-
Liu et al. (2025) Transformer-based soil moisture simulation for understanding future drying trend globally
This study introduces TSMSNet, a Transformer-based deep learning model designed to simulate global soil moisture (SM) from 2016 to 2099 under various climate scenarios. The research identifies a significant global drying trend that intensifies with higher greenhouse gas emission pathways, particularly affecting habitable regions and agricultural lands.
-
Liu et al. (2025) Exploring the dynamic relationships and mechanisms driving a long-term sequence of ecosystem services in mountains based on ecosystem service bundles
This study analyzed the long-term spatiotemporal dynamics, trade-offs, synergies, and driving mechanisms of four key ecosystem services (HQ, NPP, SC, WY) in China's Wuling Mountain Area from 2000-2020, identifying four ecosystem service bundles and proposing differentiated management strategies.
-
Zhou et al. (2025) Bayesian-factorial analysis for unveiling multi-factor interactive effect on water demand in Central Asia
This study develops an integrated Bayesian support vector machine-based two-step factorial analysis (BSVM-TFA) method to reveal the individual and interactive effects of human activities on water demand, applying it to Central Asia to project future water demand and identify key influencing factors.
-
Yan et al. (2025) Soil moisture dynamics and rainfall infiltration across vegetation types in subtropical ecosystems in Southwest China
This study investigated soil moisture dynamics and rainfall infiltration across four vegetation types in subtropical Southwest China, revealing that primary evergreen broadleaf forests maintain higher soil moisture and slower infiltration rates, which is crucial for regional drought resistance.
-
Ullah et al. (2025) Drivers and Future Risks of Groundwater Projection in Tangshan, China: Integrating SHAP, Geographically Weighted Regression, and Climate–Land-Use Scenarios
This study developed an integrated framework combining machine learning and scenario-based forecasting to evaluate spatial drivers and patterns of groundwater stress in Tangshan city and project future risks under climate and land-use change. It found that evapotranspiration and population density are key drivers of depletion, with future projections under RCP 8.5 showing highly unstable recharge and intensified depletion risks compared to RCP 4.5.
-
Qi et al. (2025) MODIS-Landsat fusion reveals two-decade 8-day lake dynamics with critical intra-annual regime shifts
The study reconstructs 8-day resolution dynamics for lakes and reservoirs in southern China from 2001 to 2020 using MODIS-Landsat data fusion. It reveals that high-frequency intra-annual variations are critical for accurate carbon budget estimates, often offsetting or equaling the impact of long-term interannual trends.
-
Weng et al. (2025) Evolution and impact of rainfall infiltration in global alpine water towers
This study develops a temperature-mediated infiltration model to quantify rainfall infiltration across 78 global Water Tower Units (WTUs) from 1980 to 2023. The findings reveal that climate warming and freeze-thaw cycles are significantly altering infiltration characteristics, threatening the stability of downstream water supplies and ecological buffering.
-
Rivas et al. (2025) Performance Evaluation of the SRM and GRxJ—CemaNeige Models for Daily Streamflow Simulation in Two Catchments with Snow and Rain Dominated Hydrological Regimes
This study evaluated the performance of the Snowmelt-Runoff Model (SRM) and the GRxJ model family in two Chilean basins with contrasting hydrological regimes. It found that SRM generally outperformed GRxJ, especially in snow-dominated catchments, highlighting the need to adapt modeling strategies to local hydrological conditions for effective water management.
-
Wang et al. (2025) Estimating soil moisture at farm scale with high spatial resolution: integrating remote sensing data, and machine learning
This study develops a machine learning-based downscaling framework that integrates evapotranspiration and groundwater depth to estimate surface soil moisture at a 30 m resolution from 9 km coarse data. The approach significantly improves soil moisture monitoring in complex agricultural environments by accounting for both upper and lower boundary conditions.
-
Maffei et al. (2025) Monitoring soil substrate influence in vineyards using Sentinel-2 time series and land surface phenology
This study investigates the potential of Sentinel-2 time-series and Land Surface Phenology (LSP) to differentiate vineyards based on varying soil substrate types. It found that both growing season and off-season metrics, particularly vegetation water content indices like Global Vegetation Moisture Index (GVMI), effectively distinguish soil substrate effects with high temporal stability, with off-season metrics showing better transferability across years.
-
Claros et al. (2025) Moisture Sources of Precipitation Using Convection‐Permitting Simulations: A Study Over South America
This study compares precipitation moisture sources over the Amazon Basin using convection-permitting (CPM) and non-CPM WRF simulations, revealing significant differences. It then introduces a revised, computationally efficient 2L-DRM model that accurately replicates CPM moisture source estimates, enabling broader climatological analyses across South America.
-
Li et al. (2025) Improvement of Snow Albedo Simulation Considering Water Content
This study developed a snow albedo model explicitly considering water content by integrating Maxwell–Garnett, Mie scattering, and four-stream discrete ordinates methods, demonstrating that liquid water content significantly impacts near-infrared albedo and validating its accuracy across diverse Chinese regions.
-
Ding et al. (2025) Phenology-adapted potato mapping index (PMI) for ground sample-free identification
This study proposes a novel Potato Mapping Index (PMI) leveraging Sentinel-2 temporal phenology for scalable, ground sample-free potato identification, achieving an average overall accuracy of 92% across six major potato-producing countries.
-
Waring et al. (2025) A Deep Learning Approach to Downscaling Microwave Land Surface Temperatures for a Clear-Sky Merged Infrared-Microwave Product
This study presents the first validated clear-sky merged land surface temperature (LST) product for the USA by combining downscaled passive microwave (PMW) data with MODIS thermal infrared (TIR) observations using a modified U-Net, demonstrating improved spatial coverage and temporal completeness over single-sensor products.
-
Jain et al. (2025) Deriving hydrological inferences from a machine learning model to understand the physical drivers of flow duration curves
This study utilizes Random Forest regression and SHapley Additive exPlanations (SHAP) to predict Flow Duration Curves (FDCs) across 991 watersheds in the contiguous United States. The research demonstrates that while climate attributes primarily determine the scale of FDCs, the baseflow index and geological features are the critical drivers of FDC shape and low-flow regimes.
-
Tan (2025) The Climatic Impacts of a Satellite‐Based Parameterization of the Wegener‐Bergeron‐Findeisen Process for Large‐Scale Models
This study develops and implements a satellite-based, temperature-dependent parameterization for the Wegener-Bergeron-Findeisen (WBF) process in CAM5.3, which significantly improves the simulation of ice mass and effective radius in mixed-phase clouds compared to satellite observations.
-
Ma et al. (2025) Estimation of All-Weather Daily Surface Net Radiation over the Tibetan Plateau Using an Optimized CNN Model
This study developed and optimized a deep learning framework using 19 CNN architectures for accurate daily surface net radiation (Rn) estimation over the Tibetan Plateau, finding Xception to be the most effective with high accuracy (R² > 0.94) and superior performance compared to existing products.
-
Rasaei et al. (2025) Can environmental clustering reveal soil profile patterns? A depth-based approach at field scale
This study numerically clustered soil profiles using multiple algorithms and a "pedogenon"-inspired methodology at a field scale to create meaningful soil management units. The Mahalanobis distance hierarchical clustering (HM) algorithm performed best, providing clear separation of soil properties with depth and aligning well with expert understanding.
-
Mo et al. (2025) Time-lag effects of vegetation gross primary production response to the hydro-climate changes in humid and semi-humid areas of China
This study investigated the relationship between vegetation gross primary production (GPP) and hydro-climate factors (precipitation, temperature, basin water storage) in the humid and semi-humid Hanjiang River Basin, China. It revealed significant time-lag effects of hydrological factors on GPP (4 months for basin water storage, 5 months for precipitation), highlighting their long-term influence compared to the immediate response to temperature.
-
Raymond et al. (2025) Distinct Favored Regions for Historical Record-Setting and Future Record-Breaking Humid Heat
This study globally identifies record-setting humid-heat days across 216 regions and assesses the likelihood of these records being broken under present-day climate forcing, revealing that humid-heat anomalies are most intense and concentrated in the deep tropics and arid subtropics, with specific regions like the eastern United States, Australia, and eastern China being particularly vulnerable to new extremes.
-
Goodwin (2025) Indicators and predictions of climate change
This chapter reviews the desirable features of climate forecasting methods and discusses various approaches, including statistical, physical, expert judgment, and combined forecasts, to inform policymakers and the public about future climate changes.
-
Waldowski et al. (2025) Data Assimilation in Integrated Subsurface Flow Models—Making Optimal Use of Cross‐Compartmental Interactions
This study investigates the risks and benefits of cross-compartmental data assimilation (DA) in integrated subsurface flow models, revealing that while single-compartment DA has trade-offs, multivariate assimilation of both soil moisture and groundwater tables yields the most robust predictions for root zone soil moisture.
-
Khan et al. (2025) Remote sensing-based cropping pattern identification and its impact on groundwater use in canal command areas of an irrigated agriculture region in Pakistan
This study integrates Sentinel-2 satellite imagery and Random Forest algorithms to map seasonal cropping patterns across eight Canal Command Areas in Pakistan's Bari Doab from 2018 to 2023. The findings quantify a rising dependency on groundwater for irrigation, driven by water-intensive crops and urbanization, leading to significant regional aquifer depletion.
