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Liu et al. (2025) Transformer-based soil moisture simulation for understanding future drying trend globally
This study introduces TSMSNet, a Transformer-based deep learning model designed to simulate global soil moisture (SM) from 2016 to 2099 under various climate scenarios. The research identifies a significant global drying trend that intensifies with higher greenhouse gas emission pathways, particularly affecting habitable regions and agricultural lands.
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Jain et al. (2025) Deriving hydrological inferences from a machine learning model to understand the physical drivers of flow duration curves
This study utilizes Random Forest regression and SHapley Additive exPlanations (SHAP) to predict Flow Duration Curves (FDCs) across 991 watersheds in the contiguous United States. The research demonstrates that while climate attributes primarily determine the scale of FDCs, the baseflow index and geological features are the critical drivers of FDC shape and low-flow regimes.
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Yan et al. (2025) Soil moisture dynamics and rainfall infiltration across vegetation types in subtropical ecosystems in Southwest China
This study investigated soil moisture dynamics and rainfall infiltration across four vegetation types in subtropical Southwest China, revealing that primary evergreen broadleaf forests maintain higher soil moisture and slower infiltration rates, which is crucial for regional drought resistance.
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Wang et al. (2025) Estimating soil moisture at farm scale with high spatial resolution: integrating remote sensing data, and machine learning
This study develops a machine learning-based downscaling framework that integrates evapotranspiration and groundwater depth to estimate surface soil moisture at a 30 m resolution from 9 km coarse data. The approach significantly improves soil moisture monitoring in complex agricultural environments by accounting for both upper and lower boundary conditions.
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Qi et al. (2025) MODIS-Landsat fusion reveals two-decade 8-day lake dynamics with critical intra-annual regime shifts
The study reconstructs 8-day resolution dynamics for lakes and reservoirs in southern China from 2001 to 2020 using MODIS-Landsat data fusion. It reveals that high-frequency intra-annual variations are critical for accurate carbon budget estimates, often offsetting or equaling the impact of long-term interannual trends.
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Weng et al. (2025) Evolution and impact of rainfall infiltration in global alpine water towers
This study develops a temperature-mediated infiltration model to quantify rainfall infiltration across 78 global Water Tower Units (WTUs) from 1980 to 2023. The findings reveal that climate warming and freeze-thaw cycles are significantly altering infiltration characteristics, threatening the stability of downstream water supplies and ecological buffering.
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Fan et al. (2025) Considering parameter seasonal variation to enhance process-based ecosystem model performance, evidence from the SWH model
This study demonstrates that incorporating seasonal variation into empirical parameters significantly enhances the performance of the SWH evapotranspiration (ET) partitioning model. A novel Monte Carlo-based calibration scheme with adaptive time windows achieved a 95% success rate and substantially improved R² values compared to traditional methods, approaching the accuracy of Extended Kalman Filtering.
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Mo et al. (2025) Time-lag effects of vegetation gross primary production response to the hydro-climate changes in humid and semi-humid areas of China
This study investigated the relationship between vegetation gross primary production (GPP) and hydro-climate factors (precipitation, temperature, basin water storage) in the humid and semi-humid Hanjiang River Basin, China. It revealed significant time-lag effects of hydrological factors on GPP (4 months for basin water storage, 5 months for precipitation), highlighting their long-term influence compared to the immediate response to temperature.
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Daiman et al. (2025) Assessing the link between changes in landscape and desertification in the chambal river basin using machine learning and remote sensing
This study analyzed the linkage between landscape changes and desertification in the Chambal River Basin (India) from 1990 to 2020 using machine learning and remote sensing. It found that anthropogenic land alterations, particularly the conversion of vegetation and agricultural lands, amplified the region's vulnerability to drought and desertification.
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Turki et al. (2025) On the use of SWOT altimetry data for monitoring coastal hydrodynamics
This study evaluates the accuracy of the Surface Water and Ocean Topography (SWOT) mission in retrieving coastal Sea Surface Heights (SSH) and Significant Wave Heights (SWH) in the English Channel. The findings demonstrate that SWOT provides high-resolution, reliable hydrodynamic data even within 3–4 km of the shoreline, significantly outperforming conventional satellite altimetry in complex nearshore environments.
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Chucuya et al. (2025) Reconstructing aquifer dynamics with machine learning: Linking irrigation expansion to groundwater decline in a data-scarce hyper-arid region
This study utilizes machine learning (BPNN) to reconstruct fragmented groundwater records in the hyper-arid Caplina aquifer, revealing a 0.6 m/yr water table decline driven by a 400% expansion of irrigated agriculture over three decades. The research highlights the critical role of seawater intrusion in maintaining stable water levels near the coast while severely degrading water quality.
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Olson et al. (2025) Long‐Term Stream Chemistry Patterns in a Boreal Watershed Underlain With Discontinuous Permafrost
This study investigated over 20 years of stream chemistry and climate trends in boreal catchments with varying permafrost extents to understand how altered flowpaths and climate change affect solute transport. It found significant declines in dissolved organic carbon (DOC) and partial pressure of carbon dioxide (pCO₂) in sub-catchments with higher permafrost extent, with moisture and discharge being key abiotic drivers.
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Khan et al. (2025) Remote sensing-based cropping pattern identification and its impact on groundwater use in canal command areas of an irrigated agriculture region in Pakistan
This study integrates Sentinel-2 satellite imagery and Random Forest algorithms to map seasonal cropping patterns across eight Canal Command Areas in Pakistan's Bari Doab from 2018 to 2023. The findings quantify a rising dependency on groundwater for irrigation, driven by water-intensive crops and urbanization, leading to significant regional aquifer depletion.
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Yin et al. (2025) A shift in drought propagation trend in the Yellow River Basin during 1980–2020 linked to climate change and vegetation greening
This study investigates the propagation of meteorological drought to soil moisture drought in the Yellow River Basin from 1980 to 2020, identifying a significant shift around the year 2000 where drought propagation time began to prolong and duration extension began to decrease due to vegetation greening.
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Patra et al. (2025) Long-term projections of global groundwater storage under future climate change scenarios using deep learning
This study utilizes a deep learning model to project global groundwater storage (GWS) variations until 2100 under CMIP6 climate scenarios, identifying maximum temperature as the primary driver of depletion. The findings indicate that over 50% of the global population will reside in regions facing GWS decline by the end of the century, with tropical and temperate zones being the most vulnerable.
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Karimzadeh et al. (2025) Climate change has increased global evaporative demand except in South Asia
Climate change has increased global evaporative demand, but this study reveals a significant decline in South Asia due to widespread irrigation, which has increased local moisture, cloud cover, and reduced solar radiation. These contrasting trends highlight how human water use can locally reshape the climate's influence on the water cycle.
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Dangare et al. (2025) Estimating transpiration dynamics of a low-density litchi orchard using crop coefficients derived from a variable leaf conductance model, canopy cover, and tree height in Northeastern South Africa
This study improved the estimation of litchi orchard transpiration in semi-arid South Africa by modifying the Allen and Pereira (A&P) crop coefficient approach to incorporate a variable leaf resistance model and a litchi-specific typical leaf resistance, achieving significantly higher accuracy compared to the original fixed-value method.
