-
Lundquist et al. (2026) Snow What? Strengths and Limitations of Different Snow Products in Western U.S. Mountains
This study evaluates various snow water equivalent (SWE) products against aerial LiDAR observations in the western United States to identify the most accurate datasets for hydrological analysis.
-
Altarhouni (2026) Predicting Sandstorms and Drought Using Artificial Intelligence Techniques Based on Climate Data: A Case Study of Jifarah Plain, Libya
This study develops an AI-based framework to predict drought severity and sandstorm occurrence in Libya's Jifarah Plain using climate data from 1985 to 2022. The results demonstrate that a weighted Ensemble model provides the highest predictive accuracy for these climate hazards.
-
Liang et al. (2026) Advancing Urban Flood Risk Mapping: A Hybrid Framework Integrating Interpretable Machine Learning and Uncertainty-Aware Expert Judgment
This study develops a hybrid framework integrating CatBoost machine learning and Z-number-based Fuzzy Analytic Hierarchy Process (Z-FAHP) to map urban flood risk in Tokyo, Japan, by combining physical susceptibility with socioeconomic exposure and vulnerability.
-
Li et al. (2026) Extreme drought converts grassland carbon sinks to sources in Inner Mongolia, but strong recovery follows: Insights from a 9-year study
This study evaluates the impact of extreme drought on carbon fluxes in Inner Mongolia's grasslands from 2010 to 2018, finding that the 2017 drought converted carbon sinks into sources, followed by a strong recovery in 2018.
-
Arancibia et al. (2026) Runoff Generation Processes and Thresholds in Agricultural Catchments of Central Chile
This study investigates runoff generation thresholds in three agricultural catchments in central Chile, finding that runoff is triggered when combined precipitation and deep soil moisture exceed ~505 mm and is significantly modulated by land-use.
-
Scagliarini (2026) Non-parametric time between events and amplitude methods for monitoring regional drought characteristics
The paper proposes a non-parametric Time Between Events and Amplitude (TBEA) framework to monitor regional drought characteristics, finding a significant increase in the frequency and severity of droughts in the Emilia-Romagna region since the early 2000s.
-
Wang et al. (2026) Multi-Scenario Optimal Allocation of Agricultural Water Resources in a Canal–Well Irrigation District Based on Irrigation Priority
The study develops a multi-objective optimization model for water allocation in the Jiamu Town Irrigation District to minimize groundwater extraction and irrigation deficits. The results demonstrate that the optimized scheme significantly improves surface water efficiency and reduces groundwater reliance while maintaining high crop satisfaction rates.
-
Xu et al. (2026) Evaluation of Irrigation Water Resources Carrying Capacity and Identification of Its Dominant Patterns of Variation in the Hetao Irrigation District
This study evaluates the irrigation water resources carrying capacity of the Hetao Irrigation District from 2001 to 2023, finding that improvements in water-use efficiency have partially offset the decline in water availability.
-
Dinneen (2026) Why are El Niños getting stronger? 1000-year-coral record points to climate change
This study utilizes a 1,000-year coral proxy record from the Galapagos Islands to demonstrate that human-induced climate change has increased the frequency and intensity of strong El Niño events.
-
Xu et al. (2026) A network-based framework for deciphering extreme precipitation propagation across China
The study develops a network-based framework to analyze the propagation of extreme precipitation across China, identifying key propagation hubs and the role of geomorphological features in facilitating hydroclimatic connectivity.
-
Abdallah et al. (2026) Development and Field Validation of WaziSense, a Low-Cost Solar-Powered IoT Smart Tensiometer for Soil–Water Monitoring and Irrigation Scheduling in Semi-Arid Agriculture
Development and field validation of WaziSense, a low-cost, solar-powered IoT smart tensiometer designed to optimize irrigation scheduling in semi-arid regions.
-
Yang et al. (2026) Cross-Scale Performance Evaluation of GPM IMERG V07 Precipitation Products in a Typical Mountainous Monsoon Region
This study evaluates the performance of GPM IMERG V07 and V06 precipitation products in Guangxi, China, finding that while V07 improves daily detection, it introduces systematic positive biases that amplify at monthly scales.