-
Wang et al. (2025) Diverging Impacts of Snow Fraction and Soil Drainage on Seasonal and Annual Water Balances Across Snow‐Influenced Catchments
This study investigates how climate and soil drainage nonlinearity control seasonal and annual water balances across 230 snow-influenced catchments in the contiguous United States. It reveals that climate dictates both regional hydrological differences and the factors driving within-region variations, with the impacts of snow fraction and soil drainage nonlinearity diverging based on prevailing water and energy balance regimes.
-
Shirani et al. (2025) An integrated machine learning framework for flood susceptibility assessment
This chapter introduces an integrated machine learning framework for flood susceptibility assessment, emphasizing the critical and complex interplay between drought conditions and subsequent flood events. The provided text is an introduction and does not present the framework's specific findings.
-
Yin et al. (2025) A shift in drought propagation trend in the Yellow River Basin during 1980–2020 linked to climate change and vegetation greening
This study investigates the propagation of meteorological drought to soil moisture drought in the Yellow River Basin from 1980 to 2020, identifying a significant shift around the year 2000 where drought propagation time began to prolong and duration extension began to decrease due to vegetation greening.
-
Li et al. (2025) Numerical simulation and analysis of groundwater response to ecological water recharge in an alluvial fan system
This study quantifies the synergistic effects of ecological water replenishment (EWR) and other restoration measures on groundwater levels in a North China alluvial fan system, revealing significant spatial differentiation and cross-layer hydraulic responses.
-
Soleimanpour et al. (2025) Application of Gravity Recovery and Climate Experiment satellite data to monitor groundwater storage and scarcity
This paper focuses on applying Gravity Recovery and Climate Experiment (GRACE) satellite data to monitor groundwater storage and scarcity, emphasizing the critical need for advanced, cost-effective monitoring systems due to widespread groundwater overexploitation and its environmental consequences.
-
Patra et al. (2025) Long-term projections of global groundwater storage under future climate change scenarios using deep learning
This study utilizes a deep learning model to project global groundwater storage (GWS) variations until 2100 under CMIP6 climate scenarios, identifying maximum temperature as the primary driver of depletion. The findings indicate that over 50% of the global population will reside in regions facing GWS decline by the end of the century, with tropical and temperate zones being the most vulnerable.
-
Karimzadeh et al. (2025) Climate change has increased global evaporative demand except in South Asia
Climate change has increased global evaporative demand, but this study reveals a significant decline in South Asia due to widespread irrigation, which has increased local moisture, cloud cover, and reduced solar radiation. These contrasting trends highlight how human water use can locally reshape the climate's influence on the water cycle.
-
Chakraborty et al. (2025) An integrated framework for flood risk forecasting utilizing global weather predictions and hydrodynamic Modelling: An appraisal of the Krishna River Basin case study, India
This study proposes a novel and scalable framework for flood risk forecasting that integrates global rainfall forecasts, hydrodynamic modeling, and socioeconomic vulnerability assessment to support response prioritization. Applied to the Krishna River Basin, India, the framework identifies hazard-driven, vulnerability-driven, and compound high-risk zones for anticipatory planning.
-
Javidan et al. (2025) The role of climatic and anthropogenic factors in drying up of lakes
This paper investigates the relative contributions of climatic and anthropogenic factors to the desiccation of lakes, asserting that human activities often play a more significant role than natural climate variability in contemporary hydrological transformations and lake decline.
-
Naghibi et al. (2025) Assessing dust storm risks in water-scarce regions: a machine learning approach
This paper aims to assess dust storm risks in water-scarce regions using a machine learning approach, emphasizing the severe environmental, health, and socio-economic impacts of dust storms and the critical need for their study and mitigation.
-
Moghaddam et al. (2025) Technological innovations in water conservation: navigating drought and water scarcity
This chapter synthesizes traditional and technological approaches to water conservation, addressing the escalating global challenges of drought and water scarcity by highlighting innovative tools and strategies for sustainable water management.
-
de Vries et al. (2025) Precipitation disaster hotspots depend on historical climate variability
This study reveals that historical precipitation variability influences current and future record-breaking precipitation probabilities, identifying regions with low current records as most at risk, while climate change disproportionately increases risk in regions with high current records.
-
Chen et al. (2025) Bias correction of subseasonal to seasonal precipitation forecasts over the Tibetan Plateau based on CMA climate prediction models
This study evaluates the effectiveness of CDF and KEM bias correction methods on subseasonal to seasonal precipitation forecasts over the Tibetan Plateau using CMA hindcast data, finding that CDF excels in systematic bias reduction while KEM improves spatial correlation and anomaly trends, with a combined approach offering synergistic benefits.
-
Olson et al. (2025) Long‐Term Stream Chemistry Patterns in a Boreal Watershed Underlain With Discontinuous Permafrost
This study investigated over 20 years of stream chemistry and climate trends in boreal catchments with varying permafrost extents to understand how altered flowpaths and climate change affect solute transport. It found significant declines in dissolved organic carbon (DOC) and partial pressure of carbon dioxide (pCO₂) in sub-catchments with higher permafrost extent, with moisture and discharge being key abiotic drivers.
-
Qin et al. (2025) The effect of deficit irrigation combining with transparent/black film and straw mulching on wheat-maize cropping system in the North China Plain
This study investigated the effects of deficit irrigation combined with various mulching materials on winter wheat-summer maize rotation in the North China Plain. It found that transparent film mulching under deficit irrigation significantly increased annual yield, water productivity, and economic profit, while deficit irrigation alone negatively impacted wheat but not subsequent maize.
-
Fan et al. (2025) Considering parameter seasonal variation to enhance process-based ecosystem model performance, evidence from the SWH model
This study demonstrates that incorporating seasonal variation into empirical parameters significantly enhances the performance of the SWH evapotranspiration (ET) partitioning model. A novel Monte Carlo-based calibration scheme with adaptive time windows achieved a 95% success rate and substantially improved R² values compared to traditional methods, approaching the accuracy of Extended Kalman Filtering.
-
Daiman et al. (2025) Assessing the link between changes in landscape and desertification in the chambal river basin using machine learning and remote sensing
This study analyzed the linkage between landscape changes and desertification in the Chambal River Basin (India) from 1990 to 2020 using machine learning and remote sensing. It found that anthropogenic land alterations, particularly the conversion of vegetation and agricultural lands, amplified the region's vulnerability to drought and desertification.
-
Yang et al. (2025) Watershed boundary extraction from digital elevation models using RBM-SegNet
This study developed RBM-SegNet, a deep learning framework, to overcome limitations in traditional watershed boundary extraction from Digital Elevation Models (DEMs), demonstrating superior accuracy compared to existing methods.
-
Matos et al. (2025) Modeling the Late Pliocene with AWI-CM3 as a contribution to PlioMIP3 core experiments
This study presents the first application of the AWI-CM3 climate model for Late Pliocene (3.205 Ma BP) simulations within the PlioMIP3 framework, demonstrating a significantly warmer climate with pronounced polar amplification, an intensified hydrological cycle, and a reorganized ocean circulation, notably a strengthened Atlantic Meridional Overturning Circulation.
-
Zhang et al. (2025) Retrieving atmospheric water vapor profiles over Europe combining NOAA-20/CrIS and ground-based GNSS-PWV data
This study proposes a novel method to improve the accuracy of atmospheric water vapor vertical profile retrievals over Europe by combining NOAA-20/CrIS infrared hyperspectral data with ground-based GNSS-PWV observations using Transformer and Random Forest models, demonstrating significant accuracy enhancements, especially under cloudy conditions, compared to official CrIS products.
-
Murton (2025) Permafrost and climate change
This chapter provides a comprehensive review of permafrost characteristics, observed trends in its conditions due to climate change, and the resulting impacts on both natural and built environments. It synthesizes current scientific understanding to highlight permafrost degradation as a critical indicator and consequence of global warming.
-
Yan et al. (2025) Biophysical feedback from earlier leaf-out enhances nonerosive precipitation in China
This study investigates the impact of earlier vegetation leaf-out on precipitation intensity and water erosion in China using remote sensing, reanalysis data, and a coupled land-atmosphere model. It finds that advanced phenology enhances nonerosive precipitation, particularly in semi-humid temperate regions, while reducing erosive precipitation in sparsely vegetated areas and during summer/autumn, driven by biophysical feedbacks that redistribute atmospheric moisture and energy.
-
Letcher (2025) Causes of climate change
This chapter reviews the primary causes of climate change, focusing on the evidence establishing carbon dioxide as the main driver of global warming, while also discussing other natural and anthropogenic factors.
-
Quadro et al. (2025) The Atmospheric Water Cycle over South America as Seen in the New Generation of Global Reanalyses
This study evaluates precipitation and key atmospheric water-cycle terms over South America using three modern global reanalyses (MERRA-2, ERA5, CFSR/CFSv2) against two observation-based datasets (CPC Unified Gauge, MSWEP-V2) from 1980–2021. It finds MERRA-2 generally exhibits the smallest precipitation biases and highest correlations, along with better moisture-budget closure, making it the most reliable for basin-scale water-budget analyses in the region.
-
Zhang et al. (2025) Assessing the Dominant Impact of Climate and Land Use Change on Runoff Through Multi-Model Simulation in the Karst Headwater Region of the Wujiang River
This study utilized multi-model simulations (SWAT, CA-Markov, CMIP6) to assess the impacts of climate and land use change on runoff in the karst headwater region of the Wujiang River, concluding that climate change is the dominant factor driving future runoff reduction.
