-
Liu et al. (2026) Compound climate hazards revealed by global modeling of drought, heatwaves, and land degradation
The study develops a scalable machine-learning framework to globally map compound climate hazards—specifically drought, heatwaves, and land degradation—identifying critical hotspots in semi-arid and transitional regions.
-
Patil et al. (2026) dfaa-analysis: Flood-to-drought and drought-to-flood transition analysis pipeline for Great Britain
This study develops and implements a computational pipeline to analyze flood-to-drought (FTD) and drought-to-flood (DTF) transitions across Great Britain, evaluating how soil moisture and snowpack influence these dynamics under various warming climate scenarios.
-
Rigo (2026) Variations in Seasonal Precipitation Patterns with Elevation in Catalonia (2016–2025)
This study models the regional precipitation regime in Catalonia using radar and lightning data, identifying distinct single and bimodal patterns of precipitation maxima throughout the year.
-
Terassi et al. (2026) Satellite and Reanalysis Dataset Dependence of ETCCDI Extreme Precipitation Trends in Paraná, Southern Brazil
This study analyzes extreme precipitation variability in Paraná State, Brazil (1983–2024), finding that detected trends are highly dependent on the dataset used, with evidence of an extended mid-year dry season and rainfall redistribution toward the austral spring.
-
Gül et al. (2026) A Skewness–Kurtosis Adjusted Expansion of the Standardised Precipitation Index for Drought Analysis
The study proposes the eXpanded SPI (X-SPI), a drought index that adjusts the classical Standardized Precipitation Index (SPI) by incorporating skewness and kurtosis to better account for local precipitation variability.
-
Hierro (2026) Decoupling Between South American Low‐Level Jet Intensity and Precipitation Extremes in Southeastern South America
This study examines the relationship between the South American Low-Level Jet (SALLJ) and extreme precipitation in Southeastern South America, concluding that while the jet significantly boosts the probability of extreme rain, it requires specific upper-level dynamical coupling to trigger convection.
-
Rottler et al. (2026) Station‐Based Assessment of Sub‐Seasonal Climate Trends in the Tyrolean Alps (Austria)
This study quantifies sub-seasonal and elevation-dependent climate trends in the Tyrolean Alps (Austria) using multi-variate observations, revealing a mean warming rate of 0.43°C/decade and shifting precipitation patterns.
-
Li et al. (2026) Deep learning tree and forest biomass from sub-meter resolution optical imagery
The study evaluates the ability of Convolutional Neural Networks (CNNs) to directly estimate forest above-ground biomass (AGB) from sub-meter resolution RGB optical imagery. The results demonstrate that CNNs can achieve high predictive performance ($R^2 = 0.71$) without requiring LiDAR-derived tree height data, approaching the accuracy of traditional height-based models.
-
Silva et al. (2026) Intensification of Heat Extremes and Spatial Variability of Rainfall in Mainland Portugal (1980–2025): Insights from ETCCDI Indices Based on ERA5-Land Data
This study analyzes 18 ETCCDI climate indices in mainland Portugal from 1980 to 2025 using ERA5-Land data, revealing a robust, spatially coherent warming trend contrasted with highly variable and less significant precipitation trends.
-
Wang et al. (2026) Spatiotemporal Dynamics of Meteorological and Agricultural Drought on the Northern Slope of the Tianshan Mountains: Trends, Propagation Processes, and Triggering Thresholds
This study investigates the propagation of meteorological drought into agricultural drought on the northern slope of the Tianshan Mountains (1980–2022), finding that agricultural droughts typically lag meteorological ones by 1–3 months with triggering thresholds between −0.81 and −0.71.
-
Nirwal (2026) Empowering the Drylands: An AI-Driven Framework for Precision Agriculture and Sustainable Rural Development in the Marathwada Region
The paper proposes an AI-Enabled Rural Development Framework (AI-RDF) for the Marathwada region of India to combat water scarcity and agricultural distress through IoT-integrated machine learning.
-
Hussien et al. (2026) Validation of Downscaled and Bias-Corrected WorldClim 2.1– CRU-TS v4.09 Climate Dataset for Hydrological Modeling in a Semi-Arid Ecotonal Catchment of Central South Africa
This study evaluates the reliability of the WorldClim 2.1 historical weather dataset against observed records in central South Africa's C5 Secondary Drainage Region from 1950 to 2023, concluding that it is a robust baseline despite underestimating extreme precipitation.
-
Burton et al. (2026) Metrics into management: which measures of landscape moisture best inform wildfire planning and response?
This review evaluates 30 landscape moisture metrics to determine their suitability for fire management decisions across four operational scales. It identifies high-potential metrics, including those currently underutilized in Australian fire management.
