-
Liu et al. (2025) Unrevealing site-dependent relationship between solar-induced chlorophyll fluorescence and gross primary productivity using the terrestrial ecosystem carbon cycle simulator
This study developed TECs-SIF, a terrestrial biosphere model integrating a radiative transfer module, to simultaneously simulate canopy solar-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) and investigate their relationship across forest ecosystems. The model accurately simulates SIF and GPP across various temporal scales, revealing that the SIF-GPP relationship is site-dependent and influenced by canopy structure and leaf traits.
-
Dong et al. (2025) Intensification of extreme cold events in East Asia in response to global mean sea-level rise
This study investigates the impact of global mean sea-level (GMSL) rise on winter extreme cold events in East Asia using climate model experiments. The findings demonstrate that GMSL rise promotes stronger and more frequent extreme cold events by altering atmospheric circulation, a phenomenon expected to intensify in the coming century.
-
Wang et al. (2025) Complex network approaches for identifying global drought teleconnection patterns
This study developed a novel complexity-based approach using a global extreme drought complex network to identify global drought teleconnection patterns, revealing major source and sink regions and an average propagation distance exceeding 11,000 km. The integrated approach provides insights into the complex, nonlinear, and asynchronous spatiotemporal associations of drought events.
-
Liu et al. (2025) Strong Contributions of Abnormal Vortex Street-Like System in the Tibetan Plateau’s Wake to Record-Breaking Strong Rainfall over Southern China in April 2024
This study investigates the unprecedented heavy rainfall in southern China in April 2024, revealing that an abnormally strong Tibetan Plateau vortex street-like system (TPVSL), driven by an extreme north-south temperature gradient linked to a preceding El Niño, provided a favorable background for approximately 78% of the precipitation.
-
Zhao et al. (2025) Regional Variations in Drivers of Extreme Reference Evapotranspiration Across the Contiguous United States
This study investigates the regional contributions of meteorological drivers to extreme reference evapotranspiration across the Contiguous United States (CONUS), revealing distinct spatial patterns where different atmospheric variables dominate extreme events.
-
Heyblom et al. (2025) Global Moisture Cycling Rate an Important Control on Regional-Mean Precipitation under Warming
This study proposes an updated framework explaining regional precipitation change under climate forcing through three additive drivers, finding that the global moisture cycling rate robustly accounts for key spatial patterns like "wet-get-wetter, dry-get-drier" across various Earth system models.
-
Tiwari et al. (2025) Underestimation of Historical Terrestrial Water Storage Droughts in Global Water Models
[Information not extractable due to corrupted input.]
-
Chen et al. (2025) Spatial heterogeneity and driving mechanism of the response of lake area to drought for lakes in China
This study developed a novel image pollution pixel filling algorithm to construct a high spatiotemporal resolution monthly lake area dataset for China from 2000 to 2021, revealing a significant upward trend in lake area with spatial heterogeneity, and identifying drought as a primary driver of lake area fluctuations, often exacerbated by human activities.
-
Abraham et al. (2025) Assessing the Flood and Drought Regulation Capacity of Dams in a Changing Climate: An Application to the Largest Hydropower Dam in Africa
This study quantifies the Grand Ethiopian Renaissance Dam's (GERD) flood and drought regulation capacity under projected climate change, demonstrating its substantial moderating effect on both extreme hydrological events in the transboundary Upper Blue Nile basin.
-
Abbasizadeh et al. (2025) Can causal discovery lead to a more robust prediction model for runoff signatures?
This study investigates whether incorporating causal relationships between catchment attributes, climate indices, and runoff signatures can lead to more robust and interpretable prediction models. The findings indicate that models trained on causally identified parent variables, particularly Bayesian Networks and Generalized Additive Models, demonstrate enhanced robustness and parsimony across diverse environments compared to models using all available predictors.
-
Harrison et al. (2025) Will landscape responses reduce glacier sensitivity to climate change in High Mountain Asia?
This paper proposes an alternative "Paraglacial Transition Model" for glacier evolution in High Mountain Asia (HMA), where increasing rock debris cover transforms glaciers into rock glaciers and other ice debris landforms, potentially prolonging ice persistence and reducing their sensitivity to climate warming, in contrast to conventional models predicting significant ice loss.
-
Nie et al. (2025) Divergent Impacts of Precipitation Regimes on Autumn Phenology in the Northern Hemisphere Mid‐ to High‐Latitudes
This study investigated the impacts of total precipitation and precipitation frequency on the end of the vegetation growing season (EOS) across the mid- to high-latitudes of the Northern Hemisphere. It found that both increased total precipitation and decreased precipitation frequency delayed EOS, and developed an improved process-based autumn phenology model by incorporating precipitation frequency.
-
He et al. (2025) Response of Mixed‐Phase Cloud Microphysics to Aerosol Perturbations at the Contrasting Sites of Limassol, Cyprus, and Punta Arenas, Chile
This study investigates the microphysical response of mixed-phase clouds to contrasting aerosol conditions at Limassol, Cyprus (dust-influenced) and Punta Arenas, Chile (continental/marine), finding that ice crystal number concentrations directly correlate with the availability and type of ice-nucleating particles.
-
Yu et al. (2025) Cloud and Snow Segmentation via Transformer-Guided Multi-Stream Feature Integration
This paper introduces a novel Transformer-guided dual-branch deep learning architecture for accurate cloud and snow semantic segmentation in remote sensing images, effectively integrating global contextual features with local spatial details to overcome spectral similarities and achieve state-of-the-art performance on challenging datasets.
-
Wang et al. (2025) Disentangling climate and policy uncertainties for the Colorado River post-2026 operations
This study evaluates the future conditions of Lakes Powell and Mead in the Colorado River Basin under climate change and various water management policies using dynamically downscaled CMIP6 climate models and unique uncertainty methods. It finds that existing policies lead to high risks of dead pool conditions for both reservoirs by 2060, while proposed alternative policies reduce but do not eliminate these risks, necessitating larger reductions for long-term sustainability.
-
Jiang et al. (2025) Responses of the East Asian Winter Climate to Global Warming in CMIP6 Models
This study evaluates the projected changes in the East Asian winter climate (EAWC) from 1979 to 2100 using a multimodel ensemble from CMIP6, revealing widespread and robust alterations including significantly shortened winters and uneven regional temperature shifts driven by greenhouse gas emissions.
-
Eccles et al. (2025) High-resolution downscaled CMIP6 drought projections for Australia
This study investigates the impacts of climate change on meteorological droughts across Australia using a high-resolution ensemble of downscaled CMIP6 climate models under three Shared Socioeconomic Pathway (SSP) scenarios. It projects consistent increases in drought frequency, duration, spatial extent, and percent time in drought, particularly for southern Australia and when considering evapotranspiration, indicating a significant shift towards more extreme climatic conditions under higher emissions.
-
Li et al. (2025) A super-resolution network based on dual aggregate transformer for climate downscaling
This paper proposes a novel Climate Downscaling Dual Aggregation Transformer (CDDAT) model that integrates a lightweight CNN and a dual aggregation transformer with multimodal fusion to enhance high-resolution climate downscaling. The CDDAT achieves state-of-the-art performance in rainfall image restoration and dew point reconstruction by effectively capturing complex details and dynamically reassigning the importance of different rainfall variables.
-
Suriano et al. (2025) Temporal trends and large-scale atmospheric and oceanic forcings of central U.S. snowfall producing weather systems, 1948–2021
This study investigates the temporal trends and large-scale atmospheric and oceanic forcings of snowfall-producing weather systems in the central U.S. from 1948 to 2021, revealing significant changes in synoptic weather type frequencies, inherent meteorological characteristics, and their links to teleconnection indices.
-
Fallah et al. (2025) The Impact of CO 2 ‐Driven Vegetation Changes on the Future of Flash Drought in the Northern Hemisphere
## Identification - **Journal:** Journal of Geophysical Research Atmospheres - **Year:** 2025...
-
Getirana et al. (2025) Inconsistencies in GRACE‐Based Groundwater Storage Estimation—A Call for a Proper Use of Land Surface Models
This comment critically examines the common practice of estimating groundwater storage anomalies by subtracting land surface model components from GRACE terrestrial water storage, highlighting implicit assumptions and structural challenges due to model simplifications. It advocates for careful interpretation and more sophisticated methods in hydrological analyses.
-
Sharma et al. (2025) Comparative Analysis of Machine Learning Methods for Imputing Missing Daily Rainfall Data in Complex Himalayan Terrain
This study evaluated seven machine learning methods for imputing missing daily rainfall data across different elevations and agro-climatic zones in Himachal Pradesh, India, finding that Multilayer Perceptron (MLP) consistently demonstrated the highest accuracy and lowest estimation errors.
-
Campos et al. (2025) Regional Aspects of Observed Temperature and Precipitation Trends in the Western Mediterranean: Insights From a Timescale Decomposition Analysis
Information not extractable from the provided corrupted text.
-
Zhang et al. (2025) Change of extreme temperatures in the Three-North Region of China and its possible mechanism
This study analyzes temperature, extreme temperature events, and land-atmosphere coupling strength in China's Three-North Region from 1961 to 2022, revealing a significant warming trend and increased extreme heat events driven by enhanced land-atmosphere coupling.
-
Gebresellase et al. (2025) Projected impacts of climate and land use changes on streamflow extremes in the upper awash Basin, Ethiopia
This study investigated the projected impacts of climate and land use/land cover (LULC) changes on streamflow extremes in the Upper Awash Basin, Ethiopia. It found that climate change is the dominant driver, significantly increasing high-flow extremes and decreasing low-flow extremes, while LULC changes had statistically non-significant effects.
-
Wang et al. (2025) Gross primary productivity is more sensitive to droughts than to heatwaves in China
This study investigated the response of Gross Primary Productivity (GPP) to droughts and heatwaves across different climate zones and vegetation types in China from 1950 to 2020 using the SEIB-DGVM model, revealing that GPP is generally more sensitive to droughts than to heatwaves, with significant spatial variations influenced by precipitation gradients.
-
Jia et al. (2025) Post-conversion vegetation restoration: PRE and VPD dominated the NDVI changes on the Loess Plateau
This study investigated the response of vegetation greenness (NDVI) to climatic factors and soil moisture on the Loess Plateau from 2000 to 2020, finding that precipitation was the dominant promoter of vegetation growth, while vapor pressure deficit (VPD) was the primary constraint.
-
Eltahir et al. (2025) A Theory on Regional Impacts of Global Warming
This paper proposes a theory explaining how relatively uniform global warming can lead to significantly diverse regional impacts by considering how local background temperatures interact nonlinearly with temperature thresholds governing natural phenomena.
-
Wu et al. (2025) Assessing blue-green infrastructures for urban flood and drought mitigation under changing climate scenarios
This study evaluates the effectiveness of Blue-Green Infrastructures (BGIs) like green roofs, rain tanks, and permeable pavements in mitigating urban floods and droughts under present and future climate scenarios in a Belgian university campus. It found that BGIs significantly reduce discharge volumes and peak flows while substantially enhancing groundwater recharge, demonstrating their potential as a climate change adaptation solution.
-
Kruskopf et al. (2025) ClimateServ: An open source earth observation climate data access tool
This paper introduces ClimateSERV, an open-source web application and API developed by the SERVIR program, designed to lower barriers for users to visualize, analyze, and download curated Earth observation climate data for water and food security applications. It details the system's architecture, performance, and usage, including a case study demonstrating its utility in drought monitoring in Southeast Asia.
-
Lorente-Ramos et al. (2025) Accurate calibration of hydrological models with evolutionary computation multi-method ensembles
This study introduces the Dynamic Probabilistic Coral Reefs Optimization algorithm with Substrate Layer (DPCRO-SL) to enhance hydrological model calibration. Applied to the *abcd* model in two Spanish river basins, DPCRO-SL consistently outperformed the benchmark SCE-UA algorithm, demonstrating superior accuracy and reliability, particularly in test scenarios reflecting future projections.
-
Sepaspour et al. (2025) Future climate prediction and projection: A systematic review of classical and advanced methodologies
This systematic review analyzes 4,276 studies (2014–2024) on climate variable forecasting methodologies, including classical, machine learning, deep learning, hybrid, and General Circulation Models (GCMs), to identify trends, gaps, and comparative effectiveness. It concludes that integrating machine learning and deep learning with high-resolution GCM outputs will be crucial for future climate forecasting.
-
Terzi et al. (2025) A novel statistical framework for constructing multivariate standardized drought indices
This study developed a novel copula-based framework for the Multivariate Standardized Drought Index (MSDI) using month-specific probability distributions and copulas to better account for seasonal variability and inter-variable dependence. The proposed methodology demonstrated improved drought detection and characterization, particularly for extreme and compound events, in two distinct Turkish river basins.
-
Barbaux et al. (2025) Integrating non-stationarity and uncertainty in design life levels based on climatological time series
This study develops a novel method to infer design life levels for extreme events under non-stationary climate conditions, integrating both non-stationarity and uncertainty. It introduces the Predictive Equivalent Reliability (PER) level, a single, interpretable indicator that accounts for stochastic and estimation uncertainties, demonstrating its utility for robust risk assessment in a changing climate.
-
Liu et al. (2025) Corrigendum to “Quantifying the effects of irrigation schedule on groundwater level variability using a linked APSIM-MODFLOW model framework” [Agric. Water Manag. 316, 2025, 109610]
This study aimed to quantify the effects of various irrigation schedules on groundwater level variability by employing a linked crop simulation and hydrological modeling framework.
-
Guo et al. (2025) Incorporation alfalfa with annual forage enhances even water use and maintains forage yield resilience in a semiarid region
This study investigated whether intercropping alfalfa with annual forage crops could improve soil water distribution and maintain forage yield resilience in a semiarid region. Findings show that intercropping significantly enhanced soil water content, water productivity, and overall yield advantage, particularly with alfalfa-maize combinations, offering a feasible solution for efficient water management.