-
Ogou et al. (2026) Observed and Simulated Decadal Variability of Precipitation in North Africa and the Mediterranean: Insights from ERA5 Reanalysis and CORDEX-CORE Simulations
This study evaluates the performance of CORDEX-CORE regional climate models in characterizing decadal precipitation variability across the Mediterranean and North Africa (MNA) using ERA5 reanalysis data. The results indicate a general tendency for models to overestimate precipitation, with the Multi-Model Ensemble (MME) providing the most reliable reproduction of precipitation patterns.
-
Ren et al. (2026) Exploring Drivers of Hydrological Drought Dynamics Across the Upper Yellow River Basin, China: Insights from the Sub-Basins Contribution, Large Reservoir Regulation, and Teleconnection
This study analyzes the spatiotemporal evolution of hydrological droughts in the Upper Yellow River Basin (UYRB), demonstrating that while natural trends show increasing drought severity, reservoir regulation significantly modifies drought intensity, duration, and the influence of large-scale climate drivers.
-
Zhao et al. (2026) Generating a consistent long-term L-band soil moisture record through Bayesian merging of SMOS and SMAP data
The study develops a Bayesian framework to harmonize SMOS soil moisture data with SMAP as a reference, creating a consistent long-term L-band soil moisture record from 2010 to 2024. The resulting merged product significantly increases the annual frequency of effective observations.
-
Biryło (2026) Regime shifts in European basins detected from the water budget and the Combined Climatological Deviation Index
The study evaluates hydroclimatic regime shifts in ten major European basins by integrating water budget components and the Combined Climatological Deviation Index (CCDI). It identifies a general hydroclimatic reorganization occurring around 2015, with regional variations in timing and magnitude.
-
Duan et al. (2026) Spatiotemporal variation of extreme precipitation and its terrain modulation in the Hengduan Mountains: machine learning and interpretable analysis
This study analyzes the spatiotemporal evolution of extreme precipitation in the Hengduan Mountains from 2005 to 2024 and utilizes interpretable machine learning to quantify the nonlinear influence of topographic factors on these events.
-
Wang et al. (2026) Integrated analysis of surface water-groundwater interactions and enhanced hydrological forecasting of glacierized river in cold and arid regions
This study employs a coupled SWAT-MODFLOW model to quantify surface water-groundwater interactions and project future runoff trends in the glacierized headwater catchment of the Bortala River under CMIP6 climate scenarios.
-
Worden et al. (2026) Decorrelating carbon and water fluxes in the Amazon Basin
The study reveals that carbon (GPP) and water (ET) fluxes in the Amazon Basin are frequently decoupled rather than tightly coupled, with this decorrelation increasing over the last 40 years due to rising vapor pressure deficit (VPD).
-
Millot-Weil et al. (2026) Asian summer monsoon orbital variability directly paced by CO2 and precession, not eccentricity
This study demonstrates that the 100-kyr variability of the Asian summer monsoon is primarily driven by greenhouse gas concentrations rather than eccentricity, while the 20-kyr variability is directly paced by precession.
-
Shen et al. (2026) Divergent Forest Cover Changes Dominate Inter‐Model Spread in Near‐Surface Wind Speed Trends Over China
This study identifies forest cover changes as the primary driver of uncertainty in global climate model simulations of near-surface wind speed (NSWS) over China.
-
Zheng et al. (2026) Aerosol modulation of precipitation systems migrating from mountains to plains in Northern China during early autumn
This study demonstrates that aerosols invigorate peak precipitation events as weather systems migrate from mountains to the North China Plain, driven by latent heat release and modulated by topographic transport mechanisms.
-
Zaifoglu (2026) Multidimensional Assessment of Hydroclimatic Changes in Northern Cyprus
This study performs a multidimensional assessment of hydroclimatic trends in Northern Cyprus, revealing spatial heterogeneity in precipitation (wetting in mountainous/western areas and drying in eastern coasts) and widespread warming in maximum temperatures.