-
Pimentel (2025) Glaciers and climate change
This chapter reviews the role of glaciers as critical indicators of contemporary climate change, detailing their observed changes, drivers, simulation, and wide-ranging global impacts.
-
Vries et al. (2025) Precipitation disaster hotspots depend on historical climate variability
This study investigates how historical climate variability and climate change interact to shape the probability of future record-breaking precipitation events and identifies global disaster hotspots where high risk combines with potentially low societal preparedness. It reveals that regions with low historical precipitation records are currently most vulnerable, while those with high records face the steepest increase in risk due to climate change, exposing over a billion people to high record-breaking probabilities by 2100.
-
Liu et al. (2025) Spatiotemporal Dynamics of Surface Energy Balance over the Debris-Covered Glacier: A Case Study of Lirung Glacier in the Central Himalaya from 2017 to 2019
This study analyzed the spatiotemporal surface energy balance of Lirung glacier (Central Himalaya) from 2017 to 2019, demonstrating that net radiation is the primary ablation driver, significantly modulated by debris cover and ice cliffs, which in turn influences proglacial lake expansion.
-
Razavizadeh et al. (2025) The effect of climatic and human factors on hydrological drought
This chapter aims to investigate the impacts of human factors (dam construction and land use changes) and climatic drought on hydrological drought within the Zohreh-Jarahi basin in southwestern Iran over a five-decade period.
-
Dong et al. (2025) Integrating prior information for improving 3D model-driven GAI estimation with application to wheat crops
This study investigated the integration of prior information (soil background, leaf optical properties, and canopy structure) into radiative transfer models to enhance Green Area Index (GAI) estimation for wheat crops. It demonstrated that stage-specific GAI retrieval with detailed prior information significantly improves accuracy compared to standard model inversion approaches.
-
Chen et al. (2025) Asymmetric East‐West Changes in Mountain Fog Driven by Urbanization and Climate Warming
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
-
Xu et al. (2025) Soil Texture Controls Terrestrial Water Storage Anomalies in the Chinese Loess Plateau
This study investigates the influence of soil texture on terrestrial water storage anomalies (TWSA) in the Chinese Loess Plateau (CLP), revealing that soil texture critically controls TWSA dynamics, decline rates, and precipitation-related hysteresis effects across different loess types.
-
Zhang et al. (2025) An Improved Change Detection Method for Time-Series Soil Moisture Retrieval in Semi-Arid Area
This study developed an improved integrated approach using Sentinel-1 C-band SAR and MODIS optical data to enhance time-series surface soil moisture (SSM) estimation, achieving high accuracy (R2 = 0.844, RMSE = 0.030 m3/m3) in heterogeneous landscapes by effectively addressing vegetation effects and anomalous surface changes.
-
Turki et al. (2025) On the use of SWOT altimetry data for monitoring coastal hydrodynamics
This study evaluates the accuracy of the Surface Water and Ocean Topography (SWOT) mission in retrieving coastal Sea Surface Heights (SSH) and Significant Wave Heights (SWH) in the English Channel. The findings demonstrate that SWOT provides high-resolution, reliable hydrodynamic data even within 3–4 km of the shoreline, significantly outperforming conventional satellite altimetry in complex nearshore environments.
-
Chucuya et al. (2025) Reconstructing aquifer dynamics with machine learning: Linking irrigation expansion to groundwater decline in a data-scarce hyper-arid region
This study utilizes machine learning (BPNN) to reconstruct fragmented groundwater records in the hyper-arid Caplina aquifer, revealing a 0.6 m/yr water table decline driven by a 400% expansion of irrigated agriculture over three decades. The research highlights the critical role of seawater intrusion in maintaining stable water levels near the coast while severely degrading water quality.
-
Maier et al. (2025) Correction to: Analysing the future trends of foehn-enabling synoptic patterns over two valleys in the Eastern Alps in CMIP5 EURO-CORDEX models
This correction addresses an error in the number of climate models used for specific Global Warming Levels (GWLs) in a previous study, clarifying the statistical significance of annual foehn occurrence trends while affirming the validity of the original paper's main conclusions.
-
Liu et al. (2025) Characterizing Shallow Cumulus and Its Drivers Over the Tibetan Plateau
This study investigates the macrophysical characteristics and environmental controls of shallow cumulus (ShCu) over the Tibetan Plateau (TP) using 15 years of satellite observations and reanalysis data. It reveals a distinct ShCu regime primarily controlled by near-surface relative humidity, with lower cloud bases compared to surrounding regions, offering insights for improved cloud parameterization.
-
Chen et al. (2025) Baseflow index dynamics and its non-monotonic drivers from a spatiotemporal heterogeneity perspective
This study analyzed the spatiotemporal dynamics and non-monotonic drivers of the Baseflow Index (BFI) in 60 Taiwanese catchments from 1960 to 2022, revealing significant non-stationarity and identifying the simple daily intensity index (SDII) as the most dominant driver.
-
Cheng et al. (2025) The 3D Evolution of Spatiotemporally Contiguous Summer Heatwaves in China: Tracks and Underlying Mechanism Associated With Compound Typhoons and High‐Pressure Systems
This study introduces a 3D perspective to track the evolution of regional summer heatwaves (HWs) in China from 1979 to 2022, revealing that the interactions between inland high-pressure systems and typhoon tracks critically modulate HW intensity, duration, and affected area.
-
Li et al. (2025) Spatiotemporal fog origins in a foggy desert through integrating isotope and satellite observations
This study establishes a new framework integrating isotope and satellite observations to identify the spatiotemporal origins of fog in the Namib Desert, revealing an increasing trend of locally generated fog over time at Gobabeb and distinct spatial patterns across the central Namib Desert.
-
Caraballo‐Vega et al. (2025) Optical imagery and digital spaces in the era of machine learning for better geospatial information and services
This chapter introduces the challenges and opportunities presented by the vast amount of Earth observation satellite data, emphasizing the critical role of machine learning methods for accurate processing and analysis to generate geospatial information and services. It highlights the need for guidance in selecting appropriate methods for remote sensing applications.
-
He (2025) A Self-Calibrating Retrieval Algorithm for Three-phase water Raman Lidar Incorporating Temperature-Dependent Spectral Characteristics
This paper introduces a novel self-calibrating retrieval algorithm for three-phase water Raman lidar, designed to derive atmospheric three-phase water profiles by incorporating temperature-dependent spectral characteristics. It also describes the associated data collected using a Raman lidar system.
-
Jiang et al. (2025) Nonlinear impacts of urban size and vegetation cover on global surface urban heat: Insights from 6022 cities
This study globally assesses surface urban heat island (SUHI) across 6022 cities to reveal nonlinear impacts of urban size and vegetation cover, finding distinct SUHI intensification patterns and compounded thermal stress in Global South cities.
-
Eppelbaum et al. (2025) The long-term variability of the hydrological characteristics of Lake Baikal and its tributaries under climate change and anthropogenic impact
This study aims to identify and quantify long-term trends in the hydrological characteristics of Lake Baikal and its tributaries, revealing the impacts of climate change and anthropogenic activities on this critical freshwater ecosystem.
-
Lari et al. (2025) Quantifying sediment yield and discharge fluctuations using the GeoWEPP in response to soil and water conservation practices
This study evaluated and calibrated the GeoWEPP model to predict runoff and sediment yield in the mountainous Amameh watershed, Iran, incorporating snowmelt dynamics and high-resolution spatial data. It assessed eight biological conservation scenarios, demonstrating that enhanced canopy cover can reduce runoff by up to 44% and sediment yield by up to 47%.
-
Sugita et al. (2025) Ship's Motion and Eddy Correlation Measurements of Surface Fluxes on the Small Research Ship NIES ' 94 in Lake Kasumigaura, Japan
[Information not extractable due to unreadable paper text.]
-
Tunca et al. (2025) Integration of UAV images and ensemble learning for root zone soil moisture estimation in sorghum
This study developed and evaluated a methodology to estimate root-zone soil moisture in sorghum using high-resolution unmanned aerial vehicle (UAV) multispectral and thermal imagery combined with machine learning. An ensemble model integrating XGBoost, Light Gradient Boosting Machine, and K-Nearest Neighbors achieved the highest accuracy (R² = 0.85, RMSE = 11.124 mm/90 cm, MAE = 8.775 mm/90 cm) for field-scale monitoring.
-
Ercan et al. (2025) Rethinking standardized drought indices for critical drought evaluation
This study investigates the differences between classical and dynamic Standardized Precipitation Index (SPI) models, revealing that dynamic models produce significantly longer drought durations, particularly at short and medium timescales, highlighting the importance of model choice for accurate drought assessment.
-
Mokhtarisabet et al. (2025) Comparison of water and flood spreading area against nonspreading and control area using remote sensing, GIS, and statistical analysis
This study aims to compare the characteristics of water and flood spreading areas against non-spreading and control areas using remote sensing, GIS, and statistical analysis.
-
Khomri et al. (2025) Optimizing Sugar Beet Irrigation in Arid Regions: A Machine Learning Approach to Soil Moisture Prediction
This study evaluated eight machine learning models for soil moisture prediction in sugar beet cultivation in southern Algeria to optimize irrigation. The deep learning models, LSTM and GRU, demonstrated superior accuracy, leading to an estimated 15–25% reduction in water usage and a 5–10% increase in crop yield potential.
