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Sharma et al. (2025) Comprehensive flood inundation mapping by integrating HEC-RAS and ArcGIS for a long stretch multi-tributary river system
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Liu et al. (2025) The impact of nonlinear surface energy partitioning on potential evapotranspiration: A machine learning study based on FLUXNET data
This study investigates the nonlinear relationship of the no-water-limited Bowen ratio (βNWL) with environmental factors using global FLUXNET data and machine learning, developing a new PET model (PETβNWL−RF) that significantly improves daily potential evapotranspiration estimation and drought monitoring accuracy.
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Ali et al. (2025) Hydrological analysis of the Oder droughts for the period 1950‒2022 in the context of the 2022 river disaster
This study analyzed Oder River drought patterns from 1950-2022, finding no significant long-term trends in annual low flows but a clear increase in summer drought frequency and duration, especially downstream, with the severe 2022 drought significantly contributing to the ecological disaster.
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Bousbaa et al. (2025) Assessing groundwater storage response to snow cover dynamics in large Moroccan river basins over the last decades using remote sensing data
This study investigates the impact of snow cover variability on groundwater storage in large Moroccan river basins over recent decades using remote sensing data. It reveals significant groundwater declines linked to snow cover loss and quantifies snowmelt contributions to recharge with a notable time lag.
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Helili et al. (2025) Assessment and intercomparison of 23 global satellite and model-based soil moisture products using cosmic ray neutron sensing observations over Europe
This study systematically evaluated 23 global satellite and model-based soil moisture products using 68 Cosmic Ray Neutron Sensing (CRNS) observations across Europe, finding that SMAP-INRAE-BORDEAUX (SMAP-IB) retrievals showed the superior consistency with CRNS measurements.
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Sun et al. (2025) Fusion of multi-source precipitation records via coordinate-based generative models
The study introduces PRIMER, a coordinate-based diffusion model framework that fuses heterogeneous precipitation data from gauges, satellites, and reanalysis. The model successfully overcomes the trade-offs between spatial coverage and local accuracy, providing a unified tool for bias correction, downscaling, and optimal interpolation.
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Esbrí et al. (2025) Intense Rainfall in Urban Areas: Characterization of High-Intensity Storms in the Metropolitan Area of Barcelona (2014–2022)
This study characterizes intense rainfall in the Metropolitan Area of Barcelona (2014–2022) by comparing radar-derived VIL density (DVIL) with rain gauge observations and urban impact data, establishing a linear relationship between DVIL and short-duration rainfall intensity and evaluating its nowcasting potential.
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Chen et al. (2025) The trend and interannual variability in the global terrestrial evapotranspiration are respectively dominated by humid regions and drylands
This study reveals that the increasing trend in global terrestrial evapotranspiration (ET) is predominantly driven by humid regions, while its substantial interannual variability (IAV) is primarily controlled by drylands.
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Modanesi et al. (2025) Accounting for scaling effects on irrigation optimization within a land surface model using satellite observations
This study optimizes the Noah-MP Land Surface Model's irrigation scheme using Sentinel-1 satellite data and a genetic algorithm to improve grid-scale irrigation estimates. The research demonstrates that incorporating a Scale Irrigation Coefficient (SIC) to account for sub-grid heterogeneity significantly outperforms traditional soil moisture threshold triggers.
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Naeini et al. (2025) A comprehensive approach to enhancing irrigation network management through the water accounting plus framework
This study utilizes the Water Accounting Plus (WA+) framework and WaPOR remote sensing data to analyze water fluxes and productivity in the Qazvin Plain irrigation network from 2009 to 2021. The findings reveal significant spatial imbalances and identify that 28% of evapotranspiration is non-beneficial, highlighting substantial opportunities for water-saving interventions.
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Liu et al. (2025) Estimating Soil Moisture Using Multimodal Remote Sensing and Transfer Optimization Techniques
This study develops a multimodal deep learning framework using a ConvNeXt v2 backbone and an intermediate fine-tuning strategy to estimate high-resolution surface soil moisture. By integrating SAR, optical, topographic, and meteorological data, the model achieved high precision ($R^2 = 0.8956$) and demonstrated robust transferability across diverse agro-ecological zones.
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Bekele (2025) Review on Crop Water Requirements in Ethiopia
This paper reviews the methodologies and challenges of estimating crop water requirements (CWR) in Ethiopia, emphasizing the need for localized calibration and integrated water management. It identifies that while the FAO-56 Penman-Monteith method is the standard, its accuracy is hindered by sparse meteorological data and the neglect of rain-fed "green water" contributions.
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Pérez-Vega et al. (2025) Monitoring Irrigated Agriculture Using Remote Sensing and Census Data: A Case Study from Guanajuato, Mexico
This study evaluates the consistency between official agricultural census data and multi-sensor remote sensing data (MODIS, Landsat, and Sentinel-2) for monitoring irrigated crop dynamics in Guanajuato, Mexico. The research demonstrates that integrating coarse-resolution temporal data with high-spatial-resolution imagery provides a robust framework for managing water resources in semi-arid regions.
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Xu et al. (2025) A global intercomparison of SWOT and traditional nadir radar altimetry for monitoring river water surface elevation
This study presents the first global-scale intercomparison between SWOT’s wide-swath Ka-band InSAR and traditional nadir radar altimetry (Sentinel-3 and Sentinel-6) for monitoring river water surface elevation. The research identifies that while high-quality SWOT data aligns well with traditional altimetry (RMSE = 0.80 m), factors such as river width, ice cover, and extreme backscatter significantly modulate data consistency.