-
Zhang et al. (2026) Large‐Scale Extreme Precipitation Events Increasingly Drive River Discharge Extremes in the Yangtze River Basin
This study analyzes the evolution of extreme precipitation events (EPEs) of varying spatial scales in the Yangtze River Basin from 1960 to 2024, finding that larger-scale events are increasing in frequency and intensity, thereby driving higher hydrological flood risks.
-
Ashi et al. (2026) Studying the effectiveness of cloud seeding on enhancing precipitation
The study develops a mathematical model to simulate cloud seeding dynamics, proposing that aerosol chemicals be introduced proportional to water vapor density to optimize precipitation while reducing costs and environmental pollution.
-
Mirzaei et al. (2026) Quantifying Future Drought Intensity and Frequency: A Multi-Scenario Study Using SPI, PDSI, and LPDF in the Mid-Atlantic Region of the US
This study evaluates future drought conditions in Maryland, USA, using meteorological and hydrological indices, finding that rising temperatures may offset increased precipitation to intensify hydrological drought.
-
Gao et al. (2026) Physical continuous-simulation SWAT + framework to develop flow duration curves within the contiguous United States
This study implements a SWAT+ framework to predict Flow Duration Curves (FDCs) across the contiguous United States (CONUS), providing a tool to characterize streamflow regimes and watershed performance.
-
Nikta et al. (2026) Lagged global relationships between teleconnection indices and hydrological drought: a continental-scale analysis using ERA5-Land runoff data
This study evaluates the lagged correlation between five teleconnection indices and hydrological drought globally to identify optimal lag times for early drought detection. It finds that optimal lags are generally shorter at continental scales than at the global scale.
-
Liu et al. (2026) A river-augmented highly parameterized linear inverse method for enhanced water table estimation across China
The study develops a river-augmented highly parameterized linear inverse method (HPLIMRG) that utilizes river-stage observations to improve the accuracy and reduce the uncertainty of water table mapping in regions where monitoring wells are scarce.
-
Li et al. (2026) Physical Constraints on Future Tibetan Plateau Summer Precipitation: Emergent Relationships and Pareto Optimal Ensembles
This study utilizes emergent constraints and Pareto optimal ensemble selection on CMIP6 models to reduce uncertainty in summer precipitation projections for the Tibetan Plateau for the period 2051–2100.
-
Mather et al. (2026) Where has all the energy gone? Quantifying advective energy fluxes with a dense tower network
The study investigates whether a high-density tower network can quantify advective energy fluxes to resolve the systematic energy imbalance observed in eddy covariance measurements.
-
Zarei (2026) A dynamic eco-hydrological drought framework integrating soil moisture memory, human pressure, and vegetation–atmosphere coupling under a non-stationary climate
The study introduces the Dynamic Eco-hydrological Drought Index (DEHDI), a process-based framework that integrates soil moisture memory, human pressure, and $\text{CO}_2$ effects to detect terrestrial water stress more accurately than traditional meteorological indices.
-
Nagavciuc et al. (2026) From Rare to Recurrent: Intensifying Hot–Dry Extremes Across Romania
This study quantifies the historical evolution and future projections of compound hot-dry months in Romania, revealing a significant increase in frequency and spatial extent, particularly in lowland regions, accelerating since the 1990s.
-
Wu et al. (2026) Spatio-temporal variations in the sensitivity of forests to seasonal precipitation across Europe
This study utilizes long-term satellite data and machine learning to analyze the sensitivity of European forests to seasonal precipitation over two decades, finding that warming and increased vapor pressure deficit (VPD) amplify these sensitivities, particularly during summer and in low-elevation, warm-dry regions.
-
Gao et al. (2026) Hydrothermal Balance and Diurnal Temperature Range Jointly Explain Maize Yield Variability in a Semi-Arid Region of North China
This study evaluates the drivers of maize yield variability in Lyuliang City, North China, from 2005 to 2024, concluding that hydrothermal balance (specifically the aridity index) is a more significant predictor of yield than precipitation or temperature alone.