-
Saha et al. (2025) Development of daily downscaled, bias-corrected CMIP6 climate datasets for estimating reference evapotranspiration (ETo) in South Asia
This study developed daily bias-corrected and downscaled CMIP6 climate datasets for South Asia, including temperature, solar radiation, wind speed, and relative humidity, which were then used to estimate reference evapotranspiration (ETo) at a 0.25° spatial resolution for historical and future climate scenarios. The resulting datasets significantly reduce biases compared to original CMIP6 outputs, providing a crucial resource for regional hydrological and climate impact assessments.
-
Blanco et al. (2025) Changes in drylands in Argentina during the phases of the Pacific Decadal Oscillation (1961–2022)
This study investigates dryland changes in Argentina between 1961 and 2022 in relation to Pacific Decadal Oscillation (PDO) phases. It finds that drylands expand during negative PDO phases and contract during positive phases, primarily in central Argentina, driven by shifts in precipitation and evapotranspiration linked to South Pacific atmospheric circulation.
-
Cao et al. (2025) Enhancing short-term PWV prediction through GNSS and ERA5 data fusion
This study developed a multi-source data fusion model combining Global Navigation Satellite System (GNSS) and ERA5 precipitable water vapor (PWV) to enhance short-term, high-accuracy, and high-spatial-resolution PWV predictions, demonstrating significant improvements in prediction accuracy using Transformer and Long Short-Term Memory (LSTM) neural networks.
-
Li (2025) Pre-processed datasets for manuscript: Divergent Subtropical Forest Functional and Structural Responses to the 2022 Yangtze River Extreme Drought.
This study investigates the varied functional and structural responses of subtropical forests in the Yangtze River region to the extreme drought event that occurred in 2022.
-
Jrayj De Melo (2025) Rain Upstream, Floods Downstream: the Case of the Uruguai River in Uruguaiana-Rs
This study investigates the relationship between upstream management in the Uruguay River basin and downstream flood events in Uruguaiana, Brazil. It found a strong correlation between upstream rainfall volumes and river level rise in Uruguaiana, with response times ranging from two to four days.
-
Akor et al. (2025) Impact of Cloud Microphysics Schemes and Boundary Conditions on Modeled Snowpack in the Central Idaho Rocky Mountains, USA
This study investigates how the choice of cloud microphysics parameterization and lateral boundary conditions in the Weather Research and Forecasting (WRF) model impacts hydrometeorological forcings and snow conditions in mountainous regions, revealing significant variability in precipitation, radiation, and snow metrics due to these configuration choices.
-
Zhang et al. (2025) Hydrological drivers of maize productivity: A new analytical framework
This study developed the Crop-Oriented Hydrological Variables Zoning and Quantification Framework (COHV-ZQ), integrating Multiscale Geographically Weighted Regression (MGWR) and clustering analysis, to spatially delineate hydrological-crop zones and quantify the spatiotemporal relationships between hydrological variables and maize net primary productivity (NPP) in Songyuan City, Northeast China, finding evapotranspiration to be the dominant direct driver of maize NPP.
-
Zeroualı et al. (2025) Linking the North Atlantic Oscillation to rainfall variability and dynamics in Algeria through GIS and wavelet theory
This study regionally analyzes the relationship between the North Atlantic Oscillation (NAO) and rainfall in northeastern Algeria using wavelet theory and GIS, revealing a strong and consistent NAO influence in the north that diminishes southward, offering potential for improved rainfall forecasting.
-
Guillou (2025) Approche machine learning pour le suivi des changements de l'occupation / utilisation du sol des zones humides littorales de Bretagne
[Unable to extract information due to unreadable paper text.]
-
Mali et al. (2025) Three R: Recharge, Retention, and Reuse of water
This paper synthesizes the "Three R" approach (Recharge, Retention, and Reuse) for sustainable water resource management, illustrating its practical application and positive outcomes through a case study of watershed development in Maharashtra, India.
-
Xu et al. (2025) Construction and Application of Soil–Water Characteristic Curve Model Considering Water Mineralization Degree
This study developed and validated a modified Van Genuchten (VG) model for the soil–water characteristic curve (SWCC) that explicitly accounts for irrigation water salinity, demonstrating improved accuracy in predicting soil water dynamics in saline environments of southern Xinjiang.
-
Nouri et al. (2025) Mitigating crop modeling uncertainties through machine learning in drylands
This study developed a novel machine learning (ML)-based clustering–unbiasing–ensembling framework to improve the reliability of gridded meteorological data for the CSM-CERES-Wheat crop model in data-scarce drylands of Iran. The framework, particularly when correcting all meteorological variables, significantly enhanced wheat yield and water stress simulations in approximately 60% of cases, outperforming classical methods.
-
Xin et al. (2025) A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl
This paper introduces HydroModels.jl, a flexible and differentiable Julia-based framework designed to overcome challenges in integrating deep learning with hydrological models by supporting automatic differentiation and symbolic programming for hybrid modeling applications.
-
Webster et al. (2025) Hourly potential light availability maps at 10 m resolution over Switzerland
This paper introduces the SwissRad10 dataset, providing novel nationwide light availability maps for Switzerland at 10 meter spatial and hourly temporal resolution across an entire annual solar cycle, accounting for detailed terrain, individual tree shadows, and seasonal foliage changes.
-
Cho et al. (2025) Disentangling geomorphic equifinality in sediment and hydrologic connectivity through the analyses of landscape drivers of hysteresis
## Identification - **Journal:** Earth Surface Processes and Landforms - **Year:** 2025...
-
Papalexiou et al. (2025) Machine unlearning: bias correction in neural network downscaled storms
This study evaluates four machine learning models for downscaling precipitation using synthetic benchmark storms, demonstrating that combining machine learning with post-processing bias correction ("machine unlearning") is crucial for reliable outputs, especially for Wasserstein Generative Adversarial Networks (WGANs). It finds that raw neural network outputs struggle to reproduce key statistical properties and wet/dry boundaries, necessitating systematic bias correction for operational use.
-
Jenny et al. (2025) Mass Change in Antarctica from 2002 to 2025 Using GRACE and GRACE-FO
This study compares five GRACE(-FO) Level-2 gravity field solutions using a mascon inversion method to estimate gridded mass change across Antarctica, revealing consistent negative mass trends from 2002 to 2025, with regional variations where Glacial Isostatic Adjustment model error exceeds inter-solution differences.
-
Zhao et al. (2025) Linking deterministic and probabilistic paradigms: a peak-sensitive prediction framework for heterogeneous runoff processes
This study introduces a peak-sensitive hybrid framework combining time-varying filtering-based empirical mode decomposition (TVF-EMD) with deep learning to improve seasonal runoff forecasting under hydroclimatic nonstationarity and human regulation. The framework delivers superior point predictions and well-calibrated, peak-aware prediction intervals, supporting risk-informed water-resources management.
-
Zhou et al. (2025) Impacts of Subseasonal Silk Road Pattern on Extreme Heat Events in Eurasia
This study investigates the subseasonal Silk Road Pattern (SRP) and its significant impact on the occurrence and development of individual extreme heat events in Eurasia, revealing its quasi-biweekly oscillation and its synergistic effect with the British Baikal Corridor Pattern (BBC) on 2 m temperature anomalies.
-
Maurya et al. (2025) Geospatial information services for climate-resilient natural resource management in the Indian Himalayan Region
This paper advocates for the application of geospatial information services, including GIS and remote sensing, to enhance climate-resilient natural resource management in the Indian Himalayan Region.
-
Athira et al. (2025) Multi-model ensemble improves evapotranspiration estimation over India
This study develops and evaluates multi-model ensemble techniques for improving evapotranspiration (ET) estimation over India at both in-situ and 1 km spatial scales, demonstrating that machine learning-based ensembles significantly enhance accuracy compared to individual models and simpler ensemble methods.
-
Yin et al. (2025) Reconstruction of Daily Runoff Series in Data-Scarce Areas Based on Physically Enhanced Seq-to-Seq-Attention-LSTM Model
This study proposes a Physics-enhanced Seq-to-Seq Attention LSTM (PSAL) model to reconstruct high-accuracy daily streamflow from remote sensing data in data-scarce regions, demonstrating significant performance improvements over a baseline model on the Jinsha River.
-
Nayak et al. (2025) Evaluating Physical Climate Model Emulators for Global Warming Projections
This study develops a framework to systematically evaluate the robustness of physical climate model emulators against global climate model (GCM) large ensembles, revealing that while emulators can match GCM warming projections, they often do so for incorrect physical reasons due to compensating biases.
-
Wang et al. (2025) Gap-Filling for Daily Latent Heat Flux Observations with the Full-factorial method at Global Flux Sites
This study developed and validated a novel median-adjusted full-factorial and iterative method to accurately fill gaps in daily latent heat flux (LE) observations from 265 global eddy covariance sites, demonstrating superior performance compared to existing LE products and providing a high-quality, continuous dataset.
-
Rao et al. (2025) Long-term assessment of crop water dynamics and irrigation dependency for selected major Kharif crops under climate variability using the CROPWAT Model in the Araniar command area, Andhra Pradesh, India (1990–2024)
This study estimated crop water requirements (CWR), net irrigation requirements (NIR), and irrigation dependency ratios (IDR) for major Kharif crops (Paddy, Groundnut, Bajra) in the Araniar command area, India, from 1990-2024 using the CROPWAT 8.0 model, revealing significant variations in water dependency among crops. The findings highlight Paddy's high irrigation dependency and provide insights for sustainable water management.
