-
Maines et al. (2026) Recent and future variability in 1-day precipitation extremes in Trentino – South Tyrol (Eastern Italian Alps) based on observations (1956-2023) and climate model projections
This study comprehensively assesses 1-day precipitation extremes in Trentino – South Tyrol (eastern Italian Alps) using observations (1956-2023) and climate model projections, finding increasing intensity and frequency in both past observations (especially in the north, summer/autumn) and future projections, with rarer events becoming more probable under higher warming levels.
-
Chuphal et al. (2026) Development of Gridded Root-Zone Soil Moisture Product for India, 1981–2024
This study developed a high-resolution (0.05°), long-term (1981–2024) daily root-zone soil moisture dataset for India using a hybrid modeling and machine learning approach, providing a crucial resource for drought monitoring and agricultural planning.
-
Bani et al. (2026) Application of artificial intelligence-based modelling to investigate spring streamflow predictability under ENSO and IOD forcing
This study developed Artificial Neural Network (ANN) models, driven by lagged El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) indices, to forecast spring streamflow in Victoria, Australia. The ANN models consistently and substantially outperformed traditional Multiple Linear Regression (MLR) across diverse catchments, demonstrating enhanced predictive accuracy and better representation of nonlinear climate-streamflow interactions.
-
Wang et al. (2026) A novel dual-polarization SAR-based method for high-accuracy 768 km river mapping in steep mountainous regions
This study proposes a novel dual-polarization SAR-based method for high-accuracy river mapping in steep mountainous regions, effectively overcoming challenges of backscatter heterogeneity, layover, and shadow distortions, achieving superior accuracy (Kappa coefficient 95.53%) compared to traditional and machine learning methods.
-
Xi et al. (2026) Widespread biophysical cooling effects due to post-fire greening
This study globally assesses the biophysical cooling effects of post-fire greening on land surface temperature (LST) and its relationship with carbon use efficiency (CUE) from 2004 to 2019. It finds that while post-fire greening generally induces a cooling effect due to enhanced evapotranspiration, this cooling weakens as primary productivity recovery lags behind structural (LAI) recovery, leading to decreased CUE and potential warming in some regions.
-
Muhaimeed et al. (2026) Monitoring and Assessment of Agricultural Drought Using Satellite Data: Case Study in Afaj District, Al-Qadisiya Governorate, Southern Iraq
This study analyzed spatiotemporal drought severity in Iraq's Afaj District using Sentinel 2 indices for 2019 and 2025, revealing a projected increase in drought severity by 2025, marked by a 28% expansion of bare land and significant impacts on agricultural areas.
-
Sun et al. (2026) Response and adaptation of global terrestrial vegetation production to extreme precipitation
This study quantifies the global terrestrial vegetation's gross primary productivity (GPP) response, adaptation, and recovery to extreme precipitation events (EPEs) using FLUXNET data and modeling, finding that EPEs significantly reduce carbon sequestration but most ecosystems recover within 12 days.
-
Saber et al. (2026) Enhancing groundwater recharge mapping in arid regions with geospatial multi-criteria analysis in the Eastern desert of Egypt
This study developed an integrated methodology combining Analytic Hierarchy Process (AHP), Remote Sensing (RS), and Geographic Information System (GIS) to map groundwater recharge potential zones in the Qift–El Quseir area of Egypt's Eastern Desert. The research successfully identified and validated these zones, estimating a basin-scale recharge of approximately 9.67 × 10^6 m^3/year.
-
Senjaliya et al. (2026) A comprehensive review of machine learning and deep learning approaches for rainfall forecasting: current progress, challenges, and future directions
This comprehensive review synthesizes nearly 150 studies to compare statistical, machine learning (ML), and deep learning (DL) approaches for rainfall forecasting, identifying current progress, persistent challenges, and outlining future research directions for robust and climate-aware prediction systems.
-
Lv et al. (2026) Precipitation and soil moisture coupling constrains subseasonal predictability of a prolonged extreme heatwave
This study investigates the subseasonal predictability of the August 2022 Yangtze River Valley heatwave, finding that precipitation-soil moisture coupling is the primary factor limiting forecast accuracy due to its significant influence on surface temperature.
-
Corona et al. (2026) Extreme Precipitation Variability and Soil Texture Controls on Water-Table Response
This study used one-dimensional modeling to evaluate water-table response time, displacement, recession time, and total recharge under various extreme precipitation events across twelve soil textures, revealing that recharge is primarily governed by event magnitude and soil properties, rather than event duration.
-
Xu et al. (2026) Integrating LSTM and Transformer for Improved Daily Runoff Prediction: A Parallel Computing Approach
This study proposes a novel LSTPencoder model, integrating LSTM and Transformer encoder via a parallel computing and information-sharing approach, to improve daily runoff prediction accuracy. Optimized by the AOAAO algorithm, the model effectively captures both local and global sequence features, demonstrating superior forecasting performance and offering a more effective explanation of complex causal relationships in runoff sequences.
-
Fang et al. (2026) Machine Learning Calibration of Groundwater Table Depth in ELM: Impact on Land Surface Hydrology and Land‐Atmosphere Fluxes
[Information not extractable due to corrupted paper text.]
-
Cremaschi et al. (2026) Karst record of Holocene climate and human-induced changes in surface processes in the northern Apennines of Italy
This study reconstructs Holocene environmental changes in the northern Apennines of Italy using clastic and speleothem sediments from Tana della Mussina Cave, revealing the interplay between natural climatic variability and human-induced land use changes, particularly deforestation and pastoralism, in shaping the Earth's Critical Zone dynamics.
-
Luintel et al. (2026) Cross-comparison of national drought monitoring products in Central Europe using a new drought impact database
This study evaluated six national drought monitoring products in Central Europe using a novel, high-resolution drought impact database derived from national newspaper reports (2000–2023). It found varying effectiveness in detecting impact occurrence and capturing impact severity across countries and indices, highlighting the need for multi-index approaches and improved impact data for operational drought management.
-
Çelik (2026) Shifting aridity patterns in Türkiye: a comparative assessment of De Martonne, UNEP, Erinç and Budyko Dryness indexes
This study comparatively assessed shifting aridity patterns in Türkiye using four dryness indexes and high-resolution climate data for two climate normal periods. It found a consistent expansion of semi-arid areas, primarily driven by increasing potential evapotranspiration rather than precipitation variability, with aridity changes concentrated in semi-arid transition zones.
-
Batool et al. (2026) Development of co-integrated standardized procedure for the joint monitoring, forecasting and probabilistic characterization of climate extremes under global climate models
This research develops the Adaptive Joint Standardized Drought and Heatwave Index (AJSDHI) for joint monitoring, forecasting, and probabilistic characterization of climate extremes using multi-model ensembles from CMIP6 GCMs. The study finds K-Component Gaussian Mixture Distribution (K-CGMD) to be the most suitable fitting approach and shows that machine learning models (ELM, MLP) generally outperform ARIMA for forecasting, with moderate wet and cold events having higher long-term probabilities than moderate dry and hot events in the Tibetan Plateau.
-
Tang et al. (2026) Fast response of satellite fluorescence-derived plant physiology to drought stress
This study globally disentangles the sequence and drivers of vegetation physiological and structural responses to drought using satellite data. It reveals that satellite fluorescence-derived plant physiology responds to drought within approximately 3 days, significantly faster than structural changes which emerge after approximately 12 days.
-
García-Marquez et al. (2026) Environment90m – globally standardized environmental variables for freshwater science at high spatial resolution
This paper introduces Environment90m, a global, high-resolution dataset of 104 standardized environmental variables aggregated to 726 million freshwater sub-catchments, designed to support large-scale freshwater biodiversity research and conservation efforts.
-
Bennour et al. (2026) Increasing Irrigated Agriculture Area and Its Related Water Consumption Set Djorf Aquifer at Risk of Water Quantity Depletion
This study investigates the relationship between expanding irrigated agriculture and groundwater level dynamics in the Djorf aquifer, southeastern Tunisia. It found that a significant expansion of irrigated cropland led to substantial reductions in groundwater levels, particularly a rapid decrease after 2015.
-
Chao et al. (2026) Simulation of Extreme Flood Events Based on Precipitation Fusion: A Multi-Method Fusion Framework Combining RF and BMA
This study introduces the Hybrid Downscaling and Multi-source Precipitation Fusion (HDMPF) framework to enhance the spatial resolution and accuracy of precipitation estimates, significantly improving simulations of extreme precipitation and hydrological responses.
-
Jia et al. (2026) Advancing ecohydrological modelling: coupling LPJ-GUESS with ParFlow for integrated vegetation and surface-subsurface hydrology simulations
Groundwater sustains vegetation and regulates land-atmosphere exchanges, yet most Earth system models oversimplify its dynamics. This study develops an integrated framework coupling a dynamic vegetation model with the three-dimensional hydrological model ParFlow to explicitly represent groundwater-vegetation interactions, demonstrating that groundwater flow strongly regulates water exchanges and improves simulations of water cycles in Earth system models.
-
Mukherjee et al. (2026) Drying of the Himalayan Indus-Ganges-Brahmaputra Rivers: Understanding Causes and Management as a “One Water” Resource
[Information not extractable from provided text]
-
Ahmad et al. (2026) Fuzzy Modeling Strategies for Groundwater Level Forecasting: Comparing Local, Integrated, and Behavioral Frameworks for a Data-Limited Coastal Aquifer in the Eastern Mediterranean
This study comparatively analyzes three fuzzy expert system strategies for monthly groundwater level forecasting in the semi-arid Al-Hsain Basin, Syria, finding that an innovative behavioral clustering approach significantly outperforms localized and unified models in terms of directional classification accuracy and model efficiency.
-
Kim et al. (2026) Advancing understanding of parameterization effects in global hydrologic models through multi-model, multi-variable evaluation
This study investigates how parameter choices in four Global Hydrological Models (GHMs) affect hydrological simulations by optimizing them with multi-variable data across 228 global watersheds, revealing that optimized parameters generally outperform default settings and highlighting risks of high-flow overestimation with default parameters.
-
Yang et al. (2026) Variation Characteristics of Evapotranspiration and Water Consumption Effectiveness Evaluation in the Aksu River Basin Based on Multi-Source Data Fusion
This study developed a Bayesian Model Averaging (BMA)-based framework to fuse multi-source actual evapotranspiration (ETa) estimates in the Aksu River Basin from 2000–2020, revealing an increasing ETa trend and a water consumption structure dominated by low-effectiveness components.
-
Yue et al. (2026) Improving Daily Precipitation Estimates through Machine Learning-Based Downscaling, Precipitation Event Classification, and Categorical Merging
This study proposes a three-step machine learning framework for multi-source precipitation merging, integrating downscaling, precipitation event classification, and categorical merging. The framework developed a high-resolution (1 km, daily) merged precipitation dataset (MSMP) for the Pearl River Basin, demonstrating significantly improved accuracy, especially for heavy and extreme precipitation, compared to existing products.
-
Hussein et al. (2026) Hydrogeophysical characterization and recharge potential of three Wadi basins along the Red Sea Margin, Northeastern Desert, Egypt
This study integrated morphometric, geophysical, hydrochemical, and meteorological datasets to assess groundwater potential and recharge dynamics in three Wadi basins along the Red Sea Margin, Northeastern Egypt. The findings prioritize alluvial fan toes and lineament intersections as high-potential sites for managed aquifer recharge and reconnaissance drilling.
-
Qu et al. (2026) Drought propagation as a nonlinear amplifier of ecohydrological damage
This study systematically investigates how meteorological drought propagates to soil and ecological drought, revealing that ecohydrological damage is nonlinearly amplified, reaching 162% to 310% of initial meteorological drought intensity, especially beyond a standardized threshold of 2.18.
-
Trullenque‐Blanco et al. (2026) Projected Evolution of Climatic Aridity in Spain: Robust Signals and Model Uncertainties
Information not extractable from the provided corrupted text.
-
Kucheruk et al. (2026) Flash Drought Climatology Over Southeastern South America: Sensitivity to Index and Reanalysis Selection, and Potential Causes
[Information not extractable due to corrupted paper text.]
-
Vichot‐Llano et al. (2026) Projected 21st Century Changes in Precipitation and Temperature Over Italy Using CMIP6 CMCC ‐ CM2 ‐ SR5 Model and COSMO ‐ CLM Dynamical Downscaling
[Information not extractable from the provided corrupted text.]
-
Zhang et al. (2026) Multi-source precipitation fusion for hydrological models: Correction and metrics importance analysis
This study developed a lightweight Absolute Distance Inverse Weighting (ADIW) framework to merge eight precipitation datasets, evaluating the merged product's performance and bias-corrected versions through hydrological simulations using HYPE and VIC models in the Ganjiang River Basin. The ADIW+Linear Regression (LR) approach demonstrated optimal hydrological performance, with Relative Bias (RB) and Mean Absolute Error (MAE) identified as key metrics controlling hydrological reliability.
-
Abuzarov et al. (2026) Vegetation Health Indicators of Groundwater Discharge: Integration of Sentinel-2 Remote Sensing and Meteorological Time Series in the Northern Apennines (Italy)
This study evaluates the capability of Sentinel-2 derived vegetation indices to indicate groundwater discharge in forested mountainous areas. It found that during droughts, vegetation near springs exhibits significantly higher Normalized Difference Vegetation Index (NDVI), demonstrating groundwater-supported resilience and serving as a reliable indicator for discharge likelihood.
-
BAYAZIT et al. (2026) Climate-Induced Vegetation Stress Detected Through Remote Sensing of Hydroclimatic Indicators
This study investigated the effects of hydroclimatic variability and long-term trends on vegetation response in Türkiye's Meriç-Ergene Basin. Findings reveal that vegetation dynamics are increasingly driven by temperature anomalies, leading to heightened evapotranspiration and expedited phenological processes, underscoring the basin's vulnerability to warming and drying.
-
Ma et al. (2026) Divergent response of vegetation structure to land-atmosphere droughts across aridity gradients in the Northern Hemisphere
This study used machine learning to investigate the relative contributions of soil moisture (SM) and vapor pressure deficit (VPD) to leaf area index (LAI) across aridity gradients in the Northern Hemisphere, revealing a "seesaw effect" where SM's contribution decreases and VPD's increases with aridity.
-
Suleymanov et al. (2026) Three-dimensional mapping of key soil properties with multi-stage validation and big data
This study generated high-resolution three-dimensional digital soil maps for soil organic carbon (SOC) and pH in the Republic of Bashkortostan, Russia, across five depth intervals up to 1 meter using a machine learning approach. The maps, validated through a multi-stage process, showed reliable predictions in plain regions but highlighted data limitations in mountainous areas.
-
Gupta et al. (2026) Optimizing surveillance efficiency with deep learning-driven flood segmentation
This paper introduces Flood-X, a novel deep learning architecture for pixel-level flood segmentation in ground-level surveillance images, utilizing an Xception-based encoder and a custom lightweight decoder. The model achieves state-of-the-art performance with a mean Intersection over Union (mIoU) of 94% on a combined augmented dataset, outperforming existing methods and demonstrating superior efficiency and accuracy.
-
Samara et al. (2026) 2021–2023: Extreme Years of Global Drought in the Context of Long- and Short-Term Hydroclimate Trends
This paper performs a global assessment of dryness and wetness using an ensemble of soil moisture datasets and drought indices, revealing consistent drying trends in several regions and identifying 2023 as one of the driest years on record, with the 2021–2023 period marking the longest consecutive extreme global drought since the early 20th century, consistent with anthropogenic climate change.
-
Lee et al. (2026) ESCAPE: An ensemble-based self-calibrated autoencoder with physics-informed estimation of high-resolution soil moisture and surface roughness from ALOS-2/PALSAR-2 polarimetric observations
This study introduces ESCAPE, an ensemble-based self-calibrated autoencoder with physics-informed estimation, to retrieve high-resolution soil moisture (SM) and surface roughness (Hrms) from ALOS-2/PALSAR-2 polarimetric observations without requiring in-situ SM for direct training, demonstrating robust performance and improved generalization across diverse environments.
-
Han et al. (2026) Combined evaporation estimation model of upper and lower reservoirs of pumped storage plants in arid areas under floating coverage
This study developed and validated a combined evaporation estimation model for upper and lower reservoirs of pumped storage plants (PSP) in arid regions, accounting for unique operational conditions and floating ball coverage. The model demonstrated a significant water-saving effect of 61.2% at a 74% coverage rate, along with substantial energy and environmental benefits.
-
Chen et al. (2026) Sen2GF3Floods: A Benchmark Multi-Source Flood Dataset with Dual-Temporal and Active Learning Annotation
This paper introduces Sen2GF3Floods, a novel multi-source flood dataset integrating pre-disaster Sentinel-2 optical and post-disaster Gaofen-3 SAR imagery, annotated using a dual-temporal and active learning framework to enhance flood detection algorithm development.
-
Jung et al. (2026) Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data
This paper introduces a fully automated and scalable method for mapping dynamic surface water extents from single-acquisition Sentinel-1 SAR imagery, integrating adaptive thresholding, fuzzy-logic classification, region growing, dark land estimation, and a bimodality test. The approach achieves classification accuracies exceeding 85% globally, providing a robust tool for near-real-time monitoring of floods, droughts, and water resources across diverse environmental conditions.
-
Mwangi et al. (2026) Uncertainties in long-term ensemble estimates of contextual evapotranspiration over southern France
This study applies the EVASPA ensemble contextual tool over southern France (2004–2024) to estimate evapotranspiration (ET) using MODIS data, demonstrating that ensemble-based modelling provides reliable ET estimates and a meaningful uncertainty spread, with land surface temperature (LST) and evaporative fraction (EF) formulations being the dominant sources of uncertainty.
-
Achemrk et al. (2026) Tracking Quarter-Century Spatio-Temporal Soil Salinization Dynamics in Semi-Arid Landscapes Using Earth Observation and Machine Learning
This study developed an integrated framework combining multi-temporal remote sensing and machine learning to model soil salinity dynamics in the Sehb El Masjoune (SEM) semi-arid region, Morocco. The framework revealed a nearly 10% expansion of moderately to highly saline areas from 2000 to 2025, primarily driven by recurrent droughts and inefficient drainage.
-
Wei et al. (2026) Evaluating and enhancing the performance of satellite precipitation products by considering uncertainty in rain gauge observations
This study develops a machine-learning-driven hierarchical framework to evaluate and correct biases in satellite precipitation products (SPPs) by incorporating uncertainty in rain gauge observations as interval-valued data. Applied to Guangxi, China, the framework significantly reduces SPP bias, especially for heavy precipitation events, through a novel evaluation index and a neural network-based correction model.
-
Zakharova et al. (2026) Exploring the potential of Sentinel-3 and Sentinel-6 SAR altimetry measurements for discharge estimation: Case studies from the Rhine and Po rivers
This study investigates the potential of Sentinel-3 and Sentinel-6 SAR altimetry for discharge estimation in medium-sized rivers, using the Rhine and Po as case studies. It demonstrates that enhanced 80 Hz altimetry processing and rigorous data filtering significantly improve water level and discharge accuracy, achieving NRMSE values as low as 4% with empirical methods and 8% with refined physical models.
-
Zafar et al. (2026) Temporal transferability of spatially derived Manning’s roughness across flood regimes in the Mississippi River
This study evaluated the temporal transferability of spatially derived Manning's roughness values across different flood regimes in the Middle Mississippi River using a 2D hydrodynamic model. It demonstrated that a single set of spatially variable, temporally invariant roughness values provided excellent model performance over a decade, suggesting that hydrological forcing, rather than temporal changes in roughness, is the primary cause of model discrepancies.