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Lemus‐Canovas et al. (2025) More intense heatwaves under drier conditions: a compound event analysis in the Adige River basin (Eastern Italian Alps)
This study analyzes a severe compound drought and heatwave (CDHW) event in the Adige River basin (Eastern Italian Alps) in May 2022, revealing that similar events are now significantly hotter (by 1–4 °C) and drier (with pronounced precipitation deficits) due to climate change, exacerbating water stress and shifting streamflow seasonality. It also highlights the inability of many regional climate models (EURO-CORDEX) to accurately reproduce these observed changes in both magnitude and sign.
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Lari et al. (2025) Quantifying sediment yield and discharge fluctuations using the GeoWEPP in response to soil and water conservation practices
This study evaluated and calibrated the GeoWEPP model to predict runoff and sediment yield in the mountainous Amameh watershed, Iran, incorporating snowmelt dynamics and high-resolution spatial data. It assessed eight biological conservation scenarios, demonstrating that enhanced canopy cover can reduce runoff by up to 44% and sediment yield by up to 47%.
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Sugita et al. (2025) Ship's Motion and Eddy Correlation Measurements of Surface Fluxes on the Small Research Ship NIES ' 94 in Lake Kasumigaura, Japan
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Tunca et al. (2025) Integration of UAV images and ensemble learning for root zone soil moisture estimation in sorghum
This study developed and evaluated a methodology to estimate root-zone soil moisture in sorghum using high-resolution unmanned aerial vehicle (UAV) multispectral and thermal imagery combined with machine learning. An ensemble model integrating XGBoost, Light Gradient Boosting Machine, and K-Nearest Neighbors achieved the highest accuracy (R² = 0.85, RMSE = 11.124 mm/90 cm, MAE = 8.775 mm/90 cm) for field-scale monitoring.
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Ercan et al. (2025) Rethinking standardized drought indices for critical drought evaluation
This study investigates the differences between classical and dynamic Standardized Precipitation Index (SPI) models, revealing that dynamic models produce significantly longer drought durations, particularly at short and medium timescales, highlighting the importance of model choice for accurate drought assessment.
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Weynants et al. (2025) Dheed: an ERA5 based global database of compound dry and hot extreme events from 1950 to 2023
This study introduces Dheed, a novel global database of compound dry and hot (CDH) extreme events from 1950 to 2023, derived from ERA5 reanalysis data, and confirms a significant increase in the frequency and spatial extent of these events over recent decades.
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Zeroualı et al. (2025) Linking the North Atlantic Oscillation to rainfall variability and dynamics in Algeria through GIS and wavelet theory
This study regionally analyzes the relationship between the North Atlantic Oscillation (NAO) and rainfall in northeastern Algeria using wavelet theory and GIS, revealing a strong and consistent NAO influence in the north that diminishes southward, offering potential for improved rainfall forecasting.
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Cho et al. (2025) Disentangling geomorphic equifinality in sediment and hydrologic connectivity through the analyses of landscape drivers of hysteresis
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Papalexiou et al. (2025) Machine unlearning: bias correction in neural network downscaled storms
This study evaluates four machine learning models for downscaling precipitation using synthetic benchmark storms, demonstrating that combining machine learning with post-processing bias correction ("machine unlearning") is crucial for reliable outputs, especially for Wasserstein Generative Adversarial Networks (WGANs). It finds that raw neural network outputs struggle to reproduce key statistical properties and wet/dry boundaries, necessitating systematic bias correction for operational use.
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Ramirez et al. (2025) Hydrological response to thinning in forest stands: analysis of soil volumetric water content and soil water flux
This study investigates the impact of masticator thinning on soil moisture dynamics in a semiarid mixed conifer forest. The findings indicate that thinning increases soil water storage at the soil-bedrock interface and induces upward water flux during dry periods, potentially enhancing forest resilience to drought.
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Peng et al. (2025) Analysis of Spatiotemporal Changes in NDVI-Derived Vegetation Index and Its Influencing Factors in Kunming City (2000 to 2020)
This study analyzed the spatiotemporal changes and driving factors of vegetation cover in Kunming City from 2000 to 2020 using MODIS NDVI and climate/socioeconomic data, finding an overall increase in vegetation cover primarily influenced by precipitation, with urbanized areas showing lower vegetation.
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Goudiaby et al. (2025) Hydrological evaluation of gridded rainfall products for streamflow simulation in West Africa
This study evaluates the hydrological performance of 23 gridded precipitation products for streamflow simulation in eight West African river basins using GR2M and GR4J models, finding that multi-source products like IMERGDF, MSWEP, GPCP, and TAMSAT are the most reliable alternatives in data-scarce regions.
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Wang et al. (2025) Spatiotemporal evolution characteristics of multi-type drought propagation processes in the Yellow River Basin, China
This study systematically investigates the spatiotemporal characteristics and propagation mechanisms of meteorological drought to hydrological, agricultural, and ecological droughts in the Yellow River Basin (YRB) from 1982 to 2018, revealing distinct propagation patterns and regional disparities.
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Rapella (2025) Modélisation et évaluation de la solution agrivoltaïque au nexus climat-eau-énergie-alimentation dans le contexte du changement climatique dans la région euro-méditerranéenne
This study integrates a photovoltaic module into the ORCHIDEE land surface model to assess the regional impact of agrivoltaics across the Euro-Mediterranean. The findings reveal that agrivoltaics significantly improve crop yields and resource efficiency in arid southern regions like the Iberian Peninsula, while providing limited or negative impacts in wetter northern regions like the Netherlands.
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Abdelrahim et al. (2025) A Self‐Supervised Seasonal Anomaly Embedding ViT for Label‐Free Drought Mapping in the Horn of Africa
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Marrocu et al. (2025) Integrating deficit irrigation, crop modelling and Water–Energy–Food nexus to enhance durum wheat resilience in Mediterranean climate conditions
This study evaluates the impact of deficit irrigation on durum wheat yield and quality in southern Sardinia over two cropping seasons and two soil types, integrating agronomic monitoring, remote sensing, and crop modeling within a Water–Energy–Food (WEF) nexus framework. It found that moderate deficit irrigation (50% of plant water requirement) significantly improved grain yield and was comparable to full irrigation in efficiency, offering a sustainable strategy for food security in drought-prone Mediterranean regions.
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Tafesa et al. (2025) Assessing groundwater and climate susceptibility in Masgeredo-Bulal catchment, Ethiopia
This study assesses groundwater potential and climate change impacts in Southern Ethiopia's Masgeredo-Bulal catchment using GIS, remote sensing, and climate modeling, revealing that 51.6% of the catchment has good groundwater potential while future projections indicate increased temperature and decreased precipitation.
