biases and improving interannual variability representation across Vietnam's climatic sub-regions.
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Zhang et al. (2026) A Study on Integration of Topographic Clustering and Physical Constraints for Flood Propagation Simulation
This study develops a high-accuracy and efficient flood evolution simulation method for flood storage and detention basins (FSDBs) by combining terrain clustering and physical propagation constraints. The method achieves errors within 10% for water level and inundation extent, and improves computational efficiency by over 60% compared to traditional methods.
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Vojtek et al. (2026) Transferability of machine/deep learning-based prediction of fluvial flood extent to distinct river sections in Slovakia based on benchmark flood maps and high-resolution spatial data
This study investigates the transferability of machine learning (ML) and deep learning (DL) models for predicting fluvial flood extent across distinct river sections in Slovakia under three flood scenarios. It finds that transferability is most effective between similarly sized river sections, with HAND, distance from river, and slope being the most influential predictors, offering high potential for near real-time flood mapping.
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Cao et al. (2026) Effect of regional marine cloud brightening on land climate
This study uses the CESM Earth system model to investigate the land climate consequences of regional Marine Cloud Brightening (MCB), finding that while MCB stabilizes global temperature and offers benefits like reduced drought stress and increased GPP, its abrupt termination leads to severe and rapid land warming with significant ecological risks.
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Najafzadeh et al. (2026) Assessment of flood susceptibility in Minab County, Iran, through the integration of topographic, climatic, and land-surface indices using ensemble machine learning models
This study developed a high-resolution flood susceptibility map for Minab County, Iran, by integrating multi-source geospatial datasets with seven machine learning models. It found that ensemble tree-based models (CatBoost and Random Forest) provided the most balanced and generalizable performance, identifying short-term precipitation and surface moisture as dominant flood drivers, with approximately 53% of the study area classified as high to very high flood risk.
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Chen et al. (2026) Impact of deep-inland typhoon track uncertainty on the 2023 record-breaking rainfall over North China: an ensemble-based analysis
This study applied ensemble sensitivity analysis to ECMWF ensemble data to diagnose the large-scale circulation controls on the unprecedented Beijing–Tianjin–Hebei rainfall event in July-August 2023, finding that rainfall extremes were tightly governed by the precise trajectory of Typhoon Doksuri's remnants, which modulated the critical spatial alignment of moisture flux, typhoon intensity, and terrain-enhanced convergence.
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Jong et al. (2026) Reversal of extreme precipitation trends over the Northeast US in response to aggressive climate mitigation in GFDL SPEAR
This paper assesses projected changes in extreme precipitation over the Northeast US under an aggressive overshoot mitigation pathway, finding that while warm-season extremes decline quickly after greenhouse gas reductions, cold-season extremes exhibit a delayed response and hysteresis, returning to mid-century levels by 2100.
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Sheil (2026) How Forests May Reduce the Incidence of Destructive Tropical Cyclones, Hurricanes and Typhoons
This review systematically examines whether and how forests influence tropical cyclone frequency, intensity, and behaviour. It finds strong support for post-landfall effects, such as slowing storms and curbing flooding, while pre-landfall influences remain less certain but warrant further investigation.
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Rivoire et al. (2026) The future is in the past? A flexible resampling approach to generate multivariate time series
This paper introduces a straightforward method for generating synthetic climate time series by constrained sampling of observations, demonstrating its ability to preserve physical consistency and multivariate dependencies while simulating multi-day extremes under future climate scenarios.
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Bhattacharjee et al. (2026) Spatiotemporal reorganization of drought characteristics across India under changing monsoon variability
This study assesses the spatiotemporal reorganization of drought characteristics across India (1902-2013) using a non-stationary drought index (NSPI) and non-linear trend analysis (EEMD). It reveals a significant post-1950s increase in intrinsic drought duration (~61%) and severity (~62%), driven by changing monsoon variability, with NSPI demonstrating superior detection skill compared to traditional stationary indices.
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Zhou et al. (2026) Synchronized Heat Extremes in the Northern Hemisphere Based on a Complex Network
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Gangwar et al. (2026) Consistent increase in Southwest Monsoon rainfall in Telangana, India: insights from bias-corrected CMIP6 simulations
This study projects future changes in Southwest Monsoon rainfall in Telangana, India, using statistically downscaled and bias-corrected CMIP6 models, finding a consistent and significant increase in the frequency and intensity of extreme rainfall events by the end of the 21st century under both moderate and high emission scenarios.
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Chu et al. (2026) Future changes in precipitation and temperature using cmip6 model based on topsis method: focus on Songhua river basin
This study projects future precipitation and temperature changes in the Songhua River Basin (SRB) using a Weighted Multi-Model Ensemble (WMME) based on the TOPSIS method with CMIP6 GCMs. It finds significant increases in both variables across the basin, with precipitation rising by 5.7% to 26.6% and temperature by 1.32 °C to 5.44 °C depending on the scenario.
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Aryal et al. (2026) A novel approach for river discharge prediction in Lancang Mekong River Basin: Incorporation of multisource remote sensing and LSTM model
This study developed a Long Short-Term Memory (LSTM) model framework integrating multi-mission satellite altimetry and optical sensor data to predict daily river discharge in the Lancang Mekong River Basin (LMRB). The model achieved robust performance, particularly in downstream reaches, and demonstrated spatial transferability to ungauged locations, though predictive accuracy declined with increasing distance from training sites.
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Fu et al. (2026) Global distribution of the terrestrial moisture dynamics response to the meteorological dry-wet abrupt alternation
This study quantifies the global patterns and trends in the coincidence between meteorological dry-wet abrupt alternation (M-DWAA) and terrestrial dry-wet abrupt alternation (T-DWAA) events. It finds that approximately 17.4% of M-DWAA events globally coincide with a T-DWAA within the subsequent season, with the M-DWAA alternation velocity being the dominant influencing factor.
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Li et al. (2026) Declining Ecological Water Consumption of Marsh Wetlands and the Driving Forces in Semi-Arid Plateau Region: A Case Study in the Bashang Plateau, China
This study investigated the spatiotemporal dynamics and driving forces of marsh wetland ecological water consumption (EWC) in the Bashang Plateau, China, from 1986 to 2021, revealing a significant decline in wetland area and EWC primarily driven by precipitation, surface water area, and indirectly by increased forest EWC due to large-scale afforestation.
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Tang et al. (2026) Elevation‐Dependence of Different Precipitation Phases Concentrations in the Asian Water Tower: Differences in Rainfall and Snowfall Concentration Changes
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Dadaser‐Celik et al. (2026) Future Hydrological Trajectories of Burdur Lake Under Climate Change and Basin‐Scale Human Interventions
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Xu et al. (2026) Compound effects of dams and levees reshape Yangtze flood dynamics and reveal substantial risk misestimations from ignoring levees
This study assesses the compound effects of dams and levees on Yangtze River flood dynamics using the CaMa-Flood model, revealing their distinct and complementary roles in regulating flow and inundation, and demonstrating that ignoring levees leads to a significant overestimation of flood risk.
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Zewdu et al. (2026) Utilizing GIS and fuzzy logic for groundwater resource mapping in gubalafto woreda, Ethiopia: a spatial analysis approach
This study developed a GIS-based fuzzy logic framework to map groundwater potential zones in Gubalafto Woreda, Ethiopia, integrating multiple hydrogeological and topographic factors. The model identified that 17% of the area has high groundwater potential, demonstrating robust predictive accuracy with an AUC of 0.752.
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Sridhara et al. (2026) Historical and future extremes of cauvery basin analysed using cmip6 models and ETCCDI indices
This study validates and ranks 13 CMIP6 models against IMD observations using Multi-Criteria Decision-Making (MCDM) techniques to project historical (1951–2023) and future (2025–2100) extreme temperature and precipitation events in the Cauvery Basin, India, revealing significant increases in heat stress and alternating flood/drought risks under high-emission scenarios.
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Putri et al. (2026) Groundwater recharge from intense rainfall in Indonesia: evidence from East Kalimantan
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Ganiyu et al. (2026) Enhancing flood simulation in data-sparse Niger central hydrological area river basin in Nigeria using machine learning-based data fusion
This study enhances flood event simulation in the data-sparse Niger Central Hydrological Area River Basin in Nigeria by fusing daily downscaled PERSIANN-CDR satellite precipitation with observed rainfall data using machine learning models. The Random Forest (RF) model demonstrated superior accuracy in data fusion, significantly improving precipitation estimates and subsequently leading to more reliable flood simulations with the HEC-HMS hydrological model.
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Dong et al. (2026) How Lateral Flow Impacts Heavy Rainfall in Complex Terrain: A Composite Analysis Over the Southern Anhui Mountainous Region
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Wang et al. (2026) Spatiotemporal patterns and zonation of typhoon and non-typhoon extreme rainfall hazards in the typical coastal region of southeastern China
## Identification - **Journal:** Geomatics Natural Hazards and Risk - **Year:** 2026...
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Sulca et al. (2026) An Integrated Analysis to Delineate Groundwater Flow Systems and Recharge Dynamics in the Chili River Sub-Basin, Southern Peru
This study characterizes the poorly understood aquifer systems, recharge mechanisms, and chemical evolution in the arid Chili River sub-basin, Peru. It identifies three aquifer types, distinct groundwater flow systems, and reveals a chemical evolution from high to low elevations, with high-altitude rainfall being the primary recharge source for wells.
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Çelik et al. (2026) Solar Radiation–Driven Machine Learning for Modelling Reference Evapotranspiration Using ERA5 ‐Land in a Semi‐Arid Microclimatic Basin in Türkiye
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Si et al. (2026) Evolution trends and drivers of glacier and snowmelt induced floods in Upper Yarkant River Basin, Karakoram (1954–2020)
This study developed a multi-temporal framework to classify flood events in the Upper Yarkant River Basin (UYRB) from 1954 to 2020, focusing on the hydrological evolution and driving mechanisms of Glacier and Snowmelt Floods (GSMFs). It found that GSMFs are the predominant flood type, exhibiting increased peak discharge, reduced duration, earlier peak timing, and greater variability, primarily driven by extreme nocturnal warming and the 0 °C isotherm height with a 3-day lag.
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Legese et al. (2026) Spatiotemporal analysis of extreme precipitation and temperature variability, trends, and vulnerability hotspots in coffee growing districts of Ilubabor zone, Ethiopia
This study analyzed 42 years of daily temperature and precipitation data in coffee-growing districts of Ethiopia's Ilubabor Zone to assess spatiotemporal variability, trends, and ecological vulnerability hotspots, revealing significant warming, declining heavy rainfall, increasing dry spells, and identifying Bure and Mettu districts as highly vulnerable.
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George et al. (2026) Submarine groundwater discharge and associated fluxes along the Kanyakumari coast of India using radon and nutrient mass balance approach
This study quantified submarine groundwater discharge (SGD) and associated nutrient fluxes along the Kanyakumari coast of India using radon and nutrient mass balance approaches, revealing significant seasonal variations influenced by monsoonal recharge and tidal dynamics.