-
Huang et al. (2026) Unravelling Groundwater–Precipitation Interactions in Karst Aquifers Under the Dual Pressures of Climate Variability and Human Disturbance
N/A - Information not extractable from the provided text due to corrupted format.
-
Ultee et al. (2026) CMIP6 climate model spread outweighs glacier model spread in 21st-century drought buffering projections
This study quantifies 21st-century glacial drought buffering across 75 major river basins using an ensemble of three global glacier models forced by 11 CMIP6 climate models, finding that climate model uncertainty significantly outweighs glacier model uncertainty in projections.
-
Selek et al. (2026) Multi-Index evaluation of meteorological drought across Türkiye: a temporal and seasonal perspective
This study evaluates the temporal and seasonal dynamics of meteorological drought across Türkiye using SPI, SPEI, and PNI from 1972-2023, revealing an increasing frequency and severity of temperature-driven droughts, particularly in summer and autumn, with a significant intensification after 2000.
-
Wang et al. (2026) Evolution characteristics and driving mechanisms of ecological drought from a terrestrial ecosystem perspective
This study systematically investigated the spatiotemporal evolution patterns and driving mechanisms of ecological drought in the Yellow River Basin (YRB) from 1982 to 2022 using a novel Standardized Ecological Water Deficit Index (SEWDI). It found a significant basin-wide drying trend with westward migration of drought centers, primarily driven by evapotranspiration (ET) and the Atlantic Multidecadal Oscillation (AMO).
-
Ali et al. (2026) Regional drought assessment using multi-site probabilistically integrated precipitation by Bayesian network
This study proposes a Regional Standardized Precipitation Drought Index (RSPDI) for regional drought assessment by integrating precipitation dynamics from multiple stations using Bayesian Network (BN) theory. Application to two regions in Pakistan showed strong agreement between RSPDI and SPI, demonstrating enhanced spatial coherence and robust probabilistic consistency for regional drought monitoring.
-
Rizzoli et al. (2026) Water Resource Management in Wetland: Developing a Predictive Model for Climate Resilience in the Pantanello Natural Park, Italy
This paper describes a hydrological model for the Pantanello Natural Park to identify effective water-resource management strategies. The model demonstrates that a targeted water supply of 0.01 m³/s from April to September significantly reduces dry conditions in the wetland system from 53% to 10%.
-
Sánchez-Dávila et al. (2026) Recent water cycle changes in Spanish forests are driven by stand structure more than climatic changes
This study modeled the Spanish forest water cycle from 1990 to 2020 to assess the relative impacts of climate change and forest stand structure on green and blue water. It found that changes in stand structure, particularly leaf area index growth, had a stronger influence on the water cycle than climatic changes, leading to increased green water and decreased blue water.
-
Rastipishe et al. (2026) Water-Driven Soil Erosion in Iran’s Agricultural Lands: A Nationwide Synthesis of Drivers, Impacts and Management
This systematic review synthesizes evidence from 399 peer-reviewed studies to evaluate the drivers, impacts, assessment, and management of water-driven soil erosion across Iran's agricultural lands. It reveals a mean annual soil erosion rate of 16.5 t ha⁻¹ yr⁻¹ nationwide, underscoring the critical need for integrated, climate-resilient management strategies.
-
Moussaid et al. (2026) K-means clustering applied to vegetation indices for mapping cultivated areas using high-resolution Moroccan Mohammed VI satellite imagery
This study developed a pixel-based unsupervised classification method combining K-means clustering with vegetation indices (NDVI, MNDWI) and the Near-Infrared band to accurately map cultivated areas using high-resolution Moroccan Mohammed VI satellite imagery. The method achieved a low relative error of 1.41%, demonstrating its superior performance compared to traditional approaches for field-scale agricultural mapping.
-
Gurazada et al. (2026) ENSO’s influence on co-occurring hot-dry and hot-wet extremes across global croplands
This study investigates the influence of the El Niño Southern Oscillation (ENSO) on co-occurring hot-dry (HD) and hot-wet (HW) extreme events across global croplands. It finds that El Niño years significantly increase the localized risk and global exposure of croplands, particularly for staple crops like rice, to both HD and HW extremes, necessitating integration into agricultural early warning systems.
-
Tang et al. (2026) Characterizing extreme climate events at different time scales and their contributions to agricultural drought and flooding areas
This study investigates multi-scale extreme climate events (1961–2020) in Hunan Province, China, and their contributions to agricultural drought and flooding using Modified Mann–Kendall, correlation, and Random Forest models, revealing asymmetric hydroclimatic shifts with distinct thermal-driven drought and precipitation-controlled flood mechanisms.
-
Xu et al. (2026) Rapid Prediction of Compound Flood Based on Hydrological-Hydrodynamic Model and Convolution Neural Network
This study proposes a hybrid approach coupling a hydrological-hydrodynamic model (PCSWMM) with a Convolutional Neural Network (CNN) for rapid and accurate prediction of compound flood processes in coastal urban areas, demonstrating a significant increase in computational efficiency while maintaining high prediction accuracy for flood depths.
-
Gnann et al. (2026) On Utilizing Spatial Gradients to Discover Functional Relationships in Hydrology
This paper reviews functional relationships in hydrology, defining them as variable relationships characterizing hydrological system functions along environmental gradients, and discusses their critical value for advancing hydrological theory, improving observational data, and enhancing computational models.
-
Gualdi et al. (2026) Seasonal Predictions and Their Applications in the Mediterranean Region: Part II—Prediction‐Based Services
This review paper examines the development and application of seasonal climate predictions as actionable information for decision-making in the Mediterranean region, illustrating the iterative process of transforming probabilistic forecasts into tailored climate services for key sectors.
-
Ren et al. (2026) Global River Forecasting with a Topology-Informed AI Foundation Model
## Identification - **Journal:** arXiv (Cornell University) - **Year:** 2026...
-
Jing et al. (2026) Spatiotemporal Variability of Runoff Coefficients and Rainfall–Runoff Responses in a Mountainous Basin Using Integrated Hydrological Analogy and SCS–CN Models
This study developed a three-stage coupling framework integrating hydrological analogy, SCS-CN calibration, and reciprocal modeling to quantify spatiotemporal runoff coefficient variability in a mountainous basin, demonstrating enhanced predictive reliability and mechanistic interpretability for runoff estimation under data-scarce conditions.
-
Rezvani et al. (2026) An XGBoost-SHAP framework for interpretable and probabilistic flood susceptibility mapping
This study develops an interpretable framework for flood susceptibility mapping by integrating Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP). Applied to the Karkheh Basin, Iran, the framework achieved high predictive performance (AUC of 0.89) and provided transparent insights into the influence and interactions of key environmental factors on flood susceptibility.
-
Janzing et al. (2026) Spatial streamflow drought in the larger Alpine region
This dataset provides hyper-resolution hydrological model outputs for the larger Alpine region, enabling the study and reproduction of spatiotemporal dynamics of streamflow drought between 1990 and 2019.
-
Mashaly et al. (2026) Dynamic Water Budget Modeling to Fill Knowledge Gaps and Improve Water Management in Arid and Semi‐Arid Regions
[Information not extractable due to corrupted input.]
-
R et al. (2026) SoilNet‐TF: A TabNet and DenseNet‐121 fused model for soil moisture forecasting and monitoring
[Information not extractable due to unreadable paper text.]
-
Dommange et al. (2026) Climatology of long-term extreme precipitation using rain gauges data over France
This study presents a comprehensive analysis of extreme precipitation events (EPs) across France from 1950 to 2023 using 352 quality-controlled rain gauges, revealing significant intensification of extreme precipitation in southern regions, particularly the Cévennes, alongside a national decrease in the frequency of rainy days, and identifies large-scale atmospheric drivers.
-
Castaldo et al. (2026) Evaluating SEAS5 ECMWF seasonal forecasts for Sicily Mediterranean Island: Retrospective analysis and bias correction
This study evaluates ECMWF SEAS5 seasonal forecasts for temperature and precipitation over Sicily, comparing traditional and Artificial Neural Network (ANN) bias correction methods. It finds that raw forecasts have systematic biases, and the ANN with Individual Member Separated Monthly (IMSM) correction significantly improves forecast accuracy, especially for precipitation, reducing Root Mean Square Error (RMSE) by up to 45 %.
-
Lee et al. (2026) A novel approach for soil moisture retrieval from Sentinel-1 SAR via temporal stability-based backscatter analysis
This study developed a novel temporal stability analysis (TSA)-based masking method for Sentinel-1 SAR to improve high-resolution soil moisture retrieval by effectively filtering noisy pixels. The TSA method significantly enhanced correlations and reduced errors compared to existing methods across diverse monitoring networks.
-
Islam et al. (2026) Modeling evapotranspiration in diverse climatic zones of Pakistan using Surface Energy Balance Algorithm for Land (SEBAL) through geospatial technologies
This study utilized the SEBAL model with Landsat imagery and meteorological data to estimate actual evapotranspiration (ET) and assess its spatiotemporal variations across Pakistan's diverse climatic zones, revealing significant environmental degradation and increased climatic stress linked to urbanization.
-
Wang et al. (2026) Improved estimation of evapotranspiration and gross primary productivity by incorporating soil moisture feedbacks into a coupled ecosystem model
This study developed a fully coupled ecosystem model that explicitly incorporates dynamic soil moisture feedbacks to improve the consistent estimation of gross primary productivity (GPP) and evapotranspiration (ET). The model demonstrated robust performance across 32 diverse sites, significantly enhancing GPP and ET predictions, especially under dry soil conditions, compared to non-coupled approaches.
-
Gao et al. (2026) Applicability of the Richards equation in infiltration simulation: A comparative study with the two-phase flow model
This study systematically evaluates the applicability of the Richards equation (RE) for simulating water infiltration in the vadose zone by comparing its performance against a two-phase (TP) flow model, identifying specific hydrogeological conditions where RE overestimates infiltration due to restricted air escape.
-
Sun et al. (2026) Streamflow prediction in the Danube River Basin using a multi-source graph-integrated GCN-LSTM model
This study developed a multi-source graph-integrated GCN-LSTM model for regional streamflow forecasting in the Danube River Basin, demonstrating its superior performance over conventional baselines and highlighting the critical role of daily temporal resolution for capturing extreme events, while revealing limitations in spatial generalization to ungauged sites.
-
Nikoo (2026) Integrating deep learning into future hydrological modeling under climate change scenarios in an arid region
This study developed a hybrid deep learning framework for downscaling climate projections and a hybrid HEC-HMS–LSTM model for streamflow simulation to assess climate change impacts on an arid region in Oman, revealing significant future changes in precipitation, temperature, and streamflow under different SSP scenarios.
-
Hu et al. (2026) A New Model Incorporating Soil–Vegetation Interaction Scattering for Improving SAR-Based Soil Moisture Retrieval in Croplands
This study enhances the Water Cloud Model (WCM) by explicitly incorporating soil–vegetation interaction scattering to improve active microwave-based soil moisture retrieval accuracy, especially under dense vegetation. The proposed model demonstrates improved soil moisture retrieval performance across diverse vegetated areas through multi-year validation.
-
Makhasana et al. (2026) Understanding Land-Atmosphere Interactions During Coupling Whiplash Events
This study investigates the role of Land-Atmosphere (L-A) interactions in driving hydrometeorological whiplash events, characterized by abrupt dry-to-wet and wet-to-dry transitions. It identifies that lower atmospheric moisture availability is critical for whiplash intensity, while convective potential responds to seasonal thermal changes, revealing distinct L-A feedback mechanisms and global high-risk regions.
-
Wu et al. (2026) Global Desert Variations During 1985–2024 Associated With Effective Water Availability
[Information not extractable due to corrupted input.]
-
Jeung et al. (2026) Sensitivity of hydrological machine learning prediction accuracy to information quantity and quality
This study investigates how the quantity and quality of information in training data influence the prediction accuracy of hydrological machine learning (ML) models. It demonstrates that while the highest accuracy is achieved with all available data, incorporating high-quality outputs from calibrated mechanistic models most efficiently improves ML prediction accuracy.
-
Li et al. (2026) Skills in sub-seasonal to seasonal terrestrial water storage forecasting: insights from the FEWS NET land data assimilation system
This study evaluates subseasonal to seasonal (S2S) terrestrial water storage (TWS) forecasts over Africa from the Famine Early Warning Systems Network (FEWS NET) land data assimilation system (FLDAS) using GRACE/FO observations. It finds that the NASA Catchment Land Surface Model (CLSM) generally outperforms Noah-MP, primarily due to its more accurate reanalysis-based initial conditions and stronger representation of TWS interannual variability.
-
Masmoudi et al. (2026) Modelling and Mapping of Soil Salinity Related to Soil Characteristics and Irrigation in a Semi-Arid Context
This study aimed to model and map soil salinity in semi-arid regions by correlating the Normalised Differential Salinity Index (NDSI) with various physicochemical parameters. The developed model accurately estimated soil salinity (R²=0.90), revealing that a significant portion of the studied area exhibits moderate to extreme salinity levels.
-
Pei et al. (2026) Flooding algorithm combining hydrology and dynamic seed growth
This study develops and validates an improved GIS-based seed propagation algorithm for rapid and accurate flood inundation simulation in data-scarce small and medium-sized river basins, demonstrating superior spatial accuracy (F1 score of 0.76) compared to HEC-RAS models.
-
Chen et al. (2026) Enhancement of Global Flood Risk Due To Greater Flood Magnitude and Variability Under Anthropogenic Activities
[Information not extractable due to corrupted input.]
-
Fatma et al. (2026) Analysis of precipitation and temperature trends in the gomti river basin, India (1980–2023)
This study analyzed long-term precipitation and temperature trends in the Gomti River Basin, India (1980–2023), revealing a significant decrease in annual and monsoon rainfall and a slight, seasonally varied increase in mean annual temperature, with implications for water resources and agriculture.
-
Debnath et al. (2026) Impacts of Climate Change and Demographic Growth on Future Water Supply and Demand Gap in a River Basin
This study developed a Water Evaluation And Planning (WEAP) model for the Kesinga Sub Catchment of the Mahanadi River basin in India to assess future water supply-demand gaps under climate change and demographic growth, finding significant unmet domestic water demand (up to 20%) in specific demand sites by the 2030s, particularly during lean periods.
-
Rahimi et al. (2026) Integrating geospatial intelligence and machine learning for flood susceptibility mapping
This study evaluated five machine learning algorithms and an ensemble voting model for flood susceptibility mapping, demonstrating that the ensemble approach significantly improves accuracy and reliability in identifying flood-prone areas.
-
Gülcan et al. (2026) Unveiling the performance of pre-processing approaches in machine learning based flood susceptibility mapping
This study systematically evaluates various pre-processing techniques for machine learning-based flood susceptibility mapping in the San Joaquin River Basin using the XGBoost algorithm. It identifies that robust scaling with a 70/30 train-test split, combined with Random Under Sampling at a 10x class imbalance ratio, yields the most accurate flood susceptibility predictions.
-
Zhang et al. (2026) Fill-spill process-guided hydrologic modeling: enhanced identification of hydrologically sensitive zones and simulations in semi-arid basins
This study proposes the Hydrologically Sensitive Fill-Spill Zone (HSFSZ) concept and develops the CASC2D-HSFSZ model to improve flood simulation accuracy in semi-arid regions by explicitly incorporating depression-storage and threshold-activated connectivity, demonstrating enhanced performance over the original CASC2D model.
-
Alemaw et al. (2026) Editorial: Climate, water and land in Africa: research trends and challenges
This editorial introduces a Research Topic on "Climate, Water and Land in Africa: Research Trends and Challenges," synthesizing 11 original research articles to highlight current scientific understanding and prerequisites for sustainable water and land management on the continent. It emphasizes the need for scientific innovation combined with stakeholder participation and improved governance to address severe pressures on Africa's water resources.
-
Zhang et al. (2026) Scale-dependent model-observation inconsistencies in global terrestrial water storage models
This study evaluates scale-dependent model-observation inconsistencies in seven global terrestrial water storage models against GRACE observations across global, climate-zone, and basin scales, finding that satellite observation-constrained assimilation significantly reduces these inconsistencies compared to model-driven products.
-
Sumith et al. (2026) Comparative Analysis of Deep Learning and Machine Learning Models for Evapotranspiration Prediction in Semi-Arid Regions: Statistical Model Evaluation with a Paired t-Test and Bootstrap Resampling
This study comprehensively evaluates deep learning (LSTM, RNN, GRU) and traditional machine learning models (DT, RF, SVM, ANN, GBM) for evapotranspiration prediction in semi-arid regions using climatic factors. The Long Short-Term Memory (LSTM) model demonstrated superior predictive accuracy and statistical significance, establishing it as the most effective and reliable model for this application.
-
Li et al. (2026) Integrating multi-dimensional features for remote sensing–based drought monitoring and driver analysis during the vegetation growing season: a case study in northern Xinjiang
This study develops a novel three-dimensional drought index (kTVPDI) for Xinjiang, integrating kernel-based NDVI, land surface temperature, and precipitation, demonstrating improved accuracy over traditional indices and identifying key drivers of drought intensification from 2000 to 2024.
-
Rahman et al. (2026) Spatiotemporal dynamics of flood susceptibility under future precipitation variability, population growth, and land cover change
This study assesses the spatiotemporal dynamics of flood susceptibility in the Kabul River Basin (KRB) from 2020 to 2100 under future precipitation variability, population growth, and land cover change. Findings indicate a significant increase in areas with "Very High" flood susceptibility and a decline in "Very Low" susceptibility zones, particularly in the central and southern regions, driven by these interacting dynamic factors.
-
Qi et al. (2026) Waterlogging Simulation Model Based on Bidirectional Coupling Between Runoff Production and Confluence in Plain Areas
This paper develops a raster-based waterlogging simulation model for plain areas, introducing a novel bidirectional coupling mechanism between runoff production and confluence to overcome the limitations of traditional unidirectional models. The model significantly improves the accuracy of plain waterlogging simulation, with peak flow errors within ±15% and Nash-Sutcliffe Efficiency (NSE) values over 0.84, providing enhanced tools for flood warning and water resource management.
-
Yakubu et al. (2026) A Bias-Corrected HighResMIP Dataset for Impact Assessment Studies
This paper introduces the BC-HiRMIP dataset, a globally consistent, comprehensive collection of bias-corrected climate data from High Resolution Model Intercomparison Project (HighResMIP) experiments, designed for impact assessment studies. It provides daily meteorological variables at 0.5° longitude by 0.25° latitude spatial resolution for historical (1979-2014) and future (2015-2050) periods.
-
Yang et al. (2026) Enhancing Multi-Step Ahead Daily Runoff Prediction via HydMoE Model with Local-Global Hybrid Attention
This study proposes HydMoE, a deep learning model integrating a Mixture of Experts architecture with Time2Vec temporal embedding and Local-Global Hybrid Attention, to enhance multi-step ahead daily runoff prediction and provide interpretability for diverse hydrological patterns. It achieves superior performance over baselines for 1 to 7-day lead times on the CAMELS dataset.
-
Cannon et al. (2026) Deep Learning for Multi-Satellite Precipitation Retrievals: Impact of Tomorrow.io’s Microwave Sounders
This study introduces a novel satellite-based precipitation retrieval system that integrates publicly available geostationary and polar-orbiting satellite data with commercial microwave sounders, utilizing a convolutional neural network to provide near-surface precipitation rates every 10 minutes at 4 km resolution, demonstrating significant accuracy improvements over existing products.