-
Hisam et al. (2025) Precipitation downscaling with the integration of multiple precipitation products, land surface data and gauge stations using explainable machine learning algorithms: A case study in the Mediterranean region of Turkiye
This study downscaled monthly gridded precipitation data to a 0.04° spatial resolution in the Mediterranean region of Türkiye by integrating multiple precipitation products and land surface characteristics using explainable machine learning, finding Random Forest to be the most accurate model.
-
Luque-Sánchez et al. (2025) Optimizing water management: Identifying strategies to enhance irrigation efficiency under drought conditions
This study analyzed the water-energy nexus in a traditional irrigation community under drought conditions (2020–2024) to identify management strategies for enhanced irrigation efficiency, demonstrating the critical importance of adaptive water management and sensorization for adjusting practices to limited water availability.
-
Nasar-u-Minallah et al. (2025) Spatial and temporal assessment of drought dynamics in Bahawalpur (Pakistan) using remote sensing and meteorological data
This study assessed spatial and temporal drought dynamics in Pakistan's Bahawalpur division (2012-2022) using remote sensing and meteorological data, identifying 2012, 2017, and 2022 as severe drought years and forecasting future temperature trends for the region.
-
Baillarget et al. (2025) Permafrost Degradation: Mechanisms, Effects, and (Im)Possible Remediation
This review synthesizes the mechanisms and consequences of permafrost degradation, highlighting its widespread impacts on hydrological, ecological, and engineered systems. It concludes that systematic remediation efforts are largely unfeasible given the current pace of climate change, necessitating a strategic shift towards adaptation.
-
Ishikawa et al. (2025) Evaluation of a Width‐Based Satellite Discharge Algorithm for Detecting Longitudinal Flow Changes in a Human‐Regulated Continental River Basin
This study investigated the capabilities and limitations of estimating spatially continuous river discharge using satellite-observed river width and the AMHG-based algorithm (BAM) along the Yellow River. It found that the method can reproduce spatial discharge patterns, with improved accuracy using irrigation-corrected prior discharge, but faces significant challenges in levee-confined reaches.
-
Sánchez et al. (2025) One Country, Several Droughts: Characterisation, Evolution, and Trends in Meteorological Droughts in Spain Within the Context of Climate Change
This paper analyzes drought variability in Spain from 1950–2024 using the Standardised Precipitation–Evapotranspiration Index (SPEI) at multiple timescales, revealing a convergence of rising drought severity and persistence across interior Spain.
-
Feng et al. (2025) Disentangling atmospheric, hydrological, and coupling uncertainties in compound flood modeling within a coupled Earth system model
This study leverages the Energy Exascale Earth System Model (E3SM) with multi-component coupling to disentangle atmospheric, hydrological, and coupling uncertainties in compound riverine and coastal flood modeling. It demonstrates the critical role of two-way river-ocean coupling and antecedent soil moisture conditions in amplifying flood impacts, advocating for a broader definition of compound flooding.
-
Tang et al. (2025) Analyzing the Driving Mechanism of Drought Using the Ecological Aridity Index Considering the Evapotranspiration Deficit—A Case Study in Xinjiang, China
This study developed a multivariate comprehensive drought index (MCDI) by integrating evapotranspiration deficit (ED) for the first time, alongside atmospheric water deficit, soil moisture, and runoff, using both Copula and nonparametric methods. It found that the nonparametric method was superior, soil moisture was the main driver of ecological drought in Xinjiang, China, and a strong synergistic effect exists between soil moisture and ED.
-
Liu et al. (2025) Response of water cycle over drylands to a warming future
This study investigates the comprehensive response of the global dryland water cycle to future warming using CMIP6 multi-model projections, finding an overall acceleration of the water cycle but with significant regional and seasonal variations and increased drought risk.
-
Yang et al. (2025) Spatial heterogeneity and environmental drivers of drought vulnerability in the Yangtze River Basin
This study assessed the spatial heterogeneity and environmental drivers of drought vulnerability in the Yangtze River Basin (YRB) from 2001 to 2023, integrating exposure, sensitivity, and resilience. It found that resilience is the dominant determinant of vulnerability, with the central-northern YRB exhibiting the highest vulnerability due to concurrent high exposure and sensitivity with low resilience.
-
Kupzig et al. (2025) A controlled model experiment for the global hydrological model WaterGAP3: Understanding recent and new advances in the model structure
This study conducts a controlled model experiment on the global hydrological model WaterGAP3 to evaluate if increased model complexity in river routing, reservoir algorithms, and snow on wetlands leads to better process representation. It finds that volume-dependent river routing generally worsens performance, while reservoir algorithms and snow on wetlands show benefits but require further development.
-
Song et al. (2025) Mediterranean rapid warming drives abrupt runoff decline in South China around 2002
This study investigates a pronounced decadal abrupt change (DAC) in summer runoff over South China around 2002 and its teleconnection mechanisms. It reveals that rapid warming of Mediterranean Sea surface temperature (SST) triggered an eastward-propagating atmospheric wave train, establishing an anomalous high-pressure system over East Asia, which led to regional moisture divergence, enhanced surface drying, and ultimately an abrupt decline in South China's summer runoff.
-
Daraghma et al. (2025) Impacts of climate change on the water budget elements in the Faria catchment, Palestine
This study assesses the impacts of climate change on water budget components in the semi-arid Faria catchment, Palestine, using climate projections and hydrological modeling. It projects significant reductions in groundwater recharge and surface runoff, alongside increased evapotranspiration, leading to decreased water yield and threatening regional water security.
-
Wang et al. (2025) Investigating the meteorological causes of hydrological drought through the integration of spatiotemporal cubes and interpretable machine learning: A case study of the Yangtze River Basin
This study investigates the meteorological causes of hydrological drought in the Yangtze River Basin (1980-2019) using spatiotemporal cubes and interpretable machine learning (XGBoost-SHAP, CNN-SHAP). It found that climate change is the dominant driver in the upper reaches (85% contribution), while human activities have a larger influence in the downstream (climate change 55% contribution), with specific meteorological factors driving drought in each region.
-
Yu et al. (2025) A Novel Quality Control Framework for Long‐Term Hourly Sunshine Duration Data in China
This study develops a systematic four-step quality control framework to enhance the reliability of historical hourly sunshine duration data across China from 1951 to 2023, effectively identifying and flagging significant data errors linked to historical digitization limitations and improving data quality for solar energy and climate studies.
-
Zhang et al. (2025) Increases in global hot droughts across multiple timescales
This study comprehensively investigated global hot drought changes across weekly to annual timescales, revealing significant historical increases in frequency and spatial extent attributed to anthropogenic influences, with further exacerbation projected under future climate scenarios.
-
Prakash et al. (2025) An integrated assessment of hydro-meteorological extremes and water scarcity in a mountainous river catchment under climate change
[Information not extractable due to corrupted input.]
-
Luo et al. (2025) Synergistic enhancement of Asian monsoons to westerlies intensifies the drying trend of arid Central Asia over the last 20 years
This study investigates the long-term dry-wet climate change in Arid Central Asia (ACA) and finds that a synergistic enhancement of Asian monsoons to westerlies has intensified the region's drying trend over the last 20 years.
-
Quispe (2025) Modélisation intégrée du bilan hydrique du lac Titicaca : un cadre pour identifier les facteurs de variabilité et atténuer les risques de sécheresses et d’inondations
N/A
-
Sato et al. (2025) Improving soil moisture estimation in wet soils using L-band Synthetic Aperture Radar (SAR) through polarization and filtering optimization
This study optimized L-band Synthetic Aperture Radar (SAR) parameters for soil moisture estimation in wet soils (volumetric water content > 0.3 m³/m³), finding that HH polarization with the Frost filter and VH polarization with Lee Sigma or Refined Lee filters, combined with spatially matched ground-truth data, significantly improved accuracy.
-
Ravinandrasana et al. (2025) The first emergence of unprecedented global water scarcity in the Anthropocene
This study estimates the Time of First Emergence (ToFE) of "Day Zero Drought" (DZD) events globally, attributing their timing and likelihood to human influence, and finds that many regions, including major reservoirs, face high DZD risk by the 2020s and 2030s, with urban populations particularly vulnerable at 1.5 °C warming.
-
Naeem et al. (2025) Simulating and predicting lake dynamics by fusing HBV modeling, machine learning approach and remote sensing data
This study comprehensively analyzes historical (1990-2023) and projected (up to 2060) hydrological dynamics and land use changes in the Hongjiannao Lake Basin by integrating HBV modeling, Random Forest, CA-Markov, and remote sensing, revealing significant lake area fluctuations and predicting future increases despite ongoing pressures.
-
Yu et al. (2025) Cross-scale soil moisture content monitoring of winter wheat by integrating UAV and sentinel-1/2 data
This study developed an innovative framework integrating ground, UAV, and satellite data to accurately estimate and map soil moisture content (SMC) in winter wheat fields across scales, demonstrating significantly improved accuracy compared to traditional ground-satellite models.
-
Qing et al. (2025) Delayed formation of Arctic snow cover in response to wildland fires in a warming climate
This study reveals that from 1982 to 2018, Arctic wildland fires significantly increased, causing a substantial delay in snow cover formation primarily due to fire-induced albedo reduction and temperature increases. Projections under a high-emissions scenario indicate a 2.6-fold increase in burned area and an 18-day decrease in annual mean snow cover duration by 2100.
-
Bharghavi et al. (2025) Evaluating climate change impact on drought: a comprehensive review of drought indices and future projections
This review systematically evaluates the performance of key drought indices across ten global regions under climate change, finding that climate change exacerbates drought conditions and that the Standardised Precipitation Evapotranspiration Index (SPEI) consistently performs well, while highlighting the increasing role of remote sensing, AI, and ML in drought monitoring and prediction.
-
Wang et al. (2025) Meta-learning-driven intelligent ensemble approach for robust drought evaluation across China
This study develops a Comprehensive Drought Monitoring Model based on a Meta-learning Ensemble Algorithm (CDMMMLEA) that integrates multi-source remote sensing and geospatial data to enhance drought monitoring accuracy and robustness across China from 2001 to 2023, demonstrating superior performance over benchmark models and revealing spatiotemporal drought evolution patterns.
-
Hasan et al. (2025) Satellite data and physics-constrained machine learning for estimating effective precipitation in the Western United States and application for monitoring groundwater irrigation
This study developed a physics-constrained machine learning framework to accurately estimate effective precipitation for irrigated croplands in the Western United States at a spatial resolution of approximately 2 kilometers and a monthly temporal scale from 2000 to 2020. The framework's effective precipitation estimates were successfully integrated into a water balance model to monitor groundwater irrigation, showing good skill with an R² of 0.78 and a PBIAS of –15 % when compared to in-situ pumping records.
-
Yu et al. (2025) Irrigated agriculture expansion drives groundwater storage decline in Black Soil Region of Northeast China
This study analyzed the spatiotemporal distribution and drivers of groundwater storage changes in the Black Soil Region of Northeast China using high-resolution GRACE data and a random forest model. It revealed a significant overall decline in groundwater storage, primarily driven by the expansion of irrigated agriculture, particularly in long-term trends.
-
Beyá-Marshall et al. (2025) Water stress thresholds for walnuts: Stem water potential baselines to maximize yield and water productivity
This study established cultivar-specific midday stem water potential (Ψₓ) baselines and detrimental thresholds for 'Chandler' and 'Serr' walnuts, demonstrating that optimizing irrigation based on these baselines can improve water productivity by 20–25% while sustaining high yields under water-limited conditions.
-
Zhang et al. (2025) Global optimization of a water-constrained two-leaf light use efficiency model through multi-biome FLUXNET observations
This study developed and globally optimized a water-constrained two-leaf light use efficiency (WTL-LUE) model using multi-biome FLUXNET observations to improve terrestrial gross primary productivity (GPP) estimation, particularly under water stress. The optimized WTL-LUE model significantly enhanced GPP simulation accuracy and stability across various ecosystems compared to existing LUE and machine learning models.
-
Samanta et al. (2025) Evaluating the potential of no-tillage in enhancing resilience of agricultural watersheds to extreme climatic conditions
This study assessed the effectiveness of no-tillage (NT) in enhancing the resilience of agricultural watersheds to extreme climatic conditions compared to conventional tillage (CT). Results demonstrate that NT significantly reduces future annual soil evaporation, surface runoff, and soil erosion, minimizing the destructive effects of climate change.
-
Farokhzadeh et al. (2025) Drought Characteristics and Risks in Iran: A Four‐Dimensional Copula‐Based Approach Under Future Climate Scenario
This study investigated historical (1966–2019) and future (2020–2050) drought dynamics in Iran under the SSP2-4.5 scenario using SPI and copula models, revealing a general reduction in drought severity, duration, and magnitude but an increased likelihood of more frequent, less severe droughts and a moderate increase in severe drought probability for longer return periods.
-
Folland et al. (2025) A Review of 25 Annual Forecasts of Global Mean Surface Temperature Including the Record Warm Years 2023 and 2024
This study evaluates the skill of real-time global mean surface temperature forecasts issued annually for 2000–2025, finding high skill in capturing interannual variability for 2000–2024, though the extreme 2023 warming was missed, while 2024's record warmth was better predicted, especially by dynamical models.
-
Fu et al. (2025) Remote sensing-based monitoring of compound drought-waterlogging stress in groundwater-sensitive agroecosystems in arid regions
This study developed a remote sensing-based framework to monitor groundwater-driven compound drought-waterlogging stress in arid Groundwater-Sensitive Agroecosystems (GWSA) in Northwest China. By downscaling root-zone soil moisture data to 30 m resolution and employing novel indices, the research revealed significant spatial heterogeneity in stress patterns and identified groundwater level, precipitation, and temperature (via snowmelt) as key drivers, offering critical insights for agricultural resilience.