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Santos (2026) Projected Intensification of Hydroclimatic Extremes in Rio Grande do Norte, Brazil, Under CMIP6 Scenarios
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Cui et al. (2026) Coupled dominant factors analysis, dual attention deep learning, and uncertainty quantification for long-term pan evaporation ensemble prediction in the Wuding River Basin, China
This study develops a novel framework integrating dominant factors analysis, dual-attention deep learning, and uncertainty quantification to improve long-term pan evaporation (Epan) ensemble prediction in the Wuding River Basin, China. The framework, utilizing a DA-LSTM model and an improved C-Vine Copula-based multi-model processor (CMMCP), significantly enhances Epan prediction accuracy and reliability by reducing uncertainty.
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Cheung et al. (2026) Expanding Temporal Glacier Observations Through Machine Learning and Multispectral Imagery Datasets in the Canadian Arctic Archipelago: A Decadal Snowline Analysis (2013–2024)
This study presents the first decadal (2013–2024) satellite-derived time series of late-summer snowline altitude (SLA) for six Canadian Arctic Archipelago (CAA) glaciers, revealing that annual peak SLA correlates positively with summer warmth and that glacier hypsometry strongly modulates climatic sensitivity.
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Rahmani et al. (2026) Wetlands set the pace of annual runoff in the northern Great Plains
This study reveals that in North America's Prairie Pothole Region, annual wetland inundation extent, rather than climate drivers, is the dominant factor explaining interannual variability in runoff and high-flow in 69% of 109 studied catchments over 38 years, with most catchments exhibiting threshold-like buffering behavior linked to geographically isolated wetlands.
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Becker et al. (2026) A map of high-altitude wetlands in the world’s major mountain regions
This study presents the first global high-resolution (30 meters) map of high-altitude wetlands across the world's major mountain regions, identifying a total area of over 134,700 square kilometers of these critical ecosystems.
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Chen et al. (2026) An explainable correction and fusion framework for global bare-earth DTM generation in mountain areas
This study developed an explainable correction and fusion framework to generate high-accuracy global bare-earth Digital Terrain Models (DTMs) in mountainous regions, addressing height biases in existing Digital Surface Models (DSMs). The proposed framework, combining AutoML-SHAP, a CNN-Transformer, and a fusion model, achieved significant vertical accuracy improvements (43.13%–76.86%) over current GDEMs and correction methods.
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Hao et al. (2026) Partitioning precipitation moisture sources in a cold-temperate forest: Seasonal dominance of advection and transpiration in the Greater Khingan Range, China
This study investigated seasonal precipitation moisture sources in the northern Greater Khingan Range using stable isotopes, backward trajectories, and moisture uptake diagnostics. It found that cold-season precipitation is dominated by long-range advection, while warm-season precipitation shows enhanced local recycling primarily driven by transpiration, though advection remains the largest single source.
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Qin et al. (2026) Gain–phase characteristics of groundwater responses to barometric pressure: interpreting subsurface confinement in layered systems
This study applies frequency-domain barometric response functions (BRF) to analyze groundwater response to atmospheric pressure in a layered aquifer. It demonstrates the utility of BRF analysis for interpreting pressure transmission and confinement in vertically heterogeneous aquifers, revealing distinct gain-phase relationships for different confinement types.
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Nandi et al. (2026) Combined hydro-meteorological drought assessment of Ganga-Brahmaputra Basin: insights of the control of total water storage anomaly in drought occurrence
This study assesses hydro-meteorological drought in the Ganga-Brahmaputra Basin (GBB) using a combined drought index (CCDI) derived from GRACE-based Terrestrial Water Storage Anomaly (TWSA) and precipitation, revealing significant TWSA declines and identifying TWSA as the dominant driver of drought severity in the Upper Ganga and Yamuna-Chambal Basins.
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Hiraga et al. (2026) Numerical experiments of cloud seeding for mitigating localization of heavy rainfall: a case study of Mesoscale Convective System in Japan
This study numerically investigated the potential of cloud overseeding to mitigate localized heavy rainfall from a mesoscale convective system in Japan, finding that an optimal seeding configuration could reduce area-averaged 3-hour accumulated rainfall by 11.5% and maximum rainfall by 32% in the heavy rainfall region.
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Hassan et al. (2026) Climate adaptation-aware flood prediction for coastal cities using Deep Learning
This study develops a novel, lightweight Convolutional Neural Network (CNN)-based model, CASPIAN-v2, for rapid and accurate prediction of high-resolution coastal flooding in urban areas under various sea-level rise scenarios and shoreline adaptation strategies. The model significantly outperforms state-of-the-art methods, reducing mean absolute error by nearly 20%, and offers a scalable tool for climate adaptation planning.
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Guo et al. (2026) Wind-water energy characteristics and sediment transport prediction in sandy coarse sand basin
This study developed an energy-based wind-water composite watershed erosion and sediment transport model for the sandy coarse sand area of the Yellow River, demonstrating its high accuracy in predicting sediment yield across different soil types and revealing significant spatiotemporal differentiation of erosion energies.
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Zhao et al. (2026) Integrated linear and non-linear assessment of remote sensing drought indices for soil moisture monitoring across multiple temporal scales in China
This study systematically evaluated remote sensing drought indices (Vegetation Condition Index, Vegetation Water Index, Temperature Condition Index) against soil moisture across China using linear correlation and a Copula-based framework, revealing that optimal index performance varies significantly by ecosystem, temporal scale, and drought severity, especially under extreme conditions.
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Mutar et al. (2026) Assessment of Haditha Dam’s Operation Under Historical Hydrological Conditions: Comparison Between Actual and Simplified Operation Using the HEC-HMS Model in Different Scenarios
This study evaluated the HEC-HMS model's applicability for simulating inflow hydrographs and supporting reservoir operation at Haditha Reservoir, Iraq, under historical hydrological conditions. It found that a rule-based operation scenario significantly improved reservoir storage and water levels during dry periods compared to existing and hydraulic-based operational policies.
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Kajiyama et al. (2026) City boundaries for global urban water scarcity assessment
This study introduces HydroUrbanMap (HUM), a global gridded dataset of hydrologically-informed city boundaries for 1,604 cities at 5 arcmin resolution, designed to improve urban water scarcity assessments by accurately delineating water-served populations and identifying accessible surface water sources. It demonstrates that HUM's approach overcomes limitations of existing urban delineation methods, providing a more realistic basis for city-specific water resource assessments.
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Khosravi et al. (2026) A geographically weighted XGBoost framework for Pixel-Level modeling of vegetation responses using Multi-Source Earth Observation data
This study introduces Geographically Weighted XGBoost (GW-XGBoost), a hybrid and interpretable framework, to model pixel-level vegetation responses to climate extremes in the Middle East. The model, calibrated with 30 years of multi-source Earth Observation data, outperforms baseline models and reveals a significant ecological transition where vegetation sensitivity has shifted from cold/precipitation constraints to warm temperatures and episodic moisture pulses.
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Dixit et al. (2026) Integrating SMART principles in flood early warning system design in the Himalayas
This study integrates SMART principles with low-cost, real-time hydrometeorological monitoring to design an urban flood early warning system (EWS) in the data-scarce Lesser Himalayas. It demonstrates how community-engaged monitoring captures crucial spatiotemporal rainfall variability and watershed dynamics, which are poorly represented by secondary datasets, providing foundational insights for effective, community-centered EWS implementation.
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Pérez et al. (2026) Regional synchronization patterns between climate indices and colombian hydroclimatic variables
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Seo et al. (2026) Global 0.25-degree gridded Snow water equivalent data derived from machine learning using in-situ measurements
This study developed SWEML, a novel global daily snow water equivalent (SWE) product at 0.25° (~25 km) resolution for 1980–2020, utilizing a machine learning-based Random Forest algorithm trained on in-situ measurements. SWEML demonstrated superior accuracy (overall RMSE 10.33 mm) compared to ten existing reference datasets, particularly in high-elevation regions, and showed robust performance even in data-sparse areas like the Andes.
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Nana et al. (2026) Assessing the ability of the ECMWF seasonal prediction model to forecast extreme September–November rainfall events over Equatorial Africa
This study assesses the European Centre for Medium-Range Weather Forecasts seasonal prediction system 5.1 (ECMWF-SEAS5.1) in forecasting extreme September–November (SON) rainfall events over Equatorial Africa (EA). It finds that the model generally reproduces observed rainfall patterns and teleconnections with tropical sea surface temperatures well, with better skill for September initial conditions, but tends to underestimate the magnitude of extreme events and shows limitations in representing certain atmospheric features at longer lead-times.
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Uysal et al. (2026) A data-assimilated SEAS5 forecasting framework for seasonal hydropower inflows in a snow-dominated basin
This study developed a seasonal hydropower inflow forecasting framework for a snow-dominated basin by integrating a variational data assimilation (VarDA) scheme into the HBV hydrological model, demonstrating significant improvements in inflow and snow water equivalent predictions, especially at short lead times.
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Wang et al. (2026) Modeling Climate Change Impacts on Large and Small Lakes of the Tibetan Plateau: Responses and Drivers
This study evaluates the FLake model's performance in simulating thermal structure and heat fluxes in large and small lakes on the Tibetan Plateau using in situ observations. It finds that the model generally reproduces seasonal variations but underestimates diurnal amplitudes, and that long-term warming trends are primarily driven by downward longwave and shortwave radiation and air temperature.
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Leistert et al. (2026) Accumulation-based Runoff and Pluvial Flood Estimation Tool (AccRo v.1.0)
This paper introduces AccRo (Accumulation-based Runoff and Flooding), a computationally efficient model designed to estimate maximum inundation depth, flow velocity, and specific discharge for pluvial flood events at larger spatial scales. The model demonstrates high accuracy in reproducing analytical solutions for design cases and closely matches the results of state-of-the-art 2D hydrodynamic models for real-world scenarios, offering a robust alternative for flood hazard assessment.
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Brum et al. (2026) Assessing the environmental costs of multi-scale recurrent neural networks for sustainable extreme rainfall nowcasting
This study evaluates the Multi-scale Recurrent Neural Network (MS-RNN) framework for improving computational efficiency and predictive accuracy in extreme precipitation nowcasting using real weather radar data. It quantifies the environmental costs (energy, CO2 equivalent emissions, and water usage) of deep learning models to support sustainable and accessible AI solutions for climate resilience in resource-limited regions.
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Leopardi et al. (2026) A satellite-based approach for estimating runoff and river discharge in the Pan-Arctic region from 2003 to 2022
This study presents daily, long-term (2003–2022) satellite-based estimates of river discharge and gridded runoff at 0.25° × 0.25° spatial resolution across the continental Pan-Arctic region. Integrating various satellite observations into the adapted STREAM hydrological model, it demonstrates high performance (median Kling-Gupta Efficiency of 0.83) and quantifies freshwater fluxes to the Arctic Ocean at 4760 ± 619 km³ yr⁻¹.
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Chen et al. (2026) Rapid recovery from permafrost thaw subsidence after extreme warmth inferred from InSAR
This study examines the response of ice-rich permafrost in Northwest Alaska to the 2019 extreme warmth, revealing a short-lived subsidence of approximately 6 cm followed by a partial recovery of about 3 cm over three years, suggesting substantial resilience rather than widespread sustained degradation.
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He et al. (2026) Contrasting trends in climatic and ecohydrological aridity over one-fifth of global drylands
This study reveals that nearly one-fifth (22.3%) of global vegetated drylands exhibited contrasting trends in climatic and ecohydrological aridity over the past four decades, primarily driven by the opposing effects of elevated atmospheric CO2 on vegetation structure and stomatal conductance.