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Huimin et al. (2025) The response of meteorological drought to extreme climate in the water-receiving area of the Tao river diversion project in China
This study analyzed the spatiotemporal variations, interrelationships, and driving factors of meteorological drought and extreme climate events in the water-receiving area of the Tao River Diversion Project, China. It found a persistent drying trend since 1988, primarily driven by annual total precipitation, cold days, and summer days, with most extreme climate factors exhibiting complex nonlinear influences and critical thresholds on drought.
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Weng et al. (2025) Spatially coherent changes in Chinese annual flood peaks revealed by a consensus-based machine learning framework for regionalization
This study develops a consensus-based machine learning framework to identify homogeneous flood regions across China, revealing predominant trends of decreasing annual flood peak magnitudes and delayed occurrences in most regions, primarily driven by climate factors.
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Tügel et al. (2025) Extreme precipitation and flooding in Berlin under climate change and effects of selected grey and blue-green measures
This study quantifies the projected increase in extreme precipitation in Berlin under climate change (RCP8.5) and its impact on urban flooding, demonstrating that a 46 % increase in 1 h 100-year rainfall leads to a 51 % increase in maximum water depth. It further assesses the effectiveness of grey infrastructure, infiltration, and retention roofs in mitigating these impacts, highlighting the non-linear relationship between rainfall and flooding and the need for combined adaptation strategies.
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Shah et al. (2025) Global patterns of reservoir fullness and fluctuations during droughts
This study assesses the storage conditions and fluctuations of 6634 global reservoirs during major river basin-scale droughts from 1999 to 2018, revealing significant regional, functional, and socio-economic disparities in reservoir resilience and a strong link to large-scale climate variability.
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Holwerda et al. (2025) A comparison of drought indices for crop yield loss detection: The role of green-up onset alignment and spatial resolution
This study compares six drought indices for detecting rainfed crop yield loss in the Central Plateau of Mexico, finding that aligning indices to satellite-derived green-up onset improves performance, with ALEXI-based Evaporative Stress Index and MODIS NDVI anomaly showing the strongest relationships with yield anomalies.
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Singh et al. (2025) Integrated trend analysis and meteorological drought forecasting using ANN in the adjacent semi-arid and arid regions
This study integrated trend analysis and meteorological drought forecasting using an Artificial Neural Network (ANN) model in adjacent semi-arid and arid regions of Rajasthan, India, finding increasing precipitation trends in several periods and a decrease in drought severity with longer time scales.
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Kulkarni et al. (2025) Near-global Agro-climatological Drought Monitoring Dataset
This study introduces the Near-global Combined Drought Monitoring (NEC-DROMO) dataset, integrating soil moisture, vegetation water content, rainfall, and temperature at a 0.25-degree monthly resolution from 2002-2021, demonstrating superior reliability in capturing global drought patterns compared to traditional indices.
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Alifujiang et al. (2025) Spatiotemporal patterns and trends of meteorological drought in the Kaidu-Kongque River Basin
This study analyzed the spatiotemporal patterns and trends of meteorological drought in the Kaidu-Kongque River Basin from 1960 to 2023 using SPI, SPEI, Pettitt test, and Modified Innovative Trend Analysis (MITA). It revealed a significant "dry west and wet east" spatial divergence, increased drought persistence, and a critical shift in drought drivers from precipitation-dominated to water-heat coupling-dominated around 1999.
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İpek et al. (2025) Integrating Spatio-Probabilistic mapping and remote sensing for comprehensive drought risk assessment
This study developed a Spatio-Probabilistic Drought Mapping (SPDM) framework by integrating multiple drought indices with remote sensing and land cover analysis to assess drought dynamics and environmental impacts in the Küçük Menderes Water Basin. The research identified western regions as high-risk areas and quantified severe impacts on vegetation, agriculture, and forest fires during major drought episodes.
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Dasari et al. (2025) A regionalization based machine learning framework for bias correction and downscaling of ESACCI soil moisture in data limited region: A case study over India
This study developed a regionalization-based machine learning framework for bias correction and downscaling of ESACCI soil moisture data in data-limited regions like India, demonstrating significant bias reduction (over 90%) and effective downscaling with high containment ratios (over 89%).
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Xu et al. (2025) The applicability of statistical post-processing techniques for quantitative precipitation forecast in the Huaihe River Basin
This study evaluates seven post-processing methods for quantitative precipitation forecasts in the Huaihe River Basin, demonstrating that spatiotemporal deep learning models (specifically ConvLSTM) significantly outperform traditional statistical and time-series methods, particularly during flood seasons and in complex terrains.
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Pimentel et al. (2025) Asymmetry in snow-water nexus in mountain areas mainly governed by meteorological seasonal changes
This study analyzes 548 mountain catchments globally to quantify the nexus between snow cover and streamflow, revealing that only 5% of catchments show simultaneous significant trends in both variables. The findings highlight an asymmetric relationship where seasonal meteorological drivers, such as summer temperature increases or winter precipitation shifts, often decouple snow cover changes from annual water yield.
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Mathbout et al. (2025) Europe’s Double Threat: Evolving patterns of compound heatwaves and droughts
This study quantifies the spatiotemporal evolution of Compound Hot and Dry Events (CHDEs) across Europe from 1980 to 2023, revealing a significant post-2000 intensification and northward/eastward expansion, primarily driven by heatwaves, with urban areas showing disproportionately higher increases.
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Yuan et al. (2025) A global drought dataset for Multivariate Composite Drought Index (MCDI) and its constituent drought indices
This study developed and validated a global, high-resolution (0.1°, monthly, 1980-2019) drought dataset based on the Multivariate Composite Drought Index (MCDI) and its constituent indices, demonstrating its effectiveness in characterizing comprehensive drought dynamics and ecosystem responses by accounting for time lag and cumulative effects.
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Sinore et al. (2025) Agricultural and meteorological drought variability assessment over the Rift Valley Lake Basin of Ethiopia
This study assessed the spatiotemporal variability of meteorological and agricultural droughts in Ethiopia's Rift Valley Lake Basin using multi-source remote sensing data and advanced statistical models. It revealed significant drought severity variations, with specific major events, and highlighted the compounded effects of thermal and moisture stress on vegetation.
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Yonaba et al. (2025) Hydrological evaluation of top-down and bottom-up rainfall products in West Africa: Model performance, parameter range and uncertainty propagation
This study evaluated the hydrological performance of four top-down and three bottom-up satellite rainfall products in three West African Sahelian river basins using the SWAT model. It found that model skill varied across basins, with some gridded products outperforming gauge observations, and demonstrated that carefully selected rainfall products can significantly enhance hydrological modeling and water resource planning in the region.
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Park et al. (2025) Climate change paradox: worsening droughts amidst increasing average precipitation across South Korea
This study reveals a climate change paradox in South Korea, demonstrating that droughts have worsened in frequency and intensity over the past century despite an overall increase in average precipitation, driven by enhanced meteorological variability and temperature-driven evapotranspiration, with strong implications for water resources.