-
Singh et al. (2025) From bias to forecast: advancing satellite rainfall accuracy and flood prediction with transformer modeling in the Kosi basin (India)
This study enhances satellite rainfall product (SRP) accuracy through Random Forest bias correction and integrates the best-performing SRP (IMERG) into a Transformer model for real-time flood forecasting in the Kosi River basin, India, achieving robust water level predictions up to 14 days in advance.
-
Ghazi et al. (2025) Drought characteristics in the Middle East simulated by raw and bias-corrected CMIP6 models
This study compares raw and bias-corrected CMIP6 GCMs (NEX-GDDP) to project future drought characteristics in the Middle East under various SSP scenarios, finding a significant increase in drought probability, particularly when considering the increasing role of warming-driven evapotranspiration.
-
Lu et al. (2025) Sentinel-1 vegetation optical depth retrievals over the international soil moisture network
Information not available from the provided text.
-
Lee et al. (2025) Effect of Slope Gradient and Litter on Soil Moisture Content in Temperate Deciduous Broadleaf Forest
This study analyzed the impacts of litter and slope gradient on soil moisture content (SMC) in a temperate deciduous broadleaf forest over two years, finding that both factors significantly influence SMC dynamics, with litter interception and slope affecting rainfall absorption and SMC response, which also varies seasonally and temporally due to litter decomposition.
-
Asmus et al. (2025) The Role of Horizontal Resolution in Modeling Irrigation Effects With a Coupled Regional Climate Model System Up To Convection‐Permitting Scale
[Information not extractable due to corrupted input text.]
-
Arra et al. (2025) Evaluating Droughts and Trends in Data-Scarce Regions: A Case Study of Palestine Using ERA5, Standardized Precipitation Index, Bias Correction, Classical and Innovative Trend Approaches
This study evaluates temporal and spatial drought characteristics and trends in data-scarce Palestine using bias-corrected ERA5 precipitation data and various trend analysis methods. The research found that bias correction significantly improves ERA5 data accuracy, and while short- and medium-term droughts show no significant trends, long-term droughts (SPI-12) exhibit a significant intensifying trend across the region.
-
Wang et al. (2025) Impacts of irrigation on hot extreme in the Yangtze River Basin using observed analysis and model simulation
This study investigates the impact of irrigation on hot extremes in the Yangtze River Basin using observational analysis and WRF model simulations, revealing that irrigation reduces extreme high temperatures by an average of 0.3 °C, with a maximum decrease of 1.4 °C, and identifies a threshold for cooling benefits at an irrigated area fraction of 0.3.
-
Chebbo (2025) Intégration de l’identité et de la diversité fonctionnelle dans un modèle de surface terrestre : une étude de la productivité et de la stabilité d’un écosystème dans un contexte de changement climatique
Information could not be extracted from the provided unreadable text.
-
Yang et al. (2025) Propagation dynamics of meteorological, agricultural, and vegetation droughts in China
This study quantified the propagation mechanisms of meteorological, agricultural, and vegetation droughts across China, revealing distinct propagation times and seasonal variations influenced by climate and vegetation types. The findings provide a scientific foundation for enhanced drought monitoring and early warning systems.
-
Keel et al. (2025) Representing the Teleconnection Between the Jet Stream and Extreme Cold Air Outbreaks Over North America
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
-
Gao et al. (2025) The exposure threat of wet heat extremes is substantially increasing to the 65+ elderly population in Asia
This study analyzes past trends (2001-2020) and projects future exposure (2030-2100) to wet heatwaves across Asia, particularly focusing on the elderly population. It finds a significant increase in wet heatwave days and projected exposure, with population growth contributing more than climate change to the heightened exposure.
-
Devanand et al. (2025) The influence of lateral flow on land surface fluxes in southeast Australia varies with model resolution
Lateral flow significantly increases evapotranspiration near major river channels in high-resolution (1 and 4 km) land surface simulations in southeast Australia, consistent with observations. This inclusion reveals resolution-dependent spatial patterns, such as drier ridges at 1 km, which are crucial for improved drought and water availability modeling.
-
Castillo et al. (2025) Evaluation of Monin‐Obukhov Similarity Theory Wind Profiles in Convective Storm Environments and Cold Pools at the ARM Southern Great Plains Atmospheric Observatory
This study evaluates the performance of Monin-Obukhov similarity theory (MOST) in fair-weather and convective storm environments using observational data, finding that MOST accurately predicts wind profiles in fair weather but systematically overestimates wind shear in cold pools following convective storms.
-
Tang et al. (2025) Radiative–Convective Equilibrium over an Idealized Land Surface with Fixed Soil Moisture
This study uses theory and simulations to understand radiative-convective equilibrium (RCE) over idealized land surfaces with fixed soil moisture, a crucial step for land climate modeling. It finds that potential evapotranspiration primarily scales with surface net radiation, implying only modest increases in global mean aridity with warming, contrary to some prior predictions.
-
Turiano et al. (2025) Evaluating Country-Scale Irrigation Demand Through Parsimonious Agro-Hydrological Modeling
This study introduces WaterCROPv2, an agro-hydrological model designed to estimate national-scale irrigation water demand, demonstrating enhanced reliability and accuracy through validation for maize cultivation in Italy, and identifying potential water savings from efficient irrigation technologies.
-
Fang et al. (2025) The applications of radiocesium-137 to soil redistribution and related studies in NE China: a review
This review systematically analyzes the applications of radiocesium-137 (137Cs) in soil redistribution studies in Northeastern China, identifying key research areas and future needs, including the necessity for alternative radionuclides due to 137Cs decay.
-
Rabiei et al. (2025) Deep Learning-Based Short- and Mid-Term Surface and Subsurface Soil Moisture Projections from Remote Sensing and Digital Soil Maps
This study develops a convolutional long short-term memory (ConvLSTM) framework to generate short- and mid-term forecasts of surface and subsurface soil moisture across the contiguous U.S., demonstrating its skill in supporting large-scale drought and flood monitoring despite varying accuracy with lead time, soil texture, and land cover.
-
Suri et al. (2025) Optimal Rain Gauge Network: A Data‐Driven Design for Enhanced Precision in Rainfall Measurements Over Northwest Himalayas
## Identification - **Journal:** International Journal of Climatology - **Year:** 2025...
-
Yoon et al. (2025) Non-Destructive Drone-Based Multispectral and RGB Image Analyses for Regression Modeling to Assess Waterlogging Stress in Pseudolysimachion linariifolium
This study evaluated the waterlogging stress responses of *Pseudolysimachion linariifolium* using non-destructive drone-based multispectral imagery, finding that vegetation indices like NDVI and GNDVI effectively quantify stress and correlate strongly with soil moisture content.
-
Zhang et al. (2025) Land use and economic development influenced the hotspots of groundwater storage gains and losses in mainland China in the past 20 years
This study identifies groundwater storage change hotspots across mainland China over the past two decades using GRACE/GRACE-FO satellite data. It reveals that land use and economic factors are the primary drivers of these changes, with their influence varying geographically.
-
Schmidt et al. (2025) Analysis of the Meso‐Scale Climate of the Galápagos Archipelago by Dynamical Downscaling of Reanalysis Data
[Information not extractable due to corrupted input.]
-
Zeng et al. (2025) The Spatial Patterns and Trends of Sensible and Latent Heat Fluxes in the Northern Drought‐Prone Belt of China
[Information not extractable due to corrupted paper text.]
-
Hui et al. (2025) Refine Extreme Hot Day Predictions With the Sea Surface Temperature Tendency
This study investigates the underlying mechanisms influencing extreme hot days over Western North America (WEHDs) and aims to improve their seasonal prediction, revealing that two independent sea surface temperature (SST) precursor signals enable robust and enhanced prediction using a physics-informed convolutional neural network.
-
Kannan et al. (2025) Hydrological drought assessment using drought indices in semi arid zones of Vaippar river sub basins, Tamil Nadu, India
This study assessed hydrological drought in the semi-arid Vaippar river sub-basins of Tamil Nadu, India, using the Streamflow Drought Index (SDI) and Groundwater Resource Index (GRI). It found contrasting conditions, with groundwater experiencing mild to severe drought while surface water remained near-normal to wet, underscoring the critical need for integrated surface and groundwater management.
-
Shi et al. (2025) Improving runoff simulation in cold alpine regions based on VIC-glacier by combining LSTM error correction technology
This study developed and optimized a coupled Variable Infiltration Capacity-Glacier (VIC-glacier) model for the upper Hotan River Basin, demonstrating that integrating Long Short-Term Memory (LSTM) error correction significantly enhances runoff simulation accuracy in cold alpine regions.
-
Gu et al. (2025) Energy and mass balance of glaciers in the Ulugh Muztagh driven by climate warming over 44 years
This study systematically investigated the mass balance and energy exchange characteristics of glaciers in the Ulugh Muztagh region from 1980 to 2023 using calibrated reanalysis data and the COSIPY model. It found a mean glacier mass balance of −0.06 ± 0.05 m water equivalent per year, with accelerated mass loss after 2000, primarily driven by net radiation and air temperature increases.
-
Mondal et al. (2025) Hot Drought of Summer 2023 in Southwestern North America
[Information not extractable due to unreadable paper text.]
-
Yu et al. (2025) Research on quantitative monitoring of new meteorological-agricultural compound drought characteristics
This study developed the Meteorological-Agricultural Compound Drought Index (AMDI) using Copula methodology to integrate meteorological and agricultural drought variables for China's Huang-Huai-Hai region. The AMDI effectively monitors emerging compound drought patterns, demonstrating higher sensitivity and accuracy than traditional indices for early warning and precise characterization.
-
Rydén (2025) Statistical analysis of monthly precipitation in Sweden using the Tweedie distribution
This study applies the Tweedie distribution to model monthly precipitation across 14 sites in Sweden, extending previous research primarily focused on Australia. The findings demonstrate that the Tweedie distribution is a relevant model for Northern European precipitation, revealing seasonal and geographical patterns in its index parameter and similarities to Australian rainfall characteristics.
-
Struglia et al. (2025) Impact of spatial resolution on multi-scenario WRF-ARW simulations driven by the CMIP6 MPI-ESM1-2-HR global model: a focus on precipitation distribution over Italy
This study dynamically downscales CMIP6 global climate projections over Italy and the Mediterranean to 5 km resolution, demonstrating the added value of high resolution for precipitation, and projects a general warming, mean precipitation reduction over the Mediterranean, and a strong increase in extreme precipitation intensity over the Italian Peninsula by 2100, especially under SSP5-8.5.
-
Mohammadnezhad et al. (2025) A novel hybrid model for actual evapotranspiration estimation in data-scarce arid regions: Integrating modified Budyko and machine learning models using deep learning
This study developed a novel hybrid model integrating a modified Budyko framework with machine learning (XGBoost) using deep learning to accurately estimate monthly actual evapotranspiration (ETa) in data-scarce arid regions, demonstrating superior performance over standalone models.
-
Bruno et al. (2025) Imprints of increases in evapotranspiration on decreases in streamflow during dry periods, a large-sample analysis in Germany
This study quantifies the contribution of increased actual evapotranspiration (E) to decreased streamflow (Q) during dry periods in 363 small German catchments. It finds that increases in E are a significant driver of decreasing summer low flows, particularly in more arid eastern regions, and are linked to shifts in the precipitation-streamflow relationship during multi-year droughts.
-
Morales et al. (2025) Geochemical, hydrochemical and remote sensing study of an Andean calcareous wetland in Huanta, Peru
This study provides the first integrated geochemical, hydrochemical, and remote sensing assessment of the Huaper Wetland, revealing early signs of water quality deterioration, declining surface moisture, and a weakening of its natural geochemical buffering capacity due to anthropogenic pressures and climatic variability.
-
Serkendiz et al. (2025) Machine learning and geographic information systems-based framework for multidimensional analysis of cascading drought impacts using remote sensing and in-situ data
This study proposes a multidimensional framework to assess cascading drought impacts on the agricultural sector, demonstrating its application in the Konya Closed Basin. It reveals severe groundwater depletion coinciding with intensified drought periods and a significant conversion of over 510,000 hectares of irrigated land to non-irrigated areas between 1990 and 2018, highlighting maladaptive agricultural practices.
-
Liu et al. (2025) GEDI reveals decline in overstorey and increase in understorey canopy cover of protected forests in Central Europe since 2019
This study utilized GEDI spaceborne lidar data to investigate changes in the vertical forest structure of protected areas in Central Europe since 2019, revealing a widespread decline in overstorey canopy cover coupled with a simultaneous increase in understorey canopy cover in both coniferous and broadleaved forests. The findings highlight GEDI's unique capability to monitor understorey regeneration, providing a more nuanced understanding of forest disturbance and recovery dynamics than traditional forest loss products.
-
Xie et al. (2025) Agricultural Water Sustainability Evaluation in Guangdong Province of China Through Perspective of Water Footprint
This study developed a multi-dimensional framework to evaluate agricultural water sustainability in Guangdong Province, China (2010-2020), revealing a 6.98% reduction in total agricultural water footprint and a 14.46% improvement in sustainability.
-
Lapyai et al. (2025) Developing a Composite Hydrological Drought Index Using the VIC Model: Case Study in Northern Thailand
This study introduces a Composite Hydrological Drought Index (CHDI) for a northern watershed in Thailand, integrating multiple Variable Infiltration Capacity (VIC) model outputs via Principal Component Analysis (PCA) to capture the multidimensional complexity of water scarcity. The CHDI demonstrated significant predictive skill in monitoring hydrological drought, effectively capturing seasonal and interannual variability and identifying low-flow events.