-
Markogianni et al. (2026) Watershed Dynamics in the Prespa Lakes: An Integrated Assessment of Stream Inflow Effects
This study investigated the ecological and hydrological impacts of stream inflows on the Greek part of the Prespa Lakes system, revealing distinct drivers for water storage variability in each lake and highlighting the influence of anthropogenic pressures on stream ecological status and lake water quality, necessitating cross-border management.
-
Akaffou et al. (2026) Comparing bias adjustment methods for CMIP6 extreme precipitation projections in the San-Pédro River Basin (Côte d’Ivoire, West Africa)
This study evaluates four bias adjustment methods for CMIP6 extreme precipitation projections in the San-Pédro River Basin, Côte d’Ivoire, identifying CDFt SSR as the most robust for accurately reproducing observed distributions and projecting increased extreme precipitation, which heightens flood risks.
-
Rico-Bordera et al. (2026) Emerging Links between Droughts, Heatwaves and Extreme Precipitation in a Western Mediterranean Hotspot: Evidence of Intensifying Compound and Sequential Hazards
This study analyzes the increasing occurrence and impacts of compound and sequential climate events (heatwaves, droughts, forest fires, and extreme precipitation) in the Western Mediterranean Valencian Community from 1979 to 2021, revealing a rising frequency of concurrent hazard days and an increasing influence of Mediterranean Sea warming on autumn flood risks.
-
Kalvāne et al. (2026) More frequent warm and dry spells along persistent cold and wet spells in the Baltics
This study analyzed the temporal and spatial variability of warm, cold, wet, and dry spells in the Baltics (Estonia, Latvia, and Lithuania) from 1961 to 2020 using ERA5-Land reanalysis data. The findings reveal a significant increase in the frequency and duration of warm and dry spells, while cold and wet spells moderately decreased, indicating an amplification of extreme weather conditions in the region.
-
Lyu et al. (2026) Warming overwhelms CO2-driven drought mitigation in alpine vegetation on the Qinghai-Tibetan Plateau
This study investigates the combined effects of rising atmospheric carbon dioxide (CO₂) and warming on alpine vegetation drought responses on the Qinghai-Tibetan Plateau. It finds that while CO₂ rise alone mitigated drought-induced productivity losses by 5.7%, concurrent warming reversed this benefit, intensifying drought stress by 5.2% due to increased plant water demand.
-
Song et al. (2026) Decoupling of gross primary productivity and transpiration revealed through a daily remote sensing modelling approach in arid irrigated farmland
This study investigated the relationship between gross primary productivity (GPP) and transpiration (T) in arid, irrigated farmlands, revealing a weak coupling and significant decoupling where transpiration rates were disproportionately high relative to photosynthesis, especially during the middle to late growing season.
-
Dai et al. (2026) Analysis of Seasonal Propagation Dynamics and the Potential Driving Factors from Meteorological to Soil Moisture Drought
This study investigates the seasonal propagation dynamics from meteorological drought to soil moisture drought (SMD) and their driving factors in the Luanhe River Basin, China, using the Soil and Water Assessment Tool (SWAT) model and copula functions. It reveals distinct seasonal propagation times, with the shortest in summer and longest in spring, and identifies hydrometeorological, teleconnection, and land use factors as significant drivers of these dynamics.
-
Apak et al. (2026) Multi-resolution adaptive channel fusion transformer encoder LSTM for accurate streamflow prediction
This study proposes a novel hybrid deep learning model, MR-ACF-TE-LSTM, for accurate and interpretable univariate streamflow prediction by effectively capturing multi-scale temporal patterns. The model consistently outperforms baseline and state-of-the-art methods across benchmark datasets, demonstrating significant reductions in prediction error and enhanced generalization capabilities.
-
Moumane et al. (2026) Desertification monitoring in arid oasis environment using Google Earth Engine, machine learning, and field-based hydrogeological assessment
This study assessed desertification dynamics in the Ternata Oasis (southeastern Morocco) over four decades (1984–2024) by integrating Google Earth Engine-based machine learning, remote sensing, hydrogeological fieldwork, and socioeconomic surveys. It revealed a significant decline in oasis vegetation, groundwater depletion, and salinization, driven by climate variability and anthropogenic overexploitation, with the Gradient Tree Boosting model achieving 87.2% accuracy for desertification mapping.
-
Zou et al. (2026) Identifying hotspots and impact factors of multi-type compound events over major global river basins
This study identifies hotspots and impact factors of 12 types of compound events (CEs) across 520 major global river basins, revealing their spatial heterogeneity and the significant influence of atmospheric circulation anomalies.
-
Abid et al. (2026) Analysis of spatiotemporal droughts using order statistics and archetype analysis of remotely sensed relative productivity index
This study evaluates the effectiveness of a satellite-derived productivity index (KV) combined with order statistics and archetype analysis for spatiotemporal agricultural drought monitoring in northern Tunisia, demonstrating archetype analysis as a robust method for identifying drought years and severity consistent with official reports.
-
Canul-Macario et al. (2026) Quantifying resilience for storm drainage management in karst environments with shallow water table
This study quantifies the resilience of storm drainage systems (SDSs) in karst environments with shallow water tables, revealing that traditional SDSs in Merida, Mexico, experience reduced efficiency for approximately 24 days and remain vulnerable to failure for up to 156 days after extreme hydrometeorological events.
-
Gorsevski (2026) Predicted Streamflow Sensitivity to Climate Change Using TOPMODEL with CLIGEN Weather Generator in a Data-Sparse Medium-Sized Mediterranean Watershed
This study develops a transferable framework to assess future streamflow sensitivity to climate change in data-sparse Mediterranean regions, confirming historical declines and predicting significant future reductions of 32% to 50% under 1.5 °C and 3.0 °C temperature increases, respectively.
-
Nawaz et al. (2026) Integrating Meteorological and GRACE-Based Indices to Assess Groundwater Drought Under Climate Change in Data-Scarce Mediterranean Basins
This study integrates satellite-derived data (CHIRPS, CHIRTS-ERA5, GRACE) to investigate the relationship between meteorological (SPI, SPEI) and hydrogeological (GGDI) drought indicators in Lebanon, particularly the Al Assi River Basin, under climate change scenarios, revealing a projected increase in groundwater drought severity by the late 21st century.
-
Türk et al. (2026) Catchment transit time variability with different SAS function parameterizations for the unsaturated zone and groundwater
This study investigated whether stable water isotope (δ2H) measurements in streamflow can effectively represent preferential flow in the unsaturated zone and groundwater using StorAge Selection (SAS) functions. It found that δ2H data are sensitive to preferential flow in the unsaturated zone but insufficient to constrain groundwater preferential flow due to the damping effect of large passive groundwater storage.
-
Pinto et al. (2026) User‐Relevant Climate Indices and Associated Uncertainties From Transient Convection‐Permitting Climate Model Projections
[Information not extractable due to corrupted input.]
-
Abidi et al. (2026) Long-term streamflow reconstruction of The Medjerda River, Tunisia, from tree rings
This study presents the first tree-ring-based reconstruction of Medjerda River discharge in Tunisia, extending the natural flow record from 1876 to 2009 CE using seven *Pinus halepensis* chronologies and a hydrological model. The reconstruction explains 54 % of discharge variability and provides crucial long-term context for hydroclimatic extremes, aiding water resource management in this semi-arid region.
-
Rahman et al. (2026) Characterization of drought in the Arabian Peninsula: A multi-index approach
This study characterizes drought in the Arabian Peninsula (AP) from 1975 to 2024 using a multi-index approach (SPI, SPEI, EDDI) and ERA5 reanalysis data, revealing a marked increase in drought frequency and severity, particularly in the Southeast and Southwest zones, primarily driven by dewpoint temperature, precipitation, and maximum temperature.
-
Évin et al. (2026) Uncertainty sources in a large ensemble of hydrological projections: Regional Climate Models and Internal Variability matter
This study quantifies uncertainty sources in a large ensemble of hydrological projections for Metropolitan France using the QUALYPSO method. It finds that low flows are projected to decrease in southern France, with emission scenarios and regional climate models being dominant uncertainty sources, and highlights that internal variability is often as significant as climate change response uncertainty.
-
Chiara et al. (2026) Assessing the effects of climate trends and drought periods on the undeterred aquifer of Castelporziano Nature Reserve (Rome, Italy)
This study analyzed 30 years of groundwater level trends in the Castelporziano Presidential Estate, revealing a significant decline in the Coastal and Central sectors driven by increased evapotranspiration due to rising temperatures, despite stable rainfall, leading to a critical aquifer crisis since 2016.
-
Sun et al. (2026) A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation
This paper proposes HyDPNet, a novel hybrid deep learning model integrating a differential Xinanjiang model with dynamic parameters and an LSTM post-processor, demonstrating superior flood simulation accuracy in the Lushui River basin compared to benchmark models.
-
Fu et al. (2026) Remote sensing-based monitoring of spatiotemporal waterlogging variations in groundwater-sensitive agroecosystems
[Information not extractable due to unreadable paper text.]
-
Hartmann (2026) The hydrologic cycle
This chapter provides a comprehensive overview of the hydrologic cycle, detailing its essential components, processes, and the methods for its measurement and modeling within the Earth's climate system.
-
Recanatesi et al. (2026) Anthropogenic impact on peri-urban natural systems in mediterranean area: the case of Castelporziano Nature Reserve, (Rome)
This study investigates the anthropogenic impact of urbanization and land-use changes on the Castelporziano Nature Reserve, a protected peri-urban Mediterranean ecosystem. It reveals a significant decline in deciduous oak forest health and groundwater recharge due to increased impervious surfaces and excessive water withdrawals for residential and recreational uses.
-
Stahl et al. (2026) Towards an operational European Drought Impacts Database (EDID)
This paper details the development and implementation of the European Drought Impact Database (EDID) for operational application within the Copernicus European Drought Observatory, providing a comprehensive baseline of drought impacts across Europe and revealing spatial and temporal patterns. The study finds that agriculture, public water supply, and aquatic ecosystems are the most frequently impacted systems, with an increasing trend in reported impacts and severity over time.
-
Graham et al. (2026) The Met Office Unified Model Global Atmosphere 8.0 and JULES Global Land 9.0 configurations
This paper describes the Global Atmosphere 8.0 and Global Land 9.0 (GA8GL9) configurations of the Met Office Unified Model and JULES land surface model, detailing their scientific advancements over previous versions (GA7GL7) and evaluating their improved performance across weather and climate timescales. GA8GL9 demonstrates reduced errors, enhanced spatial structure in numerical weather prediction, and improved mean climate, particularly in top-of-atmosphere outgoing shortwave radiation.
-
Cheng et al. (2026) Hydrologically driven coordination of transboundary floods
This study develops a hydrologically driven coordination framework integrating physical flood routing into an open-loop differential game for transboundary flood management in the Yarlung Tsangpo–Brahmaputra River Basin. It demonstrates that cooperative strategies, accounting for hydrologic travel time, significantly reduce downstream flood peaks and suprathreshold flow durations compared to noncooperative approaches.
-
Kim et al. (2026) Comparative Simulation of Hillslope Runoff Using Two Infiltration Equations
This study compared the performance of Horton and Green-Ampt infiltration equations, coupled with a physically-based overland flow model explicitly accounting for rill-interrill microtopography, in simulating hillslope runoff against field data. It found the Green-Ampt equation superior in validation due to its consideration of antecedent soil moisture, and highlighted that runoff is more sensitive to infiltration parameters than to surface friction.
-
Zhao et al. (2026) Quantifying the drivers of river thermal regimes in the Hanjiang River Basin under climate change and reservoir construction
This study developed an integrated SWAT-HHO-LSTM modeling framework to disentangle the impacts of climate change and dam-heightening on river water temperature in the Hanjiang River Basin, revealing a spatial transition from dam-dominated cooling upstream to climate-driven warming downstream.
-
Hamel et al. (2026) River temperature response to atmospheric heatwaves is modulated by discharge and meltwater
This study investigates how river water temperature in Alpine regions responds to atmospheric heatwaves, revealing that only 47% of atmospheric heatwaves lead to riverine heatwaves, with discharge and meltwater anomalies significantly modulating this response.
-
Boras et al. (2026) Changes in compound dry–hot extremes in Croatia: Spatio-temporal features and the dominant role of temperature
This study investigates the spatio-temporal characteristics and intensity of compound dry and hot (DH) extreme events in Croatia during summer (June-August). It finds a significant increase in the frequency and intensity of these events across all regions of Croatia, primarily driven by rising temperatures, with distinct regional patterns in how temperature contributes to event intensity.
-
Zhou et al. (2026) A high-resolution dataset revealing the dynamical variations of the relative humidity in China
This study developed a high-resolution (1 km × 1 km) daily relative humidity dataset for China (1951–2020) using Random Forest interpolation and analyzed its spatiotemporal variations, revealing a significant downward trend of −0.26% per decade from 1956 to 2020.
-
Yan et al. (2026) Determination of irrigation water use from multiple soil moisture observations at a fine spatial resolution: Preferred model optimization and fusion strategy
This study proposes a framework to determine high-resolution irrigation water use (IWU) across China using multiple soil moisture (SM) observations, identifying the optimal SM depth and an effective data fusion strategy for improved estimation. The framework demonstrates that surface SM (0–10 cm) combined with an optimal fusion strategy yields the most accurate IWU estimates at a 1 km resolution.
-
Maddison et al. (2026) Using seasonal forecasts to enhance our understanding of extreme wind and precipitation impacts from extratropical cyclones
This study utilizes nearly 700 years of seasonal forecast model output to quantify the likelihood of unprecedented wind and precipitation impacts from European extratropical cyclones (ETCs). It finds that the probability of an ETC having an impact more extreme than any observed is generally between 0.5 % and 1.6 % for wind and 0.2 % and 0.7 % for precipitation, with the North Atlantic Oscillation strongly influencing wind impact likelihood.
-
Fu et al. (2026) Estimation of the monthly change in soil water storage in two watersheds of contrasting vegetation cover
This study evaluated the impact of different vegetation restoration types (plantation and grassland) on actual evapotranspiration (ETa) and monthly changes in soil water storage (ΔW) by integrating the Generalized Complementary Relationship (GCR) into a water balance model. The findings suggest that natural grassland restoration offers a more balanced approach to soil moisture management compared to artificial plantations, emphasizing the need for rational soil moisture regulation in the first half of the year.
-
Mu et al. (2026) Understanding the tradeoff between the flood risk and the hydropower benefit from the integrated flood control operation of a reservoir group and a flood detention basin
This study proposes a framework to analyze the tradeoff between flood risk and hydropower benefit from the integrated operation of a reservoir group and a flood detention basin (FDB). It finds that integrated operation enhances this tradeoff for extreme floods but weakens it for non-extreme floods due to FDB operational inflexibility.
-
Xin et al. (2026) A multi-scale assessment of the effects of runoff/sediment discharge in karst catchments as shown by vegetation-related remote sensing indicators
This study assessed the effectiveness of five satellite-derived vegetation indicators in capturing runoff and sediment discharge variability across 15 karst catchments globally from 2003–2020, identifying Solar-Induced chlorophyll Fluorescence (SIF) as the most effective explanatory variable.
-
Kayhomayoon et al. (2026) Developing the fusion of MODFLOW simulation and data-driven approaches for river-aquifer recharge modeling
This study developed a hybrid MODFLOW-machine learning approach to simulate and predict river-aquifer recharge in the Guilan aquifer, Iran, demonstrating its effectiveness for complex groundwater management and potential to reduce water loss by up to 30%.
-
Vergnes et al. (2026) Assimilation de données pour la prévision des débits d’étiage
Information could not be extracted due to unreadable paper text.
-
Allafta et al. (2026) Rainfall–Surface Runoff Estimation Using SCS-CN Model and Geospatial Techniques: A Case Study of the Shatt Al-Arab Region, Iraq–Iran
This study addresses the scarcity of runoff data in the Shatt Al-Arab Region by applying the Soil Conservation Service–Curve Number (SCS–CN) method integrated with remote sensing and GIS to predict surface runoff over 35 years, finding an average annual runoff of 233 mm and demonstrating the method's suitability for the region.
-
Zhu et al. (2026) HieraBoost-Q: interpretable karst discharge prediction from multi-site electrical conductivity with SHAP-based mechanism insights
This study proposes HieraBoost-Q, an interpretable hybrid framework integrating multi-site electrical conductivity and hierarchical XGBoost with SHAP, to improve discharge prediction and elucidate recharge mechanisms in karst catchments. It significantly enhances prediction accuracy and provides insights into the spatiotemporal dynamics of karst recharge.
-
Yıldırım et al. (2026) Spatiotemporal assessment of aridification in Europe (1950–2024) using bias-corrected high-resolution reanalysis dataset
This study comprehensively assesses spatiotemporal aridification in Europe from 1950 to 2024 using bias-corrected high-resolution ERA5-Land data, revealing an accelerated, evapotranspiration-driven shift towards increased aridity across large parts of the continent in the 21st century.
-
Springer et al. (2026) A review of current best practices and future directions in assimilating GRACE/-FO terrestrial water storage data into numerical models
This review synthesizes insights from approximately 60 GRACE/-FO data assimilation studies to identify best practices and future directions for integrating terrestrial water storage anomaly data into numerical models, revealing that effective strategies leverage ensemble Kalman filters, localization, and explicit accounting for correlated observation errors.
-
Cheng et al. (2026) Dynamic water delay time estimation using dispatch data for improved inflow forecasting in cascade hydropower reservoirs
This study proposes an operation-data-driven probabilistic framework to improve inflow forecasting in cascade hydropower systems by dynamically estimating and quantifying the uncertainty of water delay time. Applied to the Lancang River cascade, the method significantly reduces forecasting errors compared to benchmark models, providing a practical, self-sufficient, and uncertainty-aware solution for dispatch centers.
-
Oloniyo et al. (2026) Assessment of Heat Stress Hazards in Africa Using CMIP6 and NEX-GDDP Datasets
This study assesses the ability of CMIP6 and NEX-GDDP datasets to reproduce heat stress characteristics across Africa, focusing on heat stress hazard. It finds that while CMIP6 simulations exhibit substantial biases, the NEX-GDDP dataset significantly improves the representation of heat stress hazards by correcting many of these biases, though some overcorrection occurs.
-
Ogunrinde et al. (2026) Evaporative demand drought index for monitoring and analyzing drought conditions in arid regions of Asia and Africa
This study introduces and evaluates the Evaporative Demand Drought Index (EDDI) as a complementary tool for monitoring flash droughts and evapotranspiration-driven moisture stress in arid regions of Asia and Africa, demonstrating its ability to detect drought onset weeks earlier than precipitation-based indices.
-
Forecasts (2026) ERA5-Land Reanalysis
This paper describes ERA5-Land, a high-resolution global land surface reanalysis dataset, detailing its spatial and temporal coverage, variable content, and applications for environmental monitoring.
-
Pegas et al. (2026) A Comprehensive Evaluation of Evapotranspiration in Mainland Portugal Based on Climate Reanalysis Data
This study comprehensively compares spatial patterns of potential and reference evapotranspiration (Ep and Eto) derived from various models and high-resolution datasets over mainland Portugal (1980–2023) and analyzes Eto trends, revealing an overall increase in atmospheric evaporative demand, particularly in recent decades, suggesting a progressively longer dry season.
-
Zhu et al. (2026) A global framework for subsurface soil moisture estimation: Coupling fractal Richards equation with Bayesian optimization
This study develops a global, satellite-based framework (ExpF-FRE) to extend near-surface soil moisture (SM) measurements (0–5 cm) to subsurface depths of 20 cm and 50 cm at 400 m daily resolution, demonstrating robust performance against in situ observations and reanalysis products without site-specific calibration.