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Yu et al. (2025) Impacts of the mega cascade reservoirs on riverine hydrothermal regimes based on deep learning
This study investigates the impacts of four mega cascade reservoirs on the Lower Jinsha River's downstream hydrological and water temperature regimes using an LSTM-based hydro-thermal model, revealing significant alterations in flow, temperature, and their coupling, with implications for ecological risks.
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Luo et al. (2025) Surface Soil Moisture Retrieval over Winter Wheat Fields Based on Fused Multispectral and L-Band MiniSAR Data
This study proposes a high-accuracy surface soil moisture (SSM) retrieval method for winter wheat fields by fusing Sentinel-2 satellite and UAV multispectral data with L-band MiniSAR observations. The results demonstrate that multi-platform data fusion combined with machine learning significantly outperforms single-satellite approaches, particularly at shallow soil depths.
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Zhang et al. (2025) Influence of drought identification methods on analyzing and assessing responses of water quality to droughts
This study compares fixed (FDT) and variable (VDT) drought identification methods to assess their influence on water quality responses in the Harp Lake catchment, Ontario. Findings reveal that the choice of drought identification method significantly impacts the assessment of water quality parameters (dissolved organic carbon, total nitrogen, total phosphorus) and their dynamics during drought events.
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Guzzon et al. (2025) Improving extreme precipitation forecasts in Catalonia (NE Iberian Peninsula) using analog methods: A comparison with the GFS model
This study evaluates novel analog-based methods (AMs) to enhance 24-hour extreme precipitation forecasts in Catalonia, aiming to support flood risk management. The findings demonstrate that AMs integrating Seasonal Standardization and the Perfect Prognosis framework significantly improve forecasts compared to the operational Global Forecast System (GFS), particularly in reproducing the intensity and spatial distribution of extreme events.
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Ferdinand et al. (2025) Spatio-temporal variability of flooded areas in the Ouémé floodplain (Benin, West Africa) from 2015 to 2023
This study assessed the spatio-temporal variability of flooded areas in the Ou´em´e floodplain (Benin) from 2015 to 2023 using remote sensing and in-situ data, revealing a significant upward trend in flood extent driven by cumulative rainfall and river water surface elevation thresholds.
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Melón-Nava (2025) Patterns of snow cover distribution in the Cantabrian Mountains (NW Spain)
The study evaluates the performance of the ISBA land surface model and the mHM hydrological model across metropolitan France to improve the national hydrometeorological reanalysis. It demonstrates that while mHM excels in river discharge simulation due to its multiscale parameterization, ISBA provides a more comprehensive representation of surface energy fluxes.
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Chakraborty et al. (2025) The mirage of the silver bullet: Exploring the limitations of high-resolution data in flood model validation
This study explores the limitations of high-resolution data in flood model validation, demonstrating that while beneficial, it does not resolve all discrepancies, which often stem from a complex interplay of observed data limitations, model uncertainties, and structural differences between datasets.
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Liu et al. (2025) Global warming intensifies extreme day-to-day temperature changes in mid–low latitudes
Global warming is intensifying extreme day-to-day temperature changes (DTDTs) in mid-low latitudes, a distinct and largely ignored extreme weather event, posing substantial risks to human health and ecosystems, with climate models projecting further amplification by 2100.
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Bourbour et al. (2025) Pre-harvest forecasting of rainfed wheat yield in Iran using multi-source remote sensing and machine learning
This study developed and compared machine learning models integrating multi-source remote sensing and meteorological data to forecast rainfed wheat yield across 22 Iranian provinces from 2001 to 2021. The XGBoost algorithm achieved superior accuracy (R²=0.64, MAE=0.25 t/ha) two months pre-harvest, outperforming Random Forest and Support Vector Regression.
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González-Ramón et al. (2025) Integration of 3d geological models and groundwater flow models for the improvement of the management of complex multilayer aquifers under intensive exploitation. The case of the Loma de Úbeda (Southern Spain)
This study integrates 3D geological and numerical groundwater flow models to improve the management of the complex, intensively exploited multilayer aquifer system of Loma de Úbeda, Southern Spain. The research successfully corroborates the conceptual hydrogeological model and provides a validated tool for sustainable water resource management, highlighting the shift in water storage and flow dynamics due to prolonged pumping.
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Silber-Coats et al. (2025) Beyond scarcity: Science-based solutions for water and agriculture in the Western United States
This editorial synthesizes 14 research contributions focusing on science-based demand management strategies for sustainable agriculture in the water-scarce Western United States, demonstrating that agricultural productivity, environmental sustainability, and economic resilience are mutually compatible goals.
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Wang et al. (2025) Recent south-central Andes water crisis driven by Antarctic amplification is unprecedented over the last eight centuries
This study reconstructs 827 years of Negro River streamflow in northern Patagonia using tree-ring records, revealing an unprecedented decline in recent decades. This decline is primarily driven by Antarctic amplification, which exacerbates temperature rise and disrupts circulation patterns, intensifying regional aridity.
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Kabe et al. (2025) Impact of hydrogeological regime changes on the Bakhtegan-Tashk lake system under groundwater overextraction
This study investigated the impact of human-induced hydrogeological regime changes on the desiccation of the Bakhtegan-Tashk Lake (BTL) system in southern Iran. It revealed a reversal in the natural groundwater-surface water flow, with the lake now losing approximately 10.5 million cubic meters of water annually to overexploited adjacent aquifers, accelerating its desiccation.
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Li et al. (2025) Societal and environmental interconnections: future directions for flood inundation models
This review synthesizes the evolution of flood inundation models from 1970 to 2023, highlighting the transition toward large-scale simulations and identifying eight interdisciplinary frontiers for future research.
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Robelin et al. (2025) RECHARGE, a model of potential recharge of aquifers applied to mainland France
The study introduces the RECHARGE model, a simplified soil water balance approach designed to estimate potential groundwater recharge across mainland France by correlating effective precipitation with a cartographic infiltration index (IDPR). The model provides a robust, large-scale estimation of renewable groundwater resources, validated against observed river flows and the physically-based SURFEX model.
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Cherie et al. (2025) Agricultural drought dynamics in East Gojjam: Insights on soil moisture, drought indices, and crop sustainability
This study investigates agricultural drought dynamics in East Gojjam, Ethiopia, from 2013 to 2024 by integrating remote sensing indices (NDVI_Max, SWDI, SMA, SPEI) and rainfall data with machine learning models. It found significant interannual drought variability, identifying 2022 as a critical year, and highlighted Soil Water Deficit Index (SWDI) and Normalized Difference Vegetation Index (NDVI_Max) as the most influential predictors of vegetation response.
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Cenobio-Cruz et al. (2025) Uncertainty propagation from gridded precipitation datasets to streamflow simulations: application to the Reno River basin (Italy)
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Yaseen et al. (2025) Quantitative assessment of best management practices for soil and water conservation: A case study from the Tarquinia plain
This study utilized the Soil and Water Assessment Tool (SWAT) to quantitatively evaluate individual and combined Best Management Practices (BMPs) in the Tarquinia plain, Italy, demonstrating that combined BMPs significantly reduce river sediment load by up to 33.9 %, total nitrogen by 27 %, and total phosphorus by 27.5 %.