-
Mashoudi et al. (2025) Topographic Influences on Seasonal Drought in Morocco’s Western Rif: Insights from a 40-Year SPI-3 Analysis
This study investigated the influence of static topographic factors on seasonal drought patterns in Morocco's Western Rif over 40 years, revealing that distance to the coast is the most significant predictor of winter drought intensity, while topographic influence is weaker in autumn and spring.
-
Jesse et al. (2025) Sub-shelf melt pattern and ice sheet mass loss governed by meltwater flow below ice shelves
This study presents a novel coupled ice sheet–sub-shelf melt model (IMAU-ICE/LADDIE) to resolve 2D horizontal meltwater flow and compares its impact on Antarctic ice sheet evolution against traditional melt parameterizations. The findings reveal that resolving 2D meltwater flow introduces critical feedbacks, particularly along shear margins, leading to distinct transient volume loss and sensitivity to ocean warming not captured by simpler parameterizations.
-
Mazzolini et al. (2025) Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter
This study presents a novel spatio-temporal data assimilation framework to integrate sparse ICESat-2 laser altimeter snow depth profiles with Sentinel-2 fractional snow-covered area (fSCA) observations into a snow model. It demonstrates that jointly assimilating both data types significantly improves the accuracy and spatial distribution of simulated snow depth, particularly during the accumulation season, compared to using fSCA alone.
-
Achite et al. (2025) Performance enhancement of daily reservoir evaporation rate estimation models using stacking regression by discretization with AI methods
This study developed an advanced machine learning framework based on Regression by Discretization (RD) and ensemble methods to accurately predict daily reservoir evaporation rates at the Sidi-M’Hamed Ben Aouda Dam Basin in Algeria. The RD-Bagging model demonstrated superior performance with high predictive accuracy and low bias, making it a reliable tool for water resource management.
-
Chalmers et al. (2025) Defining the Agricultural Wet Season in Africa Using Soil Moisture From the Soil Moisture Active‐Passive Satellite
This study estimates root-zone soil moisture across Africa using SMAP satellite data from 2016 to 2023 to redefine the wet season, finding that soil moisture timing correlates more strongly with vegetation timing than precipitation in African croplands and better captures early season rainfall events.
-
Hannaford et al. (2025) Have river flow droughts become more severe? A review of the evidence from the UK – a data-rich, temperate environment
This extended review assesses whether river flow droughts in the UK have become more severe, synthesizing existing literature and conducting new analyses. The study finds little compelling evidence of worsening hydrological droughts in the UK, a finding that appears to contradict near-future climate projections and common assumptions about human impacts.
-
Simon et al. (2025) Evaluation of different bias-corrected EURO-CORDEX databases and the expected future changes in precipitation over Hungary
This study investigates projected changes in mean precipitation characteristics and extremes over Hungary using raw and bias-corrected EURO-CORDEX simulations, finding that a newly created HuClim-based bias correction performs best and projects increased annual precipitation (up to 30% in highlands) but fewer wet days by the end of the century.
-
Lakshmi et al. (2025) Precipitation Data Accuracy and Extreme Rainfall Detection for Flood Risk Analysis in the Akçay Sub-Basin
This study evaluates the performance of GPM-IMERG and CHIRPS satellite precipitation data against rain gauge observations in Türkiye’s Akçay Sub-Basin, finding that GPM-IMERG shows good agreement at the monthly scale but reduced accuracy at the daily scale, particularly for extreme events.
-
Hassan et al. (2025) Advancing convection-permitting regional climate modeling for monsoon extremes in data-scarce, topographically complex regions of South Asia
This study evaluates the convection-permitting RegCM5 model's performance in simulating extreme monsoon precipitation events in data-scarce, topographically complex South Asia, demonstrating that the 3 km MOLOCH configuration significantly improves the accuracy of precipitation intensity, spatial distribution, and temporal variability compared to coarser hydrostatic simulations.
-
Laan et al. (2025) Decadal re-forecasts of glacier climatic mass balance
This study presents the first application of decadal re-forecasts to simulate global glacier climatic mass balance, bridging the gap between seasonal forecasts and long-term projections. It demonstrates that forcing the Open Global Glacier Model (OGGM) with decadal re-forecasts generally outperforms persistence forecasts and historical General Circulation Model (GCM) simulations for multi-annual glacier mass balance, offering moderate improvements for near-term predictions.
-
Thakur et al. (2025) Unveiling the impact of potential evapotranspiration method selection on trends in hydrological cycle components across Europe
This study evaluates the impact of selecting different potential evapotranspiration (PET) methods on trends in actual evapotranspiration (AET), runoff (Q), and total water storage (TWS) across 553 European catchments. It finds that annual and seasonal trends are variably sensitive to the PET method choice, depending on the hydrological component and catchment type, underscoring the importance of careful method selection for robust hydrological assessments.
-
Dash et al. (2025) Sedimentation in Saudi Arabia’s 574 reservoirs: Nationwide assessment using remote sensing and erosion modeling
This study presents the first nationwide assessment of sedimentation across 574 reservoirs in Saudi Arabia, combining long-term Landsat imagery (1986–2024) with erosion modeling. It reveals a median annual water extent decline of –1.5% per year and estimates a 32% reduction in total usable storage capacity, highlighting a critical threat to the nation's water security.
-
Guan et al. (2025) Study of the Correlation Between Water Resource Changes and Drought Indices in the Yinchuan Plain Based on Multi-Source Remote Sensing and Deep Learning
This study integrates multi-source remote sensing data with deep learning to model water resource dynamics and their relationship with drought indices in the Yinchuan Plain, China, finding strong correlations with SPEI and superior performance of LSTM for predictions, offering a robust foundation for water management.
-
Bellat et al. (2025) Soil information and soil property maps for the Kurdistan region, Dohuk governorate (Iraq)
This study provides the first detailed, high-resolution (30 m) soil property and depth maps for the Dohuk governorate, Kurdistan Region of Iraq, outperforming global models and offering crucial data for local land management in a data-poor, arid/semi-arid environment.
-
Khan (2025) New insights into 21st-century drought characteristics under climate change from CMIP6 multimodel ensemble analysis
This study projects 21st-century drought characteristics (frequency, duration, severity, intensity, and peak) across different categories in Pakistan using CMIP6 multimodel ensemble data, revealing varied regional shifts in drought dynamics under climate change.
-
Kulkarni et al. (2025) Global assessment of socio-economic drought events at the subnational scale: a comparative analysis of combined versus single drought indicators
This study globally assesses socio-economic drought events at the subnational scale by comparing a novel combined drought indicator (CDI) with single-parameter indices, finding that CDI significantly outperforms individual indices in identifying GDIS-documented socio-economic drought impacts.
-
Hu et al. (2025) Driving Factors of Hala Lake Water Storage Changes from 2011 to 2023
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
-
Ma et al. (2025) Runoff Forecast Model Integrating Time Series Decomposition and Deep Learning for the Short Term: A Case Study in the Weihe River Basin, China
This paper introduces a novel framework integrating segmented decomposition sampling with a multi-input neural network to address forward data contamination in decomposition-based runoff prediction models, demonstrating improved accuracy and reliability for daily runoff estimation.
-
Gacu et al. (2025) Application of Artificial Intelligence in Hydrological Modeling for Streamflow Prediction in Ungauged Watersheds: A Review
This review synthesizes recent advancements in artificial intelligence (AI) for streamflow modeling in ungauged watersheds, demonstrating that AI-based models, particularly deep learning architectures, consistently outperform traditional models in capturing nonlinear hydrological responses.
-
Sun et al. (2025) Season-Specific CNN and TVDI Approach for Soil Moisture and Irrigation Monitoring in the Hetao Irrigation District, China
This study develops a year-round, field-scale framework for soil moisture retrieval and irrigation mapping in arid regions by introducing a season-stratified TVDI scheme and a multi-source inversion. The framework, leveraging Sentinel-1 SAR and Landsat data with a CNN regressor, successfully maps complementary seasonal irrigation patterns and provides operational evidence for water management.
-
Qian et al. (2025) A Multi-Scale Comprehensive Evaluation for Nine Evapotranspiration Products Across Mainland China Under Extreme Climatic Conditions
This study comprehensively evaluates nine evapotranspiration (ET) products across grid, basin, and site scales in China under varying climatic conditions from 2003 to 2014, finding that while products like GLEAM perform well, their accuracy significantly decreases under extreme conditions, a limitation largely overcome by integrating daily ET products into machine learning models.
-
Moradikian et al. (2025) Identifying and Characterizing Dust-Induced Cirrus Clouds by Synergic Use of Satellite Data
This study develops an algorithm to identify and characterize dust-induced cirrus clouds using synergic satellite data, revealing that these clouds are thicker, form at higher altitudes, and are more frequent in the Aral Sea and Iberian Peninsula regions, with significant seasonal and regional variations.
-
Shen et al. (2025) Ocean State Estimation in CESM via a Localized Particle Filter: Joint Assimilation of Satellite SST and In Situ TS Profiles
This study extends the Localized Particle Filter (LPF) to the Community Earth System Model (CESM) for assimilating multisource ocean observations, demonstrating its significant improvement in subsurface and deep ocean states, but revealing challenges with sea surface temperature (SST) assimilation when temperature and salinity (TS) profiles are already used.
-
Guan et al. (2025) Characterization of spatial and temporal distribution of flood impacts in China
This study develops a novel dynamic clustering framework and a Flood Impact Index (FII) to characterize the spatiotemporal distribution of flood impacts in China from 1993 to 2023. It reveals an overall declining trend in flood impacts, with concentrations in central and southern regions, providing a quantitative basis for disaster mitigation.
-
Li et al. (2025) Generative Downscaling and Bias Correction of Multivariable Earth System Model Simulations
This study introduces a multivariate generative downscaling model (MVGDM) to simultaneously downscale global climate simulations from 100 km to 25 km resolution and correct inherent biases, significantly improving the simulation of key climate variables, phenomena like ENSO and IOD, and climate extremes.
-
Cho et al. (2025) Enhancing production efficiency of tomatoes through soil moisture–dependent multifaceted responses in three-layered soil
This study identified an optimal irrigation range for enhancing water use efficiency in tomato cultivation within a three-layered soil system by integrating real-time soil moisture monitoring with morpho-physiological and biochemical analyses, demonstrating that maintaining volumetric water content between 15% and 25% significantly improved irrigation water use efficiency without negatively affecting plant growth.
-
Kraulich et al. (2025) The impact of aerosol forcing on the statistical attribution of heatwaves
This study demonstrates that the standard statistical method for attributing heatwaves, which relies solely on global mean temperature, produces significant biases in regions with strong aerosol trends. Incorporating regional aerosol optical depth as an additional covariate in the Generalized Extreme Value distribution model substantially reduces these biases and improves heatwave return period estimates.
-
Kallas et al. (2025) Modeling the Hydrological Regime of Litani River Basin in Lebanon for the Period 2009–2019 and Assessment of Climate Change Impacts Under RCP Scenarios
This study investigates the combined impacts of climate change and land use changes on water resources and soil conditions in the Litani River Basin, revealing significant declines in infiltration, runoff, and soil moisture, especially under severe climate scenarios and exacerbated by deforestation and urban expansion.
-
Xu et al. (2025) Impact of different rainfall patterns on flood risk on the urban-rural gradient
This study developed a framework combining hydrodynamic modeling and multi-indicator decision analysis to quantify the dynamic response of flood risk to different rainfall patterns along the urban-rural gradient. It found that flood risk is significantly higher in rural areas compared to urban and suburban areas, with differences worsening under heavier rainfall, and that rainfall peak timing influences flood response time.
-
Abdelrhman et al. (2025) Optimizing Drip Irrigation and Nitrogen Fertilization for Sustainable Wheat Production in Arid Soils: Water–Nitrogen Use Efficiency
This study investigated the effects of integrated water and nitrogen fertilizer management under drip irrigation on wheat performance in arid regions of Egypt, finding that optimal strategies for yield, water productivity, and nitrogen use efficiency vary depending on the specific combination of irrigation regime and nitrogen application rate.
-
Murasheva et al. (2025) Information support for monitoring of agricultural lands based on the remote sensing and geoinformation technologies in Kon tum province of the Republic of Vietnam
This paper develops a methodology for monitoring agricultural and environmental lands in Kon Tum province, Vietnam, using remote sensing and GIS technologies via Google Earth Engine, demonstrating its effectiveness for sustainable resource management.
-
Mohammed et al. (2025) Deep-Learning-Based Probabilistic Forecasting of Groundwater Storage Dynamics in Sudan Using Multisource Remote Sensing and Geophysical Data
This study integrates GRACE satellite data with GLDAS land surface variables to assess and forecast groundwater storage (GWS) dynamics in Sudan, revealing a positive GWS recovery across all regions, particularly strong in the south and southwest.
-
Zhang et al. (2025) Applicability of a Sine–Random Forest Hybrid Method for meteorological and energy variables
This study proposes a Sine-Random Forest Hybrid Method to reduce bias and enhance the accuracy of meteorological and energy variables in reanalysis datasets, demonstrating its effectiveness in improving agreement with measured data and capturing diurnal patterns.
-
Ahmed et al. (2025) Evapotranspiration Estimation in the Arab Region: Methodological Advances and Multi-Sensor Integration Framework
This study reviews evapotranspiration (ET) estimation techniques in the Arab world, highlighting the dominance of traditional methods while demonstrating the potential of machine learning (ML)-based fusion for improved ET estimation in data-scarce regions.
-
Quang et al. (2025) Future Intensity‐Duration‐Frequency Curves of Extreme Precipitation in the Midwest United States From Convection‐Permitting Modeling
[Information not extractable due to corrupted input text.]