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Song et al. (2026) Simulation of Nitrogen Migration and Output Loads Under Field Scale in Small Watershed, China
This study investigated nitrogen transport dynamics in an agricultural watershed using high-resolution UAV-derived digital elevation models (DEMs) and coupled hydrological–erosion modeling. It found that decimeter-scale DEMs are essential for accurately capturing microtopographic regulation, which predominantly controls nitrogen migration and spatial heterogeneity of exports.
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Gaona et al. (2026) Spatial Downscaling of the CHIRPS Rainfall Product Using Machine Learning Methods: The Catamayo–Chira Transboundary Basin (Ecuador-Peru) Case
This study spatially downscaled the 5 km CHIRPS rainfall product to 1 km for the Catamayo–Chira Transboundary Basin (Ecuador-Peru) using various single-variable and multivariable machine learning methods, demonstrating significant improvement in precipitation estimates and successfully capturing "El Niño" event differences.
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Wang et al. (2026) Evaluating WRF simulated temperature uncertainties across Northern Hemisphere climate zones with different land surface models and land cover datasets
This study systematically evaluated the temperature simulation performance of four land surface models (LSMs) and three land cover (LC) datasets within the WRF model across the Northern Hemisphere. The CGLC-Noah combination demonstrated the best overall performance, though significant temperature underestimations were found in tropical and polar regions.
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El-Yazidi et al. (2026) Climate Change Projections: Application of the Statistical Downscaling Model in the Souss-Massa Watershed
This study analyzed historical (1982–2022) and projected future (2025–2099) climate variability in the semi-arid Souss-Massa watershed, finding a statistically significant increase in mean annual temperature historically and projecting substantial future warming and precipitation decreases, indicating a trend towards arid conditions.
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Lu et al. (2026) Future water erosion on the Tibetan Plateau: Projections from coupled model intercomparison project phase 6 (CMIP6)
This study projects future water erosion on the Tibetan Plateau using CMIP6 models, revealing that 'hot' models (high climate sensitivity) in unconstrained ensembles predict higher mean annual soil erosion rates (up to 30.37 t⋅ha⁻¹⋅a⁻¹) compared to constrained ensembles, highlighting the critical role of model selection and the need for adaptive soil conservation strategies.
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Mahamat-Nour et al. (2026) Hydrogeological functioning of the Massenya floodplain, Lake Chad Basin: insights from stable isotopes and hydrochemistry
This study investigates the hydrogeological functioning and water quality of the Massenya floodplain in the Lake Chad Basin using hydrodynamics, stable isotopes, and hydrochemistry. It reveals a dual groundwater recharge system, with recent precipitation and floodwater replenishing shallow aquifers and older, fossil waters in deeper horizons, highlighting the critical role of floodplains in sustaining water resources despite anthropogenic pressures and climate variability.
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Massart et al. (2026) Filling the Gap: Elevation-Based Sentinel-1 Surface Soil Moisture Retrieval over the Austrian Alps
This study introduces a novel approach to retrieve surface soil moisture (SSM) across the complex topography of the Austrian Alps by aggregating Sentinel-1 backscatter into 100 m elevation bands. The resulting product provides consistent and elevation-stratified SSM information across over 80% of the region, demonstrating satisfactory agreement with ERA5-Land and capturing precipitation-driven anomalies.
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Cui et al. (2026) Diagnosing interaction between vegetation greening and terrestrial water storage changes in the arid and semi-arid Mongolian plateau
This study investigates the complex, often bidirectional, interactions between vegetation greening and terrestrial water storage anomaly (TWSA) in the arid and semi-arid Mongolian Plateau. It reveals that vegetation greening intensifies groundwater depletion by reducing soil moisture recharge, while limited deep subsurface water recharge restricts vegetation greening, emphasizing the critical role of subsurface water in restoration efforts.
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Bidabadi et al. (2026) Spatial risk assessment of drought-induced operational failures in interconnected irrigation canals: application to the Mahyar–Jarghooye district, Iran
This study develops a stakeholder-scale, map-based risk assessment framework to evaluate drought-induced operational failures in interconnected irrigation canals under water shortages (WS) and inflow fluctuations (IF). Applied to the Mahyar–Jarghooye district in Iran, it generates spatial vulnerability, consequence, and risk maps to identify hotspots and inform management.
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Gyamfi et al. (2026) Physics-informed spatio-temporal graph neural networks for evapotranspiration prediction: Case of the Korean Peninsula
This study develops a physics-informed spatio-temporal graph neural network for evapotranspiration prediction across the Korean Peninsula, integrating climate variables, soil moisture, and a surface energy-balance constraint. The model demonstrates strong skill, particularly under dry conditions, and projects substantial increases in evapotranspiration under future climate scenarios, highlighting increasing evaporative demand and water stress.
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You et al. (2026) Quantifying climate-induced cascading effects on runoff in a cold region using a glacier-enhanced Budyko framework
This study introduces a novel Budyko-based attribution framework, integrating glacier mass balance and ridge regression, to comprehensively separate the impacts of direct climate change, cascading climate effects, and human activities on runoff in cold regions. Applied to an alpine watershed, the framework robustly quantified these drivers, revealing an antagonistic effect where direct climate increased runoff while cascading effects and human activities suppressed it.
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Li et al. (2026) Weakening vegetation control on global terrestrial evapotranspiration in a warmer world
This study quantifies future changes in the sensitivity of terrestrial evapotranspiration (ET) to leaf area index (LAI) under projected warming scenarios, finding that vegetation control on ET will weaken globally by 2100 due to reduced stomatal conductance outweighing CO₂ fertilization. This weakening will diminish LAI-driven evaporative cooling, leading to enhanced water conservation.
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Besnier et al. (2026) Crossing the Threshold: Land Cover Change Triggers Hydrological Regime Shift in Brazil’s Itaipu Hydropower Region
This study investigates hydrological transitions and their statistical associations with land cover changes in the Itaipu study region from 2002 to 2023. It identifies a significant basin-wide shift in Terrestrial Water Storage Anomalies (TWSAs) in mid-2009, strongly coupled with agricultural expansion and land cover changes, leading to increased runoff generation.
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Rehbein (2026) Reconstructing nineteenth-century Danube river water levels with transformer-based computer vision
This study developed a semi-automated workflow using transformer-based computer vision to convert nineteenth-century hand-drawn Bavarian Danube gauge charts into daily water-level series. The method achieved high accuracy (mean composite score 0.979) across three representative gauges while reducing manual effort by an order of magnitude, providing openly available, transparently documented historical hydrological data.
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Li et al. (2026) Ecological flow guarantee rate along the Xijiang River mainstream at different scales based on multiple probability distributions
This study quantifies ecological flow guarantee rates along the Xijiang River mainstream by reconstructing quasi-natural runoff using a random forest model and applying a probabilistic framework with multiple distribution functions. It finds that ecological flow guarantee rates decreased during the change period, particularly in the upper reaches and during the flood season (July to October), identifying these as priority areas and sensitive periods for ecological flow management.
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HONG et al. (2026) Characteristics and influencing factors of soil moisture memory across mainland China
This study utilized 2,218 daily in situ soil moisture observations across mainland China to characterize the spatial patterns and influencing factors of soil moisture memory (SMM) using an exponential drydown model, highlighting its heterogeneity and discrepancies with satellite/reanalysis products.
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Lin et al. (2026) Is the recently enhanced mesoscale convective systems in East Asia due to global warming or decadal variability?
This study investigates the drivers of increased mesoscale convective system (MCS) precipitation in the Yangtze River Basin, finding that decadal variability, primarily through increased MCS frequency, is the main cause, with anthropogenic warming playing a smaller, reinforcing role.
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Li et al. (2026) Spectral albedo, vegetation greenness, and radiative forcing responses of the Amazon to drought and wet conditions from 2005 to 2016
This study investigates the responses of spectral albedo, vegetation greenness, and albedo-driven radiative forcing to drought and wet conditions in the Amazon (2005-2016) across evergreen broadleaf forest, grassland, and savannas. It finds that visible and shortwave albedo negatively correlate with wetness over grasslands and savannas, while evergreen broadleaf forests show less pronounced and more complex responses, with significant implications for surface radiative forcing.
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Li et al. (2026) Evaporation, surface energy balance, and water-heat-salt transport under saline shallow groundwater: Lysimeter and modeling insights across soil textures
This study investigated the coupled water, heat, and salt transport in two soil textures (silt loam and sand) under shallow saline groundwater and natural conditions using field lysimeters and numerical modeling, revealing texture-dependent salt precipitation patterns that influence evaporation resistance and soil temperature.
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Adnan et al. (2026) Assessing the transferability of LSTM-based streamflow models under varying source basin diversity and target data availability (Mangla Basin, Pakistan)
This study evaluates the transferability of LSTM-based streamflow models in the data-scarce Mangla Basin, Pakistan, demonstrating that transfer learning significantly improves predictions, especially with limited local data, though its advantage lessens as local data availability increases.
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Hancock et al. (2026) 21st century hydrological trends in the Mississippi River basin intensify the east to west moisture gradient
This study validates 19 CMIP6 models against historical observations to project future monthly hydroclimate changes in the Mississippi River system under the SSP3-7.0 pathway. It finds consistent increases in precipitation but decreases in soil moisture due to enhanced evaporative demand, with highly divergent and regionally varied trends for runoff and discharge driven by large-scale atmospheric and oceanic patterns.
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Dutta et al. (2026) Correction: Effect of doppler radar reflectivity and radial velocity assimilation on lightning and rainfall prediction of a severe thunderstorm over Odisha, India
This document is a correction notice for an article that investigated the impact of assimilating Doppler radar reflectivity and radial velocity data on the prediction of lightning and rainfall associated with a severe thunderstorm over Odisha, India.
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Elsahabi et al. (2026) Evaluating evaporation and seepage losses in lakes using sentinel images and the water balance equation
This study assessed evaporation and seepage losses in Aswan High Dam Lake (AHDL) by integrating Sentinel-3 imagery, field data, and the water balance equation, demonstrating the method's reliability for estimating these water losses and evaporation rates.
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Hacker et al. (2026) Multidecadal reconstruction of terrestrial water storage changes by combining pre-GRACE satellite observations and climate data
This study reconstructs multidecadal terrestrial water storage anomalies (TWSA) for global land from 1984 to 2020 by optimally combining pre-GRACE geodetic satellite observations (SLR and DORIS) with climate data-driven regression models, providing a long-term consistent dataset (TWSTORE) for climate change attribution and hydrological studies.
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Aboelnour et al. (2026) Mapping tomorrow’s flood: a probabilistic, equity-centered risk assessment for the Indianapolis Metropolitan Area
This study develops a high-resolution, probabilistic framework to map current and future urban flood risk in the Indianapolis Metropolitan Area by integrating stochastic precipitation, surface runoff, and a Composite Flood Risk Index (CFRI) that includes social poverty vulnerability and exposure. It finds that climate change will significantly intensify and redistribute flood risk, increasing very-high CFRI zones sevenfold by century's end, especially in previously low-risk suburban areas.
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Ethaib et al. (2026) Water Resources in South of Iraq: Current State, Future Evolutions, Challenges, and Potential Solutions
This paper provides an in-depth review of the factors contributing to the severe water resources crisis in southern Iraq, analyzing the current situation using indicators like marsh water availability, Shatt al-Arab salinity, and cultivated area, and identifying key challenges and potential solutions.