-
Esch et al. (2025) Modelling runoff in a glacierized catchment: the role of forcing product and spatial model resolution
This study evaluates the reliability of glacio-hydrological simulations in a Swiss glacierized catchment using the GERM model, investigating the impact of meteorological forcing products and spatial model resolution. It finds that precipitation forcing significantly affects results, coarser resolutions (above 1000 m) compromise accuracy, and calibration strategies present trade-offs between annual and seasonal accuracy.
-
Lemus‐Canovas et al. (2025) More intense heatwaves under drier conditions: a compound event analysis in the Adige River basin (Eastern Italian Alps)
This study analyzes a severe compound drought and heatwave (CDHW) event in the Adige River basin (Eastern Italian Alps) in May 2022, revealing that similar events are now significantly hotter (by 1–4 °C) and drier (with pronounced precipitation deficits) due to climate change, exacerbating water stress and shifting streamflow seasonality. It also highlights the inability of many regional climate models (EURO-CORDEX) to accurately reproduce these observed changes in both magnitude and sign.
-
Wu et al. (2025) A comparison of water use strategies between pure and mixed forests on the Chinese Loess Plateau
This study investigated the water use strategies of pure and mixed forests on the Chinese Loess Plateau over two years, revealing that mixed forests exhibit more conservative water use, characterized by deeper water acquisition and stricter stomatal regulation, compared to pure forests.
-
Weynants et al. (2025) Dheed: an ERA5 based global database of compound dry and hot extreme events from 1950 to 2023
This study introduces Dheed, a novel global database of compound dry and hot (CDH) extreme events from 1950 to 2023, derived from ERA5 reanalysis data, and confirms a significant increase in the frequency and spatial extent of these events over recent decades.
-
Zheng et al. (2025) GDROM v2: An Inventory of Operation Variables Time Series and Rules for 2,017 Large Reservoirs across the CONUS
This paper introduces GDROM v2, a nationwide dataset for 2,017 large reservoirs across the Contiguous United States (CONUS), providing daily time series of inflow, release, and storage, along with operation rules derived through data fusion and transfer learning. The dataset addresses the scarcity of complete reservoir operation records, offering a comprehensive resource for hydrological modeling and water management studies.
-
Ávila-Díaz et al. (2025) Projections of Atmospheric Moisture Transport Over South America in a Changing Climate
This study projects future atmospheric moisture transport over South America using CMIP6 models and ERA5 data, revealing significant basin-level variations in Vertically Integrated Moisture Flux (VIMF) and its convergence (VIMFC) under different SSP scenarios, with implications for regional precipitation regimes and water resource management.
-
Dangare et al. (2025) Estimating transpiration dynamics of a low-density litchi orchard using crop coefficients derived from a variable leaf conductance model, canopy cover, and tree height in Northeastern South Africa
This study improved the estimation of litchi orchard transpiration in semi-arid South Africa by modifying the Allen and Pereira (A&P) crop coefficient approach to incorporate a variable leaf resistance model and a litchi-specific typical leaf resistance, achieving significantly higher accuracy compared to the original fixed-value method.
-
Rixen et al. (2025) Influence of Mesoscale Organization on the Formation of Humidity Haloes
This study investigates humidity haloes around trade wind shallow cumulus clouds using observatory data, finding they increase downward longwave radiation by 10 W/m² and are primarily driven by mesoscale organization rather than single-cloud detrainment.
-
Wang et al. (2025) Application of High-Precision Classification Method Based on Spatiotemporal Stable Samples and Land Use Policy in Oasis–Desert Mosaic Landscape Areas
This study developed a high-precision land cover classification method for oasis–desert mosaic landscapes by integrating spatiotemporally stable samples, a novel Canopy Growth Index, and land-use policy constraints. The method achieved an overall accuracy of 91.9% and significantly reduced spatiotemporal inconsistencies in land cover products.
-
Li et al. (2025) A hybrid framework for sub-seasonal to seasonal streamflow prediction: integrating numerical and statistical models
This study develops a hybrid framework integrating a distributed hydrological model (DRIVE) with a probabilistic statistical model (BJP) to enhance sub-seasonal to seasonal (S2S) streamflow prediction, demonstrating improved forecast skill for flood events in the complex Pearl River Basin.
-
Zhao et al. (2025) Impact of Joint Assimilating AWS and Radar Observations on the Analysis and Forecast of a Squall Line with Complex Terrain
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
-
Verbesselt et al. (2025) Datasets Biodiversa+ Habitat Pilots workshop Flanders - Habitat condition indicators
This paper describes the datasets and methodology used during a workshop to demonstrate the potential and challenges of Sentinel-2 L2A time series for monitoring soil moisture and inundated areas in grasslands and wetlands, without presenting statistically validated results.
-
Roychowdhury et al. (2025) Web based geospatial artificial intelligence for earth resource management toward climate change adaption
This chapter introduces the integration of web-based geospatial artificial intelligence (GeoAI) with remote sensing and GIS for earth resource management, emphasizing its role in climate change adaptation and disaster management through accessible, cost-effective solutions.
-
Chang et al. (2025) Resolution Dependence of Tropical Poleward Energy Transport in Aquaplanet GCMs
This study benchmarks the resolution dependence of tropical poleward energy transport in two aquaplanet atmospheric general circulation models without convective parameterizations, finding that mean meridional circulation transport increases while transient eddy transport decreases with higher resolution, primarily due to changes in gross moist stability and explicit deep convection.
-
Hassan et al. (2025) Land use and land cover analysis for climate-resilient disaster management with remote sensing and geographic information system approach
This paper focuses on analyzing land use and land cover changes, particularly those driven by rapid urbanization in India, utilizing remote sensing and Geographic Information System (GIS) approaches to support climate-resilient disaster management.
-
Huang et al. (2025) Urban heat forecasting in small cities: evaluation of a high-resolution operational numerical weather prediction model
This study evaluates the performance of the High-Resolution Rapid Refresh (HRRR) model in forecasting urban heat dynamics, including temperature, humidity, nocturnal cooling, and urban heat advection (UHA), in a small, semi-arid city (Lubbock, Texas). Findings reveal systematic biases in HRRR forecasts and limitations in its urban representation, highlighting the need for improved urban parameterizations in numerical weather prediction models for small to mid-sized cities.
-
Zongjie et al. (2025) Increased rainfall alters soil moisture dynamics in alpine meadows
This study investigated the impact of increased precipitation on soil moisture dynamics and water sources in alpine meadows on the Qinghai-Tibet Plateau, revealing that enhanced rainfall significantly reorders the vertical sequence and timing of soil water sources, shifting recharge towards supra-permafrost water.
-
Samson et al. (2025) Comparative study of single and hybrid deep learning models for daily rainfall prediction in selected African cities
This study comprehensively compares single (CNN, LSTM, ANN, RNN) and hybrid (RNN+ANN, LSTM+ANN, LSTM+RNN) deep learning models for daily rainfall prediction in five diverse African cities. It finds that single deep learning models, particularly RNN, generally outperform hybrid models across most locations, although hybrid models can be superior in specific complex rainfall regimes.
-
Mani et al. (2025) Geospatial information and services for assessing land use change and climate change impacts on water resources for climate-resilient disaster management
This introductory chapter emphasizes the critical need to assess land use change and climate change impacts on water resources to facilitate effective water management and planning for climate-resilient disaster management. It highlights the increasing global concern over water scarcity driven by climate change and population growth.
-
Allred et al. (2025) Sentinel-2 based estimates of rangeland fractional cover and canopy gap class for the western United States
This paper develops and applies a temporal one-dimensional convolutional neural network using Sentinel-2 satellite data to produce annual, 10 m resolution estimates of rangeland fractional cover and canopy gap size classes for the western United States from 2018 to 2024, demonstrating improved accuracy over previous Landsat-based methods.
-
Ramirez et al. (2025) Hydrological response to thinning in forest stands: analysis of soil volumetric water content and soil water flux
This study investigates the impact of masticator thinning on soil moisture dynamics in a semiarid mixed conifer forest. The findings indicate that thinning increases soil water storage at the soil-bedrock interface and induces upward water flux during dry periods, potentially enhancing forest resilience to drought.
-
Wang et al. (2025) Machine learning-based inversion and sensitivity analysis of soil moisture in Hemerocallis cultivation
This study developed a machine learning framework integrating TSM640 sensor data, multi-source remote sensing (Sentinel-1/2), and meteorological datasets to analyze soil moisture (SM) dynamics across soil layers and their impact on Hemerocallis yield. The framework successfully estimated SM (BPNN R² = 0.64) and predicted yield (RF R² = 0.63), identifying bolting and squaring as critical moisture-sensitive growth stages for optimizing irrigation.
-
Yin et al. (2025) The Preliminary Assimilation and Impacts of FY‐4B Geostationary Interferometric Infrared Sounder (GIIRS) Cloud‐Cleared Radiances
This study evaluates the impact of assimilating Cloud-Cleared Radiances (CCRs) from the Fengyun-4B Geostationary Interferometric Infrared Sounder (GIIRS) on Numerical Weather Prediction (NWP). It finds that CCRs significantly increase assimilated observations and provide modest positive contributions to analysis and forecast fields, particularly for typhoon events.
-
Idowu et al. (2025) Open problems in uncertainty quantification for flood modelling: A systematic review
This systematic review identifies eight critical open problems in uncertainty quantification for flood modelling, highlighting a system-level mismatch between flood complexity and current fragmented modelling practices, and advocating for seamless, end-to-end probabilistic pipelines.