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García‐García et al. (2025) Intercomparison of Earth Observation products for hyper-resolution hydrological modelling over Europe
This study comprehensively evaluated high-resolution Earth Observation (EO) products for precipitation, snow cover area, surface soil moisture, and evapotranspiration over Europe against observational references. It identified specific merged precipitation, MODIS/Sentinel-2 snow, and NSIDC SMAP soil moisture products as best performing for hyper-resolution hydrological modeling, while evapotranspiration products showed similar overall performance.
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Liu et al. (2025) Agricultural rapid-onset droughts in southern China’s grain-producing regions: Spatiotemporal evolution and potential drought-crop risks
This study investigated the spatiotemporal evolution and seasonal characteristics of agricultural rapid-onset droughts and their coupling with critical crop growth stages across southern China's major grain-producing regions from 1950 to 2022. It revealed significant spatial heterogeneity in drought characteristics and identified critical crop-drought coupling risks during specific phenological windows, emphasizing the need for phenology-aligned drought management.
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Hameed et al. (2025) Groundwater storage changes in the United States using baseflow recession method: Comparison with GRACE and well observations
This study quantifies long-term groundwater storage changes in over 1000 minimally disturbed watersheds across the contiguous United States using a novel event-based baseflow recession algorithm, demonstrating its reliability by comparing estimates with GRACE-DA and well observations.
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Jalilvand et al. (2025) Characterization of irrigation timing using thermal satellite observations, a data-driven approach
This study presents a data-driven framework using thermal satellite observations and change point detection to estimate irrigation timing and individual events. By comparing cropland land surface temperature (LST) with nearby natural vegetation, the method accurately identifies irrigation schedules in diverse agricultural regions.
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Deng et al. (2025) Enhanced water stress on vegetation productivity with climate warming over the Northern Hemisphere
This study investigates the inter-annual changes in gross primary productivity (GPP) in the Northern Hemisphere from 1982 to 2018, revealing that GPP trends stalled after 1998 due to enhanced atmospheric dryness (vapor pressure deficit, VPD) and that dynamic global vegetation models (DGVMs) fail to accurately capture these changes.
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Huang et al. (2025) Differential sensitivities of three types of compound drought and heatwave events to human-induced climate change across the globe
This study quantifies the differential influences of human-induced climate change on three types of compound drought and heatwave (CDHW) events (precipitation-based, runoff-based, and soil-moisture-based) using CMIP6 simulations, revealing greenhouse gas forcing as the dominant driver of global CDHW intensification, particularly for soil-moisture-based events, and projecting significant future severity growth and population exposure under high-emission pathways.
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Leivadiotis et al. (2025) Understanding Flash Droughts in Greece: Implications for Sustainable Water and Agricultural Management
This study investigates the spatiotemporal variability of flash droughts in Greece from 1990 to 2024 using ERA5-Land root-zone soil moisture data. It reveals distinct regional patterns in flash drought characteristics, including frequency, duration, and recovery, providing a data-driven framework for water management and adaptation strategies in Mediterranean agriculture.
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Pellicone et al. (2025) Assessment of Multiple Satellite Precipitation Products over Italy
This study evaluated five satellite precipitation products (CHIRPS, GPM, HSAF, PDIRNOW, SM2RAIN) against high-resolution ground data in Italy to address rainfall estimation uncertainties. It found that no single product performs optimally across all metrics, with GPM showing the most balanced performance, and product suitability depending on the intended hydrological application.
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Chen et al. (2025) A global long-term (2002–2022) C-band vegetation optical depth record retrieved after merging AMSR-E, AMSR2 and WindSat
This study developed a global, long-term (2002–2022) C-band Vegetation Optical Depth (C-VOD) dataset by merging observations from AMSR-E, AMSR2, and WindSat sensors using a combined inter-calibration method. The resulting merged C-VOD exhibited substantially improved temporal consistency across sensors, reducing global discrepancies between AMSR-E and AMSR2 from 6.20 % to 0.34 %.
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Liang et al. (2025) Quantifying anthropogenic drivers of water storage decline to support sustainable water management in a coal-mining semi-arid region
This study quantifies the anthropogenic drivers of terrestrial and groundwater storage decline in China's Mu Us Sandyland from 2003 to 2020 using a water balance framework, finding that ecological restoration and irrigation are the primary drivers, with coal mining also significant in energy-intensive areas, and proposes spatially differentiated management strategies for future sustainability.
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Majid et al. (2025) Virtual water gauge from the Synthetic Aperture Radar (SAR) altimeters for small reservoirs in tropical regions
This study evaluates the effectiveness of Sentinel-3 SAR altimetry for monitoring water surface elevation in small tropical reservoirs in Malaysia. The research demonstrates that SAR altimetry can achieve high correlations (>0.95) with in-situ gauges, providing a viable "virtual gauge" for complex tropical landscapes.
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Zareian et al. (2025) Adapting to dryness: Two decades of agricultural transformation in Iran’s arid zone through the water-energy-food-carbon lens
This study analyzed agricultural transformations in Iran's Isfahan Province (2004–2023) using the Water-Energy-Food-Carbon (WEFC) Nexus framework, revealing that declining groundwater availability, rather than meteorological drought, drove shifts towards water-efficient, high-value crops, improving water productivity and reducing environmental footprints despite a decrease in total cultivated area.
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Cruz et al. (2025) Long-term basin trends confirm a record 2022–2024 hydrological drought and water-storage losses in western Amazonia
This study quantifies long-term hydrological trends (1981-2024) in Western Amazonia and diagnoses the unprecedented 2022-2024 hydrological drought, revealing significant delays in high-runoff season onset, decreased low-flow discharge, and record-low terrestrial water storage. The findings underscore the region's increasing vulnerability and the urgent need for adaptive water resource management.
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Madaula et al. (2025) Hypersaline recharge in Mediterranean coastal aquifers: The role of aquifer–lagoon connectivity
This study investigates seasonal salinity variations in the shallow aquifer of the La Pletera salt marsh using time-lapse electrical resistivity tomography (ERT) and continuous monitoring. It reveals that hypersaline lagoons are a primary source of aquifer salinization, particularly after rainfall events, often exceeding the influence of seawater intrusion.
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Soltani et al. (2025) Enhancing Flood Forecasting with Machine Learning Informed by Integrated ParFlow-CLM Hydrological Modeling
This study integrates a fully coupled hydrological model (ParFlow/CLM) with a Gated Recurrent Unit (GRU) Convolutional machine learning model to enhance flood forecasting. It demonstrates that incorporating physically-derived soil water content (SWC) significantly improves the accuracy of river discharge predictions, outperforming standalone AI and hydrological models.