-
Strijker et al. (2025) The dynamics of peak head responses at Dutch canal dikes and the impact of climate change
This study investigated the dynamics of peak hydraulic heads in Dutch canal dikes at a national scale using non-linear time series models calibrated on extensive observation data. It found that climate change will significantly alter the frequency of extreme peak heads, with projections indicating occurrences between 3 times less and 8 times more frequently by 2100, depending on the climate scenario and dike characteristics.
-
El-Basir et al. (2025) Developing Rainfall Spatial Distribution for Using Geostatistical Gap-Filled Terrestrial Gauge Records in the Mountainous Region of Oman
This study assesses and applies geospatial interpolation techniques to fill rainfall data gaps in the arid mountainous region of northern Oman, aiming to provide continuous records for flood mitigation and water resource management. It found that geostatistical interpolation techniques were superior for generating spatial distributions of maximum and total yearly precipitation, enabling the calculation of extreme precipitation return periods.
-
Jin-xi et al. (2025) Influence of Soil Background Noise on Accuracy of Soil Moisture Content Inversion in Alfalfa Fields Based on UAV Multispectral Data
This study develops and evaluates drone-based multispectral remote sensing models for estimating topsoil moisture (0–10 cm) in alfalfa, finding that the XG-Boost model using spectral reflectance is most effective and that removing soil background noise does not significantly improve estimation accuracy in this specific environment.
-
Najafi et al. (2025) The skill of RegCM4 in forecasting Iran’s precipitation: a basin-scale intra-seasonal to seasonal analysis
This study evaluates the skill of the RegCM4-CFSv2 model in forecasting intra-seasonal to seasonal precipitation over seven basins in Iran. It finds that the model exhibits moderate skill, particularly at shorter lead times and for extreme events, but performance declines with increasing lead time and shows significant regional variability.
-
Cheang et al. (2025) RUSH: Rapid Remote Sensing Updates of Land Cover for Storm and Hurricane Forecast Models
This study developed the RUSH (Rapid Remote Sensing Updates of Land Cover for Storm and Hurricane Forecast Models) tool, an open-source application that generates high-resolution (3 meter) coastal land cover maps from Planet SuperDove imagery. The tool provides near-real-time or historical land cover data with overall accuracies of 93% to 94%, crucial for improving hydro-morphological models used in hurricane impact forecasting.
-
Cooper et al. (2025) Greenland ice sheet runoff reduced by meltwater refreezing in bare ice
This study combines field measurements and numerical modeling to demonstrate that extensive meltwater retention and refreezing occur in bare glacier ice, significantly reducing runoff from the Greenland Ice Sheet's ablation zone. This overlooked process explains why current climate models overestimate runoff by 9–15% in southwest Greenland, impacting sea-level rise projections.
-
Deleuze (2025) Nonlinear internal gravity wave modes and settling particles in stratified fluids : two pathways to mixing
This study experimentally investigates energy transfer and diapycnal mixing in stratified fluids, focusing on internal gravity waves and particle sedimentation. It reveals complex non-linear wave dynamics, including resonant triadic instability and a feedback loop between wave evolution and background stratification, alongside quantifying mixing induced by sedimenting particle clouds.
-
Yang et al. (2025) Improving Weeks 1–2 Temperature Forecasts in the Sierra Nevada Region Using Analog Ensemble Postprocessing with Implications for Better Prediction of Snowmelt, Water Storage, and Streamflow
This study applies analog ensemble (AnEn) postprocessing to improve subseasonal 2-meter temperature (T2m) forecasts in the Sierra Nevada during the spring snowmelt season, demonstrating significant accuracy enhancements, particularly at higher elevations, compared to dynamical benchmarks and basic bias correction methods.
-
Yong et al. (2025) Analysis of hydrological time-lag effects using multiple GNSS techniques: GNSS-R-retrieved soil moisture, GNSS-derived coordinates, and GNSS-based water vapor data
This study proposes an integrated approach using multiple Global Navigation Satellite System (GNSS) techniques, including GNSS reflectometry (GNSS-R) soil moisture retrieval, GNSS positioning, and GNSS-based water vapor data, to analyze hydrological time-lag effects in the Southern United States and Central America. The research reveals significant and spatially heterogeneous time-lag relationships among precipitable water vapor (PWV), soil moisture (SM), GNSS vertical displacement, and vegetation water content (VWC).
-
Verjans et al. (2025) The Greenland Ice Sheet Large Ensemble (GrISLENS): simulating the future of Greenland under climate variability
This study introduces GrISLENS, the first large-ensemble ice sheet model resolving individual glaciers and calibrated to observations, to quantify the impact of internal climate variability on Greenland Ice Sheet evolution. It finds that internal climate variability significantly contributes to ice sheet mass change uncertainty on decadal timescales, but its relative importance diminishes on longer timescales compared to anthropogenic forcing.
-
Yin et al. (2025) DACSA: deformable average channel and spatial attention model for wildfire prediction and drivers
This study proposes the Deformable Average Channel and Spatial Attention (DACSA) model, integrated with Location-aware Adaptive Normalization (LOAN), for improved wildfire prediction and analysis of driving factors using remote sensing data. The model demonstrates superior performance over state-of-the-art methods and provides quantitative insights into the importance of various wildfire drivers.
-
Tauqir et al. (2025) Elevation-Dependent Trends in Himalayan Snow Cover (2004–2024) Based on MODIS Terra Observations
This study investigates the altitude-specific dynamics and snow mass balance implications of snow cover in the Himalayas, revealing distinct regional variabilities in snow cover area, climatic drivers, and mass balance trends over 20 years.
-
Pan et al. (2025) Identifying Optimal Reanalysis and Remote Sensing Data Combinations for Multi-Scale SPEI-Based Drought Assessment in Zhejiang Province, China
This study evaluates nine reanalysis and remote sensing data combinations for multi-scale Standardized Precipitation Evapotranspiration Index (SPEI) estimation in Zhejiang Province, China, identifying the optimal combination and subsequently analyzing spatiotemporal drought variations from 1980–2020. The research found that the CMFD V2.0 precipitation and GLEAM v4.2a evapotranspiration combination is most reliable, revealing a significant "wetter winters, drier springs" pattern and distinct spatial drying trends in southern/southeastern regions.
-
Peng et al. (2025) Spatiotemporal Reconstruction of Annual Glacier Mass Balance in Central Asia (2000–2020) Using Machine Learning
This study reconstructs annual glacier-wide mass balance for glaciers in the Tien Shan and Pamir from 2000 to 2020 using machine learning, revealing an average mass loss of -0.39 meters water equivalent per year with significant spatiotemporal variability and accelerated loss for smaller glaciers.
-
Mehdizadeh et al. (2025) Assessing Orographic Cloud Seeding Impacts Through Integration of Remote Sensing from Multispectral Satellite, Radar Data, and In Situ Observations in the Western United States
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
-
Yousaf et al. (2025) A novel Bi-weight Mid Correlation Coefficient Divergence (BMCCD) approach for multi-model ensemble-based drought assessment
This study introduces a novel Bi-weight Mid Correlation Coefficient Divergence (BMCCD) weighting scheme for multi-model ensemble (MME) drought assessment, demonstrating superior performance in correlation and error reduction compared to existing methods, and utilizes it to project future drought characteristics on the Tibetan Plateau.
-
Parisouj et al. (2025) Hourly streamflow forecasting across diverse climate zones on Oʻahu Island, Hawaiʻi
This study proposes a novel hybrid Honey Badger Algorithm-optimized Multilayer Perceptron (HBA–MLP) model for hourly streamflow forecasting across diverse climate zones on Oʻahu Island, Hawaiʻi, demonstrating exceptional performance in arid and semi-arid zones while identifying challenges in subhumid regions.
-
Linhares et al. (2025) Areas susceptible to desertification in Brazil: An approach based on the frequency of annual aridity classes
This study analyzed annual aridity class frequencies across Brazil from 1961 to 2020 using non-stationary approaches, revealing a 30% expansion of dryland conditions over the last 30 years, particularly in the Southeast and Pantanal regions, driven primarily by declining precipitation.
-
Mohammed et al. (2025) Comprehensive Assessment of GPM-IMERG and ERA5 Precipitation Products Across Ireland
This study comprehensively assesses the accuracy and reliability of GPM-IMERG (Early, Late, Final) and ERA5 precipitation products against ground observations across Ireland (2014–2021). It finds ERA5 superior for low-to-moderate rainfall and seasonal consistency, while IMERG-Final excels in detecting high-intensity, short-duration events, suggesting a hybrid approach for enhanced hydrological applications.
-
Su et al. (2025) Space-time deep hybrid boosting learning for investigating day-night hourly seamless air temperature distribution from FY-4A over China
This study developed a Space-Time Deep Hybrid Boosting (ST-DHB) model to generate day-night hourly seamless 0.04-degree air temperature (Ta) distributions across China from Fengyun-4A data, achieving high accuracy (R² > 0.94, RMSE < 2.6 °C) and outperforming existing methods. The resulting Ta data reveals significant geographical, seasonal, and diurnal disparities in heatwave exposure, particularly in urban and farmland areas.
-
Ma et al. (2025) Evaluating the Effectiveness of Water-Saving Irrigation on Wheat (Triticum aestivum L.) Production in China: A Meta-Analytical Approach
This meta-analysis quantitatively assessed the effects of various water-saving irrigation (WSI) methods on wheat yield, water use efficiency (WUE), and partial factor productivity of nitrogen (PFPN) across China, finding that optimized WSI, particularly drip and micro-sprinkler systems, significantly improved resource use efficiency without yield penalties.
-
Verrelst et al. (2025) Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications
This review comprehensively surveys recent developments in surrogate modeling (emulation) for vegetation and atmospheric radiative transfer models (RTMs) in optical remote sensing, highlighting methodologies, applications, and future challenges to address the computational cost of RTMs.
-
Liang et al. (2025) The Asymmetry of the El Niño–Southern Oscillation: Characteristics, Mechanisms, and Implications for a Changing Climate
This review synthesizes over two decades of research on El Niño–Southern Oscillation (ENSO) asymmetry, detailing its observed characteristics, evaluating competing physical mechanisms, and analyzing challenges in climate modeling. It concludes that ENSO asymmetry is driven by complex nonlinear atmospheric and oceanic processes, which current climate models largely underestimate, leading to uncertainties in future climate change projections.
-
Attia et al. (2025) Mapping spatial zones of climate vulnerability and adaptive potential for major crops in the Texas high plains
This study integrates process-based crop modeling with geospatial analysis to identify spatial zones of climate vulnerability and adaptive potential for four major crops in the Texas High Plains, revealing significant regional variability in yield responses that necessitate targeted adaptation strategies.
-
Elenius et al. (2025) Where can rewetting of forested peatland reduce extreme flows? Model experiment on the hydrology of Sweden
This study used a national hydrological model to investigate the impact of rewetting drained forested peatlands in Sweden on extreme water flows. It found that rewetting has a negligible effect on extreme flows in larger catchments (≥10 km²), but can significantly alter local runoff and groundwater levels in small streams draining only peatlands, with effects depending on pre-rewetting conditions and tree cover reduction.
-
Zhou et al. (2025) Neglecting land–atmosphere feedbacks overestimates climate-driven increases in evapotranspiration
This study develops a theoretical framework to disentangle land-atmosphere interactions, achieving consistent evapotranspiration (ET) projections between offline and coupled models. It reveals that neglecting these feedbacks leads to a 25–39% overestimation of climate-driven global ET increases and a 77–121% exaggeration of negative land surface contributions, causing significant discrepancies in hydrological projections.
-
Idhirij et al. (2025) Water management innovations for adapting to climate water stress: new evidence from the American Southwest
This study developed and applied a basin-scale hydroeconomic optimization model to evaluate the economic outcomes of different water shortage sharing institutions in a drought-prone watershed of the American Southwest. The findings indicate that market-based water trading institutions significantly outperform proportional allocation in minimizing income losses and reallocating water to higher-value uses during periods of water scarcity.
-
Olaniyan et al. (2025) Performance Evaluation of Real-Time Sub-to-Seasonal (S2S) Rainfall Forecasts over West Africa of 2020 and 2021 Monsoon Seasons for Operational Use
This study evaluates real-time ECMWF S2S rainfall forecasts during the 2020–2021 West African monsoon seasons for operational use, comparing them against satellite observations and hindcasts. The results demonstrate that ECMWF rainfall forecasts are skillful and actionable, especially up to 2–3 dekads ahead, providing confidence for early-warning and planning systems in the region.
-
Liberty-Levi et al. (2025) The Role of Ocean Processes in Future Northern Hemisphere Midlatitude Winter Precipitation Changes
This study quantifies the relative roles of dynamic and thermodynamic ocean processes in the projected intensification of Northern Hemisphere midlatitude winter net precipitation, finding that thermodynamic processes dominate over land and the Pacific, while dynamic processes are key over the Atlantic.
-
Feng et al. (2025) Compound drought-heatwaves in China: driving factors and risks
This study mapped the spatiotemporal patterns, identified driving factors, and assessed the risks of compound drought-heatwaves (CDHs) across China from 1961 to 2020, revealing an overall increasing trend, particularly after 1990, driven mainly by temperature changes.
-
Sharma et al. (2025) Leveraging Sentinel-2 Data and Machine Learning for Drought Detection in India: The Process of Ground Truth Construction and a Case Study
This study investigates the use of multispectral Sentinel-2 remote sensing indices and machine learning to detect drought conditions in three regions of India during the Rabi season. XGBoost, combined with a seasonal majority voting strategy, achieved 96.67% accuracy, precision, and recall, identifying Normalized Multi-band Drought Index (NMDI) and Day of Season (DOS) as the most influential features.