-
Kumar et al. (2025) Spatiotemporal analysis and mechanisms of drought flood abrupt alternation events in India
This study comprehensively analyzes the spatiotemporal patterns, characteristics, and teleconnection drivers of Drought-Flood Abrupt Alternation (DFAA) events across India from 1901 to 2022, identifying key hotspots and an increasing trend in rapid drought-to-flood transitions influenced by ENSO, NAO, and IOD.
-
Solanki et al. (2025) River drought forcing of the Harappan metamorphosis
This study integrates high-resolution paleoclimate archives with palaeohydrological reconstructions from transient climate simulations to identify severe and persistent river droughts, lasting from decades to centuries, that affected the Indus basin between approximately 4400 and 3400 years before present, contributing to the Harappan metamorphosis.
-
Calcar et al. (2025) Bedrock uplift reduces Antarctic sea-level contribution over next centuries
This study quantifies the impact of heterogeneous solid Earth structure on Antarctic ice sheet retreat and its contribution to barystatic sea-level rise. It finds that including realistic 3D Earth structures in coupled ice-bedrock models delays grounding line retreat by 50 to 130 years and reduces the Antarctic sea-level contribution by 9–23% over the next centuries, an effect that can be twice as large as the uncertainty arising from different climate models.
-
Zhang et al. (2025) A Fuzzy Credibility-Constrained Fuzzy Multi-Objective Programming Model for Optimizing Irrigation Strategies to Balance Citrus Yield and Quality Under Uncertainty
This study develops a novel fuzzy credibility-constrained fuzzy multi-objective programming (FCC-FMOP) model to optimize irrigation strategies, demonstrating its effectiveness in simultaneously enhancing crop yield and fruit quality under water scarcity in a citrus-producing region of Southwest China.
-
Tagliabue et al. (2025) Appraising retrieval schemes from spaceborne hyperspectral imagery for mapping leaf and canopy traits in forest ecosystems
This study investigated and compared machine learning regression algorithms (MLRA) and hybrid approaches for retrieving forest traits from PRISMA hyperspectral imagery. It demonstrated that hybrid models accurately quantify key leaf and canopy traits, including Leaf Chlorophyll Content (LCC), Leaf Nitrogen Content (LNC), Leaf Water Content (LWC), Leaf Mass per Area (LMA), and Leaf Area Index (LAI), in forest ecosystems, even under drought conditions.
-
Niaz et al. (2025) BAMPP: A novel Bayesian network enhanced by average marginal posterior probabilities to identify critical ground truth meteorological stations for drought monitoring
This study introduces BAMPP, a novel Bayesian network enhanced by Average Marginal Posterior Probabilities, to identify critical meteorological stations for regional drought monitoring based on the Standardized Precipitation Index (SPI) at multiple timescales, demonstrating its effectiveness in Ankara, Türkiye. The method revealed distinct spatiotemporal patterns, with critical stations varying seasonally for short-term droughts but Beypazari consistently being key for medium- and long-term droughts.
-
Liang et al. (2025) Disentangling and integrating spatiotemporal features: Deep learning-based downscaling of groundwater storage anomalies from GRACE and GRACE-FO satellites
This study developed a deep learning downscaling framework to enhance GRACE/GRACE-FO derived Groundwater Storage Anomaly (GWSA) data from 0.5° to 0.1° resolution in Xinjiang, China, finding the Geographically and Temporally Weighted Neural Network Regression (GTNNWR) model most effective and revealing a significant groundwater depletion rate of 5.03 ± 9.42 mm/year from 2002 to 2023.
-
Wang et al. (2025) Evaluation of Aircraft Cloud Seeding for Ecological Restoration in the Shiyang River Basin Using Remote Sensing
This study quantitatively evaluates the effectiveness of aircraft-based cloud seeding in the Shiyang River Basin, China, demonstrating a significant increase in precipitation and subsequent marked ecological restoration through enhanced vegetation coverage.
-
Nair et al. (2025) On the effects of climate downscaling for projecting hydrologic response of catchments in High Mountain Asia
This study investigates the impact of climate downscaling techniques on hydrological projections for glacierized catchments in High Mountain Asia (HMA). It finds that downscaling significantly reduces biases from original Global Climate Models (GCMs) and provides more realistic estimates of future water availability, streamflow components, and flood risks, highlighting the importance of downscaling for regional climate impact assessments.
-
Skroufouta et al. (2025) Rainfall Disaggregation in Data-Scarce Regions Using the Random Bartlett-Lewis Rectangular Pulse Model
This study evaluates the Random Bartlett-Lewis Rectangular Pulse Model (RBLRPM) for rainfall disaggregation in data-scarce Mediterranean regions, comparing it against a machine learning benchmark and assessing pulse intensity distributions and parameter uncertainty. It finds that RBLRPM effectively reproduces essential rainfall properties, with the Gamma distribution generally outperforming the Exponential, offering a robust stochastic approach for hydrological applications.
-
Callahan et al. (2025) Author Correction: Increasing risk of mass human heat mortality if historical weather patterns recur
This paper quantifies the increasing risk of mass human heat mortality under recurring historical weather patterns, demonstrating high predictive accuracy through statistical modeling. This specific document is an author correction updating key statistical values to ensure accuracy.
-
Meskelu et al. (2025) Assessment of climate change impacts on crop and irrigation water demand in the Awash River basin of Ethiopia using CMIP6 models
This study assesses climate change impacts on crop and irrigation water demand in the Awash River Basin using bias-corrected CMIP6 models, projecting increased water demands for most crops, except wheat, which experienced reductions, indicating growing pressure on water resources.
-
Cui et al. (2025) Energy flux, evapotranspiration, and crop coefficients of drip-irrigated kiwifruit and citrus orchards in Southwest China
This study investigated energy flux, evapotranspiration (ET), and crop coefficients (Kc, Kcb) in drip-irrigated kiwifruit and citrus orchards in Southwest China using eddy covariance and sap flow measurements, providing locally calibrated coefficients and insights into their biophysical regulation for improved irrigation management.
-
Schmidt et al. (2025) Effectiveness of Irrigation Tanks for Enhancing Groundwater Recharge
This study evaluates the effectiveness of traditional Indian irrigation tanks in facilitating groundwater recharge using numerical modeling and satellite data, confirming their significant contribution, especially in cascade systems, and identifying key influencing factors for optimizing their dual function.
-
Tu (2025) Poyang Lake Basin Meteorological Data Set（Original data）
This dataset provides compiled meteorological data, including average temperature, potential evapotranspiration, and soil moisture, for the Poyang Lake Basin.
-
Galiana et al. (2025) AquaINFRA Marine Modeling System
This paper introduces the AquaINFRA Marine Modeling System, an integrated workflow built around the MIT General Circulation Model (MITgcm) for marine simulations. It provides a complete chain of tools for data preprocessing, model execution, and post-processing, specifically tailored for the northwestern Mediterranean Sea and designed for interoperability within the Galaxy platform.
-
Choi et al. (2025) Probabilistic Deep Learning Framework for Greenhouse Microclimate Prediction with Time-Varying Uncertainty and Covariance Analysis
This study developed a probabilistic deep learning framework to predict greenhouse microclimate variables with time-varying uncertainty and covariance analysis. The framework, based on a 1D CNN, demonstrated comparable predictive accuracy to deterministic models while providing explainable uncertainty interpretation and robust decision support for greenhouse operators.
-
Zeng et al. (2025) A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA‐2 Reanalysis Meteorological Data
This study develops a deep machine learning (DML) physical parameterization for volumetric cloud fraction (VCF) using satellite lidar-radar measurements and reanalysis data. The DML model, particularly an LSTM network, effectively captures cloud physical processes, outperforming MERRA-2 reanalysis in representing various cloud types and improving VCF histograms across different spatial and temporal scales.
-
Cao et al. (2025) Study on the characteristics of rainfall variation in the Huaihe River Basin based on multi-method analysis
This study comprehensively analyzes the spatiotemporal evolution of precipitation in the Huaihe River Basin from 1923 to 2023 using multiple statistical methods, revealing an overall upward trend, distinct spatial patterns, significant periodic fluctuations, abrupt change points, and widespread multifractal characteristics.
-
Woldemeskel et al. (2025) Assessment of JULES Land Surface Model Coupled With CaMa ‐Flood for an Operational Streamflow Forecasting Across Australia
This study implemented the Catchment-based Macro-scale Floodplain (CaMa-Flood) model across Australia and evaluated its performance when coupled with various land surface models (JULES, AWRA-L) and reanalysis datasets (BARRA-R2, ERA5-Land) for operational streamflow forecasting. It found that offline JULES and AWRA-L performed well but with regional biases, while reanalysis products largely underestimated runoff, and all models struggled in ephemeral catchments.
-
Zou et al. (2025) Quantifying drivers of lake dynamics in Xinjiang: Evaluation of water storage and climatic sensitivity from 1990 to 2022
This study quantified lake surface area (LSA) and water storage changes (LWSC) for 324 lakes larger than 1 km² in Xinjiang from 1990 to 2022 using remote sensing and a lake water balance model. It revealed a significant expansion in both LSA and LWSC, primarily driven by groundwater recharge and surface runoff, with lake surface evaporation acting as the main water loss.