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Eishoeei et al. (2025) Soil moisture measurements: a review
This review evaluates the evolution of soil moisture measurement techniques, from traditional in situ methods to modern remote sensing and data assimilation. It highlights the transition toward high-resolution global mapping and the integration of emerging technologies like UAVs and IoT for improved hydrological and agricultural management.
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Simard et al. (2025) Delta-X: An airborne remote sensing framework to calibrate hydrodynamic and ecogeomorphic processes responsible for land building in coastal deltas
This paper presents the Delta-X framework, an airborne remote sensing and in situ data integration strategy used to calibrate and validate hydrodynamic and ecogeomorphic models. The framework was implemented in the Mississippi River Delta to quantify the processes of mineral sediment deposition and organic matter production that determine deltaic land sustainability.
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Sarkar et al. (2025) Characterizing seawater intrusion and groundwater vulnerability to salinization along the east coast of India
This study evaluates seawater intrusion (SWI) across 37 districts on India's east coast, revealing that over 55% of groundwater samples are affected by salinization. The research identifies excessive groundwater extraction and permeable lithological layers as the primary drivers of landward saline water movement.
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Dou et al. (2025) Comparative Analysis of Rainfall-Based and Discharge-Based Early Warning Methods for Flash Floods
This study evaluates the comparative performance of rainfall-based (RW) and discharge-based (DW) early warning methods for flash floods using historical case studies and hydrological simulations. The findings identify specific environmental and infrastructural scenarios where each method excels, advocating for an integrated, dynamically weighted warning framework.
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Naik (2025) Comparative Study of Wavelet–ANN and Wavelet– ARIMA Models for Groundwater Level Forecasting
This study evaluates hybrid forecasting models for groundwater levels in Britona, Goa, comparing Wavelet Transform integrated with Artificial Neural Networks (WT+ANN) against Wavelet Transform with ARIMA (WT+ARIMA). The results indicate that WT+ANN is superior for capturing nonlinear fluctuations and flood forecasting, while WT+ARIMA is better suited for long-term baseline trend analysis.
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Saghiry et al. (2025) Towards More Reliable Gridded Precipitation Estimates: Gauge-Based Multi-Scale Evaluation and Machine Learning Bias Correction
This study evaluates four high-resolution gridded precipitation products against 12 rain gauges in Morocco's Moulouya Basin across multiple spatio-temporal scales and applies machine learning (ML) for bias correction. It finds that GPM IMERG-Final (GPM-F) performs best uncorrected, and ML models (Random Forest for daily, eXtreme Gradient Boosting for monthly/seasonal) significantly enhance its accuracy, providing reliable precipitation inputs for hydrological applications in data-scarce regions.
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Arshad et al. (2025) Enhancing assimilated soil moisture prediction from environmental data using advanced machine learning
This study enhances the prediction of top-layer soil moisture (0–10 cm) in arid irrigated and rainfed regions of Pakistan by integrating t-Distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction with machine learning models. The results demonstrate that t-SNE-enhanced Gradient Boosting Regression significantly outperforms standard models, providing a more reliable tool for drought early warning and agricultural management.
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Pypkowski et al. (2025) The Whittle likelihood for mixed models with application to groundwater level time series
- **N/A:** Due to the unreadable nature of the source text, a summary of the research objective and findings cannot be generated.
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Muraharirao et al. (2025) Regional scale analysis of the internal propagation of groundwater droughts in Andhra Pradesh, India
This study develops the Groundwater Drought Instantaneous Propagation Speed (GDIPS) framework to analyze the internal dynamics of groundwater droughts across Andhra Pradesh, India. The findings reveal that groundwater droughts generally develop faster than they recover, with significant regional variability driven by rainfall patterns and aquifer hydrogeology.
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Pypkowski et al. (2025) The Whittle likelihood for mixed models with application to groundwater level time series
This paper introduces a frequency-domain approach using the Whittle likelihood to jointly estimate fixed and random effect parameters in mixed models. The method significantly reduces computational complexity for large groundwater level datasets while remaining robust to missing values and non-Gaussian noise.
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Alioune et al. (2025) Integrated Analysis of Erosion and Flood Susceptibility in the Gorgol Basin, Mauritania
This study investigates the hydrological functioning and erosion susceptibility of the Gorgol River tributaries to support sustainable watershed management, finding that southern basin areas with fragile soils are most vulnerable to erosion, primarily driven by rainfall erosivity, and identifying flood-prone zones.
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Hidalgo et al. (2025) Coupled Cellular automata – Snowmelt Runoff Model: A Novel Framework for Assessing Climate Change Impacts on Streamflow
This study develops a novel coupled modeling framework (CAM–SRM) to simulate snow cover area and streamflow under climate change scenarios. The results project a significant reduction in annual streamflow (19.4–32.9%) and a two-month shortening of the snow season in Mediterranean mountainous regions.
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Master’s Program in Disaster Management et al. (2025) Analysis of Meteorological–Hydrological Drought Propagation for the Development of a Drought Early Warning System in the Special Region of Yogyakarta (DIY): A Case Study of the 2023 El Niño Event
This study analyzes the spatiotemporal propagation of the 2023 El Niño-induced drought in Yogyakarta, Indonesia, identifying a clear progression from meteorological to hydrological stages. The research quantifies specific time lags between drought types, providing a framework for regional early warning systems.
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Al-Jahwari et al. (2025) Robust Rainfall Gap-Filling in Coastal Arid Regions Using Ensemble Fusion Models
This study implemented and evaluated multiple machine learning and novel ensemble fusion techniques to fill daily rainfall data gaps across 88 stations in Oman from 1993 to 2024, finding that the Gradient-Boosting Trees (GBT) model performed best and ensemble fusion significantly improved prediction accuracy.