-
Chen et al. (2025) Projecting forecast quality before events through machine learning: Preliminary results of cloud-resolving quantitative precipitation forecasts in Taiwan for westbound typhoons
This study develops a neural-network machine learning model to project the expected similarity skill score (SSS) of cloud-resolving quantitative precipitation forecasts (QPFs) for westward-moving typhoons in Taiwan, demonstrating its ability to provide objective guidance on forecast quality before events.
-
Yang et al. (2025) Characteristics, prediction model and driving mechanism of multidimensional daily scale propagation from meteorological to agricultural drought in Guangxi, China
This study investigated the daily propagation characteristics, developed a prediction model, and elucidated the driving mechanisms of meteorological drought to agricultural drought in Guangxi, China, revealing distinct seasonality, time-lag effects (average 10.4 days), and scaling effects, primarily driven by precipitation and potential evapotranspiration.
-
Karimi et al. (2025) Evolution of mass loss at Alamkouh Glacier in Iran using multi-temporal high-resolution DEMs between 2010 and 2023
This study quantifies the surface elevation changes and mass loss of Alamkouh Glacier, Iran, between 2010 and 2023 using multi-temporal high-resolution DEMs, revealing an average mass balance of −0.20 ± 0.04 meters water equivalent per year and highlighting the significant impact of supraglacial features on melt rates.
-
Li et al. (2025) Exploring runoff variation and attribution analysis based on the SWAT model and the Budyko framework in the Huangyang River of the Northwest Inland Region, China
This study investigated runoff variations and their attribution in the Huangyang River Basin, China, from 1956 to 2023 using the SWAT model and Budyko framework. It found a significant decrease in annual runoff, primarily driven by human activities (approximately 69%) rather than climate change (approximately 30%).
-
Hu et al. (2025) CycloneWind: A Dynamics-Constrained Deep Learning Model for Tropical Cyclone Wind Field Downscaling Using Satellite Observations
This study introduces CycloneWind, a novel deep learning framework designed to downscale tropical cyclone surface wind fields, achieving an 8-fold spatial resolution increase. It significantly improves the accuracy of wind component reconstruction and key dynamical metrics by integrating a high-quality dataset and a dynamically constrained Transformer-based architecture.
-
Varotsos et al. (2025) CLIMADAT-GRid: a high-resolution daily gridded precipitation and temperature dataset for Greece
This study introduces CLIMADAT-GRid, the first publicly available high-resolution (1 km × 1 km) daily gridded dataset for air temperature and precipitation across Greece for 1981–2019, demonstrating superior spatial performance and closer agreement with observational data compared to global products like CHELSA-W5E5.
-
Hingerl et al. (2025) Comparative analysis of land–atmosphere interactions across three contrasting ecosystems in the West Sudanian Savanna
This study conducted a multi-year analysis of energy fluxes and land–atmosphere coupling using eddy covariance data from three contrasting ecosystems in the West Sudanian Savanna to assess land use change impacts. It found significant alterations in energy partitioning and land-atmosphere coupling, especially during dry and transitional seasons, driven by vegetation structure and soil moisture.
-
Qi et al. (2025) Global Increase of Tropical Cyclone Precipitation Rate Toward Coasts
This study investigates the global coastward trends of tropical cyclone (TC)-induced precipitation changes over the past four decades, revealing a statistically significant landward migration of TC lifetime maximum precipitation intensities and faster growth in coastal areas linked to a warming-humidifying environment.
-
Xia et al. (2025) Quantification of Critical Thresholds in Soil Moisture and the Leaf Area Index Regulating Aeolian Dust Events: A Sensitivity Analysis of the Response of Aeolian Dust Events to Wind Velocity Variations
This study develops an empirical methodology using long-term satellite observations to assess the global sensitivity of aeolian dust events to wind velocity variations. It quantifies this sensitivity and identifies critical soil moisture and leaf area index thresholds that constrain dust emissions, projecting future changes in constrained dust events under various climate scenarios.
-
Aghelpour et al. (2025) Re-constructing and projecting vegetation coverage area variations: A numerical approach based on MRI-ESM2.0 climatic datasets
This study numerically models and predicts vegetation coverage area (VCA) in the mountainous Zagros region of Iran using machine learning and CMIP6 climatic data. It successfully reconstructs past VCA and projects a mild increasing trend for future VCA, particularly under the SSP585 climate scenario.
-
Tran et al. (2025) Coupling of SWAT and WEAP Models for Quantifying Water Supply, Demand and Balance Under Dual Impacts of Climate Change and Socio-Economic Development: A Case Study from Cauto River Basin, Cuba
This study assesses the water supply, demand, and balance in the Cauto River Basin using coupled SWAT and WEAP models under baseline and projected future conditions. It projects a 2.5% decrease in annual flow and a 16.6% surge in water demand by 2050, leading to a 52% increase in the water deficit.
-
Liu et al. (2025) Assess the impacts of climatic change and human activities on streamflow and floods by using a hybrid-physics-data (HPD) model: A case study in the Lancang-Mekong River Basin
This study utilizes a hybrid-physics-data (HPD) model, combining VIC-CaMa-Flood with LSTM, to assess the relative contributions of climatic change and human activities to streamflow and floods in the Lancang-Mekong River Basin (LMRB) from 1966–2015. It reveals that climatic change primarily influenced streamflow and floods during 1993–2007, while human activities, mainly reservoir operations, became the dominant factor in the post-2008 period, significantly altering seasonal flow and mitigating flood magnitudes and frequencies.
-
Moghim et al. (2025) Complete assessment of the heat waves and cold waves in different regions
This study comprehensively assesses heat waves and cold waves in six global cities using various definitions, indicators, and climate scenarios for historical (1990–2000) and future (2030–2040) periods. It develops a novel Extreme Wave Index (EWI) to compare regional vulnerability, revealing an increasing trend in heat wave severity and a decreasing trend in cold wave severity, with significant regional variations.
-
Mwinjuma et al. (2025) Comparisons of SPI and SPEI in capturing drought dynamics: A Global assessment across arid and humid regions
This study globally compares the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) from 1991-2020 across diverse climatic regions, revealing that SPEI identifies significantly more frequent and severe droughts in arid and semi-arid zones due to its sensitivity to rising temperatures, challenging the adequacy of precipitation-only indices in drylands.
-
Tepetidis et al. (2025) Combining Machine Learning Models and Satellite Data of an Extreme Flood Event for Flood Susceptibility Mapping
This study applies and evaluates four machine learning models for flood susceptibility mapping in Thessaly, Greece, identifying that tree-based models (Random Forest and XGBoost) achieve superior accuracy and reveal approximately 20% of the basin as highly flood-prone.
-
Zhang et al. (2025) Multi-Source Retrieval of Thermodynamic Profiles from an Integrated Ground-Based Remote Sensing System Using an EnKF1D-Var Framework
This study introduces the novel EnKF1D-Var data assimilation framework, integrating multi-source ground-based remote sensing observations to significantly reduce biases in temperature and water vapor profiles within the low troposphere, particularly during daytime.
-
Abeysingha et al. (2025) Future hydro-climate extremes in the cypress creek watershed in Texas under different CMIP6 scenarios
This study quantifies future hydro-climate extremes in the Cypress Creek watershed, Texas, using the SWAT+ model and CMIP6 projections, revealing increased drought likelihood due to rising temperatures and declining precipitation, alongside a general decrease in flood severity but high hydroclimatic variability.
-
Sawant et al. (2025) Estimation of Reference Evapotranspiration Using Empirical Methods and Cropwat 8.0 Model for the Sangli District Maharashtra India
This study estimated reference evapotranspiration (ET₀) for the Sangli district, Maharashtra, India, by comparing nine empirical methods against the FAO Penman-Monteith equation implemented in CROPWAT 8.0, identifying the most accurate methods for regional irrigation planning. The Schendel method demonstrated the highest accuracy in matching Penman-Monteith estimates, followed by the Hargreaves-Samani method.
-
Hu et al. (2025) Investigating Aerosol Hygroscopicity in the Subcloud Transition Zone and at the Surface in the Southern Great Plains
This study investigates the hygroscopicity and optical properties of aerosols in the subcloud transition zone (SCTZ) and at ground level in the Southern Great Plains, revealing distinct seasonal variations driven by aerosol composition at the surface and cloud fragmentation effects within the SCTZ.
-
Yue et al. (2025) Watershed sediment cascades across multiple timescales: Causal relationships with hydroclimate and underlying surface attributes
This study investigated watershed sediment dynamics and their causal relationships with hydroclimate and underlying surface factors across multiple timescales (event to decadal) in seven subtropical watersheds, revealing a shift in dominant controls from hydroclimate to surface properties at longer timescales.
-
Gambini et al. (2025) Uncertainty Quantification and Spatial Biases Assessment in Precipitation Forecasts: A Methodology for Real-Time Flood Forecasting Applications
This study proposes a methodology to assess and account for spatial biases in high-resolution convective rainfall forecasts to improve flood predictions in small watersheds. It identifies a systematic 20 km northeastward displacement error in the MOLOCH model's forecasts for northern Italy and suggests using a derived displacement probability density function to generate rainfall ensembles for hydrological uncertainty quantification.
-
Tshayu et al. (2025) Optimal land identification for surface irrigation in lower and middle Awash sub-basin, Ethiopia: a multi-criteria decision analysis
This study utilized Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) to identify optimal land for surface irrigation in the Lower and Middle Awash sub-basin, Ethiopia, revealing that 83.3% of the area is highly or moderately suitable, despite only 0.96% currently being irrigated.
-
Dadparvar et al. (2025) Assessment of drought trends in the Aras River Basin: Spatiotemporal changes and implications for transboundary water management
This study assesses spatiotemporal drought trends in the transboundary Aras River Basin from 1981 to 2022 using satellite precipitation and evaporation data, revealing significant drought intensification in the southern regions driven primarily by increased evaporative demand rather than precipitation deficits, with critical implications for transboundary water management.
-
Wang et al. (2025) Flood inundation mapping with CYGNSS over CONUS: a two-step machine-learning-based framework
This study developed a two-step machine learning framework using CYGNSS bistatic reflectance observations and ancillary data to retrieve daily fractional flood inundation at a 3-kilometer resolution across the contiguous United States, demonstrating comparable performance to SAR-based flood maps and other inundation products.
-
Pandey et al. (2025) Tropical cyclone heat potential monitoring and forecasting over the Indian ocean using the UM-based coupled model
This study evaluates the performance of a Unified Model (UM)-based coupled atmosphere-ocean model in monitoring and forecasting Tropical Cyclone Heat Potential (TCHP) and its relationship with Sea Surface Height (SSH) over the Indian Ocean, demonstrating its utility for tropical cyclone intensification prediction. The model accurately simulates TCHP spatial patterns and maintains the physical link between TCHP and SSH, particularly in early forecasts, despite increasing biases at longer lead times.
-
Tao et al. (2025) Permafrost vulnerability to climate change: understanding thaw dynamics and climate feedback of permafrost degradation
This editorial synthesizes findings from 35 interdisciplinary studies to advance understanding of permafrost degradation dynamics and their cascading impacts, highlighting the critical importance of integrative, cross-disciplinary approaches.
-
Kwon et al. (2025) Introducing a novel Standardized Precipitation Evaporation Differential Index (SPEDI) for improved flash drought detection and assessment: a case study in South Korea
This study introduces the Standardized Precipitation Evaporation Differential Index (SPEDI), a new composite drought index designed to better capture flash drought conditions by accounting for both precipitation deficits and evaporative demand. SPEDI demonstrated superior performance in detecting flash droughts and aligning with agricultural damage records across South Korea compared to established indices.
-
Karbi et al. (2025) Seasonally Asymmetric Projected Changes in Austral Atmospheric Waves
This study investigates the seasonally asymmetric response of Southern Hemisphere mid-latitude atmospheric waves to anthropogenic emissions, finding that scale-dependent changes (large-scale intensification in winter, small-scale weakening in summer) drive these differences, primarily due to seasonal variations in upper-level warming patterns.
-
Pinheiro et al. (2025) Enhancing machine learning-based seasonal precipitation forecasting using CMIP6 simulations
This study demonstrates that training machine learning (ML) models for seasonal precipitation forecasting with a larger number of individual simulations from CMIP6 models significantly enhances their generalization ability and improves forecasts over South America. These CMIP6-trained ML models consistently outperform those trained with limited reanalysis data (ERA5) and state-of-the-art dynamical models.
-
Ya et al. (2025) Staging effects of biological soil crust-driven coupled soil–water-vegetation mechanisms in vegetation-limited areas
This review systematically elucidates the cascade effects of biological soil crusts (BSCs) in facilitating ecological restoration in vegetation-limited areas (VLAs) by reconstructing soil systems, regulating ecohydrological processes, and promoting vegetation succession. It highlights BSCs' multifunctional roles as a pioneering solution for overcoming restoration challenges in these degraded environments.
-
Jose et al. (2025) Improvement of soil moisture estimates over the indian domain: an anomaly bias correction approach
This study introduces and evaluates an anomaly-based bias correction method for assimilating Soil Moisture Active Passive (SMAP) satellite retrievals into the Noah Land Surface Model (LSM) over the Indian domain. It demonstrates that this novel approach significantly improves soil moisture (SM) estimates and better captures irrigation signals, particularly during dry seasons, outperforming the traditional cumulative distribution function (CDF) matching method.
-
Pietras et al. (2025) Extreme Short-Duration Rainfall and Urban Flood Hazard: Case Studies of Convective Events in Warsaw and Zamość, Poland
This study evaluated the meteorological background, intensity, and spatial characteristics of two extreme convective rainfall events in Poland in August 2024. It found that both events exceeded national and international criteria for torrential rainfall, with the Zamość event being exceptionally intense (88.3 mm in one hour), driven by specific convective organization patterns.