-
Hussain et al. (2026) Climate Change Intensifies Cropland Drought Exposure in the Indus River Basin During the 21st Century
This study evaluates current and future cropland exposure to drought in the Indus River Basin (IRB), finding that exposure increases significantly over time, driven initially by climate change and subsequently by cropland expansion.
-
Gan et al. (2026) Drought dynamics and propagation in the Wei River Basin under non-stationary conditions
The study develops non-stationary meteorological (NRDI) and hydrological (NSRI) drought indices using a GAMLSS framework to more accurately analyze the propagation and correlation between these two types of droughts under changing climatic and anthropogenic conditions.
-
Brendel (2026) Climate Transitions in the Argentine Pampas Exceed Global Rates and Reshape Crop Exposure: A Multi‐Period Köppen–Geiger Analysis (1901–2020)
This study analyzes Köppen–Geiger climate zone transitions in the Argentine Pampas from 1901 to 2020, revealing that the region is a mid-latitude climate change hotspot driven primarily by shifts in precipitation seasonality and the development of winter droughts.
-
Nouri et al. (2026) Climate change and agricultural droughts: understanding the growing challenge
This chapter provides a conceptual framework for understanding drought, specifically focusing on the definitions and classifications of agricultural drought in the context of climate change.
-
Han et al. (2026) Revealing Upstream Dam Operations under Information Asymmetry for Integrated Water Resources Management
This study develops a framework to infer upstream dam operations—including water levels, inflows, and releases—using only multi-source satellite observations and hydrological modeling to reduce information asymmetry in data-restricted transboundary basins.
-
Bo et al. (2026) Physically Consistent Reconstruction of Sparse Scatterometer Ocean Surface Wind Fields Based on Physics‐Guided Generative Learning
The study develops a physics-guided generative learning network to reconstruct sea surface wind vector fields from sparse scatterometer data, ensuring physical consistency and high accuracy.
-
Frehner et al. (2026) Revisiting energetic limits on crop coefficients in heterogeneous canopies
The paper challenges the widely accepted theoretical upper limit of the crop coefficient ($K_c$) of 1.2–1.3, arguing that this limit ignores the effects of advection and canopy heterogeneity on evapotranspiration (ET).
-
Li et al. (2026) Climate driving factors of winter-spring wildfires in the Northeast and Southwest forest zones of China
This study identifies two primary high-activity wildfire regions in China (Northeast and Southwest) and demonstrates that while soil moisture deficit is the proximal driver for both, the broader climatic controls differ, involving the Arctic Oscillation in the Northeast and El Niño in the Southwest.
-
Liu et al. (2026) DeKNN: Decompositional Kriging Neural Network for efficient and interpretable spatiotemporal interpolation
The paper proposes the Decompositional Kriging Neural Network (DeKNN), a framework designed to efficiently and interpretably interpolate spatiotemporal data that is missing completely at specific locations (MCAL).
-
Liu et al. (2026) Weekly VPD and Monthly Precipitation as Contrasting Dominant Drivers of Soil Moisture in the Yangtze River Basin
This study investigates the scale-dependent meteorological drivers of soil moisture in the Yangtze River Basin, finding that vapor pressure deficit (VPD) dominates short-term variability while precipitation governs monthly trends.
-
Zhu et al. (2026) Risk Identification and Resilience Enhancement for Rainstorm Disaster Chains: An Event Evolutionary Graph–Driven Approach
The study proposes an event evolutionary graph-based framework to identify critical risk nodes and propagation pathways in rainstorm disaster chains to enhance systemic resilience.
-
Semenova et al. (2026) Spatiotemporal Characteristics and Synoptic Patterns of Dry‐Hot Winds ( Sukhoviys ) in Ukraine
This study characterizes "sukhoviys" (dry-hot winds) in Ukraine from 1973 to 2025, identifying a predominance of short-duration events and an increasing trend in high-temperature episodes since 2007.