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Rivoire et al. (2026) Identification of hydro-meteorological drivers for forest low greenness events in Europe
This study identifies hydro-meteorological drivers of forest low greenness events across Europe using a random forest model and satellite Normalized Difference Vegetation Index (NDVI) data. It reveals that warm and dry conditions in spring and early summer, along with multi-year influences, are critical predictors for forest browning, with regional and forest-type specific variations.
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Wang et al. (2026) Toward drivers of the interannual variability of warm-season extreme rainfall over the Bohai Rim, China
This study investigates the climatic drivers and physical mechanisms for interannual variations in warm-season rainfall extremes over the Bohai Rim (BHR) region of China from 1979 to 2022. It reveals that increased extreme rainfall is primarily driven by a zonally oriented dipole circulation pattern and a significant lagged influence of El Niño-like Pacific sea surface temperature warming, which induces a subtropical Western North Pacific Subtropical High (WNPSH)-resembling anomaly gyre.
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Martí et al. (2026) Implementation of a dry surface layer soil resistance in two contrasting semi-arid sites with SURFEX-ISBA V9.0
This study evaluates and improves the SURFEX-ISBA V9.0 land surface model's estimation of latent heat fluxes in semi-arid environments by implementing a dry surface layer (DSL) soil resistance. The DSL resistance successfully reduced the overestimation of bare soil evaporation, leading to a 29% to 32% reduction in the daily Root Mean Square Error of latent heat flux at two contrasting sites.
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Li et al. (2026) A tailored deep learning method to improve spatial rainfall downscaling
This study developed a tailored deep learning model, RM-ResNet, incorporating a spatial correction algorithm to downscale satellite rainfall data from 8 km to 1 km resolution. The method successfully improved the representation of rainfall spatial patterns, including extreme events and storm centers, demonstrating consistency with observations.
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Yang et al. (2026) Assessment of Hybrid Grey-Green Infrastructure for Waterlogging Control and Environmental Preservation in Historic Urban Districts: A Model-Based Approach
This study developed a quantitative assessment framework using a 1D-2D hydrodynamic model for a historic urban district to evaluate waterlogging risks and proposed a hybrid grey-green infrastructure (HGGI) system that effectively reduces waterlogged areas while minimizing intervention in cultural heritage.
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Zhao et al. (2026) A novel hybrid approach for enhancing precipitation data fusion: Bayesian and geographical regression integration for hydrological applications
This study proposes and validates a novel three-stage hybrid precipitation fusion framework, integrating Mixed Geographically Weighted Regression (MGWR) and Bayesian Three-Cornered Hat (BTCH) methods, to generate high-quality, high-resolution precipitation data. The "Correct-then-Combine" (MGWR-BTCH) pathway significantly improved precipitation accuracy and hydrological utility in the data-sparse Shahe Basin.
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Lei et al. (2026) Analysis of the Impact of Water Conservancy Projects on Water Resource Use Efficiency and Vegetation Net Primary Productivity in an Arid Inland Basin
This study investigated the mechanisms by which ecological water conveyance impacts Net Primary Productivity (NPP) in the Aiding Lake Basin, finding that despite an overall declining trend in mean annual NPP, water conveyance significantly and positively enhanced regional vegetation productivity.
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Ngai et al. (2026) Diurnal rainfall variability over the Maritime Continent: Evaluation and future projections from CORDEX-SEA simulations
This study systematically evaluates present-day diurnal rainfall simulations over the CORDEX-SEA domain and projects future changes under the RCP8.5 scenario, finding that regional climate models significantly improve the simulation of diurnal rainfall characteristics and project a widespread weakening of amplitude over land and reduced coastal propagation in the late 21st century.
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Manspeizer et al. (2026) Tracking a semi-arid Eastern Mediterranean ecotone through integration of terrestrial and atmospheric earth observation data (2000–2024)
This study developed an Earth observation method to monitor semi-arid Eastern Mediterranean shrublands in relation to climate change and subtropical high migration, finding a 13.3% decrease in aridity and a doubling in the rate of atmospheric water vapor increase between 2014 and 2024. It proposes that ecological equilibrium occurs at plagioclimax following disturbance, challenging traditional succession theories.
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Bäthge et al. (2026) A Global-Scale Time Series Dataset for Groundwater Studies within the Earth System
This paper introduces GROW, a global-scale, quality-controlled dataset integrating over 200,000 groundwater depth and level time series with 36 associated Earth system variables to facilitate understanding of groundwater dynamics and model evaluation. It provides an analysis-ready foundation for studying large-scale groundwater processes in space and time within the Earth system.
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Chen et al. (2026) Comparative analysis of high-resolution GCMs and RCMs ensembles in simulating and projecting compound extreme events in China
This study compares high-resolution CMIP6 Global Climate Model (GCM) and CMIP5 Regional Climate Model (RCM) multi-model ensembles for simulating and projecting Compound Extreme Heat-Precipitation Events (CHPEs) over China, finding that GCM ensembles generally demonstrate better capability in reproducing these events.
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Ye et al. (2026) Leveraging water vapor to extend forecast horizons for forecast-informed reservoir operations: a vapor-precipitation-streamflow three-line defense
This study proposes a vapor-precipitation-streamflow (VPS) Forecast-Informed Reservoir Operations (FIRO) scheme that leverages precipitable water vapor (PWV) to extend forecast horizons. The VPS-FIRO scheme enables earlier prerelease operations, reducing spilled water volume by 5.6% and decreasing the duration of excessive outflow from 1% to 0.3% compared to traditional precipitation-streamflow (PS) FIRO.
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Harley et al. (2026) California Temperature Since 1520 CE Shows Interactions in Extremes of Heat, Drought, and Fire
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
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Mohammed et al. (2026) Performance evaluation of CMIP6 climate models for rainfall and erosivity in the thamirabharani basin, India
This study evaluates the performance of 35 CMIP6 Global Climate Models (GCMs) for rainfall and erosivity in the Thamirabharani River Basin, India, identifying the best-performing models through a multi-criteria decision-making framework. The research projects significant increases in seasonal and annual rainfall (up to 93.3%) and rainfall erosivity (up to 71.7%) by the end of the century under high-emission scenarios, implying heightened risks for soil erosion and water resource management.
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Singh et al. (2026) Projected intensification of precipitation extremes in the Kosi Basin using CMIP6 models
This study evaluates and ranks thirteen statistically downscaled CMIP6 models for reproducing eight ETCCDI precipitation indices over the Kosi River Basin, identifying an optimal eight-member ensemble (AMME8) that projects a significant intensification of precipitation extremes under future warming scenarios.
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Liu et al. (2026) Optimality-Based Active Region Model (ARM) for Fingering Flow in the Vadose Zone: Recent Theoretical Progress
This paper presents the latest theoretical developments of the optimality-based active region model (ARM), a macroscopic framework designed to accurately describe gravitational fingering flow in the vadose zone, by providing an updated mathematical derivation and extending it to a dual-flow field model for enhanced rigor and realism.
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Maftei et al. (2026) Ecohydrology in the Context of Climate Change: Strategies for Management, Monitoring, and Modeling
This editorial synthesizes research from a Special Issue on ecohydrology, focusing on strategies for management, monitoring, and modeling in the context of climate change, highlighting advancements in understanding hydroecological coupling and adaptive resource governance through technological convergence and analytical innovation.
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Wang et al. (2026) Beyond Annual Averages: Multi‐Scale Rainfall Variability, Drought Indicators, and Seasonal Shifts Under a Changing Climate
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Shu et al. (2026) High-resolution urban flooding inundation forecasting through hydrodynamic interaction and multimodal deep learning
This research proposes a multimodal deep learning model (SMDFN) tightly coupled with a hydrological-hydrodynamic model to improve high-resolution urban pluvial flooding inundation forecasting by capturing hydrodynamic interactions and enabling multimodal feature extraction. The model demonstrates superior performance, reducing RMSE by 13.8%, and offers a multi-region collaborative forecasting solution.
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Tsiros et al. (2026) Variability of the Climate in Athens‐Greece Over the Last 165 Years of the Period 1858–2023: An Assessment Based on Thornthwaite's Climate Classification and Relevant Indices
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Arbai et al. (2026) Projected Annual and Monsoonal Precipitation Trends of CMIP 6 Over P eninsular M alaysia
This study investigated historical (1973–2022) and projected (2051–2100) precipitation trends over Peninsular Malaysia using ground-based records and CMIP6 GCMs, revealing spatially heterogeneous patterns influenced by monsoons, with future projections indicating modest increases under SSP2-4.5 but widespread declines under SSP5-8.5.
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Liang et al. (2026) WetFramework: A deep learning framework for coastal wetland boundary extraction and inundation frequency estimation
This paper introduces WetFramework, a novel deep learning framework that integrates Transformer, Mamba, and wavelet transforms to accurately extract coastal wetland boundaries and quantitatively estimate inundation frequency at microscales, demonstrating superior performance and generalization across diverse coastal regions.
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Ma et al. (2026) Response of sediment delivery ratio to water-sediment and riverbed boundary conditions during flood events in the lower yellow river since 2000
This study investigates the response of the sediment delivery ratio to water-sediment and riverbed boundary conditions in the Lower Yellow River since 2000, developing a theoretical equation that incorporates riverbed characteristics for improved accuracy in predicting sediment transport capacity during flood events. The findings highlight the crucial role of riverbed boundaries and offer practical recommendations for river management.
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Li et al. (2026) Deriving phase-contingent dynamic drought-limited water levels: An adaptive framework for managing megadrought evolution
This study develops an adaptive framework to derive dynamic, phase-contingent Drought-Limited Water Levels (DLWLs) for managing megadroughts in reservoirs, addressing the limitations of static thresholds. It demonstrates that a supervised Random Forest model, anchored in physically constrained hydrological benchmarks, reliably classifies drought severity across four evolutionary phases, enabling improved, resilient reservoir operation.
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Xu et al. (2026) Time-lag and cumulative drought effects decouple vegetation sensitivity from damage risk in the upper Yangtze River basin
This study analyzed vegetation response to drought in the upper Yangtze River basin (1990-2022) using NDVI and multi-scale SPEI, developing a composite drought sensitivity index and quantifying loss risk with a Copula-Bayes framework, revealing that drought sensitivity does not always align with actual vegetation loss probability.
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Miao et al. (2026) SM2RAIN–dual: a global rainfall fusion product derived from multi-source satellite soil moisture observations
This study addresses regional disparities in SM2RAIN-derived rainfall estimates by developing a rainfall data fusion scheme using multi-source satellite soil moisture products. The research generated a global, more reliable rainfall product called SM2RAIN–Dual, which combines SMAP and ASCAT data.
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Wang et al. (2026) Shifts in the Decoupling and Driving Mechanisms of Grassland Greening and Water Availability in the Northern Hemisphere
This study systematically assessed the spatiotemporal evolution and trend divergence of grassland greening (leaf area index, LAI) and water availability (WA) across the Northern Hemisphere from 2000 to 2100, revealing a historical widespread decoupling (greening with declining WA) that is projected to reverse in the future, with shifts in dominant climatic drivers.