-
Chevuru et al. (2025) Relevance of feedbacks between water availability and crop systems using a coupled hydrological–crop growth model
This study quantifies the fine-grained spatiotemporal feedback between crop systems and hydrology using a coupled hydrological-crop growth model. It finds that two-way coupling, incorporating dynamic feedback of crop phenology, significantly improves performance for rainfed crops compared to one-way coupling, highlighting its necessity for capturing interannual climate variability impacts on food production.
-
Zhang et al. (2025) Assessing the impacts of climate change and land use/land cover data characteristics on streamflow using the SWAT model in the Upper Han River Basin
This study assesses the impact of various land use/land cover (LULC) data characteristics and future climate change on streamflow in the Upper Han River Basin using the SWAT model, finding that LULC resolution significantly affects model performance and projecting a future increase in annual streamflow with notable seasonal shifts.
-
Loukili et al. (2025) Enhancing Flood Mapping Accuracy in North and West Africa Using Multi-Sensor Satellite Data and Machine Learning in Google Earth Engine
This study evaluates Sentinel-1, Sentinel-2, and Landsat satellite data for flood detection using Random Forest (RF) and Minimum Distance (MD) classifiers within Google Earth Engine across urban (Tetouan, Morocco) and rural (Matam, Senegal) environments. It found that Sentinel-2 excelled in rural areas, while Sentinel-1 performed better in urban settings, with the RF-MD combination enhancing overall accuracy validated against UNOSAT benchmark maps.
-
Xu et al. (2025) Hybrid ITSP-LSTM Approach for Stochastic Citrus Water Allocation Addressing Trade-Offs Between Hydrological-Economic Factors and Spatial Heterogeneity
This study developed a hybrid Interval Two-Stage Stochastic Programming (ITSP) and Long Short-Term Memory (LSTM) model to optimize stochastic water allocation for fragmented citrus cultivation, demonstrating an 8.67% increase in system-wide benefits by balancing hydrological-economic factors and spatial heterogeneity.
-
Persch et al. (2025) A Critical Role for North Pacific Meridional Mode in the ENSO Response to Orbital Precession
This study investigates how orbital precession influences El Niño–Southern Oscillation (ENSO) variability through the Pacific meridional mode (PMM) using climate model simulations. It finds that precession strongly modulates PMM variability, its effectiveness in triggering El Niño events, and ENSO diversity, primarily driven by changes in surface wind fields affecting the wind–evaporation–sea surface temperature (WES) feedback.
-
Zhong et al. (2025) Sub-diurnal asymmetric warming has amplified atmospheric dryness since the 1980s
This study reveals that sub-diurnal asymmetric warming, characterized by a faster increase in daily maximum temperature (Tmax) relative to daily minimum temperature (Tmin), has significantly amplified atmospheric dryness (vapor pressure deficit, VPD) over land since the 1980s. This asymmetry has driven a larger increase in saturated vapor pressure (SVP) than actual vapor pressure (AVP), contributing an additional ~18% to the global land VPD increase.
-
Jiang et al. (2025) Scale-dependent drivers of water use efficiency across China: integrating stable isotopes, remote sensing, and machine learning
This study investigated the scale-dependent spatial patterns and drivers of leaf-level intrinsic water use efficiency (iWUE) and ecosystem-scale water use efficiency (WUEEco) across China, revealing inverse spatial patterns and distinct controlling factors for each scale. It also generated a high-resolution national iWUE dataset using machine learning.
-
Lin et al. (2025) Linking High‐Amplitude Quasi‐Stationary Waves With Concurrent Humid‐Heat Extremes in a Warming World
This study investigates how high-amplitude quasi-stationary waves (QSWs), linked to concurrent humid-heat extremes in the Northern Hemisphere midlatitudes, respond to climate warming. Using a large ensemble climate model, the research finds that the climatological-mean amplitude of wavenumbers 3 and 7 QSWs increases in late summer with warming, leading to more frequent high-amplitude events and consequently increased regional frequencies of humid-heat extremes.
-
Yang et al. (2025) Research on Acceleration Methods for Hydrodynamic Models Integrating a Dynamic Grid System, Local Time Stepping, and GPU Parallel Computing
This paper introduces a novel integrated method combining algorithmic optimization (domain tracking, local time stepping) and GPU parallel computing to significantly accelerate hydrodynamic models for flood forecasting, demonstrating considerable speed-up while preserving computational accuracy.
-
Touhami et al. (2025) Development of a fuzzy logic-based greenhouse system for optimizing bio-fertigation
This study developed a fuzzy logic-based algorithm to optimize bio-fertigation in a greenhouse by managing temperature, humidity, soil pH, and soil moisture. The system achieved a 27.58% reduction in water use, a 58.82% decrease in fertilizer consumption, and a 47.5% increase in tomato yield, demonstrating high precision and effectiveness.
-
Zeng et al. (2025) Identification of Precipitating Marine Low‐Altitude Water Clouds by CALIPSO: Observations and Detections
This study uses CALIOP measurements to investigate microphysical and optical property changes in marine boundary layer clouds at cloud top during precipitation formation. It finds distinct lidar signatures that enable effective discrimination between precipitating and non-precipitating clouds, offering new insights into cloud life cycles and enhancing global light precipitation detection.
-
Wilke et al. (2025) Hail events in Germany: rare or frequent natural hazards?
This study investigates hail characteristics across Germany using crowdsourced observations and C-band weather radar data to assess hail frequency, spatial distribution, and size variations. It reveals a north-south gradient in hail occurrence, with June being the peak month, and highlights the challenges and biases in human observations versus radar-derived estimates.
-
Süzer et al. (2025) Remote Screening of Nitrogen Uptake and Biomass Formation in Irrigated and Rainfed Wheat
This study evaluated the effectiveness of drone- and satellite-based spectral indices combined with neural network models for estimating wheat biomass and nitrogen uptake in water-limited environments, finding that while indices predict biomass well, accurate nitrogen uptake estimation requires integrating them with complementary crop traits in nonlinear models.
-
Song et al. (2025) Physically Interpretable Emulation of a Moist Convecting Atmosphere With a Recurrent Neural Network
This study develops a recurrent neural network (RNN) for data-driven convective parameterization, combining linear and nonlinear components to predict temperature, moisture, and precipitation time series. The model demonstrates stable and realistic long-term emulation performance, revealing physically interpretable properties of convectively coupled gravity waves.
-
Diop et al. (2025) Climate change impacts on floods in West Africa: new insight from two large-scale hydrological models
This study provides a large-scale analysis of flood frequency and magnitudes across West Africa using two hydrological models driven by CMIP6 climate models, projecting consistent increases in flood frequency and magnitude under future climate change scenarios.
-
Lee et al. (2025) Multi-centennial climate change in a warming world beyond 2100
A new 10-member ensemble simulation with the state-of-the-art Earth system model was employed to study the long-term climate response to sustained greenhouse warming through to the year 2500. The findings show that the projected changes in the forced mean state and internal variability during 2101–2500 differ substantially from the 21st-century projections, emphasizing the importance of multi-century perspectives for understanding future climate change and informing effective mitigation strategies.
-
Liu et al. (2025) Interannual Variations in Water Budget and Vegetation Coverage Dynamics in Desert Ecosystems of Heihe River Basin
This study investigates interannual variations in water budgets and vegetation dynamics in two contrasting desert ecosystems of the Heihe River Basin (2016-2021), revealing divergent ecohydrological responses and vegetation-water coupling mechanisms driven by precipitation gradients, species adaptations, and the critical role of groundwater.
-
Tsigaridis et al. (2025) ROCKE-3D 2.0: an updated general circulation model for simulating the climates of rocky planets
This paper presents ROCKE-3D version 2.0, an updated generalized three-dimensional general circulation model (GCM) designed for simulating the climates of rocky planets in both the Solar System and exoplanetary contexts. It details new physics, expanded configurations, and quantifies how different component choices affect model results, demonstrating its enhanced capabilities for diverse planetary conditions.
-
Cheng et al. (2025) Spatially distinct drought patterns and influencing factors across China: a machine learning approach with a comprehensive index
This study validated the Combined Climatologic Deviation Index (CCDI) for drought monitoring in China and assessed spatiotemporal drought patterns and their driving factors, revealing intensified drought in arid and plateau regions, and varied impacts of vegetation greening across different climatic zones.
-
Khole et al. (2025) Soil Moisture Index (SMI) Estimation Using Raw Landsat-8 OLI Data, NDVI and Land Surface Temperature for Agricultural Drought Assessment
This study outlines a procedure to calculate the Soil Moisture Index (SMI) using Landsat-8 OLI and TIRS data (Land Surface Temperature and Normalized Difference Vegetation Index) during the summer season, finding that the selected study area is under drought conditions with SMI values ranging from 0 to 0.3.
-
Guo et al. (2025) Enhanced MODIS-derived ice physical properties within the Common Land Model (CoLM) revealing bare-ice–snow albedo feedback over Greenland
This study integrates an enhanced radiative transfer model (SNICAR-ADv4) and MODIS-derived bare-ice properties into the Common Land Model (CoLM) to investigate the impact of bare-ice metamorphism on the Greenland Ice Sheet's ablation zone. The research reveals a significant bare-ice–snow albedo feedback, where bare-ice darkening due to metamorphism reduces albedo, increases surface temperature, and accelerates snowmelt, further exposing bare ice and intensifying melt.
-
Pachore et al. (2025) Validation of AquaCrop for Deficit Irrigation on Garlic (Allium sativum L.) for Semi-arid Conditions
This study calibrated and validated the AquaCrop model for simulating garlic yield and evapotranspiration water productivity under various deficit irrigation regimes in semi-arid conditions, demonstrating its applicability for bulb crops. The model accurately reproduced crop production and water productivity, with variations potentially linked to crop structure and phenology.
-
Wood et al. (2025) Comparison of high-resolution climate reanalysis datasets for hydro-climatic impact studies
This study comprehensively evaluates four high-resolution climate reanalysis datasets (ERA5, ERA5-Land, CERRA, CHELSA-v2.1) against gridded observations over complex terrain in Switzerland for hydro-climatic impact studies. It concludes that CERRA generally offers the most reliable representation of precipitation, temperature, and snowfall metrics, including their variability and extreme events, making it highly suitable for a broad range of hydrological analyses, particularly in regions where snow processes and daily to inter-annual precipitation variability are crucial.
-
Menapace et al. (2025) Sensors prioritisation for hydrological forecasting based on interpretable machine learning
This study proposes an interpretable machine learning framework to prioritise hydrological sensors, aiming to enhance short-term predictions. The research demonstrates that identifying and maintaining critical sensors significantly improves forecasting accuracy and reliability, offering a data-driven approach to optimise monitoring system maintenance.
-
Yu et al. (2025) A nudging-based data assimilation method coupled with bidirectional gated neural networks for error correction
This study develops a novel nudging-based data assimilation method that integrates Bidirectional Gated Recurrent Units (BiGRU) with the Ensemble Kalman Filter (EnKF) to enhance accuracy and stability in error correction. Numerical experiments using the Lorenz-96 model demonstrate its improved resilience to noise interference and greater robustness with sparse observations.
-
Nasrollahi et al. (2025) Modeling the Effectiveness of Alternative Flood Adaptation Strategies Subject to Future Compound Climate Risks
This paper comprehensively investigates the effectiveness of resistance (levee), nature-based (green stormwater infrastructure), and managed retreat (land swap) flood risk management strategies in Eastwick, Philadelphia, under compound climate change conditions. The study found significant differences in the predicted flood extents, depths, and durations among the options, concluding that an integrated approach is likely required for optimal flood risk reduction.
-
Rostampour et al. (2025) Prediction and comparison of streamflow using hybrid and independent models in Zola dam basin
This study models and forecasts streamflow in the Zola dam basin using independent (Extreme Learning Machine, Long Short-Term Memory) and hybrid (Wavelet, Variational Mode Decomposition) machine learning models. It demonstrates that hybrid models significantly enhance prediction accuracy, with the ELM-VMD approach achieving the best performance.
-
Bouchikhi et al. (2025) Multi-criteria decision analysis and hydrodynamic modeling for flood-safe urban planning: case study of Oued Merzeg, Casablanca (NW Morocco)
This study mapped flood hazard in Oued Merzeg, Casablanca, Morocco, using Analytical Hierarchy Process (AHP) and Iber hydrodynamic modeling, finding AHP more effective in identifying widespread flood stagnation areas compared to the hydrodynamic model's focus on fluvial dynamics. The research provides a dual understanding of flood hazard for urban planning and offers preliminary insights into nature-based solutions.
-
Khandandel et al. (2025) A shift towards temperature-dominated droughts in agricultural basins of Türkiye
This study projects future drought characteristics in Türkiye's agricultural basins using climate models and drought indices, revealing an intensification of drought frequency and severity driven primarily by rising temperatures, shifting from precipitation-deficit to temperature-dominated droughts.
-
Lim et al. (2025) Remote Forcing and Prediction of the June 2023 Texas Heat Wave
This study identifies the remote forcings of the June 2023 Texas heat wave, attributing it to a Rossby wave generated by tropical Pacific heating and extratropical Pacific vorticity transients, and demonstrates its predictability up to three weeks ahead using NASA models.
-
Ghosh et al. (2025) Quantifying rainfall-induced climate risk in rainfed agriculture: A volatility-based time series study from semi-arid India
This study develops a volatility-in-mean time series framework to quantify rainfall-induced climate risk on rice yield forecasts in semi-arid Maharashtra, India, finding that GARCH-type models, particularly eGARCH and gjrGARCH with log-differenced rainfall measures, significantly improve forecast accuracy and robustness.
-
Zhang et al. (2025) Crop Redistribution Increases Regional Production While Reducing Water Deficit, Fertilizer Use, and Production Losses: Evidence from a Multi-Objective Optimization at the County Level in Northeast China
This study quantifies the potential of crop redistribution in Northeast China using a multi-objective optimization approach to enhance grain production while alleviating environmental pressures. The findings reveal significant benefits, including increased crop production, reduced yield losses, decreased water deficit, and lower nitrogen fertilizer application.