-
Simantiris et al. (2026) Unsupervised Estimation of Post-Event Standing Urban Floodwater Depth Using Aerial Imagery and Digital Terrain Models
The study proposes an unsupervised, training-free framework that estimates residual floodwater depth by combining color-based segmentation of UAV imagery with Digital Terrain Models (DTMs) based on the hydrostatic equilibrium principle.
-
Deng et al. (2026) Application of Deep Learning Semantic Segmentation Models in Remote Sensing-Based Cropland Non-Grain and Non-Agriculturalization Monitoring: A Comparative Study
This study evaluates the performance, robustness, and generalization of seven mainstream semantic segmentation models for cropland non-grain and non-agriculturalization monitoring (CNNM) using UAV-derived imagery.
-
Alharbi (2026) Multivariate Regionalization of Rainfall Stations in Saudi Arabia Using Rainfall Concentration, Short-Duration Intensity, and Physiographic Descriptors
The study develops a stability-validated multivariate framework to partition rain gauges in Saudi Arabia into homogeneous rainfall regions to facilitate hydrological design and data transfer to ungauged sites.
-
Saadoun et al. (2026) Using a statistical approach and the Standardised Streamflow Index (SSI) to analyse the impact of climate change on runoff in catchments on the north-eastern edge of Algeria
This study analyzes river flow trends in northeastern Algeria from 1958 to 2023, revealing significant declines in water availability and persistent drought conditions due to climate change.
-
Maisha et al. (2026) Variogram Assessment of Sub-Field Scale Soil Water Variability for Irrigation Management
This study employs variogram analysis to quantify small-scale soil water variability in irrigated corn fields, concluding that spatial heterogeneity is anisotropic and that sensor placement parallel to crop rows minimizes measurement noise.
-
George et al. (2026) Long-Term Air–Water Temperature Coupling and Urbanization Effects on Stream Water Temperature in Two Adjacent Watersheds in North Central Texas
This study analyzed air temperature (AT) and water temperature (WT) relationships in two Texas watersheds with contrasting urbanization levels from 2012 to 2021, finding that while atmospheric conditions drive annual trends, urbanization increases mean water temperatures and alters thermal timing.
-
Kassaye et al. (2026) Hydrological resilience to climate extremes and its implications for water security in the data-scarce Baro river basin
This study evaluates the hydrological resilience of the Baro River Basin in Ethiopia to climate extremes, finding that the basin is more vulnerable to floods than droughts, with floods exhibiting higher frequency and longer recovery periods.
-
Davaatseren et al. (2026) Soil Carbon Recovery and Hydrological Buffering in Mongolian Rangelands: Local Benefits and Limits of Earth-System Connectivity
This study evaluates the impact of soil organic carbon (SOC) restoration in Mongolian rangelands across local, basin, and Earth-system scales, concluding that while local hydrological benefits are present, they are undetectable at larger scales.
-
Sharma et al. (2026) Optimization of Irrigation Requirement of Summer Moong Bean (Vigna radiata L.) under Changing Climatic Scenario of Junagadh District of Gujarat Using CROPWAT Model
This study utilizes the FAO CROPWAT 8.0 model to estimate water requirements and establish an optimized irrigation schedule for summer green gram in the Junagadh district of Gujarat. The results provide a precise irrigation framework to mitigate the constraints of high evaporative demand and limited rainfall in semi-arid environments.
-
Li et al. (2026) UAV-Based Thermal Inversion for Canopy Temperature Retrieval and Precision Irrigation
This study develops a UAV-based thermal infrared framework for high-resolution canopy temperature retrieval and irrigation decision support in tea plantations using the Crop Water Stress Index (CWSI).
-
Sen et al. (2026) Benchmarking farmers’ irrigation decisions using farm competition data and a crop growth model
This study benchmarks actual farmer irrigation practices against optimal amounts for maize production in Nebraska using the DSSAT CERES-Maize model to quantify water management efficiency.
-
Llenas et al. (2026) EUMETNET SRNWP-EPS EFI/SOT Software: From Research to Operations in AEMET-γSREPS
Development and operational implementation of a Python-based software tool to compute the Extreme Forecast Index (EFI) and Shift of Tails (SOT) for EUMETNET's Limited Area Modelling Ensemble Prediction Systems (LAM-EPS).