-
Olaleye et al. (2026) A Decomposition-Based Hybrid Prophet–LSTM Framework for SPEI-12 Drought Forecasting in Kano State, Nigeria
The study develops a hybrid Prophet–LSTM architecture to predict drought in Northern Nigeria, demonstrating that decoupling deterministic trends from stochastic residuals significantly improves the accuracy of hydroclimatic forecasting.
-
Berkowicz et al. (2026) Atmospheric Water Harvesting in a Changing Climate and Potential of Citizen Science for Long-Term Dew Monitoring
The paper reviews atmospheric water harvesting techniques and proposes the implementation of Citizen Science to enhance the data resolution and accuracy of dew modeling.
-
Tóth et al. (2026) Impact of ASCAT Level-2 Soil Moisture Assimilation Using a Simplified Extended Kalman Filter in the AROME Model
This study evaluates the impact of assimilating ASCAT Level-2 surface soil moisture retrievals into the AROME model, demonstrating improvements in soil states, near-surface atmospheric variables, and precipitation forecasts.
-
Wang et al. (2026) Remote Sensing Dynamic Monitoring and Driving Mechanism of Lake Area in Ebinur Lake, 1992–2024
This study analyzes the spatio-temporal dynamics of Ebinur Lake from 1992 to 2024, concluding that the lake's continuous degradation is primarily driven by socio-economic factors (70%) rather than meteorological influences (30%).
-
Küçüktopçu et al. (2026) Assessing the Impact of Geographical and Meteorological Information on Machine Learning-Based Reproduction of FAO Penman–Monteith Reference Evapotranspiration
This study evaluates four machine learning algorithms for estimating reference evapotranspiration (ETo) in the Czech Republic, concluding that the availability of meteorological predictors is more critical for model accuracy than the specific algorithm selected.
-
Waseem et al. (2026) A time adaptive weighting framework for enhanced vegetation health index based drought monitoring
The study introduces an Enhanced Vegetation Health Index (EVHI) that replaces static weights with time-adaptive weights for its components to improve agricultural drought monitoring. The framework demonstrates superior performance over traditional VHI in detecting droughts across Pakistan by better accounting for the temporal dominance of thermal and vegetation stress.
-
Liu et al. (2026) GF-5 Hyperspectral Soil Moisture Content Inversion Based on Fractional-Order Differentiation and Dual-Band Spectral Index Selection
This study utilizes GF-5 satellite hyperspectral data and Fractional-Order Differentiation (FOD) to enhance the inversion accuracy of soil moisture content (SMC) in the arid Xinjiang region. The BSS-PLSR model was identified as the optimal scheme for mapping the spatial distribution of SMC.
-
Zh et al. (2026) Interpretable Groundwater-Level Prediction in an Arid Inland Basin by Integrating Dempster–Shafer Feature Screening with a Stacking Ensemble
The study develops a one-day-ahead groundwater-level prediction framework for the Zhangye Basin using a stacking ensemble of deep learning models and D-S evidence theory for optimized feature selection.
-
Li et al. (2026) Canopy Hotspot Effects Improve Root-Zone Soil Moisture Content Estimation in Winter Wheat Based on LESS Simulations and UAV Multi-Angular Observations
This study evaluates the use of hotspot-sensitive multi-angular observations, simulated via the LESS model and captured by UAVs, to estimate root-zone soil moisture content (SMC) in winter wheat. The results demonstrate that backscattering directions, particularly at 560 nm, significantly enhance the accuracy of SMC estimation.
-
Pan et al. (2026) A refined reservoir evaporation loss estimation approach through improved wind function expression accuracy
The study proposes a weighted wind direction method to refine reservoir evaporation estimates by accounting for reservoir geometry and wind field heterogeneity, applying this approach to 6,853 large reservoirs globally from 1985 to 2021.
-
López-Hernández et al. (2026) Sentinel-2 and Unmanned Aerial Vehicle (UAV) Imagery for Irrigation Scheduling in Fodder Maize: A Comparative Remote Sensing Approach
This study compared satellite- and UAV-derived NDVI models for estimating crop coefficients (Kc) in forage maize to optimize irrigation scheduling. It found that while UAV models had higher calibration accuracy, satellite-based scheduling provided the best balance between water efficiency, forage yield, and nutritional quality.
-
Zhang et al. (2026) Flash drought characteristics based on three identification methods in the North China Plain, China
This study introduces a new identification method called the mean-scaled evaporative stress ratio (MESR) to analyze the spatiotemporal characteristics and hotspots of flash droughts (FD) in the North China Plain from 1981 to 2022.