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Rosso et al. (2025) Drought hazard assessment across Sweden's diverse hydro-climatic regimes
This study assesses meteorological, agricultural, and hydrological drought hazard across Sweden using multiple standardized indicators and hydrological model simulations. It reveals distinct regional drought patterns, with central-eastern and south-eastern Sweden experiencing increasing dry conditions, while northern and western Sweden show wetting trends.
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Lopez et al. (2025) Evaluating a Simple Algorithm for an Evapotranspiration Retrieval Energy Balance Model in Mediterranean Citrus Orchards
This study evaluates the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model's ability to estimate actual evapotranspiration in a Mediterranean citrus orchard using Sentinel-2 imagery and Eddy Covariance data. The findings indicate that while the model is highly accurate during wet seasons, its performance declines during dry periods due to its limited sensitivity to plant physiological water stress.
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Ioniță et al. (2025) Multi-Indicator Drought Variability in Europe (1766–2018)
This study compares three long-term European drought reconstructions (PDSI, SPEI, and SMI) from 1766 to 2018, finding that the identification of "extreme" drought years and decades varies significantly depending on the indicator used. While the 2015–2018 event was exceptional in several metrics, its "unprecedented" status is indicator- and region-dependent, though all indicators consistently link drought to large-scale atmospheric blocking.
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Janicka-Kubiak (2025) Hydrological drought trends and seasonality in selected Polish catchments between 1993 and 2022 using a threshold based approach
This study investigates long-term trends and seasonality of hydrological droughts in selected Polish lowland catchments from 1993 to 2022, revealing a significant increase in summer and autumn low-flow events and a strong correlation between drought intensification and land use changes, particularly urbanisation.
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P. et al. (2025) Integration of Soil Moisture and Meteorological Data Using Deep Learning for Flash Drought Detection in Northeastern Brazil
This study developed and validated a deep learning U-Net model to integrate meteorological and satellite-derived soil moisture data for detecting flash drought events in Northeastern Brazil (NEB) from 2015–2023. The model accurately reproduced flash drought frequency and duration, demonstrating its potential for high-resolution monitoring and improving early-warning systems in data-scarce regions.
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Huang et al. (2025) Simulating precipitation-induced karst-stream interactions using a coupled Darcy–Brinkman–Stokes model
This study developed a coupled Darcy–Brinkman–Stokes model to simulate precipitation-induced karst-stream interactions, integrating water-air two-phase flow and variably saturated conditions. It found that rainfall intensity is the dominant driver, leading to complex multi-media interactions and shifting discharge contributions, with groundwater stored in porous media significantly influencing subsequent stream levels.
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Bouregaa (2025) Comparative evaluation of machine learning models for regional agricultural drought prediction in Algeria using SHAP analysis
This study comparatively evaluated eight machine learning models for regional agricultural drought prediction in Algeria, finding that optimal model performance is highly dependent on region and timescale, and that efficient feature selection can maintain accuracy while SHAP analysis reveals key climate drivers.
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Nguyen‐Duy et al. (2025) Performance and added value of a high-resolution (2 km) rainfall product based on WRF-downscaled ERA5 for Ho Chi Minh City, Vietnam
This study dynamically downscaled ERA5 reanalysis data using the WRF model and applied bias correction to generate a 2-km resolution rainfall product for Ho Chi Minh City, Vietnam. The bias-corrected product (WRFC-HCM) significantly improved daily rainfall accuracy and representation of extreme events compared to original reanalysis datasets, despite limited improvement at the monthly scale.
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Dorthe et al. (2025) The thermal future of a regulated river: spatiotemporal dynamics of stream temperature under climate change in a peri-Alpine catchment
This study investigates the future thermal regime of a peri-Alpine regulated river under climate change using a high-resolution process-based model. Projections indicate mean annual water temperatures may rise by up to 4 °C by 2080–2090 under RCP 8.5, with river regulation introducing distinct spatial and seasonal warming patterns, particularly in autumn and winter due to reservoir thermal inertia.
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Yan et al. (2025) Improved Near-Real-Time Precipitation Estimation From Himawari-8 Data and Gauge Observations in the Xiangjiang River Basin Using a Three-Stage Machine Learning Framework
This paper aims to improve near-real-time precipitation estimation in the Xiangjiang River Basin by developing a three-stage machine learning framework that integrates Himawari-8 satellite data with ground-based gauge observations.
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Aryal et al. (2025) Dynamics of Meteorological and Agricultural Drought in the Karnali River Basin, Nepal
This study provides a multidimensional drought analysis for the Karnali River Basin (Nepal) using 30 years of observational and satellite data, revealing a long-term greening trend despite a significant increase in meteorological drought severity, highlighting the complex interplay of climatic and anthropogenic factors.
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Zhu et al. (2025) Drought identification using standardized evaporative fraction: Perspective from surface energy partitioning
This study introduces the Standardized Evaporative Fraction (SEF) as a new drought index, derived from surface energy partitioning, to better incorporate land-atmosphere interactions in drought identification. The SEF is shown to be effective globally from 1960–2022, demonstrating good consistency with existing indices and revealing increasing drought trends in several global hotspots.
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Xu et al. (2025) Objectivization of an expert assessment framework for drought monitoring
The study develops a Comprehensive Drought Monitoring Model (CDMM) that objectivizes the expert-based U.S. Drought Monitor (USDM) framework using the Random Forest algorithm. The model successfully reproduces USDM drought categories and demonstrates high transferability by effectively capturing regional drought dynamics across China.
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Wu et al. (2025) Cascading effects of cross-year droughts on flow-sediment dynamics across distinct drought types
This study investigates the cascading effects of cross-year meteorological and hydrological droughts on flow-sediment dynamics in seven Loess Plateau tributaries. It reveals that hydrological droughts induce significantly greater reductions in sediment transport rates and larger post-drought surges compared to meteorological droughts, with afforestation further reducing sediment supply efficiency.
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Wang et al. (2025) Hidden deep soil moisture droughts
Anthropogenic climate change exacerbates global soil moisture droughts, which are now revealed to also occur in deeper layers, and are projected to become longer lasting and more severe in a warming climate.
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Al-Taher et al. (2025) Optimizing cotton green water footprint prediction using hybrid machine learning algorithms: a case study of Al-Gezira state, Sudan
This study optimizes cotton green water footprint (GWFP) prediction in Al-Gezira state, Sudan, using hybrid machine learning algorithms (RF, XGBoost, SVR) with climatic and remote sensing data from 2001-2020, demonstrating that hybrid models significantly outperform single models in accuracy and error reduction.
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Tripathy et al. (2025) Lagged Soil Moisture Controls on the Persistence of Drought and Heatwaves in the United States
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2025...
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Guan et al. (2025) Anthropogenic enhancement of subsurface soil moisture droughts
This study introduces a Lagrangian four-dimensional tracking framework to identify "deep droughts" (more extensive moisture deficits in deep than surface soils) and reveals their increasing duration and intensity globally over the past four decades due to anthropogenic climate change, with projections for further exacerbation under higher-emission scenarios.