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Gimeno et al. (2025) Hydropedological clustering: improving the representation of low streamflows in a semi-distributed hydrological model
This study evaluated how hydropedological clustering, based on soil hydraulic properties, improves the simulation of low streamflows and soil water content in the SWAT+ model, finding significant enhancements compared to traditional soil datasets and demonstrating its criticality over soil map resolution.
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Belarbi et al. (2025) Efficient Hyperparameter Optimization for Reference Evapotranspiration Estimation with Limited Parameters: A Comparison of Optuna and Grid Search in the Doukkala Region, Morocco
This study evaluates the performance of four machine learning models for daily reference evapotranspiration (ETo) estimation in a semi-arid region using limited meteorological data. The research demonstrates that the Optuna optimization framework provides a more efficient and effective alternative to Grid Search for hyperparameter tuning.
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Lee et al. (2025) Estimation of soil moisture retention using pedo-transfer functions based on soil particle-size distribution and organic matter of Korean soils
This study developed and validated new pedo-transfer functions (PTFs) specifically for Korean agricultural soils to estimate moisture retention at 10, 33, and 1500 kPa. The resulting models significantly outperformed established international models (Saxton and Saxton-Rawls), providing more accurate tools for regional water management.
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Sadeghi et al. (2025) Soil moisture dynamics under various drought resilience measures in Mediterranean vineyards of the northern Apennines, Italy
This study evaluates the impact of various green manure management measures on soil moisture dynamics at different depths in Mediterranean vineyards. The findings reveal that while certain treatments significantly enhance topsoil moisture, they may simultaneously reduce subsoil water content, highlighting the complexity of drought resilience strategies.
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Fang et al. (2025) Satellite altimetry reveals intensifying global river water level variability
This study utilizes Sentinel-3 satellite altimetry to establish a global dataset of river water levels across nearly 47,000 virtual stations, revealing that global river seasonality is intensifying while seasonal amplitudes are shrinking due to a surge in extreme hydrological events since 2021.
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VASILONI et al. (2025) Assessing the Impact of Irrigation Practices on Soil Moisture and Crop Health Using Remote Sensing and Hydrological Modelling
This study develops an integrated framework combining Sentinel-1/2 remote sensing with the SWAT hydrological model to assess irrigation efficiency. The findings reveal that drip irrigation achieves 25% water savings and 15-20% higher crop vigor compared to traditional flood irrigation.
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El-Aabssi et al. (2025) Integrating Intelligent Irrigation Systems Across Morocco’s Cultivated Spaces: A Strategic Assessment for Sustainable Water Management
This study evaluates the implementation of Intelligent Irrigation Systems (IIS) across four Moroccan agricultural sectors, identifying traditional open-field farming as the priority area for achieving water savings of up to 70%.
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Jadhav et al. (2025) Quantification of Sugarcane Crop Water Footprint Using Remote Sensing and MachineLearning Techniques: Case Study of Kolhapur District, Maharashtra, India
This study integrates Sentinel-2 remote sensing imagery with machine learning algorithms to classify sugarcane crops and quantify their Blue and Green Water Footprints (WF) in the Kolhapur district of India. The research demonstrates that Random Forest models provide the highest precision for both crop identification and the prediction of water consumption patterns.
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Adhvaryu et al. (2025) Multi-Objective Optimization of Irrigation Canal Network Using Geospatial Computing: A Case Study of the Kadi Narmada Main Canal, Gujarat
This study integrates multi-temporal satellite imagery, high-resolution UAV data, and geophysical measurements into a multi-objective optimization framework to mitigate canal seepage. The findings demonstrate that selective lining of 15% of the canal length can reduce seepage losses by approximately 20% without compromising irrigation delivery.
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SHEN et al. (2025) Spatial and Temporal Variations in Soil Salinity and Groundwater in the Downstream Yarkant River Irrigation District
This study investigates the spatiotemporal dynamics of soil salinization in the Yarkant River basin, identifying a critical groundwater depth of 2.10–2.18 m for salt accumulation. The research establishes that maintaining groundwater below this threshold is essential for mitigating soil salinity driven by shallow, highly mineralized groundwater.
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Sridhar et al. (2025) Land Use and Water Storage Dynamics in the Krishna River Basin: Insights from Satellite Observations and Machine Learning
This study utilizes the XGBoost machine learning algorithm to reconstruct 30 years of Terrestrial Water Storage Anomalies (TWSA) in the Krishna River Basin, identifying 15 major drought events. The findings reveal that while urban and forest areas have expanded, climatic variability remains the primary driver of water storage fluctuations rather than land-use changes.
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Xiao et al. (2025) Flood Prediction with Sentinel-1 Synthetic Aperture Radar from Hurricane Helene
This study develops a flood prediction and mapping framework using Sentinel-1 Synthetic Aperture Radar (SAR) data and a Support Vector Machine (SVM) classifier. The approach successfully identifies flood extents from Hurricane Helene by integrating SAR backscatter with topographic and meteorological variables.
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Phadnis et al. (2025) Innovating Groundwater Recharge using a BoreCharger Technology in Khalad Area, Pune District, India
This study evaluates the "BoreCharger" technology, an innovative in-situ casing perforation method designed to recharge deep basaltic aquifers using filtered water from shallow unconfined layers. The findings demonstrate that the technology sustains groundwater levels through the peak dry season and improves water quality without negatively impacting neighboring wells or surface water bodies.