-
Chen et al. (2026) Emergent constraints on the hydrological impacts of land use and land cover change
This study applies an observation-based emergent constraint framework to correct Earth system model (ESM) estimates of evapotranspiration changes due to land use and land cover changes (LULCC), revealing significant biases in unconstrained models and projecting stronger evapotranspiration enhancements in future afforestation scenarios.
-
Fischer et al. (2026) Quantifying evaporation of intercepted rainfall: a hybrid correction approach for eddy-covariance measurements
This study quantifies evaporation of intercepted rainfall at a coniferous forest site, revealing a systematic underestimation by eddy-covariance measurements (24% of precipitation) compared to model estimates (45%). A novel hybrid correction approach is proposed to reconcile eddy-covariance data with both energy and water budgets during interception events.
-
Ha et al. (2026) Climate and soil moisture variability and cropland exposure in Western Yemen: a spatiotemporal analysis using satellite and reanalysis time series from 2000 to 2024
This study conducted a detailed spatiotemporal analysis of climate and soil moisture anomalies and associated cropland exposure in western Yemen from 2000 to 2024 using satellite and reanalysis data. It revealed a significant transition towards warmer and drier conditions, with declining precipitation and soil moisture, and a cumulative temperature rise of approximately 1.5 °C, leading to increased cropland vulnerability.
-
Chan et al. (2026) UK Hydrological Outlook using Historic Weather Analogues
This study assesses the skill of a new Historic Weather Analogues (HWA) method for seasonal hydrological forecasts across 314 UK catchments, benchmarking it against the standard Ensemble Streamflow Prediction (ESP) and climatology. The HWA method significantly improves winter river flow forecasts nationally, especially in upland, fast-responding catchments, while maintaining comparable skill to ESP in other regions and seasons.
-
Bhattarai et al. (2026) Rainfall relief or humidity havoc? The boon and curse of precipitation during heatwaves
This study investigates the complex interaction between heat, humidity, and precipitation during heatwaves across the contiguous United States from 1980 to 2020. It reveals that while precipitation can terminate heatwaves, it often leads to increased humidity, paradoxically exacerbating heat stress, particularly in vulnerable regions.
-
Sanchez et al. (2026) Hotspots and hot moments of metal mobilization: dynamic connectivity in legacy mine waters
This study reveals that metal(loid) mobilization in abandoned underground mines is governed by episodic shifts in subsurface hydrological connectivity, particularly during low-flow and pre-flush periods, leading to disproportionate contaminant release from localized storage zones that are often overlooked by conventional monitoring.
-
Roux et al. (2026) Hydrological regime shifts in Sahelian watersheds: an investigation with a simple dynamical model driven by annual precipitation
This study investigates hydrological regime shifts in Sahelian watersheds using a simple dynamical model driven by annual precipitation. It finds that four studied watersheds (Gorouol, Nakanbé, Dargol, Sirba) experienced regime shifts during the droughts of the 1970s–1980s, transitioning from a low to a high runoff coefficient regime.
-
Calvo‐Sancho et al. (2026) Human-induced climate change amplification on storm dynamics in Valencia’s 2024 catastrophic flash flood
This study uses a kilometer-scale pseudo-global warming storyline approach to attribute the catastrophic October 2024 Valencia flash flood to anthropogenic climate change, finding that present-day conditions significantly amplified rainfall intensity and the event's overall severity through enhanced moisture and convective processes.
-
Fowé et al. (2026) Assessing the Future of Droughts Using Relative Standardized Indices: Insights from the Nakanbé River Basin, West Africa
This study assesses future meteorological and hydrological droughts in the Nakanbé River Basin, Burkina Faso, using relative standardized drought indices and CMIP6 projections. It reveals robust warming and increased evaporative demand will lead to longer, more severe, and more frequent droughts by 2100, particularly under high-emission scenarios, despite uncertain rainfall changes.
-
Wu et al. (2026) Linear time-lag effects and nonlinear interactions of global drought-flood abrupt alternation in responses to multiple factors
This study investigated the linear time-lag effects and nonlinear interactions of multiple factors, including surface energy fluxes, on global drought-flood abrupt alternation (DFAA) events. It found that accounting for time-lag effects significantly increased the explanatory power of these factors on DFAA from 33.03% to 70.05%, revealing complex spatial heterogeneity and nonlinear threshold regulations.
-
Garderen et al. (2026) The essential role of conditional attribution in understanding complex extreme weather
This comment emphasizes the critical role of conditional attribution in understanding complex extreme weather events, using the 2024 Valencia flash flood as a striking example of how human-driven climate warming intensified the storm's rainfall intensities beyond thermodynamic expectations. It advocates for an integrated approach combining conditional and unconditional attribution for comprehensive impact assessments.
-
Yılmaz et al. (2026) Neural circuit policy and hybrid deep learning models for enhanced meteorological drought forecasting performance
This study introduces the novel Neural Circuit Policy (NCP) deep learning model for meteorological drought forecasting using the Standardized Precipitation Index (SPI) at multiple time scales, demonstrating its superior performance, especially when integrated into hybrid models, for both forecasting accuracy and drought category classification.
-
Haile (2026) Intensifying Drought in the Nile River Basin (NRB) under Climate Change: Increasing Influence of Atmospheric Evaporative Demand
This study investigates the projected intensification of drought in the Nile River Basin under climate change, emphasizing the increasing influence of atmospheric evaporative demand. It utilizes daily ensemble-mean Standardized Precipitation Evapotranspiration Index (SPEI) derived from 12 NEX-GDDP-CMIP6 Global Climate Models (GCMs) to assess future drought conditions.
-
Ceballos-Tavares et al. (2026) Forecasting meteorological droughts in a hydrological basin using artificial neural networks
This study developed and evaluated artificial neural network models for meteorological drought forecasting in Mexico's Conchos River Basin, demonstrating high predictive performance (Mean Squared Error < 0.1, Coefficient of Determination > 0.90) using complex architectures and specific optimizers.
-
Gorugantula et al. (2026) An integrated water deficit index to evaluate multifaceted impacts of drought in a semi-arid river basin
This study developed an Integrated Water Deficit Index (IWDI) by combining climatic water deficit (SPEI) and terrestrial water storage deficit (TWSI) using a Clayton copula function to holistically characterize drought in a large, semi-arid river basin. The IWDI revealed distinct spatiotemporal drought patterns, with upper sub-basins experiencing longer, more severe droughts, and quantified significant reductions in water bodies (15%) and crop production (25%) during drought events.
-
Wang et al. (2026) Terrestrial water storage variations and drought characteristics in the upper yellow river basin revealed by joint GNSS-GRACE analysis
This study integrates GNSS and GRACE observations to jointly invert terrestrial water storage (TWS) changes in the Upper Yellow River Basin (UYRB) from 2011 to 2023, revealing distinct spatial heterogeneity in TWS dynamics and drought drivers, with the south primarily climate-controlled and the north dominated by human activities.
-
Danesh‐Yazdi et al. (2026) Fields of opportunity: satellite imaging uncovers water stress and potential for water savings from irrigation
[Not discernible from provided text]
-
Godet et al. (2026) Quantifying the added value of impact-based warnings for flash flood monitoring using innovative multi-source impact data
This study quantifies the added value of impact-based warnings (IBW) over traditional hazard-based warnings (HBW) for flash flood monitoring in the French Mediterranean region. Using multi-source impact data over a 13-year period, it demonstrates that IBW significantly reduce false alarms and improve the prioritization of affected areas, especially at finer spatial scales.
-
Pary et al. (2026) Trends of rainfed and irrigated crop yield influenced more by increased cultivated area than drought in Iran
This study quantifies the drivers of wheat and barley yield trends in Iran from 1995 to 2022, revealing that the expansion of cultivated area had a significantly larger influence (71–87%) on total production than drought (12–29%). While irrigation provided a buffer, rainfed systems—particularly wheat—remained highly vulnerable to climatic stressors.
-
Shahariar et al. (2026) Enhanced Streamflow Prediction Using a Hybrid CNN-LSTM Model: Integrating Climate-Driven Spatio-Temporal Features in a Deep Learning Framework
This study developed a climate-driven hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model for daily streamflow prediction in the Brahmaputra River Basin, relying solely on precipitation and temperature. The model demonstrated superior performance over standalone deep learning models and achieved comparable or superior skill to a calibrated SWAT model, particularly for low-flow and high-flow extremes.
-
Akkimi et al. (2026) FlDepth: A New Method for Estimating Fluvial and Pluvial Flood Depths from Near Real-Time Satellite-Derived Inundation Map and Topography
This study introduces FlDepth, a novel GIS-based method for estimating fluvial and pluvial flood depths using near real-time satellite inundation maps and topographic data. The method demonstrated high accuracy (errors < 20 cm, NSE ~ 1) compared to hydrodynamic models and ICESat-2 data, outperforming existing tools like FwDET in diverse terrains.
-
Suresh et al. (2026) Physics-Guided Deep Learning with Bayesian Optimization for Enhanced River Streamflow Prediction
This study introduces PIDeepONet, a novel hybrid deep learning model integrating Physics-Guided Loss (PGL) and Bayesian Optimization (BO), to enhance the accuracy and physical plausibility of river streamflow predictions using only observational data. The model effectively bridges the gap between traditional physics-based and purely data-driven approaches, demonstrating superior performance in both random and temporal data splits for two Indian river basins.
-
Malmquist et al. (2026) Effects of water infrastructure development on water balance in a temperate agricultural landscape
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2026...
-
Sivan et al. (2026) Evaluation of compound agrometeorological drought and hot events using a copula-based standardized index
This study introduces a novel Standardized Agrometeorological Drought and Hot Index (SADHI) based on a vine copula framework to analyze compound agrometeorological drought and hot events (CADHE) in Kerala, India, from 1955 to 2020, revealing a significant increase in their frequency and severity, particularly after 1980, with the 2016 event being the most widespread and extreme.
-
Singh et al. (2026) Modelling framework for asynchronous land-atmosphere coupling using NASA GISS ModelE (NASA-GISS E2.1) and LPJ-LMfire (v1.4.0): design, application and evaluation for the 2.5 ka period
This study develops and evaluates a framework for asynchronously coupling the NASA GISS ModelE climate model with the LPJ-LMfire dynamic global vegetation model to simulate paleoclimate for the 2.5 ka period, demonstrating the critical importance of bias correction for accurate land-atmosphere feedback representation and consistency with proxy data.
-
Webb (2026) A Modified Atmospheric River Scale for Flood Hazards
This study modifies the existing Atmospheric River (AR) scale for flood hazards by incorporating antecedent soil moisture conditions, significantly improving its ability to predict peak streamflow and detect flood-generating ARs.
-
Höckh et al. (2026) Screening the Future Security of Groundwater-based Water Supply on the Scale of a Regional Water Supplier
This study develops a simple, low-cost screening framework using a novel Groundwater Use Index (GWUI) to assess the future security of groundwater-based water supply under climate change. Applied to a regional water supplier in southwest Germany, the framework projects significant water stress and potential shortages by 2070, even in a historically humid region, primarily due to declining groundwater recharge.
-
Charrada et al. (2026) Assessment of groundwater salinization and nitrate pollution in a sub-humid to semi-arid agricultural area: a case study of the Sidi Smail plain in northwestern Tunisia
This study investigated the origins of salinity and nitrate in groundwater and the mechanisms governing its chemistry in the Sidi Smail plain, northwestern Tunisia. It revealed that 88% of groundwater samples were brackish/saline and 75% exceeded nitrate limits, primarily due to a combination of geogenic processes and anthropogenic factors like fertilizer overuse and evaporation.
-
Mure-Ravaud et al. (2026) Physically-based transposition of a mesoscale convective system for estimating probable maximum precipitation
This paper introduces a novel physically-based storm transposition approach, leveraging numerical weather prediction model internal variability, to objectively define transposition regions and reduce boundary condition shifts for probable maximum precipitation estimation, applied to a mesoscale convective system over the Raccoon River Watershed in Iowa.
-
Shakib et al. (2026) FloodWatch: An Automatic Machine Learning Tool for Flood Forecasting and Segmentation using Weather Data and Images
This study introduces "FloodWatch," an automatic machine learning (AutoML) tool designed for comprehensive flood forecasting and segmentation. It integrates various machine learning and deep learning algorithms, Explainable AI (XAI), Generative Adversarial Networks (GANs) for data augmentation, and GIS visualization into a user-friendly, no-code web application to enhance flood prediction accuracy and accessibility for disaster management.
-
Jagdhuber et al. (2026) Assessing the Spatial Similarity of Soil Moisture Patterns and Their Environmental and Observational Drivers from Remote Sensing and Earth System Modeling Across Europe
This study investigates the spatial similarity of soil moisture patterns between the SMAP passive microwave remote sensing product and the ECMWF IFS Earth system model across Europe. It finds that despite differences in their underlying drivers and methodologies, the two products exhibit significant spatial pattern similarities from local to continental scales.
-
Tarpanelli et al. (2026) The Potential of EO Data for Enhanced Flood Monitoring and Forecasting: A Consortium Assessment
This paper provides a consortium assessment reviewing the capabilities of Earth Observation (EO) data to enhance riverine flood monitoring and forecasting systems, evaluating their accuracy, lead time, and reliability while addressing key challenges and outlining future advancements. It concludes that despite significant scientific progress, EO data remain largely under-exploited in operational flood forecasting, particularly in data-scarce regions.
-
Sakellariou et al. (2026) Spatiotemporal Drought Assessment Projections for Climate-Resilient Planning in Distinct Mediterranean Agroecosystems
This study provides long-term projections of drought and wetness conditions for three representative Mediterranean agroecosystems (Spain, Tunisia, Lebanon) to support climate-resilient planning. It reveals significant spatial variability in future drought and wetness extremes, with differing timings and intensities of driest and wettest hydrological years across regions, emphasizing the need for spatially targeted adaptation strategies.
-
Guo et al. (2026) Advancing Precipitation Estimation in Mountainous Regions Through Deep Learning Fusion of Multi-Satellite Products
This study developed a Transformer-based deep learning framework to fuse near-real-time GSMaP-GNRT and IMERG-Early satellite precipitation products, significantly improving precipitation estimation accuracy, particularly bias reduction and monthly statistics, in the mountainous Sichuan Province, China.
-
Shi et al. (2026) Elucidating the hydrochemical and isotopic processes of surface and groundwater in response to river drying up and re-flowing in an alluvial-proluvial fan-plain transition zone
This study elucidates surface water-groundwater (SW-GW) interactions during river drying and re-flowing in an alluvial-proluvial fan-plain transition zone using hydrochemistry and stable isotopes. It identifies three distinct transformation zones and quantifies the seasonal contributions between SW and GW, revealing that river drying is primarily caused by efficient infiltration into groundwater.
-
Kikis et al. (2026) Benefits and Challenges of Artificial Intelligence in Soil Science—A Review
This review synthesizes recent advances in Artificial Intelligence (AI) applications across key soil science domains, evaluating the performance of various AI methods and identifying critical limitations to their widespread adoption for sustainable soil management.
-
Madhu et al. (2026) Publication Dataset
This dataset provides results obtained through VGST Confocal Laser Scanning Microscopy (CLSM).
-
Pachón-Acuña et al. (2026) Automated Dynamic Adjustment of Runoff Threshold in Ungauged Basins Using Remote Sensing Data
This study introduces an automated Google Earth Engine methodology to dynamically adjust the runoff threshold (P0) using satellite soil moisture, land cover, and precipitation data, demonstrating its superior ability to capture real-time soil saturation and adapt to varying moisture conditions compared to static methods.
-
Sun et al. (2026) Assessing and optimizing high-resolution global river streamflow estimates with triple collocation analysis
This study evaluates three global hydrological models (CWatM, PCR-GLOBWB, H08) at high and low spatial resolutions and optimizes streamflow estimates using Triple Collocation and simple averaging. It finds that high-resolution CWatM and Triple Collocation-based data fusion significantly improve global streamflow simulation accuracy, offering practical guidance for hydrological assessments.
-
Wu et al. (2026) Modeling runoff with incomplete data: a comparison of hydrological, deep learning, and hybrid approaches
This study systematically evaluates hydrological, deep learning, and hybrid runoff models under various data scarcity scenarios across forty catchments. It finds process-based models more reliable in data-scarce conditions, while hybrid models effectively combine physical knowledge with data-driven flexibility, underscoring the importance of model selection based on data availability.
-
Masoumi et al. (2026) Absolute validation of SWOT measurements over small reservoirs using legacy photogrammetric DEMs: A case study in Zanjan Province, Iran
This study validates SWOT satellite measurements of surface water extent and height over four small reservoirs in Zanjan Province, Iran, using high-resolution Digital Elevation Models (DEMs) derived from 1965 aerial photographs. It finds that an optimal PIXC classification variant effectively captures surface water dynamics, achieving a mean area RMSE of 0.15 km² and mean height RMSE of 2.16 m, though performance can degrade due to seasonal snow cover.
-
Achite et al. (2026) Meteorological Drought Under Climate Variability in the Wadi Sly Basin, Algeria (1967–2022)
This study analyzed the spatiotemporal characteristics of meteorological drought in the Wadi Sly basin, Algeria (1967–2022) using the Standardized Precipitation Index (SPI), revealing significant inter-annual and decadal variability with synchronized severe drought episodes across the basin.
-
Davis et al. (2026) Physics‐Based Versus AI Weather Prediction Models: A Comparative Performance Assessment of Atmospheric River Prediction
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
-
Green et al. (2026) Global Intensity-Duration-Frequency curves based on observed sub-daily rainfall (GSDR-IDF)
This study presents GSDR-IDF, a global dataset of Intensity-Duration-Frequency (IDF) curves derived from over 24,000 quality-controlled sub-daily rain gauge records, providing a crucial resource for hydrological modeling, engineering design, and flood-risk assessment. It addresses the lack of comparable global IDF estimates by applying robust extreme value analysis methods to generate return levels for various durations and return periods.
-
Zhang et al. (2026) A physics-driven hybrid transformer model for hydrologic simulation under nonstationary environmental conditions
This study proposes a Physics-Driven Hybrid Transformer Hydrologic Model (PD-HTHM) that integrates deep learning with the conceptual SIMHYD model to generate time-varying parameters, improving hydrologic simulation under nonstationary environmental conditions. It demonstrates that dynamically adjusting a few key parameters significantly enhances model robustness and predictive accuracy compared to static parameterizations.
-
Zaied et al. (2026) Water Harvesting Techniques for Assessing Land Degradation Using MEDALUS Approach and GIS Analysis: Jeffara Region, Southern Tunisia
This study assessed land degradation sensitivity in Southern Tunisia, finding nearly the entire region critically sensitive, and demonstrated that traditional water harvesting techniques (WHTs) significantly reduce this sensitivity from 99% to 77.3%.
-
Pan et al. (2026) Evaluating saturated hydraulic conductivity schemes: impacts on soil moisture simulations and soil texture-dependent applicability
This study evaluated five saturated hydraulic conductivity (Ksat) schemes against laboratory measurements and then integrated them into the Community Land Model (CLM5.0) to simulate soil moisture on the Tibetan Plateau, finding that the optimal Ksat scheme for soil moisture simulation is dependent on soil texture and organic matter.
-
Chinchella et al. (2026) On the accuracy of optical disdrometer measurements
This study systematically quantifies the instrumental bias of two widely used optical disdrometers (OTT Parsivel2 and Thies LPM) in a laboratory setting using a traceable raindrop generator, revealing significant underestimation of drop size and integral properties, and biases in fall velocity, highlighting the critical need for rigorous calibration.