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Zhou et al. (2025) A novel Hankel spectrum analysis filtering for reducing North-South stripes in GRACE gravity solutions
This study introduces the Hankel Spectrum Analysis Filtering (HSAF) framework to effectively reduce North-South striping noise in GRACE-derived terrestrial water storage anomalies. HSAF significantly improves the accuracy of water storage estimates by preserving hydrological signals and outperforming conventional filters across global and basin scales.
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Zheng et al. (2025) Corrigendum to “Coupling differentiable tau-omega model with Kolmogorov–Arnold network for soil moisture estimation over the Tibetan Plateau” [J. Hydrol. 662(Part B) (2025) 133940]
This corrigendum rectifies an error in the author affiliation order for the original article titled "Coupling differentiable tau-omega model with Kolmogorov–Arnold network for soil moisture estimation over the Tibetan Plateau."
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Wendt et al. (2025) Controls on the southwest USA hydroclimate over the last six glacial-interglacial cycles
This study uses an absolute-dated speleothem record from Devils Hole cave 2 and Earth system simulations to identify the primary drivers of hydroclimate and vegetation changes in the southwest USA over the last 580,000 years, finding that temperature-related mechanisms primarily control δ18O variability, with secondary influences from North American ice sheets, while vegetation density is forced by Northern Hemisphere summer intensity.
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Marín-Martín et al. (2025) A five-century tree-ring record from Spain reveals recent intensification of western Mediterranean precipitation extremes
This study reconstructs 520 years of quantitative precipitation in the Iberian Range, eastern Spain, using tree-ring data, revealing an unprecedented intensification in the frequency and intensity of hydroclimatic extremes during the late 20th and early 21st centuries compared to previous centuries.
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Veness et al. (2025) User priorities for hydrological monitoring infrastructures supporting research and innovation
This study identifies end-user priorities for the UK’s new GBP 38 million Floods and Droughts Research Infrastructure (FDRI), revealing that value is maximized when infrastructures move beyond simple data provision to actively enable decentralized data collection and foster collaborative research communities.
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Jääskeläinen et al. (2025) High-resolution soil moisture mapping in northern boreal forests using SMAP data and downscaling techniques
This study develops a machine-learning-based downscaling model to estimate soil moisture at 1 km and 250 m spatial resolutions across northern boreal forests. By integrating SMAP satellite data with vegetation and weather parameters, the model improves soil moisture prediction accuracy over forested sites compared to original coarse-resolution products.
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Mouassom et al. (2025) Hydrodynamics of rainfall peaks in homogeneous regions clustered using the K-means algorithm in Central Africa
This study identifies three homogeneous rainfall subregions in Central Africa using K-means clustering on 1984–2023 daily reanalysis data, revealing distinct rainfall peak patterns and their underlying hydrodynamic and thermodynamic mechanisms.
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Elias et al. (2025) Drought feature assessment unravels how temperature increase has enhanced earlier and more severe drought in Lebanon over the last 60 years
This study investigated how climate change from 1960 to 2020 affected various drought facets in Lebanon using the DFEAT tool, which analyzes daily soil moisture. It revealed a significant shift towards drier conditions, characterized by an earlier drought onset (up to 17 days) and a delayed offset (up to 5 days), primarily driven by rising temperatures despite stable annual precipitation.
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Biella et al. (2025) The 2022 drought needs to be a turning point for European drought risk management
This study analyzes the 2022 European drought by linking climate indices with a continent-wide survey of 481 water managers to evaluate sectoral impacts and management effectiveness. The findings reveal that while drought risk is perceived to be increasing across Europe, current management remains largely reactive and fragmented, prompting a call for a legally binding European Drought Directive.
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Samuel et al. (2025) Assessment of Historical and Future Mean and Extreme Precipitation Over Sub‐Saharan Africa Using NEX ‐ GDDP ‐ CMIP6 : Part II —Future Changes
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Patil et al. (2025) Over 100 global climate sensitive rivers are experiencing large and severe changes in streamflow volume and timing
This study analyzed streamflow volume and timing changes in 812 climate-sensitive rivers globally from 1950 to 2022, finding increasing streamflow and earlier timing in over half of the sites, largely driven by precipitation changes, with significant implications for river health and water management.
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Abdulahi et al. (2025) Impact of Climate Change on Drought Dynamics in the Ganale Dawa River Basin, Ethiopia
This study assessed the impact of climate change on agricultural and hydrological drought dynamics in Ethiopia's Ganale Dawa Basin using machine learning-enhanced CMIP6 projections and satellite-based indices. Findings reveal increasing variability in agricultural drought and continued recurrence of hydrological drought, especially under high-emission scenarios.
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Yasin et al. (2025) Spatially integrated standardized relative humidity index: A principal component analysis-based approach for regional drought assessment
This study introduces the Multivariate Standardized Relative Humidity Index (MSRHI), a novel drought assessment tool that integrates relative humidity data from multiple stations using Principal Component Analysis (PCA). The index provides a more stable and spatially coherent representation of regional drought conditions across Pakistan's diverse climatic zones compared to traditional station-based univariate indices.
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Zhi-jie et al. (2025) Nonstationary Spatiotemporal Projection of Drought Across Seven Climate Regions of China in the 21st Century Based on a Novel Drought Index
This study projects the spatiotemporal evolution of drought across seven climate regions of China in the 21st century using a novel CO2-aware standardized moisture anomaly index (SZI[CO2]) and nonstationary Copula-based approaches. It finds a wetting trend in Northern and Western China, while Central and Southern China are projected to experience drying, with drought characteristics exhibiting strong nonstationarity and higher joint probabilities under high-emission scenarios.
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Wang et al. (2025) The effect of rainfall variability on Nitrogen dynamics in a small agricultural catchment
This study investigates the effect of inter-annual and intra-annual rainfall variability on nitrogen (N) dynamics and water quality in a small agricultural catchment in central Germany using a coupled hydrological and N transport model driven by a stochastic rainfall generator. It finds that higher annual precipitation enhances N transformation and transport, while lower annual precipitation promotes N retention, with vegetation health critically influencing N dynamics during extreme droughts and rewetting periods.
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Huang et al. (2025) Hydropower vulnerability to drought-flood abrupt alternation under climate change
This study quantifies the global impact of drought-flood abrupt alternation (DFAA) events on hydropower, revealing that these rapid transitions significantly reduce generation and that high reservoir regulation capacity is a key factor in mitigating these losses.
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Disasa et al. (2025) Comprehensive review of drought characteristics and intensification under climate change: implications for agriculture and water resources
This review synthesizes the intensification of drought characteristics across meteorological, hydrological, and agricultural sectors under climate change. It highlights how global warming alters drought frequency and severity, leading to significant risks for water resources and crop yields.
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Anoop et al. (2025) Atmospheric aridity perturbs critical soil moisture thresholds of plant water stress over Indian biomes
This study quantifies critical soil moisture thresholds (θcrit) for Indian biomes using satellite data and two independent methodologies, revealing that atmospheric aridity (VPD) significantly perturbs θcrit, leading to seasonal and hydrological forcing-driven variations. The covariance-based method (Cov(GPP-VPD)-SM) is found to be more sensitive for assessing these dynamics.