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Baker (2026) Identification of synoptic climate and drought controls on rainfall stable water isotopic composition in the Macleay karst region of eastern Australia
This study investigates the stable water isotopic composition of precipitation, karst springs, and rivers in the Macleay region of eastern Australia to understand the influence of synoptic climate and drought on rainfall isotopes and to estimate groundwater recharge thresholds. It found that offshore low-pressure systems deliver isotopically depleted rainfall, which preferentially recharges groundwater, with a daily recharge threshold estimated between 11 mm and 40 mm.
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Chen et al. (2026) Combined effect of tides, irregular waves and beach recovery on groundwater flow and marine-sourced salt transport in coastal unconfined aquifers
This study numerically investigates the combined impact of tides and irregular waves on groundwater dynamics and marine-sourced salt transport during beach recovery. It reveals that wave action, particularly overtopping waves, significantly amplifies subsurface mixing and solute transport, leading to substantial increases in intertidal saline infiltration and submarine groundwater discharge.
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Dong et al. (2026) Surface Soil Moisture Drydown over the Tibetan Plateau from SMAP: Consistency with In Situ Observations, Spatial Patterns and Controls
This study evaluates the consistency of SMAP satellite-derived surface soil moisture drydown timescales (τ) with in situ observations over the Tibetan Plateau, maps its spatial patterns, and identifies dominant environmental controls. It finds that SMAP systematically yields shorter drydown timescales than in situ measurements, primarily due to differences in effective sensing depth and spatial representativeness, with τ exhibiting a clear southeast-to-northwest gradient driven by elevation, soil sand fraction, and vegetation.
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Goodman et al. (2026) Modeling cumulative hydrologic effects of multiple floodplain restoration projects in a 4th-order river channel network
This study used HEC-RAS to evaluate the cumulative hydrologic effects of floodplain restoration projects on flood propagation in a generic 4th-order river channel network. It found that flood attenuation generally increased with restored channel length, but project location and existing restoration significantly influenced benefits, sometimes even exacerbating flooding due to peak flow synchronization.
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Esmond et al. (2026) A multi-tracer approach to constraining water sources of culturally and ecologically significant natural springs: Combining environmental isotopes and environmental DNA
This study developed an eco-hydrogeological approach, integrating geochemical tracers (hydrochemistry, stable and radio-isotopes) with environmental DNA (eDNA), to create a robust conceptual model of groundwater flow paths and water sources for Great Artesian Basin springs in Carnarvon Gorge, Australia. The findings revealed that vertical inter-aquifer flow and multiple recharge zones control spring dynamics, with eDNA proving more sensitive than isotopes in distinguishing hydraulically separated flow paths and recharge areas.
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Wen et al. (2026) Preferential flow reduces overland flow on slopes: insights from a field experiment on the Chinese Loess Plateau
This study investigated how preferential flow influences slope runoff under various vegetation restoration conditions on the Chinese Loess Plateau, revealing that vegetation restoration significantly increases preferential flow, thereby reducing overland flow.
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Adhikari et al. (2026) Design of stormwater bioretention systems for improved volume and peak runoff reduction
This study investigated 54 bioretention system design combinations using a calibrated SWMM model to optimize hydrologic performance for both common and intense rainfall events. It found that a storage connection consistently improved performance, while higher filter media fractions enhanced volume reduction during common events, and lower fractions were more beneficial for reducing overflows during high-intensity rainfall.
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Wang et al. (2026) Projected Future Changes of Atmospheric Rivers by a High- and Low-resolution CESM
This study evaluates atmospheric river (AR) simulations and projections using high-resolution (HR) and low-resolution (LR) Community Earth System Model (CESM), finding that LR CESM systematically underestimates AR frequency, intensity, and precipitation, particularly for extreme events, while HR CESM significantly improves historical simulations and provides more robust projections of increased extreme ARs under warming.
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Kröcher et al. (2026) Monitoring changes in the landscape water balance: validation of satellite- and model-based evapotranspiration data in Lusatia, Germany
This study systematically validates three satellite- and model-based evapotranspiration (ET) products (CERv2, MODIS, Landsat) against long-term in situ measurements in Lusatia, Germany. It finds that while all products consistently capture spatio-temporal ET patterns, their accuracy in absolute ET values varies significantly, particularly under water-limited conditions.
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Dobrovolný et al. (2026) Spatiotemporal Changes in Precipitation Concentration in the Atlantic‐European Region
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Bagheri et al. (2026) RetroSight and ForeSight ensemble model (ReForM) for improved time series prediction: A case study on river temperature prediction
This study introduces ReForM, a novel data-driven and physics-informed ensemble model that leverages both historical data and future physics-based simulations to significantly improve time series predictions. Applied to river temperature forecasting, ReForM demonstrates superior accuracy, especially for long-term predictions, outperforming state-of-the-art machine learning benchmarks.
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Yuan et al. (2026) Integrating ecosystem adaptability into drought resilience assessment: a case study of the Yellow River Basin, China
This study developed an integrated framework to assess ecosystem drought resilience by incorporating adaptability as a third dimension alongside resistance and recovery. Applying this framework to the Yellow River Basin (1982–2017), the research found opposite trends and trade-offs between resistance and recovery, with overall resilience increasing over time.
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Zhou et al. (2026) A theoretical index for understanding distinct land relative humidity trends in observations, reanalyses, and models
[Information not extractable due to corrupted paper text.]
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ADOMBI (2026) DeepDiscover: towards autonomous discovery of bucket-type conceptual models – a proof of concept applied to hydrology
This study introduces DeepDiscover, a physics-embedded machine learning framework designed to autonomously infer bucket-type conceptual hydrological models from data. It demonstrates the feasibility and superior predictive performance of this approach compared to traditional benchmarks, reducing reliance on expert-defined model formulations.
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Wu et al. (2026) A two-level attribution method for water resource changes based on water budget balance and distributed simulation
This study develops and applies a novel two-level attribution method for water resource changes, integrating water balance principles and a distributed human-water relationship model. The method effectively clarifies the driving mechanisms of water resource changes across scales, revealing that climatic factors dominated runoff changes in the Qin River Basin (2001–2022), while human activities had complex effects on natural and actual runoff.
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Programme (2026) Global Groundwater Vulnerability Map to Floods and Droughts
This paper presents the "Global Map of Groundwater Vulnerability to Floods and Droughts," a dataset indicating the intrinsic vulnerability and resistance of global groundwater resources to natural disasters. It serves as a crucial tool for emergency management and global water resource discussions.
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Ma et al. (2026) The qualitative and quantitative relationship between the spatiotemporal variations of potential evapotranspiration and meteorological variables in the Hexi corridor, Northwest China
This study investigated the spatiotemporal variations of potential evapotranspiration (ET0) and its response to meteorological factors in the Hexi Corridor, Northwest China, from 1960 to 2019, finding that ET0 showed a fluctuating increase primarily driven by increasing mean temperature.
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Mhanna et al. (2026) Hydrological and ecological consequences of the Kakhovka dam collapse
This study assesses the cascading hydrological impacts of the Kakhovka dam destruction in June 2023, revealing significant decreases in total water storage, amplified river variability, episodic flooding, and long-term challenges for re-emerging wetlands due to groundwater decline.
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Ferguson (2026) Do wet or dry soils trigger thunderstorms? It depends on how the wind blows
This News & Views article discusses how the interaction between soil moisture and vertical wind shear significantly influences thunderstorm initiation, enabling more precise short-term forecasting of intense storms.
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Cerbelaud et al. (2026) Wide-swath altimetry maps bank shapes and storage changes in global rivers
This study leverages the first water year of the Surface Water and Ocean Topography (SWOT) mission to provide near-global observations of active river channel geometry and monthly water storage changes across 126,674 river reaches, revealing a global annual river storage variability of 313.1 ± 129.5 km³, which is approximately 28% lower than the lowest previously modelled estimates.
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Li et al. (2026) Modeling Land Use Dynamics under Climate and Hydrological Changes: An Integrated Hydro–Land Framework
This paper introduces LaHyFr, a cascaded land-hydrology coupled modeling framework, to simulate bidirectional soil-water interactions and land-use dynamics under climate change, demonstrating significantly improved simulation accuracy in the Hanjiang River Basin.
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Yang et al. (2026) Quantitative assessment impact of anthropogenic heat flux on global urban evapotranspiration retrieval at multiple temporal scales
This study quantified the global impact of anthropogenic heat flux (AHF) on urban evapotranspiration (ET) estimation by comparing remote sensing-derived ET with and without AHF across 668 cities worldwide. It found that neglecting AHF leads to significant ET underestimation, particularly in cold regions and megacities, with discrepancies peaking in summer.
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Sîrbu et al. (2026) Short-Term Streamflow Forecasting for River Management, Using ARIMA Models and Recurrent Neural Networks
This study conducts a controlled comparison between SARIMA and stacked LSTM models for 7-day-ahead daily water-depth forecasting using synthetic hydrographs across normal, drought, and flood regimes, concluding that both approaches exhibit statistically comparable median performance.
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Medina‐Roldán et al. (2026) Comparison of National and Regional Assessments of Soil Loss Rates by Water Erosion and Soil Erosion Control: An Application to the Tuscany Region (Italy)
This study compares regional and European-scale Revised Universal Soil Loss Equation (RUSLE) applications for Tuscany, Italy, revealing that regional high-resolution data estimates significantly higher soil erosion rates and better identifies high-risk areas compared to broader European datasets.
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Taylor et al. (2026) Wind shear enhances soil moisture influence on rapid thunderstorm growth
This study reveals that wind shear significantly enhances the influence of soil moisture (SM) contrasts on the rapid growth of thunderstorms, particularly for extreme events, by modulating mesoscale circulations that promote deep convection. Analyzing 2.2 million afternoon events across sub-Saharan Africa, the authors found 68% more extreme initiations under favorable soil conditions when wind shear was strong.
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Zhang (2026) Modulating Effects of Soil Thickness on the Spatiotemporal Evolution of Hydrological Connectivity in Heterogeneous Karst Hillslopes
This paper investigates how soil thickness influences the spatiotemporal evolution of hydrological connectivity within heterogeneous karst hillslopes.
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Luo et al. (2026) PSiam-HDSFNet: A Pseudo-Siamese Hybrid Dilation Spiral Feature Network for Flood Inundation Change Detection Based on Heterogeneous Remote Sensing Imagery
This paper proposes a novel pseudo-Siamese hybrid dilation spiral feature network (PSiam-HDSFNet) to improve flood change detection accuracy from heterogeneous SAR and optical remote sensing images, specifically addressing challenges in distinguishing small ground objects from actual inundated regions. The method significantly enhances change detection accuracy, with F1 scores improving by up to 7.704% compared to suboptimum methods.
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Dou et al. (2026) Analysis of the performance of a virtual gauge-based method in hydrological modeling of basins with no precipitation stations
This study evaluates the hydrological performance of the virtual gauge-based method (VG) for flood forecasting in basins without precipitation stations, demonstrating that VG-driven simulations achieved up to approximately 50% higher flood volume prediction accuracy and superior flood simulation capabilities compared to traditional methods.
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Haberlandt et al. (2026) Assessing the maximization potential for historical floods by spatio-temporal simulation of precipitation
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
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Yang et al. (2026) 20 years of trials and insights: bridging legacy and next generation in ParFlow and Land Surface Model Coupling
This study reviews two decades of ParFlow-land/atmosphere coupled modeling, presents a renewed recoupling of ParFlow with the updated Common Land Model (CoLM) demonstrating improved performance, and proposes a sustainable coupling framework and a community-led model intercomparison project (PLCMIP) for future development.