-
Daramola et al. (2026) Evaporative cooling exceeded albedo-induced warming in greening areas of global drylands
This study investigates the impact of greening and browning on temperature feedback in global drylands over the past two decades, revealing that evaporative cooling driven by soil moisture-controlled evapotranspiration largely outweighs albedo-induced warming.
-
Huang et al. (2026) How does gross primary production uncertainty impact evapotranspiration prediction within the carbon–water coupled model?
This study quantitatively assessed the impact of remote sensing-based Gross Primary Production (GPP) uncertainty on Evapotranspiration (ET) predictions within carbon-water coupled models, revealing significant negative effects on both accuracy and spatiotemporal patterns of ET estimates.
-
Liu et al. (2026) Mapping drawdown-zone bathymetry using SWOT observations: implications for global monitoring of lake inundation and storage changes
This study develops a novel Spatial Iterative Filtering and Weighted Average Fusion (SIF-WAF) method to integrate multi-temporal SWOT observations for high-precision mapping of lake drawdown zone bathymetry, demonstrating its potential for global monitoring of lake inundation and storage changes.
-
Yang et al. (2026) WinG-LSTM: a precipitation nowcasting model integrating swin transformer and LSTM
This paper introduces WinG-LSTM, a novel deep learning model for precipitation nowcasting that integrates Swin Transformer with a gated Multi-Layer Perceptron into PredRNN's recurrent units. The model addresses limitations of traditional CNN-RNN approaches by effectively capturing long-range spatiotemporal dependencies, demonstrating superior accuracy in predicting precipitation intensity and spatial extent on two real-world radar datasets.
-
Khazaelpour et al. (2026) Coordinated allocation of conservation incentives and water management across a river basin under hydrological uncertainty
This study develops high-resolution robust optimization models integrating surface water, groundwater, and conservation strategies to enhance water availability and ecosystem resilience in river basins. Applied to the Red River Basin, the models demonstrate that coordinated basin-wide conservation significantly improves water sustainability while supporting ecological and agricultural needs.
-
Li et al. (2026) Soil water vapor adsorption and condensation governed by groundwater depth and vadose zone lithology in arid and semi-arid regions
This study quantitatively analyzed the spatiotemporal dynamics of soil water vapor adsorption and condensate formation in arid regions, revealing the distinct impacts of groundwater depth and vadose zone lithology, and highlighting an overestimation in conventional condensation measurements due to unseparated adsorption.
-
Soares et al. (2026) River intermittency: mapping and upscaling of water occurrence using unmanned aerial vehicle, Random Forest and remote sensing landscape attributes
This study maps and models the spatio-temporal dynamics of an intermittent river using UAV surveys to classify water occurrence and Random Forest models with landscape attributes, dam presence, and satellite indices. Model (a), incorporating Sentinel MNDWI, proved most successful in simulating intermittency both temporally and spatially with approximately 80% accuracy.
-
Ma et al. (2026) A composite ecological drought index integrating multi-source water and heat stress with time-lag effects: Insights from the Yellow River Basin
This study proposes a Composite Ecological Drought Index (CEDI) using a trivariate copula framework to integrate multi-source water and heat stress with time-lag effects. Applied to the Yellow River Basin, CEDI effectively characterizes ecological drought and vegetation resistance, revealing a general weakening of drought over the past two decades despite regional vulnerabilities.
-
K et al. (2026) Forecasting land-use and land-cover change for groundwater sustainability in the Muvattupuzha basin using CA-Markov (2033–2050)
This study integrates satellite-based land-use analysis with machine learning to demonstrate that built-up areas in the Muvattupuzha basin increased from 12.3% to 44.4% between 2003 and 2023. Using CA-Markov modeling, the research forecasts continued urbanization through 2050 and identifies magnesium, calcium, and alkalinity as the primary drivers of groundwater nitrate contamination.
-
Sharma et al. (2026) MAUNet-Light: A Concise MAUNet Architecture for Bias Correction and Downscaling of Precipitation Estimates
Unable to extract a summary from the provided unreadable text.
-
Tornay et al. (2026) SMAP Soil Moisture Measurement in Hungary over Agricultural Land
This study assessed the accuracy of the NASA SMAP Level 3 soil moisture product over agricultural land in Hungary using long-term in situ measurements. It found that PM (ascending) overpasses generally showed stronger agreement with ground data, meeting the <0.04 m³/m³ unbiased Root Mean Square Error (ubRMSE) validation requirement in homogeneous areas, and that spatial averaging of in situ data significantly improved correlations.
-
Dharpure et al. (2026) Future projections of glacier mass change in High Mountain Asia using GRACE and climatemodel data
This study quantifies past (2002-2023) and projects future (2024-2100) glacier mass change in High Mountain Asia using GRACE/GRACE-FO data, machine learning for gap-filling, and a Generalized Additive Model driven by ISIMIP3b climate scenarios, revealing significant continued mass loss, especially under high-emission pathways.
-
Liu et al. (2026) Multi-satellite data fusion for improved field-scale evapotranspiration mapping on Google Earth Engine
This study developed a Google Earth Engine (GEE)-based framework to improve field-scale evapotranspiration (ET) mapping by fusing thermal infrared (TIR) observations from ECOSTRESS and VIIRS with Harmonized Landsat-Sentinel (HLS) data. The integration of multi-satellite data generally enhanced ET estimation accuracy, reducing average Mean Absolute Error (MAE) by 8.64% (daily), 14.40% (weekly), and 16.37% (monthly) compared to Landsat-only baselines.
-
Cheda et al. (2026) Heat Indices for Europe Derived from Satellite Data: A Proof of Concept
This study calculates and validates Land Surface Temperature (LST)-based heat indices for Switzerland and Europe using satellite data, demonstrating their high correlation with traditional air temperature indices and revealing significant increases in heat events since 1991.
-
Fazel‐Rastgar et al. (2026) Stratosphere–troposphere interactions and teleconnections associated with Iran’s winter warming in January 2024
This study investigates the extreme winter warming and dryness in Iran during January 2024, linking it to a weakened Siberian High, a mid-tropospheric ridge, and a split-type Sudden Stratospheric Warming event, while also identifying a long-term warming trend of 0.057 °C per year since 1980.
-
Jones et al. (2026) Collective risk modelling of multi-peril events: correlation of European windstorm gust and precipitation annual severity
This study develops and tests three collective risk frameworks to model the correlation between annual aggregated wind gust and precipitation severities from European windstorms. It finds that interannual modulation of hazard variables is crucial to accurately capture the observed negative correlations at high severity thresholds.
-
Rico-Firo et al. (2026) Prioritizing Vulnerability in Mexican Aquifers Under Climate Change Scenarios: A Multi-Criteria Approach Based on GIS for Sustainable Water Management
This study prioritizes Mexican aquifers based on water quantity vulnerability under climate change scenarios (SSP2-4.5, SSP5-8.5) using a GIS-based Analytic Hierarchy Process, finding that 111 to 136 aquifers are of "very high priority," with 70 already overexploited.
-
Xu et al. (2026) A Global Benchmark of the Vector-Based Routing Model MizuRoute: Similarities and Divergent Patterns in Simulated River Discharge
This study provides the first comprehensive global benchmark of the mizuRoute river routing framework, revealing its global fidelity and identifying key drivers of performance variability, particularly the impact of arid zone transmission losses and anthropogenic regulation.
-
Feigl et al. (2026) Distilling hydrological and land-surface model parameters from physio-geographical properties using text-generating AI
This study demonstrates the use of variational autoencoders (VAEs) as text-generating AI models to automatically derive interpretable parameter transfer functions for distributed hydrological and land-surface models. This novel approach significantly improves runoff predictions in ungauged basins across 162 German catchments compared to traditional regionalization methods and deep learning models, while maintaining physical interpretability.
-
Ben-Salem et al. (2026) The role of secondary data in estimating groundwater levels in the Iberian Peninsula
This study maps long-term average groundwater levels across the Iberian Peninsula by exploring the value of incorporating various secondary data with cokriging to overcome the scarcity of direct measurements, finding that hydrogeological context significantly improves the reliability of these maps.
-
Webb et al. (2026) Antecedent moisture enhances early warning of atmospheric river flood hazards
This study demonstrates that incorporating antecedent soil moisture conditions into the atmospheric river (AR) scale significantly improves its ability to predict flood hazards in California and central Chile, nearly doubling its correspondence with peak streamflow.
-
Prasain et al. (2026) A comprehensive review on impact of climate change and land use change on groundwater
This systematic review of 575 studies (1990-2024) comprehensively assesses the combined impacts of climate and land use change (CLUC) on groundwater quantity, quality, and system vulnerability, revealing a rapid expansion of research since 2016 but persistent geographic and thematic imbalances.
-
Gu et al. (2026) Has the latest IMERG V07 from GPM improved the performance of precipitation estimation of regional-scale compared to its predecessor?
This study systematically evaluates and compares the performance of the latest IMERG V07 precipitation product against its predecessor V06 across the Yellow River Basin. It finds that IMERG V07 significantly improves precipitation estimation and rain/no-rain detection capabilities, demonstrating reduced sensitivity to environmental factors compared to V06.
-
Muñoz‐Castro et al. (2026) How well do hydrological models simulate streamflow extremes and drought-to-flood transitions?
This study investigates how well conceptual hydrological models capture drought-to-flood transitions and identifies key modeling decisions influencing performance. It reveals that standard performance metrics do not guarantee accurate extreme event detection, with model timing being crucial, and that model representation of these transitions is generally poor, particularly in semi-arid and high-mountain regions.
-
Chen (2026) BGGI flood resilience data
This dataset provides phased-dependent flood resilience indicators for Blue-Green Infrastructure (BGGI), combining flood dynamics simulated by MIKE FLOOD with BGGI spatial attributes derived from GIS.
-
Luo et al. (2026) Hydroclimate shapes photosynthetic sensitivity to cloud cover across global terrestrial ecosystems
This study reveals that the sensitivity of terrestrial photosynthesis to cloud cover is spatially determined by hydroclimate, with clouds promoting photosynthesis in water-limited arid regions (via precipitation) and inhibiting it in energy-limited humid regions (via sunlight blockage). Under future warming, this leads to projected declines in gross primary productivity in arid regions and increases in humid regions, exacerbating regional disparities.
-
Schmitt et al. (2026) AGILE v0.1: The Open Global Glacier Data Assimilation Framework
This paper introduces AGILE v0.1, an open global glacier data assimilation framework that uses a time-dependent variational method with automatic differentiation to efficiently optimize glacier bed topography and distributed ice volume, demonstrating significant improvements over initial guesses in idealized experiments.
-
Feng et al. (2026) Global daily 9 km remotely sensed soil moisture (2015–2025) with microwave radiative transfer-guided learning
This study developed a Process-Guided Machine Learning (PGML) framework, integrating microwave radiative transfer theories with deep learning, to generate a global daily 9 km surface soil moisture (SM) dataset from 2015 to 2025. The resulting PGML SM product demonstrates superior accuracy (R=0.923, ubRMSE=0.040 m³/m³) compared to existing satellite and reanalysis products, particularly in regions with dense vegetation and complex surface conditions.
-
Blanch et al. (2026) AI image-based method for a robust automatic real-time water level monitoring: a long-term application case
This study presents a robust, automated camera gauge for long-term, near real-time river water level monitoring, employing artificial intelligence for image-based segmentation and ground control point identification combined with photogrammetric techniques. Tested over 2.5 years at four sites, the system achieved high performance with mean absolute errors ranging from 0.96 to 2.66 cm, demonstrating resilience to adverse conditions and enabling continuous 24/7 monitoring.
-
Douville (2026) How Do Projections of Meteorological Droughts Vary Across Models and Regions?
This study quantifies future changes in meteorological drought properties using Earth system models and the standardized precipitation index, identifying regional "dry spots" where drought severity is projected to increase, primarily due to prolonged duration.
-
Cai et al. (2026) Global water cycle changes in a warming climate: Projection from CMIP6 multi-model ensemble mean
This study projects a robust and progressively intensifying global hydrological cycle throughout the 21st century under warming climate scenarios (SSP2-4.5 and SSP5-8.5) using CMIP6 models, revealing a pronounced land–ocean asymmetry with greater precipitation increases over land and enhanced ocean-to-land moisture transport.
-
Reda et al. (2026) Intensification of drought characteristics in Africa’s Great Green Wall countries under climate change
This study projects the intensification of drought characteristics (area, duration, frequency, and intensity) across Africa's Great Green Wall (AGGW) under various climate change scenarios, revealing significant increases in droughted area, particularly under high-emission pathways, which threatens ecosystems and livelihoods.
-
Menachem et al. (2026) Multi-decadal drought variability in the Eastern Mediterranean and its connection to large-scale climate indices
This study investigates multi-decadal agricultural drought variability in the Eastern Mediterranean using soil-moisture-based indicators from ERA5-Land, revealing a shift to consistent drying since the 1990s and strong correlations with the Atlantic Multidecadal Oscillation (AMO) and North Sea–Caspian Pattern (NCP).
-
Chikhaoui et al. (2026) Advancing groundwater recharge zone mapping using AHP and high-resolution satellite imagery: A case study from northwestern Tunisia
This study maps groundwater recharge potential in the El Kef region of northwestern Tunisia using an integrated approach combining remote sensing, geological/hydrogeological data, and the Analytical Hierarchy Process (AHP). It identifies that 42.8% of the area has high recharge potential, validated by multi-tracer analysis, providing a framework for sustainable groundwater management.
-
Hamdouni et al. (2026) Evaluation of CMIP6-based climate projections in Northern Morocco: A bias corrected assessment of temperature and precipitation trends in three mediterranean watersheds
This study evaluates historical and future climate trends in three Northern Moroccan watersheds using bias-corrected CMIP6 models, projecting a significant temperature increase (2.5–3.5 °C by 2100) and a substantial precipitation decline (up to 30%) under high-emission scenarios, with the Oued Laou watershed identified as most vulnerable to aridification.
-
Ji et al. (2026) Contrasting feedback mechanisms drive basin-scale vegetation vulnerability to drought in cold-arid northern China
This study developed a basin-scale framework using the Standardized Precipitation Evapotranspiration Index (SPEI) and a Composite Vegetation Index (CVI) to assess vegetation vulnerability to drought in cold-arid northern China, revealing spatially differentiated vulnerability driven by contrasting negative and positive feedback mechanisms.
-
Tallapragada et al. (2026) Evaluation of GFSv16 for Near‐Real‐Time Data Impact Studies During the Atmospheric River Reconnaissance Program 2022
This study investigated the impact of assimilating dropsonde data from AR Reconnaissance on improving winter 2022 precipitation forecasts for landfalling Atmospheric Rivers (ARs) on the U.S. West Coast, finding that targeted dropsonde observations significantly enhance forecast accuracy for medium to strong AR events.
-
Seo et al. (2026) Implementation of a multi-layer snow scheme in the GloSea6 seasonal forecast system: impacts on land–atmosphere interactions and climatological biases
This study implements a multi-layer snow scheme in the GloSea6 seasonal forecast system, demonstrating improved simulation of snow seasonality, land-atmosphere interactions, and reduced climatological biases in temperature and precipitation over the Northern Hemisphere by delaying snowmelt and enhancing evaporative cooling.
-
Cortés-Torres et al. (2026) Scalability and Computational Performance of an Ecohydrological Model Using Machine Learning-Based Prediction
This study developed a general methodological framework to systematically quantify the computational scalability of distributed hydrological models, using TETIS v9.1 as a case study, and found that spatial resolution and output-gauge density are the strongest influences on runtime, while also creating a highly accurate predictive tool for performance.
-
Çiftçi et al. (2026) Deep Learning-based Seasonal Forecasting Over K-means-derived Climate Zones in Türkiye
This study developed an integrated framework using K-means clustering, Principal Component Analysis (PCA), and Long Short-Term Memory (LSTM) deep learning to redefine climate zones and enhance seasonal climate forecasting in Türkiye, demonstrating that cluster-based forecasts significantly reduce errors compared to aggregated approaches.
-
Wang et al. (2026) Revisiting isotopic time series and linkages between precipitation and water vapor: High-resolution insights in a semi-arid setting
This study investigates the dynamic variations and linkages between atmospheric vapor and precipitation stable isotopes (δ18O) at high resolution in the semi-arid Western Loess Plateau, China, using three years of continuous observation data. It reveals significant temporal variations, a strong precursor effect of vapor isotopes on precipitation, and distinct controlling factors (temperature, moisture transport, and convection) across different monsoon periods.
-
Bai et al. (2026) A decade-long seamless-continuity daily L-band soil moisture product derived from SMOS observations since 2010
This study developed a fully automated gap-filling method, Discrete Cosine Transformation with Partial Least Squares (DCT-PLS), to generate the first decade-long (2010-2020) seamless-continuity global daily L-band soil moisture product (MTMA-SC_SM) from SMOS observations. The resulting product achieved high fidelity, comparable accuracy to original retrievals, and 100% spatiotemporal coverage, providing a robust dataset for climate and land surface studies.
-
He et al. (2026) Coupling data assimilation and machine learning to improve land surface conditions and near-surface temperature and humidity forecasts
This study coupled a hybrid data assimilation-machine learning framework (DL) with the Weather Research and Forecasting (WRF) model to quantify the impacts of incorporating soil moisture (SM) and vegetation data on land surface initialization and near-surface weather forecast accuracy. The results indicate that optimizing leaf area index (LAI) and SM significantly improves the simulation of water table depth, evapotranspiration, air temperature, and humidity, and refines land surface initial conditions for improved near-surface weather forecasts.
-
Zhou et al. (2026) Integrating distributed hydrologic simulation with low-flow resilience: a spatiotemporal perspective
This study utilizes a fully distributed watershed hydrologic model to investigate low-flow resilience in a poorly gauged basin, revealing distinct spatiotemporal patterns where low flow emerges after approximately 120 days of minimal rainfall, and downstream reaches exhibit higher resilience compared to upstream areas.
-
Hu et al. (2026) Advancing hydrological prediction in South Africa with differentiable multi-source meteorological data fusion
This study developed a differentiable multi-source meteorological data fusion framework for regional runoff prediction in 188 South African basins, which significantly outperformed non-fusion baselines by adaptively weighting precipitation sources without relying on ground observations. The framework achieved a median Nash–Sutcliffe efficiency of 0.38, representing a greater than 52 % improvement over single-source models and a 23 % increase over direct splicing.
-
Qaraghuli et al. (2026) New multivariate composite remote sensing drought index based on machine learning and geospatial techniques, insights from Northern Iraq
This study developed and evaluated five machine learning models for predicting the Standardized Precipitation Evapotranspiration Index (SPEI) at 3-month and 6-month timescales in Northern Iraq using satellite-based and gridded data. Random Forest (RF) and Extreme Gradient Boosting (XGB) consistently outperformed other models, revealing precipitation as the dominant driver for short-term droughts, while temperature, vegetation indices, and soil moisture were more influential for medium-term droughts.
-
Unknown (2026) Tree rings and salt lakes give clues about ancient rainfall
This historical study aimed to accurately reconstruct ancient rainfall using tree rings, finding that variations in salt lake levels provided a crucial independent control, leading to a confident rainfall curve for the past four millennia in the Great Basin.
-
Bong et al. (2026) Water Isotope Model Intercomparison Project (WisoMIP): Present‐Day Climate
This paper presents the first results of WisoMIP Phase 1, intercomparing isotope-enabled atmospheric general circulation models nudged to ERA5 reanalysis to isolate differences in water isotope behavior. The study finds that the ensemble mean of these models best matches observations, providing a benchmark dataset and revealing uncertainties in physical processes of the global water cycle.