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Gaiolini et al. (2025) Salt migration and export via subsurface irrigation in a saline reclaimed landscape of the Po River lowland (Italy)
This study investigates the causes and quantifies the sources of dissolved salts in a saline reclaimed landscape of the Po River lowland, Italy, focusing on the impact of subsurface irrigation via tile drains. It reveals that sub-irrigation significantly accelerates salinization and salt export, with peaty lenses and decomposing halophytes acting as major salt sources, leading to high surface water salinity.
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Zhang et al. (2025) From Depletion to Recovery: Tracking Water Storage Changes in the Semiarid Region of Inner Mongolia, China
This study evaluated spatiotemporal variations in terrestrial water storage (TWS) and groundwater storage (GWS) in semiarid Inner Mongolia from April 2002 to January 2025, revealing a long-term TWS and GWS depletion that notably reversed after 2022 due to policy interventions and precipitation changes, with significant regional differences in driving factors.
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Kanno et al. (2025) Deep sowing accelerates rice emergence under water deficit: field experiments and model development
This study investigated the potential of deep sowing to accelerate rice emergence under water deficit, finding that sowing at 4 cm or deeper significantly advanced emergence in field experiments under drought. A novel process-based model was developed and validated, accurately predicting emergence dates based on sowing depth, soil temperature, and moisture, and suggesting optimal deep sowing depths to mitigate drought risk.
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Sebastian et al. (2025) Incorporating varying vegetation characteristics driven by Hydrometeorology in the land surface modeling by variable Infiltration Capacity model
This study demonstrates the critical role of dynamic vegetation in hydrological modeling, particularly for evapotranspiration in India, by integrating a machine learning model (LSTM) to simulate vegetation variability within the Variable Infiltration Capacity (VIC) model, revealing an 18% increase in annual evapotranspiration compared to static vegetation approaches.
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Anderson et al. (2025) What is a drought-to-flood transition? Pitfalls and recommendations for defining consecutive hydrological extreme events
This study assesses the suitability and differences of various threshold-level methods for defining drought-to-flood transitions using eight case study catchments, revealing that methodological choices significantly alter detected event characteristics and often fail to capture historically impactful transitions.
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Sindarov et al. (2025) Application of the AquaCrop model for cotton production under water scarce arid conditions
This study calibrated and validated the AquaCrop model for cotton production under arid conditions in Uzbekistan, identifying an optimal irrigation regime (FC 70-70-65%) that maximized yield and water productivity with high model accuracy.
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Zhang et al. (2025) Coupled surface water-groundwater-crop model considering the impact of irrigation using different calibration targets
This study developed a coupled VIC-EPIC-HYDRUS (VEH) model to improve the simulation accuracy of hydrological processes on agricultural land, considering irrigation impacts, and demonstrated its superior performance through multi-objective parameter optimization and validation.
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Gillo et al. (2025) Integrated assessment of meteorological, hydrological and agricultural drought in Abaya Chamo sub Basin, Ethiopia
This study comprehensively assessed meteorological, hydrological, and agricultural drought characteristics in Ethiopia's Abaya Chamo sub-basin from 1981-2021 using SPEI, SSI, and SSMI, revealing increasing aridity and severe to extreme drought intensities that varied spatially across catchments.
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Ali (2025) Machine learning approaches for soil moisture prediction: enhancing agricultural water management with integrated data
This study evaluates the effectiveness of nine machine learning algorithms for predicting soil moisture at two depths in New South Wales, Australia, using integrated climate, soil, and vegetation data. The results demonstrate that ensemble models, particularly Random Forest and XGBoost, significantly outperform traditional linear models, providing a robust framework for precision irrigation management.
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Sandoval et al. (2025) Towards water resilience: A multi-stage calibration framework for large-scale integrated surface–subsurface hydrological models
This study presents a multi-stage calibration framework for large-scale, high-fidelity integrated surface water–groundwater models using sensitivity analysis and Gaussian Process Regression surrogates. The approach resulted in the first robustly calibrated integrated model of the Po River District (87,000 km²), effectively capturing complex 3D subsurface dynamics and river discharge.
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Khan et al. (2025) Deep learning approach for vertical soil moisture profile estimation using hydrometeorological data
This study presents the evaluation of the eartH2Observe Tier-1 dataset, a global ensemble of ten hydrological and land surface models forced by a consistent atmospheric dataset. The research demonstrates that the ensemble mean generally provides a more reliable estimation of global water fluxes and storage than any individual model.
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Bushra et al. (2025) CAMELS-NZ: hydrometeorological time series and landscape attributes for New Zealand
This paper introduces CAMELS-NZ, the first large-sample catchment hydrology dataset for New Zealand, providing hourly hydrometeorological time series and comprehensive landscape attributes for 369 catchments from 1972 to 2024. The dataset fills a critical gap in global hydrology by representing a Pacific Island environment with complex hydrological processes, supporting diverse research applications.
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Anand et al. (2025) Balancing Productivity and Climate Impact: A Framework to Assess Climate‐Smart Irrigation
## Identification - **Journal:** Earth s Future - **Year:** 2025...
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Fiorese et al. (2025) Understanding the influence of Malta litho‐structural features on the dynamics of nitrate transport in the vadose zone
This study evaluates and compares the performance of the ISBA land surface model and the mHM hydrological model in simulating river discharge across 560 basins in France. The results demonstrate that mHM significantly outperforms ISBA in discharge simulation accuracy, while ISBA provides a more comprehensive representation of surface energy balance.
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Hong et al. (2025) Quantifying Impacts of Precipitation and Evapotranspiration on Future Runoff in the Han River Basin Using the Budyko Framework
This study quantifies the relative impacts of precipitation and potential evapotranspiration on future runoff in the Han River basin using the Budyko framework. The findings reveal that precipitation is the dominant driver of projected runoff increases, contributing between 67% and 84% to the total change.
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Eiras‐Barca et al. (2025) Revisiting the Impact of Moisture Transport Deficit on Droughts: Prospective Climate Change Analysis and Emerging Hypotheses
This comprehensive review systematically examines the pivotal role of moisture transport deficits in the genesis and progression of droughts under climate change, confirming that these deficiencies amplify drought severity by reducing precipitation or intensifying evaporative demand.
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Lin et al. (2025) Quantifying the Lifespan of 3D Flood Structures: Unlocking the Potential of Flood Detention Areas for Enhanced Flood Control in China
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Manteaux et al. (2025) Evaluation of SWAT‐RIVE's Ability to Represent the Hydrobiogeochemical Dynamics in the Vienne Watershed
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Bernard et al. (2025) Process‐Level Evaluation of the Land‐Atmosphere Interactions Within CNRM‐CM6‐1 Single‐Column Model Configuration
## Identification - **Journal:** Journal of Advances in Modeling Earth Systems - **Year:** 2025...