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Dariane et al. (2025) Reevaluating streamflow declines across the middle East and central Asia with insights from change point detection
This study identifies 1998 as a critical regional breakpoint for streamflow declines across the Middle East and Central Asia, primarily triggered by the 1997–1998 El Niño event. While climate anomalies initiated these shifts, the research demonstrates that subsequent human activities, such as irrigation expansion, significantly amplified the magnitude of hydrological stress.
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Kritidou et al. (2025) Partitioning uncertainties of extreme flood estimates using long continuous simulations
This study partitions the uncertainties in extreme flood estimates from a hydrometeorological modelling chain, revealing that uncertainty increases with return period and its dominant source varies with catchment characteristics. It highlights the critical role of hydrological model parameters in high-elevation catchments and weather generator components in lower-elevation, rainfall-dominated areas.
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Bozorgi et al. (2025) Assessing the performance of multi-timescale drought indices for monitoring agricultural drought impacts on wheat yield
This study evaluates the performance of three multi-timescale drought indices (SPEI, SPET, and SEDI) in monitoring rainfed wheat yield variability across four Spanish regions. The findings identify the Standardized Evapotranspiration Deficit Index (SEDI) as the most robust indicator for agricultural drought impact assessment.
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Katipoğlu et al. (2025) Prediction of soil moisture via feature selection, model optimization, and climate data integration
This study evaluates the performance of four machine learning algorithms in predicting soil moisture across the Konya Closed Basin, Türkiye, using long-term climate data from 1950 to 2022. The results identify Deep Neural Networks (DNN) as the most accurate model for estimating soil moisture dynamics in the region.
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Luo et al. (2025) Identification and spatiotemporal analysis of braided rivers in the Yarlung Tsangpo basin using an enhanced U-Net approach
This study develops an enhanced deep learning model, MSU-Net, to accurately identify and map complex braided river systems in the Yarlung Tsangpo River Basin. Using a fusion of Sentinel-1 and Sentinel-2 satellite data from 2018 to 2023, the research quantifies the spatiotemporal dynamics of these channels and their relationship with climatic drivers.
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Sonuç et al. (2025) Fine-Resolution Multivariate Drought Analysis for Southwestern Türkiye Under SSP3-7.0 Scenario
This study utilizes high-resolution (2.5 km) convection-permitting climate simulations to project a significant intensification of compound agricultural droughts in southwestern Türkiye under the SSP3-7.0 scenario. The findings indicate a regime shift where soil moisture becomes increasingly sensitive to rising temperatures rather than precipitation deficits by the late 21st century.
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Asoka et al. (2025) Tropical Cyclones Unevenly Shape Drought Propagation
This study evaluates the performance of the ISBA-CTRIP land surface model in simulating global hydrological cycles and land-atmosphere interactions. The research demonstrates the model's ability to accurately reproduce river discharge and soil moisture dynamics across diverse climatic zones.
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Zhu et al. (2025) Multi-source remote sensing retrieval and spatiotemporal distribution characteristics of soil moisture content in typical karst farmlands of southwestern China
This study establishes an optimized machine learning framework using the XGBoost algorithm and eight key environmental variables to accurately retrieve soil moisture in complex karst farmlands. The resulting model significantly outperforms standard ERA5-Land reanalysis data and reveals distinct spatiotemporal moisture patterns influenced by monsoon cycles and proximity to water systems.
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Gudmundsson et al. (2025) Past and future change in global river flows
This review synthesizes the current understanding of past and projected changes in global river flow, revealing that anthropogenic climate change is a significant driver of observed regional trends (e.g., increased flows in high-latitudes, decreased in mid-latitudes/subtropics, earlier seasonal flows in snow-dominated regions), with these changes projected to intensify. It highlights the complex interplay of climate change and direct human interventions, emphasizing the need for improved monitoring, modeling, and attribution frameworks.
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Clemenzi et al. (2025) Attributing European runoff changes to climatic drivers under future conditions
This study attributes future European runoff changes to precipitation and potential evapotranspiration using a Budyko-based framework across 35,408 basins. It finds that while precipitation is the dominant driver under most scenarios, potential evapotranspiration becomes equally significant in central and southern Europe under high-emission scenarios by the late century.
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Q et al. (2025) Synergistic Retrieval of Soil Moisture in Arid Regions Using GF ‐3 SAR and Sentinel‐2 Optical Data
This study performs a cross-scale evaluation of the ISBA land surface model and the mHM hydrological model to assess their consistency in simulating the European terrestrial water cycle. The findings reveal that while mHM provides superior river discharge simulations, ISBA demonstrates higher accuracy in capturing surface soil moisture dynamics.
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Luo et al. (2025) Temporal and Spatial Changes in Soil Drought and Identification of Remote Correlation Effects
This study analyzes the spatiotemporal evolution of soil drought in the Yangtze River Basin from 2000 to 2022, identifying precipitation as the primary climatic driver and the Interannual Pacific Oscillation (IPO) as the leading atmospheric circulation influence.
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Booker et al. (2025) Extended Strahler Ordering to Distinguish Mapped River Channels From Overland Flow Pathways and Consistently Compare Digital Networks
## Identification - **Journal:** River Research and Applications - **Year:** 2025...
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Miranda et al. (2025) Satellite-based land surface temperature and soil moisture observed during the 2023–2024 drought–heatwave events in the Amazon Basin
This study investigates the interaction between soil moisture and land surface temperature during the 2023–2024 drought–heatwave events in the Amazon Basin, revealing that NE, SE, and SW regions experienced significant soil moisture deficits and high temperatures, while the NW region showed resilience.