-
Migliavacca et al. (2026) Monitoring Europe’s forests in a changing climate
This article discusses the application of remote sensing to monitor accelerating biomass loss in European forests, emphasizing the ability to distinguish between harvesting and natural disturbances to inform climate policy.
-
Tong et al. (2026) Sensitivity of Vegetation Greenness to Multi-Depth Soil Moisture on the Mongolian Plateau: Nonlinear Responses Revealed by RF–SHAP
This study evaluates the sensitivity of vegetation (NDVI) to soil moisture across different depths on the Mongolian Plateau from 1982 to 2022, finding that sensitivity is highest in the subsurface layer and has increased over time due to vegetation greening.
-
Ali et al. (2026) Flood and drought risk management across the hydroclimatic spectrum in the most affected regions under changing water resources conditions
This perspective analyzes the evolving landscape of flood and drought risk management across high-risk global regions, concluding that integrated governance and nature-based solutions are essential to address escalating compound hydroclimatic risks.
-
Manatsa et al. (2026) Multiscale ENSO and Indian Ocean Dipole Controls on Drought Across Mainland SADC : A Meridional Teleconnection Dipole, Its Seasonal and Sectoral Structure and a Domain‐Gated Readiness Index
This study evaluates the influence of the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on drought in the SADC mainland, identifying a meridional teleconnection dipole dominated by ENSO.
-
Ansari et al. (2026) Terrestrial water storage monitoring in Bulgaria using Global Navigation Satellite System (GNSS) displacements and comparison with GLDAS, GLWS and GRACE products
The study estimates Terrestrial Water Storage (TWS) variations in Bulgaria by inverting GNSS vertical displacements into Equivalent Water Height (EWH), finding annual amplitudes typically between 10 and 30 mm.
-
Attipoe (2026) Artificial Intelligence Applications in Climate-Smart Agriculture: A Critical Review of Evidence, Implementation and Responsible Innovation
This critical narrative review evaluates the application of artificial intelligence (AI) in climate-smart agriculture, concluding that while AI is effective for specific perception and prediction tasks, its actual contribution to climate-smart goals is often limited by poor model transferability and a lack of causal evidence.
-
Meresa et al. (2026) Climate Change and Irrigation Effects on Hydrology and Crop Yield in the Geba Watershed, Tigray Region, Northern Ethiopia
This study evaluates the combined impacts of future climate change and irrigation expansion on the hydrology and crop yields of the Geba watershed in northern Ethiopia using the SWAT+ model. The findings indicate that while irrigation is intended to support agriculture, it may exacerbate hydrological stress by significantly reducing groundwater recharge and water yield.
-
Ullah et al. (2026) Regional Temperature Trends and Future Warming Risks in Pakistan Using Bias‐Corrected CMIP6 Ensembles and Machine Learning Techniques
This study enhances regional temperature projections for Pakistan by integrating CMIP6 models with Fuzzy C-Means clustering, statistical bias correction, and machine learning to provide localized climate insights.
-
Cording et al. (2026) Investigating the Combined Influence of the North Atlantic Oscillation and the East Atlantic Pattern on Monthly Precipitation Across Mainland Spain
This study analyzes the individual and combined impacts of the North Atlantic Oscillation (NAO) and East Atlantic (EA) patterns on monthly precipitation in mainland Spain from 1950 to 2020, concluding that their joint influence provides a more comprehensive understanding of precipitation variability than independent analysis.
-
Turman et al. (2026) High-Resolution SMAP Soil Moisture in Agriculture: Capturing Field-Scale Variability in Soil Moisture and Evapotranspiration in the San Luis Valley
This study evaluates a new 400 m resolution soil moisture product derived from 36 km SMAP observations, demonstrating its superior ability to discern field-scale variability in irrigation and crop types compared to the standard 9 km product in the San Luis Valley, Colorado.