-
Zhang et al. (2025) Precession-induced millennial climate cycles in greenhouse Cretaceous
This study uses centennial-resolution geological records from the early Campanian greenhouse to demonstrate that precession-induced insolation forcing directly and indirectly stimulates pronounced millennial (~1–6 kyr) wet-dry climate cycles, with ~4–5-kyr cycles showing amplitude modulation by eccentricity consistent with theoretical equatorial insolation.
-
Jayasuriya et al. (2025) The HYDRUS model for soil and water management: A brief review of capabilities, trends, and future directions
This paper provides a comprehensive review of the HYDRUS software suite, utilizing a bibliometric analysis of 3154 articles (1993–2024) to quantify its evolution, identify key applications, and synthesize persistent challenges and future research directions in vadose zone modeling.
-
Wei et al. (2025) Future projections of China runoff changes based on CMIP6 and deep learning
This study projects future runoff changes across mainland China at 185 hydrological stations under CMIP6 Shared Socioeconomic Pathway scenarios using deep learning models (LSTM-SA, GRU-SA) with DL-downscaled climate inputs, revealing overall runoff increases, particularly in central transitional and southern humid regions, with pronounced summer increases and winter declines.
-
Nelli et al. (2025) Drivers and Trends of Summertime Convection Over the Southeastern Arabian Peninsula
This study examines summertime convection in the southeastern Arabian Peninsula using observations, reanalysis, and climate projections, revealing a significant increase in convective events driven by regional warming and boundary layer instability, with projections indicating a continued positive trend.
-
Dowdy et al. (2025) Compound weather systems of cyclones, fronts and thunderstorms in global reanalysis
This study globally examines the climatology and long-term trends of compound weather systems (cyclones, fronts, thunderstorms) using environmental diagnostics applied to global reanalysis data from 1979 to 2020, revealing distinct spatial patterns and increasing thunderstorm-related systems in certain regions.
-
Schüller et al. (2025) Quantifying coupling errors in atmosphere-ocean-sea ice models: A study of iterative and non-iterative approaches in the EC-Earth AOSCM
This study quantifies numerical coupling errors in atmosphere-ocean-sea ice models using iterative Schwarz waveform relaxation (SWR) methods, revealing that standard non-iterative coupling introduces substantial errors in atmospheric and sea ice surface temperatures, often due to discontinuous physics parameterizations.
-
Wen et al. (2025) Connections and causes of inter-model spread in boreal summer precipitation across monsoon regions in AMIP6 simulations
This study investigates the interconnections and underlying causes of inter-model variability in boreal summer precipitation across global monsoon regions using historical AMIP6 simulations, identifying the Western North Pacific (WNP) as a key driver of these inter-regional connections. It reveals that WNP precipitation deviations modulate other monsoon systems through atmospheric dynamic processes, including Walker and Hadley circulations and various wave trains.
-
Hernández-Macià et al. (2025) BEC SMOS Sea Ice Thickness [Dataset]
This paper presents the BEC SMOS Sea Ice Thickness dataset, which provides sea ice thickness derived from SMOS L-band radiometry using a hybrid retrieval approach combining a physical emission model with a machine learning algorithm, primarily for thin ice up to 1 meter.
-
Xu et al. (2025) A Generalized New Method for Anomalous Phased Array Radar Echo Image Restoration Based on Generative Adversarial Network
This paper proposes a novel deep learning model, GCD, for restoring X-band phased array radar echo images, effectively addressing various data quality issues like echo voids and radial obstructions. The GCD model significantly improves restoration quality, particularly for strong echoes, and drastically reduces processing time compared to traditional methods.
-
Rapella (2025) Modélisation et évaluation de la solution agrivoltaïque au nexus climat-eau-énergie-alimentation dans le contexte du changement climatique dans la région euro-méditerranéenne
This study integrates a photovoltaic module into the ORCHIDEE land surface model to assess the regional impact of agrivoltaics across the Euro-Mediterranean. The findings reveal that agrivoltaics significantly improve crop yields and resource efficiency in arid southern regions like the Iberian Peninsula, while providing limited or negative impacts in wetter northern regions like the Netherlands.
-
Song et al. (2025) Downstream Amplification of Rossby Waves in Summertime Heavy Precipitation Events Over the Korean Peninsula
This study investigates long-lasting summertime heavy precipitation events (HPEs) over South Korea linked to quasi-stationary atmospheric rivers (QSARs), finding that westward-moving Pacific QSARs lead to HPEs preceded by downstream amplification of finite-amplitude local wave activity (LWA), suggesting LWA's utility for forecasting.
-
Gabriel et al. (2025) Surface nuclear magnetic resonance for studying an englacial channel on Rhonegletscher (Switzerland): Possibilities and limitations in a high-noise environment
This study evaluates the feasibility of Surface Nuclear Magnetic Resonance (SNMR) for detecting and characterizing englacial liquid water within Rhonegletscher, Switzerland, successfully identifying a thin aquifer near the bedrock at depths of 44 to 60 meters, surrounded by temperate ice with 0.3% to 0.75% liquid water content.
-
Amini et al. (2025) Participatory evaluation of an irrigation decision support system for water-saving and productivity gains in Lake Urmia Basin
This study evaluated a participatory irrigation decision support system (DSS) in the Lake Urmia Basin, demonstrating significant agricultural water savings (41% for drip, 14% for sprinkler) and improved water productivity across various irrigation systems under actual farm conditions.
-
Peng et al. (2025) Analysis of Spatiotemporal Changes in NDVI-Derived Vegetation Index and Its Influencing Factors in Kunming City (2000 to 2020)
This study analyzed the spatiotemporal changes and driving factors of vegetation cover in Kunming City from 2000 to 2020 using MODIS NDVI and climate/socioeconomic data, finding an overall increase in vegetation cover primarily influenced by precipitation, with urbanized areas showing lower vegetation.
-
Dong et al. (2025) The effect of precipitation-induced advective heat on permafrost in the Tibetan Plateau
This study quantifies the precipitation-induced advective heat flux (EPre) and infiltration dynamics in the Tibetan Plateau's permafrost using a modified CLM5.0 model and observational data, revealing a predominant net cooling effect regionally but significant warming potential during deep infiltration events.
-
Wickham et al. (2025) The Estimation of Evapotranspiration Rates from Urban Green Infrastructure Using the Three-Temperatures Method
This study evaluated the Three-Temperatures (3T) method for estimating evapotranspiration (ET) from urban green infrastructure using a plastic imitation surface, finding that while 3T-ET estimates tracked reference ET well, they consistently overestimated values and were limited to mid-morning to late afternoon due to temperature convergence issues.
-
Wang et al. (2025) Spatiotemporal evolution characteristics of multi-type drought propagation processes in the Yellow River Basin, China
This study systematically investigates the spatiotemporal characteristics and propagation mechanisms of meteorological drought to hydrological, agricultural, and ecological droughts in the Yellow River Basin (YRB) from 1982 to 2018, revealing distinct propagation patterns and regional disparities.
-
Zhu et al. (2025) A Multi-Sensor Fusion Approach for the Assessment of Water Stress in Woody Plants
This study developed an indoor multi-sensor phenotyping platform and a novel hybrid machine learning model to accurately and early diagnose plant water stress, achieving high classification accuracy for *Perilla frutescens*.
-
Zhang et al. (2025) A novel spatiotemporal transformer network with multivariate fusion for short-term precipitation forecasting
This study proposes ST-MFTransNet, a novel spatiotemporal transformer network with multivariate fusion, to improve short-term precipitation forecasting by integrating diverse meteorological variables. The model significantly outperforms existing deep learning methods, achieving notable enhancements in detection probability and critical success index for 12-hour and 24-hour accumulated precipitation forecasts.
-
Kifle et al. (2025) Assessing the effect of climate change on extreme hydrological events in the Awash River Basin using QSWAT + and CMIP6 projections
This study assessed the impact of climate change on hydrological extremes in the Awash River Basin using QSWAT+ and CMIP6 projections, revealing projected decreases in annual rainfall and streamflow (up to 31.4%) alongside significant temperature increases (up to 3.21 °C) by the 2050s. These changes highlight the urgent need for adaptive water resource management strategies in the region.
-
Venäläinen et al. (2025) Updated monthly and new daily bias correction for assimilation-based passive microwave SWE retrieval
This study updates and expands the bias correction for assimilation-based passive microwave Snow Water Equivalent (SWE) retrieval, introducing a new daily bias-corrected Snow CCI v3.1 product. The updated methodology, utilizing expanded snow course data, significantly improves Northern Hemisphere climatological snow mass estimates, bringing them into consistency with reanalysis products, especially for high SWE values and previously problematic months.
-
Piermattei et al. (2025) Editorial: Exploring wood structure and tree-ring dynamics in ecological research
This editorial synthesizes eight contributions exploring wood structure and tree-ring dynamics, demonstrating the ecological breadth and methodological depth of quantitative wood anatomy in understanding plant responses to environmental change. It highlights wood as a climate-sensitive archive and showcases advancements in analytical tools and protocols.
-
Retter et al. (2025) Revisiting the Convective Like Boundary Layer Assumption in the Urban Option of AERMOD
This study re-examines the AERMOD urban option's convective-like boundary layer assumption, which significantly overestimates nighttime sensible heat flux, and proposes replacing its population-based temperature difference parameterization with remotely sensed land surface temperature data to provide more realistic city-specific advection corrections. The proposed methodology yields sensible heat flux values consistent with observations and reveals that AERMOD's original urban option inadvertently addressed a low-level jet rather than an urban heat island effect.