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Valera-Prieto et al. (2025) Breaking the jam: Bridge wood-jam break during a flash flood in a Mediterranean river, insights from hydrodynamic modeling and post flood observations
This study reconstructs an extreme flash flood in the Francolí River to analyze how large wood (LW) transport and bridge clogging influence hydraulic behavior. The research demonstrates that wood-jam formation and sudden breakage cycles create transient surges and significantly increase upstream water levels, factors often overlooked in standard flood modeling.
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Ma et al. (2025) Propagation of different types of compound drought-hot events: Spatiotemporal patterns and response relationship
This study investigates the spatiotemporal patterns and propagation characteristics of three types of compound drought-hot events (meteorological, agricultural, and hydrological) in Northeast China. The research reveals strong interdependencies between these events, with propagation lag times significantly influenced by land use, soil organic matter, and anthropogenic water management.
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Li et al. (2025) Advancing Agricultural Drought Level Prediction in Guangdong Utilizing ERA5-Land and SMAP-L3 Data
This study introduces a feature recalibration encoder for LSTM-based models to improve agricultural drought forecasting in Guangdong Province. The research demonstrates that direct prediction of the Soil Water Deficit Index (SWDI) using satellite data (SMAP-L3) provides the most stable and accurate results for medium-to-long-term (7–14 days) drought level forecasting.
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Elsaidy et al. (2025) Groundwater drought assessment in a Mediterranean coastal catchment through a multi-index approach
This study assesses spatio-temporal groundwater drought dynamics in the Bruna River catchment, Central Italy, using a multi-index approach and a regional hydrological model, finding that groundwater droughts are detectable even with short records and are strongly linked to prolonged meteorological deficits.
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Koubodana et al. (2025) Spatial and Temporal Trend Analysis of Flood Events Across Africa During the Historical Period
This study analyzed the spatial and temporal distribution of historical flood events across Africa from 1927 to 2020, revealing a significant upward trend in flood frequency, fatalities, affected populations, and economic damage, primarily driven by extreme precipitation indices.
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Zhang et al. (2025) A novel framework for pixel-wise estimation of irrigation water use by integrating remote sensing and reanalysis data
This study develops a novel soil water balance framework that integrates satellite-derived soil moisture and evapotranspiration with reanalysis data to estimate irrigation water use at 1 km resolution across China. The resulting 20-year dataset reveals that China's irrigation water use increased from 339 to 395 km³/year between 2001 and 2020, driven primarily by the expansion of irrigated croplands.
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Barreiro-Fonta et al. (2025) Assessing Climate Change and Reservoir Impacts on Upper Miño River Flow (NW Iberian Peninsula) Using Neural Networks
This study utilizes artificial neural networks to project the impacts of climate change on the Upper Miño River, finding that while high-emission scenarios intensify hydrological extremes, reservoir operations can significantly mitigate these effects by redistributing seasonal water availability.
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Morovati (2025) Evaluating Use of Multiple Hydrologic Storage Indicators to Enhance Streamflow Forecasting
This study evaluates the integration of multiple hydrologic storage indicators—snow water equivalent (SWE), soil moisture, and January baseflow—to improve seasonal streamflow forecasting in the Western United States. The research demonstrates that incorporating these indicators, particularly soil moisture, significantly enhances the accuracy of operational forecasts in mountainous headwater basins.
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Rickard (2025) Deciphering soil structure: linking soil physics, water dynamics, carbon storage, and agricultural resilience in long-term experiments
This study evaluates the performance of the ISBA land surface model and the mHM hydrological model in simulating soil moisture and river discharge. The results indicate that while both models accurately capture soil moisture dynamics, mHM demonstrates superior accuracy in river discharge simulation.
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Zhu et al. (2025) Assessing the Use of the Standardized GRACE Satellite Groundwater Storage Change Index for Quantifying Groundwater Drought in the Mu Us Sandy Land
This study evaluates the effectiveness of a GRACE satellite-derived standardized groundwater index (GRACE_SGI) for monitoring groundwater drought in the Mu Us Sandy Land. The research identifies a significant intensifying trend in groundwater depletion and a temporal lag of up to 12 months between meteorological and groundwater drought at annual scales.
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Mats et al. (2025) Rainfall Regime Shifts as a Proxy for Hydrological Climate Change Vulnerability
This study analyzes long-term precipitation shifts in the Mykolaiv region (1980–2024), identifying a 1.7% decadal decline in rainfall and a transition toward more intense, short-duration events. These shifts serve as a proxy for increasing hydrological vulnerability, leading to regional aridification and the shallowness of the Southern Buh River.
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López-Pérez et al. (2025) A Spatial Framework for Assessing Irrigation Water Use in Overexploited Mediterranean Aquifers
This study develops the Spatial Irrigation Adequacy Index (SIAI) to evaluate irrigation performance by comparing satellite-derived actual evapotranspiration with modeled crop water requirements. The framework identifies distinct patterns of water use across Mediterranean aquifers, revealing near-optimal irrigation in vineyards, systemic over-irrigation in apple orchards, and persistent water deficits in citrus groves.
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Grosse et al. (2025) The Potsdam Soil Moisture Observatory: High-coverage reference observations at kilometer scale
The Potsdam Soil Moisture Observatory (PoSMO) provides a comprehensive two-year dataset from 16 permanent stations to monitor soil moisture at the kilometer scale. The study integrates Cosmic-Ray Neutron Sensing (CRNS) with multi-scale environmental data to create a high-coverage reference for hydrological research.
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Grosse et al. (2025) The Potsdam Soil Moisture Observatory: High-coverage reference observations at kilometer scale
The Potsdam Soil Moisture Observatory (PoSMO) provides a high-coverage, multi-sensor dataset at the kilometer scale to support hydrological research. The study integrates cosmic-ray neutron sensing (CRNS) with in-situ probes and remote sensing to capture soil moisture dynamics across diverse hydro-meteorological conditions.