-
Feng et al. (2025) Field-deployable lightweight YOLOv8n for real-time detection and counting of Maize seedlings using UAV RGB imagery
This study proposes YOLOv8-FLY, a lightweight deep learning model for real-time detection and counting of maize seedlings using UAV RGB imagery. The model achieves 96.5% detection accuracy while significantly reducing model size, parameters, and computational cost, making it suitable for resource-constrained edge devices.
-
Raab et al. (2025) A Low-Cost Small Satellite Space Radar System
This paper presents the design of a low-cost, small satellite space radar system enabled by FLAPS™ antenna technology, capable of all-weather surveillance for applications such as detecting small ships and aircraft. The system offers a lightweight, foldable, and cost-effective alternative to traditional phased array radar systems for small satellite platforms.
-
Yin et al. (2025) Characteristics and Attribution of Winter Extreme Cold Indices in China
This study analyzes winter extreme cold indices in China from 1961 to 2023, finding a general weakening trend driven by anthropogenic forcing and modulated by the Arctic Oscillation.
-
Sun et al. (2025) Complex networks reveal teleconnections across cascading floods in the Yangtze River Basin
This study applies complex network analysis to investigate the teleconnection patterns and synchronization characteristics of cascading floods across 125 subbasins in the Yangtze River Basin (YRB) from 1961 to 2020, revealing that streamflow-related networks exhibit stronger connectivity and identifying a significant large-scale propagation mechanism where downstream water yield regulates upstream precipitation.
-
Guan et al. (2025) The ability of a stochastic regional weather generator to reproduce heavy-precipitation events across scales
This study assesses a non-stationary regional weather generator's (nsRWG) ability to reproduce heavy-precipitation event (HPE) extremity across spatial and temporal scales in Germany, finding it largely excels in replicating observed extremity patterns and potential influential areas, making it suitable for flood risk assessment.
-
Papić (2025) Spatial and Temporal Dynamics of Drought in Bosnia and Herzegovina During the Period 1956–2023
## Identification - **Journal:** International Journal of Climatology - **Year:** 2025...
-
Nguyen‐Xuan et al. (2025) Assessment of future droughts in Vietnam using high-resolution downscaled CMIP6 projections
This study assesses future drought conditions in Vietnam using high-resolution downscaled CMIP6 projections and the Standardized Precipitation-Evapotranspiration Index (SPEI). It finds that while significant warming is projected, precipitation is the dominant factor for SPEI trends, with overall milder drought characteristics expected, though severity and intensity may worsen in specific regions under worst-case scenarios and higher return periods.
-
Centanni et al. (2025) Assessment of pollutants from the Canale d'Aiedda basin to the sea: SWAT model and Remote Sensing Approach
This study assesses the spatial patterns of pollutants from the Canale d’Aiedda basin to the Mar Piccolo Sea, identifying nutrient sources and their coastal fate by coupling the ecohydrological SWAT model with Sentinel-2 remote sensing. It found that agricultural subbasins are primary sources of nitrogen and phosphorus, with flash floods significantly impacting pollutant delivery and coastal turbidity.
-
Ndiaye et al. (2025) Hydrological variability of large rivers in West Africa: gap-filling with Earth observations and daily rainfall-runoff modelling
[Information not extractable from the provided corrupted text.]
-
Jian et al. (2025) Analysis of High–Low Runoff Encounters Between the Water Source and Receiving Areas in the Xinyang Urban Water Supply Project
This study analyzes the runoff trends and encounter patterns between the Huaihe River (water source) and Honghe River (receiving area) to optimize the operational scheduling of the Chushandian Reservoir.
-
Deman et al. (2025) Future changes in runoff over western and central Europe: disentangling the hydrological behavior of CMIP6 models
This study characterizes future runoff changes over western and central Europe using a large ensemble of CMIP6 models under a high-end emissions scenario, identifying diverse hydrological responses grouped into clusters. It disentangles inter-model uncertainties, highlighting the roles of large-scale circulation and the physiological effect of CO2, while finding the soil moisture-precipitation feedback important for the ensemble mean but not the inter-model spread.
-
Miftah et al. (2025) Moroccan water resources under pressure: Challenges of groundwater quality and nitrate contamination
This study provides a comprehensive overview of surface and groundwater quality across Morocco, identifying widespread nitrate contamination in numerous aquifers, primarily from agricultural fertilizers, domestic/industrial wastewater, and manure, often exacerbated by seawater intrusion, rendering much of the groundwater unsuitable for drinking and irrigation.
-
Zhou et al. (2025) Influence of Eurasian Spring Snowmelt on May and June Minimum Temperature Variability Over Northeastern China
This study examines the impact of Eurasian spring snowmelt (SSD) on intraseasonal extreme minimum temperature ($T_{min}$) variability in northeastern China during May and June, finding that decreased Siberian SSD enhances both warm and cold night extremes.
-
Günaçtı et al. (2025) Assessment of a Survivability Index for Drought Management in River Basins
This paper introduces a novel Survivability from Droughts Index (SDI) to assess drought management capacity in river basins, integrating WEF Nexus and sustainability pillars. The index was applied to the Gediz River Basin, Türkiye, demonstrating its ability to validate historical drought impacts and guide actions for improved drought survivability.
-
Min et al. (2025) A Diachronic Assessment of Advances in Seasonal Forecasting: Evolution of the APCC Multi‐Model Ensemble Prediction System Over the Last Two Decades
This study provides a diachronic assessment of the APCC Multi-Model Ensemble (MME) system since 2005, demonstrating a 34% increase in global forecast skill.
-
Lu et al. (2025) Role of Soil Moisture Gradients in Favoring Mesoscale Convective Systems in East China
This study demonstrates that mature mesoscale convective systems (MCSs) in East China preferentially propagate toward the drier side of strong soil moisture gradients.
-
Guthrie et al. (2025) Deep Root Loss and Regeneration in the Anthropocene Drive Continental‐Scale Changes in Deep Soil Structure
This study investigates how land-use changes and vegetation recovery influence root abundance and soil structure in deep subsurface horizons (>30 cm) across four soil orders. The authors find that root dynamics drive structural transformations, with less-developed soils being more susceptible to these changes.
-
Yang et al. (2025) Contribution of climate change and human activities to streamflow and lake water level variations at regional scales
This study quantifies the relative contributions of climate change and human activities to hydrological variations in the Poyang Lake Basin from 1960 to 2019, concluding that while climate change drove streamflow increases, anthropogenic factors primarily caused the decline in lake levels.
-
Shazil et al. (2025) Assessing the Accuracy of Gridded Precipitation Products in the Campania Region, Italy
This study compares four reanalysis and satellite precipitation products (ERA5-Land, CHIRPS, PERSIANN, and TerraClimate) against ground data from 2003 to 2022, finding ERA5-Land to be the most accurate in reproducing observed precipitation and identifying wet months.
-
Eisenacher et al. (2025) Lightning Density and Its Coupled Covariates Within the Continental United States
This study investigates the coupled land-atmosphere interactions influencing lightning density across the Continental United States (CONUS), finding that Convective Available Potential Energy (CAPE) is the most effective proxy for lightning, while soil moisture (SM) shows a significant, seasonally-dependent coupling with lightning, particularly in the southeastern U.S.
-
Canos et al. (2025) 650 - Vigilancia Epidemiológica En Municipios Afectados Por La Dana: Departamento Valencia-La Fe
This study conducted epidemiological surveillance of acute gastroenteritis (AGE) in six municipalities within the Valencia-La Fe department following an isolated depression at high levels (DANA) event. It found 518 DANA-related AGE cases, predominantly in resident men aged 17-49, with Campylobacter being the most isolated pathogen, and weekly AGE rates generally not exceeding historical expected values.
-
Yang et al. (2025) Climate Change and Population Aging Exacerbate Flood Risk to the Elderly in European Regions
This study evaluates the flood risk to the elderly population in Europe by integrating climate projections and socioeconomic pathways into a hydraulic modeling framework, revealing significant increases in exposure within Central Europe.
-
Lorenzo‐Lacruz et al. (2025) Losing Water by Storing It: The Oversighted Side of Intensive Water Regulation and Damming
[The provided paper text is unreadable and appears to be corrupted. A summary cannot be generated.]
-
Dietz et al. (2025) Impact of climate change on snow cover in the Pyrenees, Alps, and Andes Mountains, derived from 40 years of Landsat data
[Information not extractable from provided text due to unreadable input.]
-
Saunders et al. (2025) Sensitivity to Data Choice for Index‐Based Flood Insurance
[Information not extractable from the provided garbled text.]
-
Xiang et al. (2025) ADAF: An Artificial Intelligence Data Assimilation Framework for Weather Forecasting
The study introduces an AI-based data assimilation framework (ADAF) to generate high-quality kilometer-scale analysis fields, significantly reducing computational costs while improving short-term weather forecasts.
-
Inseeyong et al. (2025) Regionalization of standardized sediment rating curves for enhancing data continuity in ungauged catchments
This study develops a regionalization approach for sediment rating curve (SRC) parameters to estimate sediment load in ungauged catchments using only discharge data and catchment attributes. The approach, validated in the Mun River Basin, demonstrates acceptable performance (0.43 < NSE < 0.95, −58 % < PBIAS < 53 %) and provides a practical framework for data-scarce regions.
-
Liu et al. (2025) Spatiotemporal Flood Prediction From Single Frame Input With a Post‐Processing Method
The study introduces a "single frame prediction" framework using a U-Net architecture and physics-based post-processing to predict spatiotemporal flood maps based on boundary conditions and the previous time step's state.
-
Duan et al. (2025) The Impact of Soil Preconditioning on the Evolution of Heatwaves Under Constrained Circulation: A Case Study of the 2021 Pacific Northwest Heatwave
This study utilizes CESM2 ensemble simulations to evaluate the role of initial soil moisture and land-atmosphere interactions in the June 2021 Pacific Northwest heatwave, finding that while circulation is the primary driver, soil moisture state significantly modulates the event's intensity and duration.
-
McNorton et al. (2025) Hydroclimatic Rebound Drives Extreme Fire in California's Non‐Forested Ecosystems
This second annual "State of Wildfires" report systematically tracks global and regional fire activity for the March 2024 to February 2025 season, analyzing the causes of prominent extreme wildfire events and projecting their future likelihood under climate change. It finds that global fire-related carbon emissions totaled 2.2 petagrams of carbon (Pg C), 9% above average and the sixth highest since 2003, despite below-average global burned area, primarily driven by extreme fire seasons in South America and Canada, with climate change significantly increasing the likelihood and intensity of these events.
-
Memari et al. (2025) Quantifying the Impact of Groundwater on Ice Formation in the Great Lakes
This study investigates the impact of subsurface groundwater flux on ice formation and thermal stratification in Lakes Michigan and Huron, finding that moderate groundwater inputs generally promote thicker and longer-lasting ice by enhancing water column stability.
-
Zhang et al. (2025) Drip Irrigation in Dryland Agriculture Controls Soil Water‐Filled Pore Space and Reduces Greenhouse Gas Emissions: A Meta‐Analysis
This meta-analysis quantifies the impact of drip irrigation (DI) on greenhouse gas emissions in dryland agriculture, concluding that DI reduces global warming potential primarily by decreasing soil moisture.
-
Su et al. (2025) Widening Urban–Rural Precipitation Differences in China: Regionally Varied Intensification Since 2000
This study analyzes the impact of urbanization on precipitation patterns across 37 Chinese cities from 1980 to 2022, finding that urbanization generally increases total and extreme precipitation while reducing the overall number of wet days.
-
Bertoli et al. (2025) Revisiting Machine Learning Approaches for Short‐ and Longwave Radiation Inference in Weather and Climate Models
This study evaluates several machine learning (ML) architectures as parameterizations for radiative transfer within the ICON weather and climate model on GPUs, finding that a physics-informed BiLSTM model achieves stability and performance comparable to classical physics-based schemes.
-
Toro et al. (2025) Groundwater quality prediction for drinking and irrigation uses in the Murcia region (Spain) by artificial neural networks
This study developed and evaluated artificial neural network models (RProp-MLP and PNN DDA) to predict groundwater quality for drinking and irrigation in the semiarid Murcia region, Spain, using two defined quality indices (DWQI and IWQI) and demonstrating the superior performance of RProp-MLP.
-
Randriatsara et al. (2025) Historical changes in drought characteristics and their impact on vegetation cover over Madagascar
This study analyzes historical drought characteristics (1981–2022) and their impact on vegetation cover (2000–2022) across Madagascar using the Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI). It finds that drought events have intensified and become more consecutive, particularly in southern Madagascar, leading to severe vegetation losses.
-
Zhou et al. (2025) Fusion of Dual Wind Component for Radar Echo Nowcasting Based on a Deep Learning Model
The study introduces the Late Fusion Wind Field UNet (LFWF UNet), a model that integrates radar data with 3D wind field data to improve the accuracy of severe weather nowcasting.
-
Huo et al. (2025) Incremental Analysis Updates in a Convective‐Scale Ensemble Kalman Filter Using Minute‐by‐Minute Phased Array Radar Observations
This study evaluates the integration of the Incremental Analysis Update (IAU) method with the Ensemble Kalman Filter (EnKF) to mitigate physical imbalances in rapid-update data assimilation for convective precipitation. The results indicate that this combination improves forecast skill and ensemble diversity by refining the development of convective structures.
-
Bahrunani Punjabi (2025) GPR B-scan analysis with machine learning algorithms
The study develops a noninvasive robotic system using Ground-Penetrating Radar (GPR) and the YOLO computer vision algorithm to detect subsurface objects, specifically tree roots, for improved agricultural water management.