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Merlo et al. (2026) Tracking shifts in European drought hotspots
This study develops novel impact-based Combined Drought Indices (iCDIs) using a machine learning framework to directly link hydroclimatic drivers to remotely sensed vegetation stress across Europe. The iCDIs outperform traditional indices and project a significant northward shift in future drought impacts, identifying Central Europe as an emerging hotspot, contrary to conventional views.
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Sun et al. (2026) Land use and land cover change intensified soil moisture drought: evidence from CMIP6-LUMIP
This study quantifies the long-term impacts of historical land use and land cover change (LULCC) on global soil moisture drought (SMD) characteristics from 1901-2014, finding that LULCC significantly intensifies SMD over more than half of the global land area by altering surface energy partitioning and depleting soil water storage.
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Kwon et al. (2026) Synergistic impact of simultaneously assimilating radar- and radiometer-based soil moisture retrievals on the performance of numerical weather prediction systems
This study evaluates the synergistic impact of simultaneously assimilating radar-based (ASCAT) and radiometer-based (SMAP) soil moisture retrievals into the Korean Integrated Model (KIM) using a weakly coupled data assimilation system. The findings demonstrate that multi-sensor soil moisture assimilation leads to more balanced and improved analyses and forecasts of specific humidity, air temperature, and precipitation compared to single-sensor assimilation.
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Zhang et al. (2026) Long-Term Evolution of Permafrost across the Qinghai-Tibet Plateau: Perspectives from Multi-Model Ensembles and Machine Learning
This study combined CMIP6 data with machine learning models to project permafrost extent and maximum seasonal soil freeze depth (SFD) across the Qinghai-Tibet Plateau (QTP) from 2025 to 2100 under various SSP scenarios. Results indicate continuous permafrost degradation into seasonally frozen ground, with SFD declining significantly, and specific high-risk zones identified, with the Deep Neural Network (DNN) model demonstrating superior performance.
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Ravazzolo et al. (2026) Towards integrated short-term Rain-on-Grid modeling and long-term RUSLE estimates for improved erosion susceptibility assessment in the Oltrepò Pavese hills of Northern Italy
This study evaluates the complementary use of the empirical Revised Universal Soil Loss Equation (RUSLE) and a two-dimensional Rain-on-Grid (RoG) hydrodynamic model for erosion susceptibility assessment in Northern Italy. The models showed over 50% spatial overlap in identifying erosion-prone areas, with RoG better reproducing event-based erosion zones and RUSLE capturing land-cover effects, offering a practical integrated framework for data-scarce catchments.
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Bi et al. (2026) A 0.1° monthly potential evapotranspiration dataset based on the optimal models over global vegetation zones
This study developed a global 0.1° monthly potential evapotranspiration (PET) dataset for 1992–2022 by calibrating and selecting optimal PET models (Priestley-Taylor and Milly-Dunne) using observations from 124 eddy covariance sites, aiming to reduce uncertainties in existing PET products.
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Eliades et al. (2026) Forests in a semi-arid climate die with a memory: satellite signals predict forest mortality years after drought
This study investigates the relationship between satellite-derived vegetation indicators and meteorological drought indices to understand tree mortality mechanisms in semi-arid Cypriot forests, revealing that severe drought conditions trigger mortality and that vegetation response is linked to multi-year climate memory effects, with indicator effectiveness varying by species and post-mortality stage.
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zhang (2026) CATENA data
This entry describes the CATENA dataset, which provides volumetric water content data, categorized under 'Vegetable' and 'Moisture'.
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Athukoralalage et al. (2026) The impact of a mega-flood event on the water quality of the southern Murray-Darling Basin, Australia
This study investigated the impact of a 2022–2023 mega-flood and five other major flow events on Total Nitrogen, Total Phosphorus, and Dissolved Organic Carbon dynamics in the southern Murray-Darling Basin, Australia. It found that the mega-flood significantly increased nutrient loads and prolonged water quality degradation, particularly in downstream areas due to extensive floodplain inundation and delayed nutrient release.
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Premier et al. (2026) Assessing the impact of Earth Observation data-driven calibration of the melting coefficient on the LISFLOOD snow module
This study evaluates the LISFLOOD hydrological model's snow module and the impact of calibrating its snowmelt coefficient using Earth Observation (EO) snow cover fraction (SCF) data across nine European basins. It demonstrates that while EO-based calibration significantly improves snow cover representation, its impact on basin-level discharge simulations is minimal, suggesting that standard discharge-based calibration adequately captures snow dynamics for streamflow.
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Sellami (2026) Spatiotemporal Droughts Propagation and Direct Driving Variables Under Climate Change Projections: A Case Study of Tunisia
## Identification - **Journal:** International Journal of Climatology - **Year:** 2026...
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Aich et al. (2026) Conditional diffusion models for downscaling and bias correction of Earth system model precipitation
This paper introduces a machine learning framework utilizing conditional diffusion models for simultaneous bias correction and downscaling of Earth System Model (ESM) precipitation. The approach outperforms existing statistical and deep learning methods, particularly for extreme events, by improving spatial structure and statistical fidelity while preserving climate change signals.
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Wang et al. (2026) A wetland partitioning method based on the hydrological connectivity and the underlying causes of their occurrence
This study developed a wetland partitioning method based on hydrological connectivity using hydrodynamic modeling and clustering, demonstrating its effectiveness in delineating subareas in the Zhalong Wetland and revealing how human activities and topography influence connectivity across different hydrological years.
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Ling et al. (2026) An improved Hydrology-Informed attention LSTM(HIA-LSTM) model for runoff simulation with seasonal snowmelt
This study proposes a Hydrology-Informed Attention LSTM (HIA-LSTM) that embeds physical inductive biases into its neural architecture to improve runoff simulation in alpine basins with complex cryospheric processes. The HIA-LSTM significantly outperforms conventional deep learning models, achieving superior accuracy and interpretability, especially in melt-driven runoff scenarios.
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Alsumaiei (2026) Complexity-efficiency dynamics of metaheuristic-optimized recurrent neural network models for drought forecasting in hyper-arid Kuwait
This study develops and benchmarks metaheuristic-optimized recurrent neural network models (LSTM, GRU) for drought forecasting in hyper-arid Kuwait using the distribution-free Precipitation Index (PI12, PI24), finding that longer aggregation windows enhance stability and that compact architectures often achieve comparable accuracy to more complex, optimized models with greater efficiency.
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Chen et al. (2026) Contrary effects of soil moisture-atmosphere feedback on dry and humid heatwaves
This study investigates the distinct impacts of soil moisture-atmosphere feedback (SAF) on dry and humid heatwaves globally, revealing that SAF amplifies dry heatwaves but has spatially divergent effects on humid heatwaves, reducing their severity in low-to-mid latitudes while intensifying them in high latitudes. These contrary effects are primarily driven by the competition between SAF-induced thermal warming and moisture depletion, which modulate mean wet-bulb temperatures.
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Yang et al. (2026) High-resolution mapping of saturated soil hydraulic conductivity across China’s drylands
This study developed a novel machine learning approach integrating multi-sensor Sentinel-1/2 remote sensing data and environmental covariates to generate high-resolution (90 m) saturated soil hydraulic conductivity (Ks) maps across China's drylands, demonstrating superior accuracy and spatial detail compared to existing datasets.
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Liu et al. (2026) Determination of Suitable Ecological Intervals for Arid Terminal Lakes via Multi-Source Remote Sensing: A “Morphometry–Security–Efficiency” Framework Applied to Ebinur Lake
This study develops a novel framework integrating morphometric stability, ecological security reliability, and resource use efficiency to define the suitable ecological interval for Ebinur Lake, revealing a significant shrinking trend and proposing a "Spring Surplus and Autumn Deficit" water regulation strategy to optimize ecosystem services.
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Woreket et al. (2026) Remote sensing for estimating crop water productivity: a systematic review of concepts and methods
This systematic review synthesizes 93 studies (2020-2025) to critically examine remote sensing concepts and methods for estimating Crop Water Productivity (CWP) by analyzing approaches for crop yield and actual evapotranspiration (ETa), aiming to provide a consolidated reference for advancing CWP assessment.
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Liu et al. (2026) Mapping Synchronous Heatwaves in the Northern Hemisphere: Insights from Climate Network Analysis
This study identifies hotspot regions and dominant synchronization patterns of summertime synchronous extreme heatwaves across the Northern Hemisphere using a climate network method. It reveals connections to large-scale atmospheric circulation patterns, including Rossby waves and zonal wave trains, and highlights the role of positive soil moisture feedback in intensifying these events.
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Khan et al. (2026) A scalable framework for flash flood hazard assessment in data-scarce catchments using coupled modeling
This study developed a scalable framework for flash flood hazard assessment in data-scarce catchments by coupling HEC-HMS and HEC-RAS 2D with remotely sensed data and transposed rainfall. The framework successfully mapped flood hazards for various return periods, revealing a significant increase in extreme hazard zones from 514 hectares (2%) to 2,498 hectares (7%) between 10-year and 100-year return period floods.
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Huang et al. (2026) Reconstructing Lake Storage for the Major Water Bodies in the Aral Sea Basin Using Multi-DEM Hypsometry
This study developed a multi-digital elevation model (DEM) hypsometry framework to reconstruct near-monthly lake storage in arid zones, demonstrating its superior accuracy in recovering storage during low-level periods and hydrological disconnection compared to conventional methods.
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Miazza et al. (2026) Technical note: Transit times of reactive tracers under time-variable hydrologic conditions
This study derives and explores novel analytical solutions for the transit time distributions (TTDs) of reactive tracers in randomly sampled hydrological systems, demonstrating how processes like sorption, degradation, and evapotranspiration, along with input patterns, cause tracer TTDs to differ significantly from water TTDs.
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Poschlod et al. (2026) Climate change effects on river droughts in Bavaria using a hydrological large ensemble
This study investigates the impact of climate change on rare and extreme river droughts in two Bavarian catchments using a unique hydrological large ensemble. It projects a drastic increase in the frequency and intensity of summer droughts, with historical 100-year events becoming significantly more common by the far future (2070–2099) under a high-emission scenario.
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Mliyeh et al. (2026) Advancing hydrological modeling in the Mediterranean: Multi-objective calibration of the SWAT+ model using open-source data and tools
This study evaluated multi-variable calibration strategies for the SWAT+ model in the Upper Oum Er Rbia watershed, Morocco, integrating streamflow and remote sensing evapotranspiration data. The multi-variable approach achieved satisfactory and balanced performance for both streamflow (NSE = 0.75, KGE = 0.77) and evapotranspiration (NSE = 0.51, KGE = 0.64), highlighting the value of open-access remote sensing ET data in refining hydrological model parameters for data-scarce regions.
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Beguería et al. (2026) Water balance components of the Pyrenees: A 30-year modelling study in a transboundary context
This study reconstructed the regional water balance for the Pyrenees over the 1981–2010 historical baseline using two contrasting hydrological models, SASER and SWAT. Results reveal strong hydroclimatic gradients and highlight evapotranspiration, recharge, and snowmelt timing as key sources of structural uncertainty, establishing the first integrated, transboundary hydrological baseline for the region.