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Bai et al. (2025) Near Real-Time Reconstruction of 0–200 cm Soil Moisture Profiles in Croplands Using Shallow-Layer Monitoring and Multi-Day Meteorological Accumulations
This study developed a machine learning-based model to reconstruct deep-layer soil moisture (0–200 cm) using shallow-layer data and meteorological features. The approach achieves high predictive accuracy (R² up to 0.98), providing a low-cost alternative to expensive deep-probe monitoring for precision irrigation.
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Nguyen et al. (2025) Four-decades assessment of hydrological drought using streamflow reconstruction in poorly-gauged basins in Vietnam
This study evaluates the effectiveness of MSWEP and CHIRPS precipitation products for reconstructing streamflow and assessing hydrological drought in three poorly-gauged basins in Vietnam. The results demonstrate that these global datasets, combined with the SWAT model, accurately replicate historical drought events and monthly streamflow patterns over a 30-year period.
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Lai et al. (2025) Identification of spatiotemporal changes and driving factors of ecological drought during 1982–2024 across the mainland China
This study utilizes the Standardized Ecological Water Deficit Index (SEWDI) to assess ecological drought across mainland China from 1982 to 2024, revealing a general increasing trend in drought severity since 2000. The research identifies evapotranspiration, soil moisture, and irrigation water as the primary drivers of these spatiotemporal changes.
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Avila-Velasquez et al. (2025) How Good Are Drought Forecasts? Skill of multi-model Seasonal Forecast of Meteorological Droughts in a semi-arid Mediterranean Basin
This study develops and evaluates a multi-model seasonal forecasting system for meteorological drought indices (SPI and SPEI) in the semi-arid Jucar River Basin, Spain, integrating Copernicus Climate Change Service (C3S) forecasts with artificial intelligence post-processing to demonstrate high forecast skill for water management.
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Yang et al. (2025) Effects of extreme climate on hydrological dynamics in dryland apple orchards: a modeling study
This study integrates a dynamic leaf area index (LAI) sub-module into the process-oriented STEMMUS model to accurately simulate the ecohydrological responses of dryland apple orchards to climate extremes. The findings demonstrate that while increased precipitation volume enhances soil water storage, both 2°C warming and high-intensity precipitation patterns significantly reduce canopy transpiration and water use efficiency (T/ET).
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Pereira et al. (2025) Linking ROMS with watershed models for simulating hydrodynamics and thermohaline dynamics in a coastal lagoon affected by extreme weather events
The study develops and validates a linked numerical modeling system (ROMS-TETIS-SUTRA) to simulate the hydrodynamics and thermohaline structure of the Mar Menor lagoon. It demonstrates that extreme flash floods, such as the September 2019 event, trigger long-lasting vertical stratification and significantly reduce the lagoon's water renewal time.
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Bouswir et al. (2025) Assessment of empirical and physically-based approaches to simulate surface resistance for improved evapotranspiration modeling of winter wheat in semi-arid region, Morocco
The study evaluates two different methods for parameterizing surface resistance ($r_c$) in the Penman-Monteith model to improve evapotranspiration (ET) estimation for winter wheat in Morocco. It finds that while mechanistic models (Jarvis) excel under full irrigation, empirical models based on Land Surface Temperature (LST) are more effective at capturing rapid water stress dynamics under deficit irrigation.
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Rahmani et al. (2025) Susceptibility to water erosion of the Chertioua watershed (Algeria): Exploratory Analysis and Territorial Diagnosis
This study evaluates the water erosion susceptibility of the 110 km² Chertioua watershed in Algeria using a multi-criteria exploratory analysis. The findings reveal that over 66% of the territory is highly vulnerable to erosion, generating sediment loads up to 320 t/km²/yr due to steep slopes, fragile marly soils, and intensive anthropogenic pressures.
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Abidi et al. (2025) Sediment Assessment Using Coupled RUSLE-SDR Models of Future Mellegue2 Dam in Tunisia
This study utilizes integrated RUSLE-SDR and SWAT modeling to quantify soil erosion and sediment dynamics in the Mellegue2 Dam watershed, identifying a "drought-flush" mechanism that significantly accelerates siltation. The findings indicate that while the dam could lose 50% of its capacity in 24 years without intervention, targeted conservation strategies could extend its operational lifespan to over 75 years.
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Warter et al. (2025) Assessing the sensitivity of urban aquatic nature-based solutions to hydroclimate variability using stable water isotopes
This study utilizes stable water isotopes ($\delta^{18}O$ and $\delta^{2}H$) to characterize the hydrological functioning and hydroclimatic sensitivity of urban aquatic nature-based solutions (aquaNBS) across four European cities. The findings reveal that pond-based systems are highly sensitive to recent precipitation and evaporation, while stream-based systems exhibit greater resilience due to the mixing of older water sources and groundwater.
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Sucozhañay et al. (2025) Streamflow drought identification and characterization in a tropical Andean basin: effect of threshold methods
This study evaluates the **eartH2Observe Tier-1** global water resources ensemble, consisting of 10 hydrological and land surface models forced by a consistent reanalysis dataset. The research demonstrates that while the ensemble mean generally provides the most reliable global estimates, significant model spread exists, particularly in runoff and snow-dominated regions.
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Rodríguez-Casavilca et al. (2025) Sistema inteligente de monitoreo ambiental y análisis de datos para la eficiencia del riego en agricultura de precisión
This paper designs and implements a low-cost IoT-based smart environmental monitoring system for agriculture, providing real-time data and forecasting capabilities for variables like soil moisture. The system demonstrated its effectiveness in a real agricultural field by optimizing water resource usage through informed irrigation decisions.