-
Liu et al. (2026) Assessing the impact of drought on water use efficiency among ecosystems on the Mongolian Plateau
This study investigated the responses of precipitation use efficiency (PUE), soil water use efficiency (SWUE), and groundwater use efficiency (GWUE) to meteorological, soil, and groundwater drought conditions on the Mongolian Plateau from 2000 to 2020. Findings reveal that while WUE indicators generally increased, their responses to drought varied significantly by region and vegetation type, exhibiting lagged effects ranging from 2.4 to 4.4 months, with groundwater drought often reducing GWUE, particularly in grasslands.
-
Gnann et al. (2026) Uncertainty, temporal variability, and influencing factors of empirical streamflow sensitivities
This study systematically evaluates empirical methods for estimating streamflow sensitivities to precipitation and potential evaporation, revealing high uncertainties, particularly for potential evaporation. It demonstrates that these sensitivities are not static but decrease significantly over time (15 %–70 % over 50 years) as aridity increases, urging caution in their use for climate change impact assessments.
-
Yung et al. (2026) Flood monitoring: An innovative application of multisource image fusion and transfer learning
This study proposes a cross-sensor framework for robust water body mapping using multitemporal optical imagery, integrating relative radiometric normalization, spatiotemporally invariant feature extraction, a PSO-RF classifier, and cross-sensor sample transfer strategies to significantly improve flood monitoring accuracy and efficiency.
-
She et al. (2026) Copula–information gain-based identification of GPP response thresholds under multiscale agricultural drought
This study develops a Copula–Information Gain (Copula–IG) framework to objectively identify Gross Primary Productivity (GPP) drought response thresholds across multiple temporal scales in China. It reveals pronounced spatiotemporal heterogeneity and scale dependence in GPP responses, with model performance improving and thresholds becoming more stable at longer drought durations.
-
Jiang et al. (2026) Comparative analysis of spatial interpolation methods for daily rainfall data in complex terrain
This study systematically evaluated six spatial interpolation methods for daily rainfall in China's Loess Plateau from 1980-2020. It found that Thin Plate Spline (TPS) and Inverse Distance Weighting (IDW) provided the best overall accuracy and stability, outperforming machine learning methods and Co-kriging, especially in complex terrain and during extreme events.
-
Bai et al. (2026) Persistent and intensifying heat extremes in global deeper soils
This study investigated global heatwave evolution across near-surface air, land surface, and subsurface soils from 1980–2024, finding that heatwaves have strengthened across all layers, with soils exhibiting greater exposure and severity than air or land surface, implying air temperature alone underestimates heatwave risks to soil ecosystems.
-
Yanqun et al. (2026) Harnessing satellite-driven insights for dynamic soil moisture tracking in smart farming systems
This study developed and validated an integrated framework using Landsat 8 satellite data and IoT-enabled ground sensors to dynamically track soil moisture variability in the Angreb Watershed, Ethiopia, demonstrating its high accuracy and applicability for precision irrigation and smart farming.
-
Homtong et al. (2026) Mapping spatiotemporal agricultural droughts from 2019 to 2024 in Northeast Thailand using multi-temporal and multiple sensor data together with random forest algorithm
This study mapped spatiotemporal agricultural droughts in Northeast Thailand from 2019 to 2024 using multi-temporal Sentinel-2 imagery and a Random Forest Regression algorithm, with a Soil Moisture Index (SMI) derived from Landsat 8 as reference data. The models achieved high accuracy (R > 0.65), revealing consistent severe drought events between March and May annually, with irrigated areas showing lower severity.
-
Shokri et al. (2026) Better continental-scale streamflow predictions for Australia: LSTM as a land surface model post-processor and standalone hydrological model
This study evaluates two Long Short-Term Memory (LSTM)-based models (standalone and hybrid with AWRA-L) for continental-scale streamflow prediction in Australia, demonstrating their superior performance over traditional land surface and conceptual hydrological models across various validation scenarios. The findings highlight the potential of deep learning to enhance water resource management and climate adaptation strategies.
-
Zhang (2026) Dataset for the research titled "Suitable water level ranges for lake in arid-regions under hydro-environmental constraints"
This paper aims to determine suitable water level ranges for lakes in arid regions, considering various hydro-environmental constraints, supported by a dataset containing metadata for water level estimation.
-
Shi et al. (2026) Quasi-Global (50° S–50° N) of Soil Moisture and Precipitation Extremes
This study systematically evaluated the co-occurrence and temporal dependencies of extreme soil moisture and precipitation from 2000 to 2022 at a quasi-global scale, revealing a significant increase in their co-occurrence frequency and distinct directional pathways between them.
-
Gao et al. (2026) Nonlinear characteristics and driving factors of vegetation-soil moisture feedback at fine scale in Northeast China
This study investigated the nonlinear, bidirectional feedback mechanisms between vegetation gross primary productivity (GPP) and soil moisture (SM) at fine spatial scales (1 km) and different depths (0–100 cm) in Northeast China from 2000 to 2022. It revealed predominant synergistic growth and bidirectional causality, with SM's influence on GPP generally stronger, a 2–3 month lagged response, and regulation by climatic and geographical factors.
-
Kan et al. (2026) Latitudinal divergence in runoff responses to global forestation due to forest-atmosphere feedbacks
Global potential forestation leads to a latitudinal divergence in runoff responses, increasing runoff in tropical regions but decreasing it in boreal regions, a pattern primarily driven by forest-atmosphere feedbacks rather than direct effects of forest expansion.
-
Charjan et al. (2026) A Novel Hybrid Approach To Drought Forecasting: Leveraging Feature Engineering And Ensemble Methods
This study proposes a novel hybrid drought forecasting model that combines custom feature engineering based on mathematical equations with an ensemble Random Forest Classifier. The model significantly improves drought prediction accuracy, achieving a 98.52% accuracy rate by leveraging physically grounded feature transformations.
-
Merufinia et al. (2026) Long term stream flow for enhanced accuracy prediction through machine learning models (Ali Baba and the forty thieves vs. Fire Hawk Optimizer)
This study developed and evaluated novel hybrid machine learning models, integrating Artificial Neural Networks (ANN) and Support Vector Regression (SVR) with Ali Baba and the Forty Thieves (AFT) and Fire Hawk Optimizer (FHO) metaheuristic algorithms, for long-term streamflow prediction in the Kurkursar River basin. The hybrid SVR-AFT model demonstrated superior performance, improving prediction accuracy by approximately 47% compared to standalone models, achieving an R² of 0.9695 and an RMSE of 0.0813 m³/s.
-
Tian et al. (2026) Characteristics of drought evolution and response relationships on the Northern Slope of the Tianshan Mountains
This study investigated the spatiotemporal evolution and response relationships of meteorological, hydrological, and agricultural droughts on the Northern Slope of the Tianshan Mountains from 1980 to 2019, revealing an overall alleviation of drought conditions but complex propagation mechanisms influenced by climatic and anthropogenic factors.
-
Wang et al. (2026) The impact of 75 years of climate change on Mediterranean glacier mass balance
This study investigates the impact of 75 years of climate change on Mediterranean glacier mass balance and snowpack dynamics, revealing a dominant control by rising summer temperatures and a limited influence of the North Atlantic Oscillation (NAO). It highlights that local topoclimatic factors, such as avalanching snow, are critical for the survival of the region's remaining small, warm-wet glaciers.
-
Wang et al. (2026) Long‐Term Trends of Multiple Drought Types and Their Characteristics From ERA5 Since 1940 in the Yangtze River Basin
[Information not extractable due to unreadable paper content.]
-
Jiao et al. (2026) Multi attribute refined identification of flood-affected bodies based on multi-source data fusion
This study develops a multi-attribute diagnostic framework for flood-affected bodies by fusing multi-source data, addressing challenges in urban land function identification and dynamic population distribution characterization. It proposes an ensemble learning model for optimal urban land function identification and a human-land relationship matching method for high spatiotemporal resolution population mapping, demonstrating reliable support for comprehensive flood disaster assessment.
-
Claro et al. (2026) Near-Future Climate Change Impacts on Sado River (Southern Portugal) Flow Rates Using CMIP6-HSPF Modelling
This study assesses the near-future (2041–2060) climate change impacts on Sado River flow rates in Southern Portugal using CMIP6-HSPF modeling, projecting significant decreases in flow rates and riverine water volume, particularly under higher emission scenarios, due to increased temperatures and reduced autumn/spring precipitation.
-
Montiel et al. (2026) Evaluation of satellite precipitation products across climatic and topographic gradients in a basin in Northern South America
This study evaluates the performance of five gridded precipitation satellite products (GPPs) across climatic and topographic gradients in the Ranchería river basin, northern Colombia. The findings indicate that CHIRPSv3 exhibits the best overall performance and inter-scale consistency, with all products showing improved reliability with temporal aggregation but degraded performance at higher elevations.
-
Pandey et al. (2026) AI-Driven Predictive Modelling for Groundwater Salinization in Israel
[Unable to extract information due to unreadable paper text.]
-
Li et al. (2026) Retrieval of snow depth using synthetic aperture radar: past, current, and future
This paper provides a comprehensive review of snow depth retrieval using synthetic aperture radar (SAR) techniques, detailing the interaction between SAR signals and snow, various backscattering models, and methods like PolSAR, InSAR, PolInSAR, and TomoSAR, to offer a future outlook on this critical parameter.
-
Li et al. (2026) An Interpretable Index-Based Analysis and Scenario-Based Spatial Simulation of Vegetation Drought in the Yellow River Water Conservation Area
This study developed a Temperature–Vegetation–Precipitation Drought Index (TVPDI) to characterize vegetation drought dynamics in the Yellow River Water Conservation Area (YRWC) and analyzed the nonlinear responses of key factors, finding that precipitation is the primary driver and high-emission scenarios significantly exacerbate future drought severity.
-
Zhu et al. (2026) Estimating daily seamless 20-m resolution evapotranspiration using data fusion and TSEB
This study developed an efficient framework integrating cloud-filling, a high-performance spatiotemporal fusion model, multi-source remote sensing data, and the Two-Source Energy Balance (TSEB) model to produce daily seamless evapotranspiration (ET) estimates at 20 m resolution. The framework achieved robust performance, with daily ET estimates showing a coefficient of determination (R²) of 0.56, a mean bias (BIAS) of –0.08 mm/d, and a root mean square error (RMSE) of 1.05 mm/d, providing a powerful tool for precision agricultural water resource management.
-
Xu et al. (2026) Drought as a risk amplifier: The intensifying regime of compound dry-hot events in China
This study investigates the spatiotemporal changes and risk amplification of compound dry-hot (CDH) events in China from 1962 to 2022, revealing a significant increase in their frequency, duration, and severity, with drought amplifying concurrent heatwave risks by 3-7 times.
-
Ogle et al. (2026) Image-based classification of stream stage to support ephemeral stream monitoring
This study develops a low-cost, image-based machine learning method to classify relative stream stage (no, low, or high water levels) in ephemeral streams using field camera imagery from 2017-2023 in the upper Russian River watershed, California, demonstrating its utility for monitoring and quality control in data-scarce intermittent river systems.
-
Yu et al. (2026) Future Climate Change Increases Streamflow and Risks of Hydrological Hazards in the Pearl River Basin
This study projects future runoff in the Pearl River Basin (PRB) under various Shared Socioeconomic Pathway (SSP) scenarios, revealing a significant increase in overall runoff, heightened flood risks during wet seasons, and potential drought risks in specific sub-basins during dry seasons, necessitating adaptive water resource management.
-
Hou et al. (2026) Global rainfall simulator studies: review, challenges and perspectives
This review paper comprehensively examines the technological progression, types, applications, limitations, and future prospects of global rainfall simulators, emphasizing the critical need for standardization to enhance their reliability and comparability in soil erosion and hydrological research.
-
Helali et al. (2026) Analysis of precipitation anomalies in basins of Iran based on transition phases and different intensities of ENSO
This study analyzed annual precipitation anomalies in Iran's basins from 1960 to 2019, revealing that ENSO intensities have a more pronounced impact than ENSO phases, with El Niño generally causing positive precipitation anomalies and La Niña causing negative ones, significantly influencing regional hydrology.
-
Hengl et al. (2026) OpenLandMap-soildb: global soil information at 30 m spatial resolution for 2000–2022+ based on spatiotemporal Machine Learning and harmonized legacy soil samples and observations
This paper presents OpenLandMap-soildb, a global dataset providing dynamic predictions of key soil properties and types at 30 m resolution for 2000–2022+ using spatiotemporal Machine Learning. It reveals a global loss of at least 11 Pg of soil organic carbon in the topsoil over the past 25 years, primarily due to deforestation.
-
Cao et al. (2026) RRV (reliability, resilience and vulnerability)-based assessment of groundwater extraction sustainability in an over-exploited piedmont plain
This study evaluates groundwater extraction sustainability in the overexploited Beijing Plain, revealing that precipitation accounts for 86% of groundwater storage variations, and sustainability assessments based solely on groundwater storage changes significantly overestimate actual sustainability compared to those considering precipitation deficits.
-
Safna et al. (2026) Intelligent Flood Prediction and Early Warning System Using Machine Learning Models
This paper develops an intelligent flood prediction and early warning system using various machine learning models to analyze historical and real-time environmental data. The system aims to improve prediction accuracy and provide timely alerts for effective disaster management and mitigation.
-
Polls et al. (2026) Observed Effects of Near-Surface Relative Humidity on Rainfall Microphysics During the LIAISE Field Campaign
This study investigates how near-surface relative humidity influences early-stage rainfall characteristics, finding that dry conditions lead to longer precipitation descent times, fewer small drops, and higher surface radar reflectivity compared to moist conditions, despite similar surface rainfall amounts.
-
Gonzalez et al. (2026) Machine learning and predictive models for water management: a systematic review
This systematic review analyzes the application of machine learning (ML) in water management, identifying dominant algorithms, performance metrics, and methodological gaps. It concludes that ML is a strategic tool for water management, particularly for forecasting and bias correction, but requires improved reproducibility, uncertainty quantification, and integration of anthropogenic factors for operational maturity.
-
Howard et al. (2026) The Vulnerability and Resilience of Drinking Water Systems to Extreme Weather Events and Future Climate Change
This review synthesizes current evidence on the climate resilience of the drinking water sector, examining how climate hazards are changing, how resilience is measured, and what interventions are being used. It concludes that climate change poses a major and increasing threat to drinking water supplies, but current actions to improve resilience are insufficient, and measurement methodologies remain fragmented.
-
Radu et al. (2026) Riverbed Evolution Trends Based on the Channel-Forming Discharge Concept: A Climate Change Scenario Analysis to 2100 for the Ialomița River, Romania
This study projects long-term riverbed evolution of the Ialomița River at the Băleni gauging station until 2100 using effective discharge (Qe) under RCP 4.5 and RCP 8.5 scenarios, finding dynamic equilibrium for RCP 4.5 and significant erosion followed by aggradation for RCP 8.5.
-
Gaines et al. (2026) Impact of Spatial Scale on Optical Earth Observation‐Derived Seasonal Surface Water Extents
[Not discernible from the provided text]
-
Pradhan et al. (2026) Imprints of terrestrial water fluxes on tropospheric stable water isotopes revealed by satellite observations and complex network analysis
This study investigates the relationship between the isotopic composition of atmospheric water vapor (δD) and the surface water balance (evapotranspiration minus precipitation, ET-P) using satellite observations and model data. It reveals strong positive correlations and complex network teleconnections, demonstrating the utility of water vapor isotopes for climate network analysis and model evaluation.
-
Eckert et al. (2026) Soil moisture as a key predictor for regional groundwater levels: a deep learning study from Brandenburg, Germany
This study developed the first regional deep learning model (1D-CNN-LSTM ensemble) for groundwater level forecasting in Brandenburg, Germany, achieving strong performance (R² = 0.72, NSE = 0.59, RMSE = 0.11) by explicitly integrating soil moisture as a key predictor, which significantly improved accuracy, especially during drought periods.
-
Wang et al. (2026) Remote-Sensing Estimation of Evapotranspiration for Multiple Land Cover Types Based on an Improved Canopy Conductance Model
This study developed an improved canopy conductance model by integrating the K95 and Jarvis frameworks, significantly enhancing large-scale remote-sensing evapotranspiration (ET) retrieval accuracy across diverse land cover types. The model demonstrates strong temporal and spatial stability, outperforming existing models and products.
-
Moldakhmetov et al. (2026) Reaction of Minimum Streamflow of Arid Kazakhstan Rivers to Climate Non-Stationarity
This study analyzed long-term changes (1940–2022) in minimum river flow in Western Kazakhstan, revealing distinct seasonal climate controls and significant structural shifts, with summer flows decreasing due to precipitation changes and winter flows increasing due to warming and increased underground recharge.
-
Yoon et al. (2026) A heterogeneous weighting strategy for leveraging Cross-Basin data enhances the Usability of deep learning hydrological models
This study develops a novel heterogeneous weighting strategy for deep learning hydrological models to effectively leverage cross-basin data, demonstrating improved predictive performance over conventional homogeneous weighting by accounting for basin-specific characteristics. The proposed method enhances the usability of deep learning models for hydrological prediction by mitigating local performance degradation often seen in regional pooling models.
-
Zotta et al. (2026) Improving AMSR2 vegetation optical depth retrievals via land parameter retrieval model parameter optimisation
This study improves Vegetation Optical Depth (VOD) estimates from AMSR2 X-band observations by optimising key Land Parameter Retrieval Model (LPRM) parameters (surface roughness, effective temperature, single scattering albedo) through minimising brightness temperature residuals, demonstrating enhanced VOD-LAI seasonal agreement, especially in forests, but revealing trade-offs with soil moisture retrieval skill.
-
Ilgen et al. (2026) Correction: The impact of floating photovoltaic power plants on lake water temperature and stratification
This document corrects a notational error in the methodology section of a previous paper concerning the impact of floating photovoltaic power plants on lake water temperature and stratification, confirming that the original results are unaffected.
-
Ferguson et al. (2026) Renewability of fossil groundwaters affected by present-day climate conditions
This study compares hydraulic response times (HRT) and aquifer residence times (ART) across 31 major aquifer systems globally, revealing that the presence of fossil groundwater does not necessarily mean current water levels are controlled by past climates, as HRT often dictates the system's adjustment to modern conditions. It concludes that HRT is crucial for assessing groundwater renewability and sustainable management, even in aquifers containing very old water.
-
Sun et al. (2026) Dataset of typical terminal lake and surrounding oasis outlines in arid/semi-arid endorheic basins based on remote sensing data
This study developed a comprehensive, long-term dataset of terminal lake and surrounding oasis outlines in 12 arid/semi-arid endorheic basins worldwide from 1985 to 2022, using optical remote sensing and manual corrections. The dataset provides a valuable tool for analyzing spatiotemporal variations and supporting sustainable water resource management under climate change and human activities.
-
Zhou et al. (2026) An impact-based drought classification method using real-world agricultural drought records and explainable automated machine learning
This study introduces a novel impact-based framework combining causal inference with explainable Automated Machine Learning (AutoML) to classify drought severity and identify its primary drivers in China. The framework, leveraging real-world impact records, outperforms conventional methods, revealing that non-climatic factors (latitude, geopotential height) and climatic factors (soil moisture, evaporation) are key drivers, and indicating a significant intensification of drought severity across China from 1980 to 2024.
-
Saeidinia et al. (2026) High-resolution forecasting of soil thermal regimes using different deep learning frameworks under climate change
This study developed a deep learning framework to downscale soil temperature (5 cm depth) in western Iran under climate change scenarios. A hybrid CNN-LSTM model accurately projected that high-emission pathways (SSP585) cause initial cooling followed by accelerated warming, while low-emission pathways lead to stable, moderate warming.