-
Faria et al. (2026) Predicting Irrigated Rice Soil–Water Conditions Using Multispectral Remote Sensing and Machine Learning in Semi-Arid Australia
The study evaluates the use of multispectral remote sensing and machine learning to classify soil-water conditions in Australian rice fields, finding that while "Dry" and "Flooded" states are distinguishable, "Saturated" and "Flooded" states are difficult to differentiate.
-
Liu et al. (2026) Land Surface Feedbacks Regulate Atmospheric Water Demand and Maintain Global Drying Stability
The study resolves the "aridity paradox" by revising the potential evaporation (PE) formulation to treat vapor pressure deficit (VPD) as a dynamic feedback rather than an external driver. This new approach indicates that global aridity will remain relatively stable under climate warming, contradicting previous projections of intensification.
-
Mei et al. (2026) Diagnosing Cryospheric Runoff Dynamics: A Distributed Differentiable Hydrological Model With Global Transfer Learning
The study introduces dCREST, a differentiable physics-informed hydrological model, to improve runoff predictions in alpine regions by optimizing soil structure and utilizing global transfer learning to overcome local data scarcity.
-
Bolsée et al. (2026) Corrigendum to “Wind flow dynamics over a photovoltaic power plant: Comparison with a vegetated canopy” [Agricultural and Forest Meteorology 388 (2026) 111306]
This document is a corrigendum that corrects a specific parameter value (transition scale $\lambda_c$) in a study comparing wind flow dynamics over a photovoltaic power plant and a vegetated canopy.
-
Chotemankongsin et al. (2026) Evaluating the hydrological impacts of wetland restoration in a Dutch small catchment under future climate scenarios using SWAT+
This study evaluates the hydrological impact of strategic wetland restoration in the Linge catchment (Netherlands) using the SWAT+ model, finding that while NBS effectively reduce flood peaks, they may exacerbate water scarcity under dry future climate scenarios.
-
Ragno et al. (2026) The Role of Structural and Transient Compoundness in Advancing Compound Event Characterization
The paper introduces the concepts of "structural compoundness" and "transient compoundness" to quantify the contributions of individual drivers to compound events, moving beyond a sole focus on statistical dependence.
-
Ji et al. (2026) Reassessing Alpine Permafrost Thermal State by Accounting for Ground Ice
The study introduces an enthalpy-based metric ($\Delta H'$) to assess permafrost thermal state, demonstrating that it is superior to Mean Annual Ground Temperature (MAGT) because it accounts for the thermodynamic influence of ground ice.
-
Rabbia et al. (2026) Framework for spatiotemporal soil moisture assessment: an application to the integration of model-based clustering with non-parametric change points detection
The study proposes a three-phase statistical framework combining model-based clustering and non-parametric change point detection to analyze spatiotemporal soil moisture variability in Punjab, Pakistan.
-
Tiwari et al. (2026) Temporal Trends and Future Projections of Reference Evapotranspiration under Different Climate Change Scenarios in the Parbati Watershed
This study evaluates future reference evapotranspiration ($\text{ET}_0$) trends in the Parbati watershed across three climate scenarios, finding that $\text{ET}_0$ responses are non-uniform and vary significantly by month, scenario, and time period.
-
Sahoo et al. (2026) Soil Moisture Prediction Using a Scalable and Validated Random Forest Regressor Model
The study proposes a scalable and cost-effective soil moisture prediction system using a Random Forest Regressor (RFR) optimized for deployment on edge devices to improve irrigation efficiency.
-
Pan et al. (2026) Estimating daily evapotranspiration from remotely sensed instantaneous observations with surface flux equilibrium (SFE) theory and maximum entropy production (MEP) principle
The study proposes a method to estimate daily evapotranspiration by utilizing instantaneous remote sensing observations through the application of surface flux equilibrium (SFE) theory and the maximum entropy production (MEP) principle.
-
Vegad et al. (2026) Reconstructing Long‐Term Reservoir Storage in India Using Hydrological Modeling and Machine Learning
The study reconstructs long-term daily live storage for major reservoirs in India using a hybrid approach of hydrological modeling and machine learning, finding that normalized storage has moderately declined due to increased water withdrawals.