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Mamani et al. (2026) Irrigation parameterization for the ICON model
This study developed an irrigation parameterization for the ICON model to quantify the long-term impact of irrigation on surface and atmospheric variables over the EURO-CORDEX domain. Results indicate that irrigation leads to a cooling effect, increased latent heat flux and evapotranspiration, and decreased sensible heat flux in irrigated areas.
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Wei et al. (2026) A framework for long-term vegetation latent heat estimation and forecasting combining ERA5-land and Landsat data
This study developed a globally applicable framework integrating ERA5-Land reanalysis and Landsat data with machine learning to estimate and forecast monthly vegetation latent heat (LE) at 30 m resolution from 1984 to the present. It found Random Forest performed best for estimation and proposed two forecasting frameworks, LE-ML and LE-Direct, with varying performance based on training data availability.
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Ibrahim et al. (2026) An integrated approach to unravel the deep-shallow aquifer connectivity in the Eastern Sahara
This study integrates remote sensing, geophysical, and isotopic data to investigate deep-shallow aquifer connectivity in the Eastern Sahara, revealing that significant vertical upwelling from the deep Nubian Aquifer System (NAS) to overlying shallow aquifers occurs along intersecting structural trends in southern and middle Egypt, with contributions ranging from 10% to 98%.
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Marson et al. (2026) The Explore2-2022 climate projections dataset for impact studies over France.
This paper introduces the Explore2-2022 dataset, a new set of bias-corrected regional climate projections for France, sub-sampled from the EURO-CORDEX (EUR11) ensemble and consistent with CMIP6, designed to support impact studies, particularly on water resources, and characterize climate change uncertainties.
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Shahnazi et al. (2026) A novel implementation of a decomposition-enhanced hybrid GWO–KELM model with LUBE for constructing prediction intervals of groundwater drought
This study developed a novel decomposition-enhanced hybrid Grey Wolf Optimizer (GWO)–Kernel Extreme Learning Machine (KELM) model with Lower–Upper Bound Estimation (LUBE) for multi-horizon point and interval forecasting of groundwater drought (Standardized Groundwater Index, SGI). The Variational Mode Decomposition (VMD)–GWO–KELM model consistently outperformed other approaches, especially for short-term forecasts, providing reliable and sharp prediction intervals.
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TIAN et al. (2026) Intensifying droughts, heatwaves, and compound drought–heatwave events and their spatiotemporal patterns in Africa (1979–2024)
This study systematically evaluates the spatiotemporal patterns of heatwaves, droughts, and compound drought–heatwave (CDHW) events across Africa from 1979 to 2024, revealing significant intensification of all three, with CDHWs accelerating since the 2000s, particularly in Eastern and Southern Africa.
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Häberli et al. (2026) Unprecedented extreme meteorological droughts simulated in Fenno-Scandinavia with high-resolution climate models
This study assesses future meteorological drought probabilities in Fenno-Scandinavia using high-resolution convection-permitting regional climate models (CPRCMs) and a novel multi-threshold Standardized Precipitation Index (SPI) method. It projects a decrease in moderate droughts but a significant increase in unprecedented extreme droughts, particularly during the critical growing season, highlighting the added value of CPRCMs.
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Janzing et al. (2026) Spatiotemporal Dynamics of Streamflow Drought in the Larger Alpine Region
This study analyzes the spatiotemporal dynamics of streamflow droughts across the larger Alpine region using high-resolution hydrological model simulations and a novel clustering algorithm, revealing that extensive droughts exhibit growth and recovery phases, regional differences in behavior, and are primarily driven by rainfall deficits, though often by multiple interacting processes.
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Ebrahimi et al. (2026) GIS-based assessment of groundwater suitability for agricultural irrigation in central Iran
This paper focuses on a GIS-based assessment to determine the suitability of groundwater for agricultural irrigation in central Iran. The main findings are not available in the provided pre-proof.
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Zhang et al. (2026) Can Conceptual Rainfall‐Runoff Models Capture Multi‐Annual Storage Dynamics?
This study investigated if specific structural components enable conceptual rainfall-runoff models to capture multi-annual storage dynamics during droughts. It found that models incorporating a long-term store, its disconnection from direct streamflow, and a water loss mechanism from it were significantly more successful in representing long-term hydrological memory and drought response.
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Deger (2026) Exploring Monthly, Seasonal and Annual Spatio-Temporal Variability of Temperature and Precipitation Series by Classical and Innovative Techniques
This study comprehensively analyzed the spatio-temporal variability and trends of temperature and precipitation across 12 stations in Southeastern Anatolia, Türkiye, revealing a dominant significant increasing trend in temperature and a prevalence of non-significant decreasing trends in precipitation.
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Demirbaş et al. (2026) Modelling the Impact of Climate Change on the Reservoir Filling Rates of Dams Used for Drinking Water Supply Through Artificial Neural Networks
This study models the impact of climate change on the reservoir filling rates of drinking water supply dams in Ankara, Istanbul, and Izmir, Türkiye, using Artificial Neural Networks (ANNs). It quantifies the divergence between expected and observed precipitation, revealing significant water losses and the susceptibility of these urban water systems to climate change.
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Yang et al. (2026) A Global Assessment of Climate Change and Anthropogenic Effects on Changes in Streamflow
This study globally assessed spatiotemporal streamflow changes in 2264 catchments from 1961–2014, quantifying the contributions of precipitation, potential evapotranspiration, and landscape characteristics using the Budyko hypothesis. It found that precipitation was the dominant factor for streamflow changes in most catchments, with significant regional variations in trends and sensitivities to climate and anthropogenic factors.
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Mohomi et al. (2026) Projections of extreme rainfall in South Africa using CMIP6 ISIMIP models
This study projects extreme rainfall in South Africa using CMIP6 ISIMIP global climate models under SSP1-2.6 and SSP5-8.5 scenarios, finding an overall increase in extreme rainfall events, with a trend towards a drier west and a wetter east, posing significant risks to water resources and infrastructure.
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Adekilae et al. (2026) Pseudo‐diffusivity characteristic curves for surface–rootzone soil hydrologic connectivity
Undeterminable due to corrupted input text. The provided paper text is unreadable, consisting primarily of garbled characters, preventing extraction of its core objective and main findings.
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Dueñas-Tovar et al. (2026) Integration of spectral indices and precipitation data to assess river morphometric features in a tropical semi-humid environment
This study developed a reproducible remote sensing workflow using eight optical indices and a Random Forest algorithm to assess river channel mobility (lateral shift and sinuosity) in a data-limited tropical semi-humid environment. The workflow successfully identified episodic, reach-specific channel adjustments, with lateral shifts up to 500 meters, and revealed a short-term negative correlation between antecedent precipitation and lateral displacement.
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Martin et al. (2026) Corrigendum to “Estimating irrigation consumptive use for the conterminous United States: coupling satellite-sourced estimates of actual evapotranspiration with a national hydrologic model” [J. Hydrol. 662 (2025) 133909]
This document is a corrigendum to the original paper "Estimating irrigation consumptive use for the conterminous United States: coupling satellite-sourced estimates of actual evapotranspiration with a national hydrologic model," providing necessary corrections to author affiliations, definitions, references, numerical values, and figure/table captions.
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Ghasemifar et al. (2026) Widespread extreme precipitation events over Iran: Large-scale patterns and their associated global indices
This study characterizes widespread extreme precipitation events (WEPEs) over Iran, identifying their frequency, intensity, duration, and regional patterns using 25 years of satellite data. It reveals that WEPEs are primarily driven by deep troughs over the Red Sea/Arabian Peninsula and are strongly linked to the Circumglobal Wave Train (CGT), with the North Atlantic Oscillation (NAO) indirectly modulating CGT variability.
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Azghandi et al. (2026) Machine Learning–Based Characterization of Groundwater Recharge in Semi-Arid Drylands
This study characterized groundwater recharge dynamics in the semi-arid Karkheh Plain (Iran) from 2001–2024 using satellite-based water balance and machine learning, finding that ΔSoil Moisture is the dominant driver and that positive recharge peaks have significantly declined, indicating increasing groundwater vulnerability.
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Özel et al. (2026) Multi-dimensional Assessment of the Water-Food Nexus in a Semi-Arid Watershed
This study holistically assesses the water-food nexus in the semi-arid Upper Sakarya Watershed, Türkiye, by integrating hydrological modeling, economic analysis, and stakeholder perspectives to evaluate agricultural water management scenarios. It finds that effective scenarios, particularly crop pattern changes, can significantly reduce irrigation water use (up to 60 million m³ per year) while increasing farmers' net income per cubic meter of water, but implementation faces technical, practical, and political constraints.
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Fu et al. (2026) Climate change enhances the propagation from meteorological to lake drought
This study quantified the propagation time and probability from meteorological to lake droughts for 153,643 global lakes from 1985 to 2018, revealing that climate change is enhancing this propagation, particularly in arid regions and North America due to rising temperatures and vapor pressure deficit.
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Gianotti et al. (2026) Meteorological to Agricultural Drought Transitions Compounded by Heat Waves in Historical and Future Climates
## Identification - **Journal:** Water Resources Research - **Year:** 2026...
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Singh et al. (2026) Global shifts in rainfall drought relationship: weakening association in tropics
This study examines global meteorological drought dynamics from 1951 to 2016, revealing a sixfold increase in global drought frequency, particularly in tropical and subtropical regions. It finds that increased rainfall variability, rather than just rainfall deficit, is increasingly driving these droughts, leading to a 60 % rise in drought likelihood even during surplus rainfall years.
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Wolkeba et al. (2026) Water scarcity indicator based on GRACE derived total water storage for fast water scarcity monitoring
This study introduces a novel water scarcity indicator derived from GRACE total water storage anomaly, offering a robust and efficient alternative to traditional Global Hydrological Model (GHM)-based assessments. The new indicator demonstrates strong alignment with established blue water scarcity metrics and provides comparable estimates of populations and land areas under scarcity.
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Guo et al. (2026) Simulation and Rapid Prediction of Water Quantity and Quality Processes Based on Numerical Models and Deep Learning
This study develops a coupled 1D-2D numerical model (GAST-SWMM) to simulate urban water quantity and quality processes, generating a training database for a Long Short-Term Memory (LSTM) deep learning model. The LSTM model provides rapid and accurate predictions of pollutant concentrations on urban surfaces and within sewer networks, outperforming other machine learning models and significantly reducing computational time.
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Man et al. (2026) Multi-Target Water Demand Forecasting with Graph Neural Networks: A Comparative Study
This study systematically evaluates Graph Neural Networks (GNNs) for multi-target water demand forecasting (MTF), demonstrating their superior accuracy and robustness compared to traditional sequence-based models. Self-learning GNNs, specifically MTGNN and MTGODE, achieved enhanced accuracy and stability, particularly under data irregularities and for multi-step predictions.
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WU et al. (2026) Dominant drivers for geographic patterns and multi-scale variability of global land‒atmosphere coupling
This study systematically assesses global land-atmosphere (L-A) coupling from 1958-2022, identifying five distinct regional patterns and their multi-scale temporal variability, and determining the dominant physical drivers for each region using machine learning and process network analysis. The findings reveal that while interannual signals generally dominate L-A coupling variability, specific regions like the Hot Evaporative Region exhibit strong decadal signals, with dominant drivers varying significantly across regions and seasons.