-
Zuo et al. (2026) Decoding surface and root-zone soil moisture dynamics for agricultural drought assessment using multi-source climate records (1990–2019)
This study investigates the dynamics of surface and root-zone soil moisture using 30 years of ESA-CCI data to assess agricultural drought characteristics in three US states and develops a novel knowledge-guided machine learning model for improved drought prediction. It reveals distinct soil moisture responses to precipitation during prolonged versus short-duration droughts and demonstrates an 8% improvement in root-zone soil moisture prediction accuracy with the new model.
-
Sun et al. (2026) Integrating deep learning and groundwater dynamics for drought vulnerability assessment under climate scenarios
This study develops an AI-driven framework (DCDVI) integrating deep learning with climate, groundwater, and socio-environmental factors to assess future drought vulnerability, demonstrating improved groundwater prediction and highlighting increased drought severity under the SSP5-8.5 climate scenario.
-
Richter et al. (2026) Technical note: Literature based approach to estimate future snow
This study develops a resource-efficient, literature-based approach to project future snow depths and season lengths in regions lacking downscaled climate projections. By synthesizing existing literature and parameterizing reduction curves based on temperature scenarios and elevation, the method reveals significant declines in snow depth and season length across Swiss Alpine stations under a +2°C warming scenario, particularly at lower elevations.
-
Zhang et al. (2026) A framework for identifying discriminative model, key factors, and precipitation blocking threshold on triggering drought propagation in the Xijiang River Basin (XRB)
This study developed a GANs-enhanced machine learning framework to identify key factors and precipitation thresholds for meteorological-to-agricultural drought propagation in the Xijiang River Basin, finding that non-effective precipitation days, drought duration, and spatial complexity are critical, and daily precipitation exceeding 3 mm can mitigate propagation.
-
Alijanian et al. (2026) A flexible semi-continuous time series framework for hydrological water balance analysis using multi-source data
N/A (The provided paper text is corrupted and unreadable.)
-
Chaves et al. (2026) When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models
This study introduces an information theory-based metric to quantitatively evaluate the relative contributions of physics-based conceptual constraints and data-driven components in hybrid hydrological models. It finds that performance predominantly relies on the data-driven component, which often compensates for or even overwrites physics-based constraints, challenging the assumption that integrating physics inherently enhances model performance or interpretability.
-
Akhal et al. (2026) Impacts of Climate Variability and agriculture on water Resources: Adaptation and Resilience, Case of the Irrigated Perimeter of Tadla, Morocco
This paper describes the severe water scarcity and inequity in Morocco's Tadla irrigated perimeter, attributing it to climate change, declining groundwater levels (particularly in the non-renewable Eocene aquifer), and rising soil salinity, which collectively threaten agricultural productivity and drinking water supply.
-
Zou et al. (2026) Unraveling the Hydrological Dimension of Ecosystem Resilience: Drought-Induced Response of Water Retention and Nonlinear Drivers
This paper presents a dataset designed to investigate the hydrological factors and their nonlinear drivers influencing ecosystem resilience, particularly in response to drought-induced water retention changes.
-
Ali et al. (2026) Threshold analysis of rainfall and groundwater recharge in mitigating drought risks in overexploited groundwater regions
This study quantifies the thresholds of rainfall and groundwater recharge required to mitigate drought risks in the world's top ten groundwater-overexploited regions, finding that specific levels of precipitation (614.41 mm) and groundwater recharge (–0.0039 standard deviations) are critical for drought resilience.
-
Aleksova et al. (2026) Initial Spatio-Temporal Assessment of Aridity Dynamics in North Macedonia (1991–2020)
This study investigated the spatial organization and temporal variability of aridity and thermal continentality in North Macedonia using observational data from 1991–2020. It found a significant increase in mean annual air temperature and strong altitudinal control on aridity patterns, but no statistically significant long-term trends in aridity or continentality, while establishing a first climatological baseline for the region.
-
Sun et al. (2026) Climate warming-induced glacier mass loss driving peak runoff variability and cryosphere service value decline
This study investigates glacier responses to past and future climate change using an integrated ice-dynamic model. It reveals that climate warming will cause substantial and irreversible glacier mass loss, leading to declining glacier service values and shifts in peak runoff timing, particularly in High Mountain Asia.
-
Sang et al. (2026) SMAP Soil Moisture Downscaling Via Multimodal Data Fusion and Machine Learning
This study proposes a novel method to downscale Soil Moisture Active Passive (SMAP) soil moisture from 9 km to 1 km resolution by integrating Solar-induced chlorophyll fluorescence (SIF) and multi-source remote sensing data with machine learning. The Random Forest model incorporating SIF demonstrated superior performance, significantly enhancing the spatial detail and temporal consistency of the downscaled soil moisture product for improved drought monitoring and agricultural management.
-
Su (2026) SWPU-PMA2025: a global surface mass redistribution dataset derived from GRACE L1B data and a modified point‒mass modeling approach
This study introduces a modified Point-Mass Modeling Approach (PMA) and an adaptive constraint matrix to process GRACE Level-1B data, generating a global surface mass redistribution dataset (SWPU-PMA2025) with improved spatial resolution and reduced noise/leakage.
-
Qin (2026) Analysis of Extreme Precipitation Under Climate Change
The paper addresses the escalating frequency and magnitude of extreme precipitation events, highlighting them as a significant and immediate challenge posed by climate change.
-
Houénafa et al. (2026) Enhancing Conceptual Rainfall-Runoff Modeling in Data-Scarce Catchments using Machine Learning: Kolmogorov-Arnold Networks Compared to LSTMs
This study evaluates the effectiveness of Kolmogorov-Arnold Networks (KANs) in enhancing conceptual rainfall-runoff modeling in data-scarce catchments using a two-stage error-correction approach. It finds that KAN-based hybrid models, particularly when combined with wavelet transform preprocessing, generally outperform LSTM-based and standalone models, especially for high-flow predictions.
-
Elhamri et al. (2026) Geospatial and Temporal Assessment of Soil Salinization and Drought Using the Spi Index in an Irrigated Area in a Semi-Arid Zone: The Case of Beni-Amir (Central Morocco)
This study evaluates the spatial and temporal evolution of soil salinity and drought trends in the Beni Amir irrigated area of central Morocco, revealing a notable increase in deep soil electrical conductivity from 2013 to 2023. It establishes a negative statistical relationship between the Standardized Precipitation Index (SPI) and salinity, confirming that recurring droughts exacerbate soil salinization in the region.
-
Liu et al. (2026) Compound droughts dominated the reduction of vegetation productivity in China from 1982 to 2018
This study investigated the trends of atmospheric, soil, and compound droughts in China from 1982 to 2018 and quantified their impacts on gross primary productivity (GPP). It found that all drought types significantly increased over time, with compound droughts identified as a major contributor to GPP reduction across China.
-
Sguigaa et al. (2026) Retrospective Analysis and Future Projections of Bioclimatic Indices Under Climate Change: The Case of Azilal Province, Morocco
This study examines historical (1983-2016) and projected (2015-2100) bioclimatic changes, water balance, and thermal extremes in Azilal Province, Morocco, revealing significant warming, increasing aridity, and more frequent heat extremes, which collectively indicate a rising exposure to heat and water scarcity under future climate scenarios.
-
Elkbichi et al. (2026) QMNA extraction as a tool for watershed management in a semi-arid climate - the case of the Lakhdar river basin (Morocco)
This study aimed to determine the water scarcity threshold and define a reference low-flow level for the Lakhdar river basin in Morocco. Analyzing the annual minimum monthly discharge (QMNA) index from 1986 to 2022 revealed significant variability, with Admaghene station showing QMNA values from 0.72 m³/s to 23.08 m³/s, and Sgatt station experiencing periods of zero flow.
-
Blanco et al. (2026) On two definitions for precipitation intensity: differences, artifacts and ambiguities
This study assesses the differences between two widely used precipitation intensity definitions (I and I_wet) in arid and semi-arid regions using global precipitation data. It finds that I consistently inflates values compared to I_wet, with differences exceeding 100% in arid regions, and advocates for the use of I_wet for more accurate quantification.
-
Hamel (2026) River temperature response to atmospheric heatwaves in the European Alps
This study investigates how river temperature responds to atmospheric heatwaves across 275 catchments in the European Alps, finding that the response is significantly modulated by river discharge and meltwater contributions.
-
Li et al. (2026) Investigating the spatiotemporal behavior of VIC model parameters over the Tibetan plateau via global sensitivity analysis and machine learning
Information not extractable from the provided text due to severe corruption.
-
González-Leiva et al. (2026) When will meteorological droughts become the new normal climatic condition in Central-Southern Chile?
[Unable to extract due to corrupted text.]
-
Bian et al. (2026) Intercomparison and sensitivity analysis of WRF parameterization schemes for convection-permitting modeling of precipitation distribution along the Yarlung Zangbo River
This study systematically intercompares fifteen 3-kilometer WRF simulations to assess how parameterization choices influence precipitation characteristics in the Yarlung Zangbo River basin. It finds that convection-permitting models improve precipitation estimation by mitigating drizzle bias and that cloud microphysics and planetary boundary layer schemes are most influential for precipitation intensity, duration, diurnal cycles, and frequency.
-
Akhal (2026) Desertification In Irrigated Areas in Mediterranean Environments, Case of Tadla’s Irrigated Perimeter
This study aims to analyze desertification in the Tadla irrigated perimeter of Morocco, investigating how climate change impacts the vulnerability and resilience of Mediterranean agricultural-hydrology systems through irrigation practices and how this relationship has evolved over time.
-
Hussainzada et al. (2026) Comprehensive framework for agricultural water management in data-scarce regions: Integration of hydrological models and remotely sensed crop type data
This study proposes a comprehensive framework for agricultural water management in data-scarce regions by integrating hydrological modeling (WRF-Hydro) with remotely sensed crop type data and machine learning, demonstrating significant potential for water savings through improved irrigation efficiency in the Amu River Basin.
-
Zhang et al. (2026) Interpretable deep learning method integrating spatial self-attention for generating bias-corrected high-resolution GFS precipitation forecasts
This study introduces DualTransBU-Net-P, an explainable deep learning framework integrating spatial self-attention for end-to-end downscaling and bias correction of GFS precipitation forecasts, significantly enhancing accuracy and resolution while providing interpretability into its decision-making processes.
-
Kim et al. (2026) Quantitative risk assessment for the compound drought-wildfire disaster
This study develops a novel methodology for the quantitative risk assessment of compound drought-wildfire (CDW) disasters in South Korea, demonstrating that drought conditions can amplify wildfire risk by approximately three times compared to normal conditions.
-
Liu et al. (2026) Projecting future exposure to compound precipitation and wind extremes using Copula methods with Bayesian model averaging
This study developed a novel framework using Bayesian Model Averaging and Copula methods to project future population and economic exposure to compound precipitation and wind extremes (CPWEs). It found that CPWE intensity and exposure risk are projected to increase, particularly around mid-century, with high-risk areas persisting in the North China Plain and southeastern coast under climate change scenarios.
-
Amiresmaeili et al. (2026) Evaluation of GRACE satellite data for drought monitoring and groundwater management in a small aquifer in Iran
This study evaluated GRACE satellite data for drought monitoring and groundwater management in the small, arid Rafsanjan Plain, Iran, finding that the Modified Total Storage Deficit Index (MTSDI) is superior for drought assessment in anthropogenically impacted areas and that direct GRACE Total Water Storage Anomaly (TWSA) data effectively monitors regional groundwater changes without significant improvement from GLDAS component subtraction.
-
Grandjouan et al. (2026) An original approach combining biogeochemical signatures and a mixing model to discriminate spatial runoff-generating sources in a peri-urban catchment
This study developed an original approach combining biogeochemical signatures and a Bayesian mixing model to spatially decompose streamflow into eight runoff-generating sources in a peri-urban catchment, revealing highly variable source contributions influenced by hydro-meteorological conditions and land use.
-
Akhabue et al. (2026) Critical classification parameters linking species to Plant Functional Type in African ecosystems
This study systematically classified African plant species from the TRY plant trait database into Plant Functional Types (PFTs) compatible with the JULES land surface model. This effort resulted in a sixfold increase in the number of species mapped to PFTs and a fivefold increase in usable trait observations, significantly enhancing the representation of African ecosystems in global models.
-
Liang et al. (2026) Anthropogenically-driven escalating impact of soil-based compound dry-hot extremes on vegetation productivity
This study reveals that soil-based compound dry-hot extremes (CDHEs) have more severe adverse impacts on vegetation productivity in China than meteorological CDHEs. Their frequency and coverage area significantly increased from 1980-2017 primarily due to anthropogenic soil warming, and are projected to escalate further under high-emission scenarios, threatening terrestrial carbon sinks.
-
Feng et al. (2026) Subsurface stormflow concentration-discharge relationships reveal DOC and nitrate transport mechanisms across land uses in karst hillslopes
This study investigated dissolved organic carbon (DOC) and nitrate transport dynamics and concentration-discharge relationships in subsurface and epikarst flows across four land-use types in karst hillslopes. It revealed epikarst flow as the dominant nutrient export pathway and highlighted the impact of land use and rainfall patterns on carbon and nitrogen fluxes.
-
Valdivielso et al. (2026) Isotopic characterization and recharge dynamics of Karst aquifers in a mediterranean basin
This study characterizes the isotopic composition of precipitation, surface water, and groundwater in the headwaters of the Llobregat River, Spain, to understand hydrological processes in karst aquifers. It identifies dominant winter recharge from high elevations and the influence of air mass origin on isotopic signatures, supporting improved water resource management.
-
Li et al. (2026) Integrated evaluation of snow density reanalysis products in the Northern Hemisphere
This study systematically evaluated the accuracy and applicability of snow density from five reanalysis datasets (ERA5-Land, GLDAS-Noah, GLDAS-CLSM, GLDAS-VIC, JRA-3Q) across the Northern Hemisphere using 4,319 in-situ stations, finding that while ERA5-Land and GLDAS-Noah performed best in spatial and temporal representation, all reanalysis products failed to accurately reproduce observed long-term interannual snow density trends.
-
Costache et al. (2026) Fuzzy-decision trees models for flood hazard modeling in the Danube Delta
[Information not extractable due to corrupted input.]
-
Brenner et al. (2026) GeoDS (v.1.0): a simple Geographical DownScaling model for long-term precipitation data over complex terrains
This paper introduces GeoDS (v.1.0), a simple, topography-based geographical downscaling model for long-term precipitation data over complex terrains, designed for paleoclimate studies. It demonstrates GeoDS's ability to capture fine-scale precipitation patterns and improve statistical agreement with observations in the European Alps and Greenland, while being computationally inexpensive and robust.
-
wu et al. (2026) Evaluating surface fluxes in WRF using eddy-covariance flux measurements in the Western and Eastern U.S.
This study evaluates the Weather Research and Forecasting (WRF) model's surface flux simulations, specifically using the Pleim-Xiu land surface model (PX LSM), against year-long eddy-covariance measurements from 16 sites in the San Joaquin Valley (SJV) and Multi-state Mid-Atlantic (MMA) regions, revealing significant heat flux biases in the SJV primarily linked to irrigation.
-
Zhang et al. (2026) Drying Soil Moisture Dominates Enhancing Summer Soil Moisture‐Temperature Coupling Under Climate Change
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
-
Achahboun et al. (2026) LSTM and Temporal Fusion Transformers for daily evapotranspiration estimation using FLUXNET2015 and Google Earth Engine
[Information not extractable due to unreadable paper content.]
-
Benoudina et al. (2026) Flood risk assessment in a semi-arid mediterranean basin using HEC-RAS hydraulic simulation: A case study of Oued Bounouara
N/A
-
He et al. (2026) The general formulation for mean annual runoff components estimation and their change attribution
This study proposes a general and concise Modified Ponce-Shetty (MPS) model to quantify and attribute mean annual surface flow, baseflow, and total runoff. Applied across 662 catchments in China and the contiguous United States, the model accurately depicts spatial variability and simulates multi-year runoff components, revealing that surface flow is jointly controlled by precipitation and environmental factors, while baseflow is primarily influenced by environmental factors.
-
Yao et al. (2026) Persisting Modulation of Interdecadal Pacific Oscillation on Near‐Future Winter Precipitation Projections in Northern Europe
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
-
Honzíčková et al. (2026) The increasing flashiness in the Czech Republic: Natural variability or recent climate change?
This study investigates the temporal trends and physiographic controls of flash flood flashiness in 17 Czech catchments, revealing a significant increase in flashiness in recent years, particularly in small, steep mountainous catchments, and provides a comparison within a broader European context.
-
Ioniță et al. (2026) Spatiotemporal Variability of Seasonal Snow Cover over 25 Years in the Romanian Carpathians: Insights from a MODIS CGF-Based Approach
This study analyzed 25 years of MODIS satellite data to map and quantify spatiotemporal changes in seasonal snow cover phenology across the Romanian Carpathians, revealing a significant shortening of the snow season primarily due to earlier melt at lower and mid-elevations.
-
Garrido-Leiva et al. (2026) Topographic Modulation of Vegetation Vigor and Moisture Condition in Mediterranean Ravine Ecosystems of Central Chile
This study investigates the relationships between topographic metrics and vegetation condition proxies in a Mediterranean ecosystem, revealing that concave and less rugged areas exhibit higher vegetation vigor and moisture, with vegetation water content being particularly sensitive to topographic position and thermal anisotropy.
-
Huynh et al. (2026) A hybrid physics–AI approach using universal differential equations with state-dependent neural networks for learnable, regionalizable, spatially distributed hydrological modeling
This study introduces a hybrid physics–AI framework that integrates state-dependent neural networks into a spatially distributed, regionalizable, and fully differentiable hydrological model using universal differential equations (UDEs). The framework demonstrates consistently strong performance in streamflow simulations, particularly for flood modeling, by refining internal water fluxes and improving parameter regionalization.
-
Li et al. (2026) Observed declining strength of vegetation-atmosphere coupling
This study investigates the global patterns and driving mechanisms of vegetation-atmosphere coupling (VC) strength using a novel physically-based index, revealing a widespread declining trend in VC across 38.84–61.98 % of global land, primarily driven by changes in leaf area index and wind speed.
-
Laudi et al. (2026) Tracing nitrate fate in Malta’s hydrogeological system using an intensive vadose-groundwater monitoring network
This study integrated five years of vadose zone and groundwater monitoring in Malta to identify dominant non-point nitrate pollution sources from various agricultural systems. It found that potato cultivation is the primary source of nitrate loading to the Mean Sea Level Aquifer, while intensive vegetable and greenhouse farming create contamination hotspots in perched and coastal aquifers, with vadose zone thickness and nitrate storage being key controls on aquifer vulnerability.
-
Mohril et al. (2026) Exploring Response Time Influence on Rising Limb of a Hydrograph With Emphasis on Raindrop Characterisation: An Experimental Study
This study evaluates a rainfall simulator's ability to replicate natural rainfall conditions and then uses it to systematically analyze the influence of dynamic (moving) rainfall patterns on surface flow and hydrograph components. A nonlinear regression model effectively captures these rainfall-runoff interactions, demonstrating high accuracy in both calibration and validation.
-
Moazenzadeh (2026) Application of time-lagged satellite image-based crop coefficients for estimating actual evapotranspiration through FAO-56 method
This study investigated the feasibility of using 10-year time-lagged satellite-derived crop coefficients (Kc) with the FAO-56 method to estimate actual evapotranspiration (AET) in the Neishaboor watershed, Iran, finding it a useful and computationally simpler approach for water management when current Kc data are unavailable.