-
Beisenova et al. (2026) Hydroclimatic Variability and Water Balance Instability in Semi-Arid Steppe Ecosystems Under Climate Warming
This study analyzes hydroclimatic variability in the Akmola region from 2003 to 2023, finding that rising temperatures and increasing evapotranspiration are intensifying moisture deficits and drought vulnerability.
-
Shanko et al. (2026) Comparative Analysis of LSTM and Random Forest Algorithms for Streamflow Prediction: A Case Study of Diverse River Basins in the United States
This study compares Long Short-Term Memory (LSTM) and Random Forest (RF) algorithms for daily streamflow prediction across 16 diverse US basins, finding that RF generally provides superior accuracy and flood detection performance.
-
Anderson et al. (2026) Reliance on daily mean streamflow data biases inferred flood seasonality
This study evaluates how the temporal resolution of streamflow data (daily mean versus daily maximum derived from hourly data) affects the characterization of flood seasonality and the identification of extreme flood events.
-
Ouattab et al. (2026) Spatio-Temporal Variability and Trends of Precipitation and Climate Extremes over Morocco (1991–2020) Using Synoptic Observations’ Data
This study analyzes precipitation trends and extremes in Morocco from 1991 to 2020, revealing a general decline in rainfall and a structural shift toward more episodic, intense events in the north and overall weakening in the south.
-
Feng et al. (2026) A framework for analyzing spatial hydrological drought dependence based on extreme value theory and nonstationary copulas
The study proposes an integrated framework combining event-scale extreme value theory and nonstationary copulas to analyze the spatial dependence and concurrence risk of hydrological droughts within river networks.
-
Gupta et al. (2026) Impact of reservoir storage on propagation from meteorological to hydrological drought
This study quantifies how reservoir storage influences the propagation of drought from meteorological to hydrological stages in the Krishna River Basin, demonstrating that reservoirs act as buffers that delay propagation but facilitate the downstream transmission of severe droughts.
-
Yong et al. (2026) Spatiotemporal evolution and propagation of GNSS-derived PWV-based meteorological and soil moisture drought: a case study of the contiguous United States (2003–2022)
The study utilizes GNSS-derived precipitable water vapor (PWV) to develop a new meteorological drought index (SPCI) and analyzes its propagation to soil moisture drought (SMD) across the contiguous United States from 2003 to 2022.
-
Jin et al. (2026) Improving FY-4B Satellite Precipitation Retrieval over Coastal Complex Terrain of Eastern China: Deep Learning Approaches with Multi-Source Underlying Surface Data
This study develops a deep learning framework to improve precipitation retrieval from FY-4B satellite data by integrating underlying-surface information. The findings demonstrate that incorporating topographic and land-cover data enhances precipitation detection, with the degree of improvement depending on the specific neural network architecture used.
-
Lu et al. (2026) A Thermal Infrared Remote Sensing Model for Diagnosing Winter Wheat Water (Triticum aestivum L.) Stress by Integrating Angular Effects and Kernel-Driven Models
This study investigates the directional effects of canopy temperature in winter wheat using UAV thermal imagery and employs a kernel-driven model to retrieve isotropic temperature for more accurate Crop Water Stress Index (CWSI) estimation.
-
Liu et al. (2026) SPEI-Based Drought Frequency, Intensity, and Duration from CMIP6 Models Under SSP1-2.6 and SSP5-8.5 Across Tropical–Subtropical Monsoon Asia
This study evaluates future drought risk in tropical-subtropical monsoon Asia using CMIP6 models, concluding that while low-emission scenarios (SSP1-2.6) can stabilize moisture regimes, high-emission scenarios (SSP5-8.5) will lead to pervasive intensification and expansion of drought.
-
Mohamed et al. (2026) Climate Change and Climatic Water Balance in Brandenburg (Germany): Seasonal Drying and Hydro-Climatic Stress Under Multi-Model Climate Projections to 2100
This study evaluates projected changes in climatic water balance and hydro-climatic stress in Brandenburg, Germany, concluding that rising temperatures and evaporative demand will lead to a robust increase in growing-season drying by 2100.
-
Zhang et al. (2026) Unraveling the Responses of Gross Primary Productivity to Multiple Drought Types Across China Using Multi-Source Remote Sensing Data
This study evaluates the impact of four different drought indices on Gross Primary Productivity (GPP) across China from 2003 to 2020, identifying the Temperature Condition Index (TCI) as the primary driver of GPP variations.
-
Fattahi et al. (2026) Deep Learning LSTM-Based Model for Predicting SPI and SPEI Drought Indices
The study develops a deep learning LSTM-based model to predict the Standard Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI) in two Iranian watersheds, demonstrating superior accuracy and dynamic property preservation compared to traditional time-series models.