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Bondarik et al. (2025) Climate Change Risk and Resource-Saving Technologies in Agriculture
This study develops and validates differentiated irrigation schedules based on plant evapotranspiration and critical water stress to manage climate risks in agriculture. It demonstrates that these schedules can save 20% of irrigation water while increasing crop yields by 15–20% for cabbage and apple seedlings.
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Li et al. (2025) Evaluating the Performance of the STEMMUS-SCOPE Model to Simulate SIF and GPP Under Drought Stress Using Tower-Based Observations of Maize
This study simulated solar-induced fluorescence (SIF) and gross primary productivity (GPP) using the STEMMUS-SCOPE model at a semi-arid irrigated farmland to assess its accuracy and capability in analyzing water stress impacts. The STEMMUS-SCOPE model demonstrated higher accuracy than the SCOPE model, particularly under drought conditions, and reliably characterized the SIF-GPP relationship under water stress.
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Han et al. (2025) Vegetation greenness and productivity recovery following the 2022 record-breaking compound soil moisture and atmospheric drought in Yangtze River Basin
This study investigates the daily-scale recovery patterns and drivers of vegetation greenness (NDVI) and productivity (GPP) in the Yangtze River Basin following the 2022 record-breaking compound drought, revealing distinct recovery times and influencing factors for each.
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Fustos et al. (2025) Controls over debris flow initiation in glacio-volcanic environments in the Southern Andes
This study investigates the geomorphological, geotechnical, and hydrometeorological controls over debris flow initiation in glacio-volcanic environments of the Southern Andes, revealing that the combination of high water accumulation capacity, effective precipitation capture by slopes, and specific soil properties (volcanic soils over low-permeability glacial deposits) are critical for triggering these events. The research highlights the importance of monitoring soil moisture and surface deformation for predicting these hazards, especially under changing climate conditions.
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Gemechu et al. (2025) Assessing the Impact of Multi-Decadal Land Use Change on Agricultural Water–Energy Dynamics in the Awash Basin, Ethiopia: Insights from Remote Sensing and Hydrological Modeling
This study assesses the impact of multi-decadal land use and land cover (LULC) changes on agricultural water–energy dynamics in Ethiopia's Awash Basin using the WRF-Hydro/Noah-MP modeling framework, revealing that early agricultural expansion increased surface runoff while later woodland recovery promoted subsurface flow and groundwater recharge.
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Jat et al. (2025) Maize as an alternative to resource-intensive rice: Empirical insights from on-farm participatory study under diverse agricultural scenarios in the Indo-Gangetic Plains of Northwestern India
This study empirically evaluates maize as a sustainable alternative to resource-intensive rice in the Indo-Gangetic Plains of Northwestern India. It finds that while maize yields are slightly lower, its cultivation significantly enhances profitability, water productivity, and environmental sustainability compared to rice.
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Wang et al. (2025) Applicability of the Surface Energy Balance System (SEBS) Model for Evapotranspiration in Tropical Rubber Plantation and Its Response to Influencing Factors
This study evaluated the applicability of the Surface Energy Balance System (SEBS) model for estimating evapotranspiration (ET) in a tropical rubber plantation using Landsat-8 imagery and flux tower data, demonstrating high accuracy and identifying key meteorological and physiological drivers of ET.
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Gutiérrez et al. (2025) Groundwater salinization in inland basins of arid and semiarid climates: An overview
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Amin et al. (2025) Evaluating flood, sprinkler and drip irrigation systems for dragon fruit production in Bangladesh
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Otmane et al. (2025) Contribution of hydrological modeling to the estimation of groundwater deficit and/or excess in a karstic aquifer: the case of Wadi Sebdou catchment (Tafna, NW, Algeria)
This study aimed to estimate the groundwater deficit and characterize the hydrodynamic behavior and self-renewal capacity of the karstic aquifer in the Wadi Sebdou basin, northwestern Algeria. Using the GARDÉNIA hydrological model, it quantified an average groundwater deficit of 1.36 × 10^6 cubic meters per year, compensated by regional karstic system inputs, and established the aquifer's consistent hydrodynamic response to recharge.
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Hemasundari et al. (2025) Optimizing Agricultural Resource Management Through IoT-Enabled Sentinel-Based Vegetation Monitoring
This research introduces the Sentinel-IoT Adaptive Multi-Objective Resource Optimizer (SIAM-RO) model, which integrates IoT sensor data with Sentinel satellite-derived vegetation indices and uses NSGA-II for multi-objective optimization to enhance agricultural resource management. The SIAM-RO system significantly improves crop yield, water use efficiency, and nutrient use efficiency compared to traditional methods, promoting sustainable and cost-effective farming.
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Berihun et al. (2025) Correction: Modeling soil moisture and evapotranspiration dynamics from variably irrigated vegetable fields
This article is a correction notice addressing a typographical error in an author's name in a previously published paper titled "Modeling soil moisture and evapotranspiration dynamics from variably irrigated vegetable fields."
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Yang et al. (2025) A novel Improved Geographically Weighted Random Forest (IGWRF) model for low-resolution soil moisture data downscaling in Africa
This study proposes an Improved Geographically Weighted Random Forest (IGWRF) model to downscale 9 km SMAP soil moisture data to 1 km in Kenya, effectively addressing spatial heterogeneity and nonlinear relationships. The IGWRF model significantly outperforms traditional Random Forest and Geographically Weighted Random Forest, providing high-accuracy, high-resolution soil moisture data crucial for agricultural management and drought monitoring in Africa.