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Radwin et al. (2026) Multispectral Surface Reflectance as an Indicator of Groundwater Depth for Salt Crust Systems: Insights From the Bonneville Salt Flats, Utah
## Identification - **Journal:** Earth and Space Science - **Year:** 2026...
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Eatesam et al. (2026) Quantifying the attribution of ecohydrological degradation: a comparative deep learning approach in a changing environment
This paper aims to quantify the attribution of ecohydrological degradation in a changing environment using a comparative deep learning approach.
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Arabacı et al. (2026) A modelling framework for simulating 50-year thermal variation under lake drying and urban expansion: insights from Ramsar lake Burdur, Türkiye
This study developed an integrated modeling framework to simulate the long-term thermal variations around Lake Burdur, Türkiye, under lake drying and urban expansion scenarios. It found that the lake's cooling capacity will substantially weaken by 2075, significantly reducing the thermal resilience of nearby urban areas.
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Bril et al. (2026) Assessing the Effectiveness of Nature‐Based Solutions and Building‐Level Flood Risk Reduction Measures: An Open‐Source Coupled Model
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Finkel et al. (2026) Rare Event Sampling for Moving Targets: Extremes of Temperature and Daily Precipitation in a General Circulation Model
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Moumen et al. (2026) Riverine Flood Mapping Methods and Criteria: A Meta-Analysis Review and Synthesized Guidelines
This meta-analysis statistically evaluates the influence of methods, topography, area extent, reference dataset size, and criteria on riverine flood mapping (RFM) accuracy across 142 studies, synthesizing guidelines for objective and context-appropriate method and criterion selection. It finds remote sensing and machine/deep learning methods generally most accurate, with performance varying significantly by topography and area extent, and identifies distance from river, elevation, and slope as the most influential criteria.
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Wang et al. (2026) A DeepONet surrogate for accelerating distributed hydrological model simulations
This paper introduces a DeepONet surrogate model designed to significantly accelerate the simulation speed of distributed hydrological models.
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Lapides et al. (2026) Potential Impacts of Groundwater Pumping on Stream Temperature Are Greatest in Streams With Substantial Cold Groundwater Inflows
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Calvi et al. (2026) Origin, Age, and Flow Path of Groundwater Associated With High‐Mountain Springs in Arid Andean Regions
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Wang et al. (2026) Advancing Physical Realism in Hydrological Modelling: Selection and Integration — A Review and Synthesis
This review synthesizes 30 hydrological models and 186 peer-reviewed studies to propose a decision-oriented framework for model selection and integration, aiming to enhance physical realism, address data scarcity, and improve long-term and sub-daily simulations for resilient water resources management.
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Yaldiz et al. (2026) A Step Toward Rainfall Erosivity Mapping Over Türkiye Using Kriging With External Drift
This study mapped the rainfall erosivity (R) factor over Türkiye using Kriging with External Drift (KED) and satellite-derived Modified Fournier Index (MFI). The KED model significantly outperformed linear regression, ordinary kriging, and a global erosivity product, achieving a Kling-Gupta Efficiency (KGE) of 0.68.
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Pan et al. (2026) A Study on Rapid Dynamic Flood Forecasting in Small Watersheds Using a GNN-Transformer Approach Integrated with Spatial Physical Information
This study develops a novel GNN-Transformer deep learning model for rapid flood forecasting in small watersheds, integrating static physical information and dynamic rainfall data. The model achieves high accuracy (NSE > 0.99, RMAE < 7%) and significantly improved computational efficiency (100-200 times faster) compared to traditional hydrodynamic models.
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Dioha et al. (2026) Future projections of aridity change across Africa's climatic regions
This study aims to project future changes in aridity across various climatic regions of Africa, providing insights into the potential impacts of climate change on the continent.
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Wu et al. (2026) Evaluating Evapotranspiration Simulation Performance in 30 Conceptual Hydrological Models: Insights Into ET Representation Across Diverse Climates
This study investigates 30 conceptual hydrological models to assess their evapotranspiration (ET) representations and ability to reproduce state-of-the-art ET products across 507 diverse CAMELS-US catchments, providing guidance for appropriate ET modeling based on climate.
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Sánchez‐Gómez et al. (2026) Climate Change in the Upper Tagus River Basin: Impacts on Climate Variables and Hydrological Processes
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Negro et al. (2026) Integrating Satellite Data Into Meso‐Scale Habitat Modeling for Non‐Perennial Rivers and Streams
This study introduces a novel methodology using the MesoHABSIM model and satellite imagery to assess aquatic habitat dynamics in a non-perennial river, revealing species-specific vulnerabilities of fish and macroinvertebrates to flow intermittency and informing ecological flow strategies.
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Minea et al. (2026) Coupled evolution of meteorological and hydrological drought until 2100 based on changes in climate scenarios
This study analyzed the coupled evolution of meteorological and hydrological droughts in Eastern Romania from 1971-2100 using historical data and future climate scenarios, revealing strong correlations between drought types and a projected increase in severe and extreme hydrological droughts by the end of the century.
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Hanumantha et al. (2026) A Spatially Explicit Water Balance Model for Assessing Recharge Sensitivity to Climate and Land Cover Change in Central Mexico
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Risser et al. (2026) Correction: A framework for detection and attribution of regional precipitation change: application to the United States historical record
This paper develops and applies a framework for the detection and attribution of regional precipitation changes, specifically using the historical record of the United States.
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Zhang et al. (2026) A high-order Model-free Dynamic Framework for Accurate Daily Streamflow Prediction
This paper introduces a high-order lightweight dynamic framework (HoLDF) for daily streamflow forecasting, which integrates high-order structural information identified by an improved Granger causality inference approach into a reservoir computing paradigm. HoLDF significantly outperforms baseline deep learning models in accuracy, robustness, and computational efficiency, making it suitable for operational deployment.
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Khan et al. (2026) Mapping agricultural drought hotspots in Pakistan: a remote sensing-based climate–vegetation nexus
This study analyzes agricultural drought dynamics in Pakistan from 2001 to 2023 using multisensor remote-sensing indices, revealing spatially heterogeneous and seasonally structured drought occurrences with northern regions being resilient and southern/western regions highly vulnerable, necessitating region- and season-specific adaptation strategies.
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Ferreiro-Crespo et al. (2026) AWARE historic and 2024 characterization factors for Spain
This study developed an improved AWARE-based methodology for water scarcity assessment in Spain, integrating current reservoir data and refined demand estimates to provide temporally responsive and spatially resolved characterization factors. The application to 2024 data revealed an average 8.3% increase in water scarcity factors nationally, with significant regional variations highlighting entrenched hydrological polarization.
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Zhang et al. (2026) Longitudinal Mean Velocity and Turbulent Kinetic Energy Within an Emergent Canopy in Nonuniform Flows
This study investigates the longitudinal velocity and turbulent kinetic energy (TKE) dynamics in emergent canopies under streamwise varying flow conditions using laboratory flume experiments. It found that both time-mean longitudinal velocity and TKE significantly enhance downstream, and developed an analytical model revealing an effective power-law exponent of 2/3 between longitudinal TKE and mean velocity due to flow nonuniformity.
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Risser et al. (2026) Correction: Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation
This correction notice addresses and rectifies a misspelling of an author's name, Christina M. Patricola-DiRosario, in a previously published article.
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Yang et al. (2026) A hybrid method coupling physical process-driven model with generative deep learning for probabilistic flood forecasting
This paper proposes a novel hybrid method that integrates a physical process-driven model with generative deep learning to enhance the accuracy and reliability of probabilistic flood forecasting.
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Bernal‐Mujica et al. (2026) The Impact of Deciduous Forest and Topography on Snowpack Dynamics in a Headwater Catchment in the Southern Andes Cordillera
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
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Ma et al. (2026) Monitoring Reservoir Storage Using SWOT Satellite Observations and a Reservoir Operation Model
This study evaluates the accuracy of reservoir storage estimates derived from the new Surface Water and Ocean Topography (SWOT) satellite mission against in situ observations for 12 Western U.S. reservoirs, finding that SWOT provides highly accurate water surface elevation and storage data that can effectively constrain hydrological models to fill temporal gaps.
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Jiang et al. (2026) Integrating socio-hydrological modeling and climate change projections for sustainable water resource management in agricultural systems
This paper integrates socio-hydrological modeling with climate change projections to develop sustainable water resource management strategies specifically for agricultural systems.
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Rahman et al. (2026) Comprehending the impact of hydro-meteorological droughts on ecosystem vulnerability and resilience across the Indus River Basin in Pakistan
This study develops a catchment-based integrated drought index (CIDI) for the Indus River Basin by integrating the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Water Availability Index (SWAI), and assesses ecosystem vulnerability and resilience, finding CIDI to be robust and identifying extreme vulnerability in the Middle and Lower Indus Basins.
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Baioni et al. (2026) A regionally based method to identify lithology-specific hydraulic conductivity distributions in shallow aquifers using catchment-scale effective values
This paper introduces a novel method (HCDM) to infer lithology-specific hydraulic conductivity distributions in shallow aquifers using catchment-scale effective values. Validated with synthetic data and applied to 113 catchments in the Armorican Massif, the method demonstrates high predictive accuracy, with 85% of modeled conductivities falling within a 90% confidence interval of observed values.
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Tajima et al. (2026) Climate Change Alters Post‐Surge Recovery of Coastal Aquifers
This study quantifies the combined effects of increasing storm-surge intensity and decreasing frequency on coastal aquifers using integrated numerical simulations. It reveals two distinct long-term regimes—full recovery or shifted equilibrium with persistent salt accumulation—determined by critical thresholds of storm intensity and frequency, which can be predicted by a dimensionless number.
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Riche et al. (2026) Predicting LULC Changes and Assessing their Impact on Surface Runoff with Machine Learning and Remote Sensing Data
This study developed an approach integrating remote sensing and machine learning to predict future land use and land cover (LULC) changes and assess their impact on surface runoff in a semi-arid Mediterranean watershed. It found that urbanization significantly increases runoff, while forests mitigate it, with land factors having limited influence during intense rainfall events.
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Ma et al. (2026) Divergence or Convergence? A Comparison of InVEST and SWAT in Simulating Water Conservation Patterns and Drivers
This study quantitatively compares the InVEST and SWAT models in simulating water conservation patterns and drivers in the Liupan Mountain region from 2003 to 2022, finding both models show an increasing trend and consistent spatial heterogeneity, but with complementary strengths for different application contexts.
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Neri et al. (2026) On the Choice of Optimal Reservoir Operating Rules in a Changing Climate for the Sustainable Management of Drinking Water Sources
This study develops a multi-objective optimization framework to define optimal reservoir withdrawal rules for a multi-basin drinking water supply system in Northern Italy, assessing their adaptation to climate change under historical and future meteorological forcings to maximize production and minimize deficits. The research quantifies future expected relative changes in optimal operating rules and outlines corresponding patterns of withdrawal volumes and potential water system failures throughout the century.
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Lechner et al. (2026) Hydrological drivers of surface runoff during high intensity rainfall experiments in Alpine ski regions
This study investigates surface runoff behavior in 12 Eastern Alpine ski regions using 74 rainfall simulation experiments, revealing significantly higher surface runoff coefficients on ski slopes (median 0.57) compared to reference areas (median 0.07). A random forest model identified geological factors as the strongest predictors on ski slopes, while soil and land use variables were more influential on reference areas.