-
Li et al. (2026) Observation‐Driven Forecast of Global Terrestrial Water Storage and Evaluation for 2010–2024
This study develops a machine learning approach to forecast GRACE-like terrestrial water storage changes (TWSC) up to 12 months ahead, addressing the latency of GRACE/GRACE-FO products. The method demonstrates improved accuracy and robustness compared to ECMWF's seasonal forecasts, providing a viable data-driven solution for operational TWSC forecasting.
-
Rezaie et al. (2026) Evaluating the Impact of Climate and Land-Use Change on Flood Susceptibility on a Global Scale
This global-scale study investigates the combined impacts of climate and land-use change on flood susceptibility in the 21st century using SSP-RCP scenarios and machine learning, finding that Random Forest outperforms other models and projects an increase in high/very high flood susceptibility areas, particularly in Oceania, Europe, and parts of Asia and Africa.
-
Xiao et al. (2026) Flood risk assessment at electrical substations using a risk matrix coupled with a hydrodynamic model
This study developed a methodological framework for facility-specific flood risk assessment of electrical substations by coupling a hydrodynamic model with a risk matrix and Analytic Hierarchy Process (AHP). Applied to 12 substations in the Dashi River Basin, the framework identified that nearly half of them are at risk of flooding, with two facing "particularly significant" risk, providing an actionable "Flood Mitigation Priority Action List" for power grid managers.
-
Markhali et al. (2026) Regionalization of a Distributed Hydrology Model Using Random Forest
[Information not extractable from the provided text due to severe corruption.]
-
Jia et al. (2026) Enhancing Streamflow Prediction Using Cutting-edge Deep Learning Models and Seasonal-Trend Decomposition
This study systematically evaluates three cutting-edge deep learning models (FITS, FGN, PatchTST) against traditional models (LSTM, CNN, GRU) for streamflow prediction, both with and without four seasonal-trend decomposition (STD) techniques. It demonstrates that advanced models, particularly FITS, offer superior accuracy, robustness, and computational efficiency, while STD significantly improves traditional models but has limited impact on the advanced ones.
-
Jäger et al. (2026) On the Robustness of Modeled Non-Local Temperature Effects of Historical Land Use Changes
This paper demonstrates that historical land-use changes have driven robust regional non-local temperature signals, with warming up to 1 K and cooling up to 0.5 K, as shown by fully coupled CESM2 simulations. These regional effects are commensurate with historical temperature effects of all forcings, though they balance out globally.
-
Fadaee et al. (2026) Explainable Artificial Intelligence in Hydrology: A Review
This paper presents the first systematic and critical review of Explainable Artificial Intelligence (XAI) applications in hydrology and hydrogeology, synthesizing over 180 peer-reviewed studies. It concludes that XAI significantly enhances the transparency and trustworthiness of AI models while deepening the understanding of underlying physical hydrological processes.
-
Gupta et al. (2026) Mapping Swiss soil bulk density at 30 m Resolution: Insights from Machine Learning, environmental Covariates, and national data
This study generated high-resolution (30 m) soil bulk density maps for Switzerland at four standard depths (0 m, 0.3 m, 0.6 m, 1.0 m) using a Quantile Random Forest model and national soil data, revealing that croplands and the Central Plateau exhibit the highest bulk density, which generally increases with depth.
-
Güler et al. (2026) Influence of teleconnection patterns on daily extreme precipitation in Turkey
This study investigates the relationship between four major teleconnection patterns (NAO, AO, NCP, EA) and the frequency and intensity of daily extreme precipitation across Turkey from 1950 to 2023. It finds that the North Atlantic Oscillation (NAO) is the dominant influence, particularly in western Turkey during winter and autumn, with distinct responses for extreme precipitation frequency versus intensity.
-
Ougahi et al. (2026) Investigating Deep Learning Knowledge Transfer in Streamflow Prediction From Global to Local Catchment
This study evaluates transfer learning approaches using Long Short-Term Memory (LSTM) models to improve streamflow prediction in data-scarce regions by leveraging data from data-rich areas and catchment characteristics. It demonstrates that pre-training LSTMs on hydrologically similar basin clusters, followed by fine-tuning with limited local data, significantly enhances prediction accuracy in data-poor regions.
-
Choudhary et al. (2026) Simulating Streamflow Scenarios Using Hydrological Modeling Integrated with Future Land Use and CMIP6 Climate Projections
This study develops a unique modeling framework integrating an ensemble of CMIP6 General Circulation Models (GCMs) with future land use land cover (LULC) projections to assess their combined impacts on the hydrology of the Upper Krishna River sub-basin. It projects significant increases in temperature, precipitation, surface runoff, and water yield, leading to heightened and prolonged flood risks by the end of the century, particularly under high-emission scenarios.
-
Cai et al. (2026) Contrasting responses of Eurasian extreme precipitation to climate change: a multi-regional assessment using NEX-GDDP-CMIP6
This study evaluates the NASA-NEX-GDDP-CMIP6 dataset's performance in simulating six extreme precipitation indices across four Eurasian regions (1985–2014) and projects future changes under Shared Socioeconomic Pathways (2071–2100), revealing contrasting hydroclimatic responses from increased monsoon extremes in South Asia to hydroclimatic whiplash in Central Asia.
-
Martins et al. (2026) Multicriteria Approach to Define Adequate areas for the Implementation of Ecosystem-based Adaptation Strategies
This paper develops a multicriteria approach using the Analytic Hierarchy Process (AHP) to identify optimal locations for Ecosystem-based Adaptation (EbA) strategies, specifically in-channel Managed Aquifer Recharge (MAR) sand dam reservoirs, to increase groundwater resources, mitigate water scarcity, and prevent wildfires in the Alva River basin, Central Portugal. The study successfully mapped suitable areas, highlighting the potential for integrating treated wastewater as an alternative water resource.
-
Yazdandoost et al. (2026) Dynamic assessment of compound flooding through a risk index approach
This study introduces the Compound Dynamic Risk Index (CDRI), a daily-resolution framework that integrates copula-derived joint exceedance probabilities of river discharge and storm surge with a curvature-based diagnostic to provide anticipatory signals of compound flood risk escalation. Applied to the Fraser and Potomac Rivers, the CDRI effectively identifies historical flood events and demonstrates high predictive accuracy (up to 88.95%) when coupled with deep learning models, particularly Deep Echo State Networks.
-
Malcheva et al. (2026) Analysis of Precipitation and Regionalization of Torrential Rainfall in Bulgaria
This study comprehensively analyzes precipitation regimes and their links to atmospheric circulation in Bulgaria (1991–2020) and proposes a regionalization of torrential rainfall, finding it primarily associated with low-pressure systems, easterly/northeasterly flows, and weak-gradient pressure fields.
-
Cecchetto et al. (2026) Beyond Water: Mapping Sediment Bars to Enhance Satellite Monitoring of River Dynamics
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2026...
-
Neuhauser et al. (2026) Seasonal hazard-vulnerability patterns between drought and wildfire in New Caledonia derived from remote sensing products
This study analyzed seasonal and directional temporal relationships between vegetation drought and wildfire activity in New Caledonia using remote sensing and in-situ data from 2000-2024, revealing distinct seasonal patterns where vegetation stress precedes fires in the early dry season, and fires are followed by altered vegetation conditions later.
-
Şimşek et al. (2026) Multifractal characterization of meteorological droughts in Türkiye’s mediterranean region using visibility graph approaches
This study introduces a novel framework based on visibility graph (VG) and its upside-down variant (UDVG) to assess the multifractal structure of meteorological droughts in Türkiye’s Mediterranean region. It finds that the Standardized Precipitation Evapotranspiration Index (SPEI) consistently displays stronger multifractality than the Standardized Precipitation Index (SPI), and the UDVG framework is more sensitive to low-amplitude, persistent droughts, offering complementary insights for early warning systems.
-
Huang et al. (2026) Assessment of dynamic drought risk and transition characteristics by combining an indicator-based approach and Markov chain model
This study assessed dynamic drought risk and its spatial transition characteristics in Hunan Province, China, from 1960 to 2021, by integrating an indicator-based approach (vulnerability, exposure, resilience) with traditional and spatial Markov chain models, revealing significant spatial autocorrelation and neighborhood influence on drought risk transitions.
-
Ghaneei et al. (2026) An Effective Monitoring of Evolving Groundwater Drought via Multivariate Data Assimilation and Machine Learning
This study introduces an observation-informed approach to produce daily groundwater drought maps at 1/8° resolution across the contiguous United States, revealing distinct and persistent dry clusters, particularly severe in the Western and Northeastern regions.
-
Wang et al. (2026) Event‐Type‐Based Multi‐Dimensional Diagnostics of Process Limitations in Hydrological Models
## Identification - **Journal:** Water Resources Research - **Year:** 2026...
-
Li et al. (2026) Parsimonious and Transferrable Parameterization of Reservoir Operations: A Modular Approach for Large‐Scale Modeling
[Information not extractable from the provided corrupted text.]
-
Amaddii et al. (2026) Assessing road-watercourse crossing overtopping potential using GIS and remote sensing: a large-scale screening approach
This study develops a large-scale screening method using GIS and remote sensing to assess the overtopping potential of road-watercourse crossings (RWCs) based on the height difference between the road surface and riverbanks. The method, applied to the Magra River Basin, found that approximately 25% of identified bridges exhibited a high overtopping potential, particularly those on residential roads and with lower deck heights.
-
Keserci (2026) Comparative Performance of Global Datasets and Ground‐Based Precipitation and Temperature Products in the Eastern Mediterranean Basin: The Case of Türkiye
N/A - Content unreadable due to corrupted input.
-
Viviroli et al. (2026) Cascading downstream impacts of water cycle changes in mountain regions
This perspective synthesizes the cascading downstream impacts of climate-induced water cycle changes in mountain regions, highlighting how these alterations affect diverse social-ecological systems and identifying critical research gaps and adaptation challenges.
-
Song et al. (2026) Physics‐Informed, Differentiable Hydrologic Models for Capturing Unseen Extreme Events
[Unable to extract due to corrupted paper text.]
-
Alkanjo et al. (2026) Machine Learning as a Tool to Predict Reference Evapotranspiration
This study predicted monthly reference evapotranspiration (ET) in Siirt, Türkiye, using various machine learning and statistical regression models, along with a Design of Experiments (DoE) approach. The DoE model achieved the highest accuracy (R²=0.987), identifying average temperature as the most influential variable.
-
Mohajane et al. (2026) Machine Learning-Based Flood Susceptibility Mapping Using Geoenvironmental Factors in Central Morocco
This study evaluates and compares three machine learning models (CART, SVM, XGBoost) for flood susceptibility mapping in the Tensift Watershed, Central Morocco, identifying Classification and Regression Trees (CART) as the most accurate model with an Area Under the Curve (AUC) of 0.882.
-
Akhter et al. (2026) Quantification of Different Uncertainties in Streamflow Simulations and Their Propagation Across Multiple Catchments
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
-
Arnáez et al. (2026) Hydrological Challenges and Competing Demands in the Mediterranean Region
This review examines the severe hydrological challenges in the Mediterranean region, highlighting the growing conflicts between declining water resources in mountainous headwaters (due to climate change and revegetation) and increasing water demand in lowlands (driven by agriculture, tourism, and urban expansion), which leads to environmental and social tensions.
-
Othman et al. (2026) A comparative analysis of long-term spatiotemporal variability in precipitation 85 year-long reanalysis and observation data from 150 stations over arid MENA-T region
This study comprehensively analyzed long-term spatiotemporal precipitation variability and trends across the MENA-T region using 85 years of in-situ and ERA5 reanalysis data, revealing a distinct north-south hydroclimatic dipole with increasing aridity in the south and intensifying extremes in the north.
-
Kumar et al. (2026) Developing a Comprehensive and Spatially Explicit GIS–Fuzzy TOPSIS Framework for Drought Vulnerability Assessment
This study developed a comprehensive and spatially explicit GIS–Fuzzy TOPSIS framework for drought vulnerability assessment in Maharashtra, India, integrating diverse environmental and socio-economic indicators. The framework revealed that nearly half of the study area (47.52%) falls into high and very high vulnerability zones, primarily driven by low rainfall, depleted groundwater, and persistent soil moisture deficits.
-
Hoseingholi et al. (2026) Optimal Hedging Rules Determination for Dam Reservoir Operation Under Climate Change
This study proposes a novel approach combining hydrological modeling, optimization, and climate projections to determine optimal discrete and continuous hedging rules for the Marun Dam reservoir in Iran under climate change. It finds that the discrete hedging rule significantly outperforms standard operation policy and continuous hedging rules in maintaining reservoir storage and reducing severe water shortages, offering a more robust strategy for future water resource management.
-
Li et al. (2026) ELM‐MOSART‐DOC: A Large‐Scale Riverine Dissolved Organic Carbon Model and Its Application Over the United States
## Identification - **Journal:** Journal of Advances in Modeling Earth Systems - **Year:** 2026...
-
Giggy et al. (2026) Changes in Dominant Streamflow Drivers as Network‐Scale Flow Regime Shifts From Intermittent to Ephemeral Across a Multi‐Year Drought
N/A (Paper text is corrupted and unreadable, preventing extraction of this information.)
-
Oduor et al. (2026) Future of Water Security in Mediterranean Reservoirs: Advancing SWAT + Modeling of Hydrological Response To Climate Change in Central Spain
This study applied the SWAT+ model with an innovative multi-criteria calibration to simulate hydrological behavior and assess climate change impacts in two reservoir catchments in central Spain. Projections indicate significant declines in precipitation and water availability, coupled with increased potential evapotranspiration, leading to an accelerating transition towards an arid hydrological regime by the end of the century.
-
Shi et al. (2026) Spatiotemporal drought variability in Gansu Province based on reconstructed land surface temperature
This study developed a novel framework integrating reconstructed Land Surface Temperature (LST)-derived Temperature Vegetation Drought Index (TVDI) with the Standardized Precipitation Index (SPI) to analyze spatiotemporal drought variability in Gansu Province from 2003 to 2022, revealing a "south mild, northwest severe" drought pattern with overall intensification since 2008.
-
Sadeghzadeh et al. (2026) Deep learning fusion modeling of reference evapotranspiration with multi-source remote sensing data through addressing noise impacts
This study developed a deep learning-based Convolutional Neural Network (CNN) fusion method to estimate daily reference evapotranspiration (ETo) using multi-source remote sensing data, specifically evaluating its performance under noisy input conditions. The Random Forest model coupled with CNN fusion (RF-CNN) significantly outperformed other fusion and direct methods in accuracy and stability across both humid and arid regions, even with added Gaussian noise.
-
Wit et al. (2026) Subsurface Irrigation in Regional Water Management: a System Dynamics Approach to Support Decision Making
This study developed a system dynamics model (SDM) to simulate the non-linear hydrological impacts of upscaling controlled drainage with subirrigation (CDSI) from local to regional scales, demonstrating its utility as a decision support tool for water management authorities in assessing feasibility and supporting decision-making.
-
Watson et al. (2026) Evaluating CORDEX ‐ CORE Climate Projections for Simulated Hydrological Processes in a Mesoscale Catchment, South Africa
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
-
Hou et al. (2026) Advancing Near‐Real‐Time Flood Inundation Mapping in Australia
This study develops and evaluates a near real-time, 5-meter spatial resolution flood monitoring framework for Australia, integrating gauge data, hydrological models, and multi-source satellite observations. It demonstrates the critical role of low-latency gauge data and high-resolution LiDAR DEMs, while also showing the effectiveness of ensemble modeling and multi-source remote sensing for ungauged areas.
-
Verma et al. (2026) CTRIP‐HyDAS: A Global‐Scale Data Assimilation Framework for SWOT‐Derived Discharge Using Synthetic Observations at High Resolution (1/12°)
[Information not extractable due to unreadable paper text.]
-
Toum et al. (2026) A New Process‐Based Approach for Evaluating Gridded Precipitation Products in Mountain Watersheds: Test Cases From the Central Andes of Argentina
## Identification - **Journal:** Hydrological Processes - **Year:** 2026...
-
Liang et al. (2026) Understanding Multiscale Hydrological Interactions From Spectral Perspective: A Large Sample Investigation Across the United States
Information not extractable from the provided corrupted text.
-
Hidayatulloh et al. (2026) From dry Wadi bed to flashflood: decoding climate-driven flood hazards in arid environments, Saudi Arabia
This study quantifies future climate-driven flash flood hazards in the Wadi Ibrahim catchment, Makkah, by coupling downscaled climate projections with 2D hydrodynamic modeling, revealing a significant intensification of flood volumes, depths, and hazard zones under RCP 4.5 and RCP 8.5 scenarios.
-
Noffz et al. (2026) Development of a Dual‐Domain Karst Flow Model Under Consideration of Preferential Film‐Flow Dynamics and Analysis of Compartment‐Specific Parameter Sensitivities
[Information not extractable due to unreadable paper content.]
-
Deepthi et al. (2026) Chaos Theory-Based Downscaling for Future Rainfall Projections from Climate Models
This study applies a novel chaos theory-based downscaling method to project future monthly rainfall across India (0.25° × 0.25° resolution) using five CMIP6 General Circulation Models (GCMs) under four Shared Socioeconomic Pathway (SSP) scenarios (2015-2099), revealing significant regional shifts in both southwest and northeast monsoon rainfall patterns.
-
Neto et al. (2026) Streamflow Elasticity to Precipitation Distribution and Potential Evapotranspiration Across South America
[Information not extractable due to unreadable paper text.]
-
Choursi et al. (2026) Ensemble Machine Learning for Meteorological Drought Assessment and Forecasting with Satellite and Climate Data (Urmia Lake Basin, Iran)
This study developed and evaluated ensemble machine learning models, particularly Extremely Randomized Trees (ERT), for meteorological drought assessment and forecasting in the Urmia Lake basin, demonstrating superior accuracy and the shifting influence of local vs. teleconnection drivers across different timescales. The ERT model consistently outperformed other algorithms, providing reliable 3–6 month drought forecasts with high accuracy.
-
Sayl et al. (2026) Sustainable Water Management for Agriculture to Mitigate Climate Change Effects
This study developed an integrated methodology using the WATSUIT model and Geographic Information System (GIS) to mitigate drought impacts in Iraq by assessing the suitability of saline water from the Main Outfall Drain (MOD) for irrigating salt-tolerant crops, complemented by rainwater harvesting for soil washing. The findings indicate that while Basra's MOD water is too saline, other regions along the MOD can utilize this water with appropriate leaching fractions and rainwater harvesting to support sustainable agriculture.
-
Said et al. (2026) Water Demand and Surface Water Supply Dynamics in the Changing Climate of Semi-Arid Basins
This paper quantifies the short- and long-term impacts of climate change on potential evapotranspiration (PET) and surface water flow (Q) in the ungauged, semi-arid Kherbet Qanafar sub-basin (Lebanon), projecting significant declines in water supply (Q) by 38–52 % in the short term and up to 60 % in the long term, while PET changes are nominal.
-
Zhan et al. (2026) Integrating SWOT With Multi‐Source Satellite Observations for Near‐Daily Reservoir Water Level Monitoring
## Identification - **Journal:** Water Resources Research - **Year:** 2026...
-
Yang et al. (2026) Trigger thresholds and drivers of meteorological to agricultural drought propagation in the Hanjiang basin under climate change
This study quantifies how climate change alters meteorological drought trigger thresholds for agricultural drought in the Hanjiang basin, finding that milder meteorological deficits will increasingly trigger agricultural drought by 2100, with temperature dominating annual shifts and precipitation/evaporation driving seasonal changes.