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Saurı́ et al. (2025) Risks into benefits?: The current relevance of traditional flood management practices in Southeastern Spain
This paper highlights how unplanned urbanization and conventional hydraulic engineering exacerbated flood risk in Valencia, advocating for the integration of Nature-based Solutions inspired by traditional Mediterranean "Boquera" irrigation systems to mitigate floods and enhance resource management. It finds a strong connection between these traditional systems and current alternative flood management approaches, both leveraging natural hydrological processes.
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Alcalde et al. (2025) Remote Sensing Standardized Soil Moisture Index for Drought Monitoring: A Case Study in the Ebro Basin
This study evaluates the satellite-derived Standardized Soil Moisture Index (SSI) for drought monitoring across various timescales in the Ebro Basin, demonstrating its robustness and superior spatial resolution compared to precipitation-based indices, and its capability to monitor hydrological droughts without relying on in situ measurements.
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Duque Gardeazábal (2025) Some sources of hydroclimate and renewable energy variability in tropical South America
This thesis investigates how ocean-atmospheric modes and external aerosol forcing modulate hydroclimatic variability and potential solar and wind energy in tropical South America. It reveals distinct regional and seasonal impacts of Atlantic modes, the influence of these modes and ENSO on renewable energy capacity factors, and the differing effects of anthropogenic and volcanic aerosols on rainfall.
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Daoud et al. (2025) Comprehensive hazard susceptibility assessment in Port Sudan city using AHP: emphasizing flash flood risk, soil moisture, and salinity dynamics
## Identification - **Journal:** Geomatics Natural Hazards and Risk - **Year:** 2025...
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Lozano‐Parra (2025) Exploring the aridity risk in agricultural lands of southwest Spain: the Extremadura region
This study quantified future climate variations and aridification trends in irrigated and rainfed agricultural areas of southwestern Spain using CMIP6 projections. It found a strong aridification trend characterized by significant precipitation decline, temperature increase, and a widespread shift towards semi-arid and arid conditions, necessitating urgent adaptive measures.
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Marchionni et al. (2025) Urban greening and water strategies are key to adapt Australian cities to climate change and urban growth
This paper explores how Australian cities integrate urban greening with water-sensitive urban design (WSUD) for climate adaptation, demonstrating that effective water management is crucial for the long-term success of urban greening in building climate-resilient cities.
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Fassnacht et al. (2025) Ski Resort Snow Surface Roughness
This study investigates the spatial variability of snow surface roughness at a Spanish ski resort using 2-D roughness boards and 3-D iPad scans, finding an order of magnitude difference in aerodynamic roughness length (z0) across natural and groomed surfaces, which significantly impacts modelled sublimation.
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Zhu et al. (2025) Large-scale irrigation area mapping: Status and challenges
This paper provides a comprehensive synthesis of large-scale irrigation area mapping methodologies and datasets, benchmarking ten global and regional products against EUROSTAT 2020 gridded statistics in Europe. It concludes that integrating ground-based statistics with geospatial information significantly improves mapping accuracy, especially in humid regions, while highlighting the critical need for standardized, accessible, and high-quality ground-truth and statistical data.
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Chahed (2025) Hydro‐Climatic Modelling for Water Resources: From Processes to Adaptive Management and Governance
This paper synthesizes advances in hydro-climatic modelling, particularly within the CMIP framework, to demonstrate how improved model design and uncertainty management inform practical water resource strategies and adaptive governance, while also identifying persistent challenges and promising new approaches.
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Zabaleta et al. (2025) Changing rivers: Hydrological shifts in the Pyrenees revealed by daily streamflow indicators (1950–2019)
This study provides the first region-wide assessment of daily streamflow trends across the Pyrenees from 1950 to 2019, revealing a robust long-term decline in mean, low, and high flows, alongside an emerging shift towards rain-dominated winter regimes.
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Tiwari et al. (2025) A two-step iterative data assimilation and calibration approach for improving large-scale hydrological processes in the Ganga basin
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Abbasi et al. (2025) Increased Streamflow Intermittence in Europe Due To Climate Change Projected by Combining Global Hydrological Modeling and Machine Learning
## Identification - **Journal:** Earth s Future - **Year:** 2025...
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Beguería et al. (2025) Water balance components of the Pyrenees: a 30-year modeling study in a transboundary context
This study reconstructed the Pyrenean water balance for 1981–2010 using two hydrological models, revealing strong hydroclimatic gradients, declining groundwater recharge, and a shift towards water-limited conditions, thereby establishing the first integrated transboundary hydrological baseline for the region.
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Palazón Tabuenca et al. (2025) The PIRAGUA_atmos_analysis and PIRAGUA_hydro_analysis: water balance components data sets for the Pyrenees.
This paper introduces and documents the PIRAGUA_atmos_analysis and PIRAGUA_hydro_analysis datasets, which provide comprehensive meteorological and water balance components for the Pyrenees region to support water resource assessment and management.
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Zhang et al. (2025) On the Use of Knowledge‐Informed Machine Learning and Multisource Data for Spatially Explicit Estimation of Irrigation Water Withdrawal
## Identification - **Journal:** Earth s Future - **Year:** 2025...
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García-Maroto et al. (2025) Snow cover over the Iberian mountains in km-scale global climate simulations: evaluation and projected changes
This study evaluates the IFS-FESOM storm-resolving model's performance in simulating historical seasonal snow cover over Iberian mountains and projects significant future reductions in snow season length under the SSP3-7.0 scenario, primarily due to rising temperatures and decreased precipitation.