-
Dong et al. (2025) Warm and wet anomalies persist across the pan-Arctic after carbon dioxide removal
This study investigates Pan-Arctic climate responses to carbon dioxide removal (CDR) using CMIP6 models, revealing significant hysteresis and a persistent warming of approximately 1.5 °C and increased precipitation of about 0.1 mm d⁻¹ even after carbon dioxide concentrations return to pre-industrial levels.
-
Goudiaby et al. (2025) Hydrological evaluation of gridded rainfall products for streamflow simulation in West Africa
This study evaluates the hydrological performance of 23 gridded precipitation products for streamflow simulation in eight West African river basins using GR2M and GR4J models, finding that multi-source products like IMERGDF, MSWEP, GPCP, and TAMSAT are the most reliable alternatives in data-scarce regions.
-
Hwang et al. (2025) Large‐Scale Moisture Sources and Delivery Pathways Contributing to Winter Floods in the US
This study updates previous research on US winter flood hydroclimatology by identifying large-scale moisture delivery pathways and quantifying source contributions across the Conterminous United States (CONUS). It finds that oceanic sources dominate coastal floods, while land is a key contributor to Midwest floods, influenced by moisture dynamics over mountain ranges.
-
Otryakhin et al. (2025) Comparison of simulations from a state-of-the-art dynamic global vegetation model (LPJ-GUESS) driven by low- and high-resolution climate data
This study explores the differences in vegetation outcomes from the dynamic global vegetation model LPJ-GUESS when driven by high- (0.05°) versus low-resolution (0.5°) climate data across Europe. It reveals significant systematic discrepancies, particularly in mountainous regions where high-resolution simulations show substantially smaller carbon pools (e.g., total carbon 37 %–39 % smaller), and quantifies the impact of under-represented orographic climate variability and shoreline features on regional predictions.
-
Hakami-Kermani et al. (2025) Evapotranspiration and water requirement changes of main crops under climate change conditions in a semi-arid region
This study projects future changes in evapotranspiration and water requirements for major crops (barley, wheat, alfalfa, cotton) in Iran's semi-arid Garmsar Plain under various climate change scenarios (2025-2100), finding a general increase in temperatures, reference evapotranspiration, and crop water demands, particularly for alfalfa and cotton.
-
Zhao et al. (2025) A Hybrid Modeling Approach for Improved Simulation of Thermal‐Hydrological Dynamics in Active Layer on the Qinghai‐Tibet Plateau
This study presents a novel hybrid modeling approach combining the SHAW model with random forest-corrected Noah LSM simulations to accurately model active layer dynamics on the Qinghai-Tibet Plateau, significantly outperforming existing models in simulating soil temperature and moisture.
-
Su et al. (2025) Simulation of the climatic conditions required for the existence of ice sheet on the Tibetan Plateau
This study combines numerical experiments using atmospheric and ice sheet models to establish the temperature-precipitation constraints required for widespread glaciation across the Tibetan Plateau. The findings indicate that a contiguous ice sheet during the Last Glacial Maximum was unlikely, as it would have demanded an average regional cooling of at least 10 °C, far exceeding reconstructed proxy data.
-
Alsanoosy et al. (2025) Predicting plant stress using SAM-L: novel self-adaptive-meta learner with XAI based on soil moisture and chlorophyll analysis
This study proposed a novel framework integrating Sparse Additive Models with Learning (SAM-L) and Explainable Artificial Intelligence (XAI) to predict plant stress using soil moisture and chlorophyll content. The framework achieved an overall accuracy of 89.2% on a multi-class classification task, providing adaptive and interpretable stress predictions for precision agriculture.
-
Lv et al. (2025) Validation of Soil Temperature Sensing Depth Estimates Using High-Temporal Resolution Data from NEON and SMAP Missions
This study validates the τ-z model's ability to estimate soil temperature sensing depth (zTeff) using high-temporal resolution data from the NEON and SMAP missions. It demonstrates the model's high accuracy, especially under monotonic soil conditions, enhancing confidence in passive microwave remote sensing for soil moisture and temperature retrieval across diverse ecosystems.
-
SHALOO et al. (2025) Modeling daily reference evapotranspiration and evaluating uncertainty analysis in machine learning under limited meteorological data conditions for Northern India
This study evaluated the performance of Random Forest, Artificial Neural Networks, and Long Short-Term Memory models for daily reference evapotranspiration (ET0) estimation under varying meteorological data availability in Northern India, finding LSTM to be the most reliable model, especially in data-scarce conditions.
-
Ursu et al. (2025) Hail Damage Detection: Integrating Sentinel-2 Images with Weather Radar Hail Kinetic Energy
This study integrates weather radar-derived Hail Kinetic Energy (HKE) with Sentinel-2 Normalized Difference Vegetation Index (NDVI) differencing to accurately assess and map short-term vegetation damage from hailstorms in northeastern Romania. The research demonstrates a strong spatial correspondence between high HKE values and significant NDVI reductions, highlighting that damage detection is most effective shortly after the event and varies by land use type.
-
Farahnak et al. (2025) Overland Flow Generation Under Clear-Cut, 40% Thinning, and Control Conditions in a Japanese Cypress Plantation
This study investigated overland flow and soil water content in Japanese cypress plantations under clear-cutting, thinning, and control conditions over one year, revealing that increased ground cover in clear-cut plots significantly reduced overland flow and altered its generation mechanism compared to control plots.
-
Detre et al. (2025) Sentinel‐1 SAR Estimates of Snowmelt Onset Coincide With SNOTEL Soil Moisture Pulses Across the Western United States
This study investigated snowpack conditions around Sentinel-1 SAR-derived snowmelt runoff onset and evaluated these estimates against SNOTEL soil moisture pulses, finding that SAR-derived onset corresponds to increasing liquid water content but can differ temporally from in-situ melt signals due to local climatological conditions.
-
Yu et al. (2025) Spatio‐Temporal Network With Self‐Attention Mechanism for Improved ENSO Prediction
This study proposes ACTNet, a novel deep learning model combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a self-attention mechanism, to improve long-lead prediction of the Niño 3.4 index and classify different ENSO types.
-
Abdelrahim et al. (2025) A Self‐Supervised Seasonal Anomaly Embedding ViT for Label‐Free Drought Mapping in the Horn of Africa
[Information not extractable due to corrupted paper text.]
-
Frei et al. (2025) Characterizing and correcting for global climate models’ biases in multiyear extreme precipitation scenarios
This study introduces the Multiyear Precipitation Variability Bias Correction (MPVBC) method to address the systematic underestimation of multiyear extreme precipitation variability by Global Climate Models (GCMs). The method successfully corrects GCMs to match observed variability, leading to significantly more realistic future extreme drought and pluvial scenarios for water supply system resiliency assessments.
-
Ling et al. (2025) Global warming weakens Maritime Continent barrier effect on MJO propagation
This study projects that the probability of Madden-Julian Oscillation (MJO) events successfully propagating through the Maritime Continent will increase by 29.3% by the late 21st century under global warming, driven by a zonal asymmetry in moistening associated with the expanding Indo-Pacific warm pool.
-
Meydani et al. (2025) Scenario-driven Decision Support System for assessing water balance to support agricultural and ecosystem sustainability
This study proposes a novel scenario-driven decision support system (DSS) that integrates hydrological modeling and multi-objective optimization to evaluate complex water allocation strategies in arid regions. Applied to Iran's Lake Urmia basin, the DSS identified optimal strategies capable of increasing environmental water supply by 31% while simultaneously boosting agricultural profit by 26%.
-
Ma et al. (2025) Impacts of Triple La Niña Events on Forest Gross Primary Productivity in China from 2020 to 2022
This study investigated the impact of the 2020-2022 triple La Niña event on China's forest Gross Primary Productivity (GPP), revealing an initial inhibition followed by gradual recovery, with distinct regional and seasonal variations driven by climatic factors.
-
Zhang et al. (2025) Physics-Informed Deep Learning for 3D Wind Field Retrieval of Open-Ocean Typhoons
This study proposes a physics-informed deep learning framework for high-resolution three-dimensional (3D) typhoon wind field reconstruction over the open ocean using multi-channel Himawari-8/9 satellite data, achieving improved accuracy and physical consistency by embedding the continuity equation.
-
Yang et al. (2025) Evaluating Agricultural Drought in the Haihe River Basin Using an Improved Crop Moisture Index
This study developed an improved Crop Moisture Index (CMI) by incorporating crop coefficients, water stress coefficients, and an auto-irrigation threshold method into its soil water balance equation to more accurately assess agricultural drought in large irrigated regions. Applied to the Haihe River Basin, the improved CMI significantly enhanced soil moisture simulation accuracy and drought identification compared to the original CMI, providing a more realistic representation of drought under intensive human management.
-
Yang et al. (2025) Modulation of Warm‐Season Heavy Precipitation Microphysics by Synoptic Patterns in the Yangtze‐Huaihe River Basin: Insights From GPM‐DPR and Principal Component Classification
This study investigates the microphysical characteristics of warm-season heavy precipitation in the Yangtze–Huaihe River Basin across six identified synoptic patterns, revealing that while warm-rain processes generally dominate, monsoon-related patterns lead to high concentrations of small-to-medium raindrops, whereas convective patterns enhance ice-phase processes producing larger, less concentrated raindrops.
-
Aravinth et al. (2025) A Deep Learning Model for Integrating Landsat-8 and Sentinel 2 Satellite Images to Improve the Spatiotemporal Fusion Network for Drought Monitoring
This paper proposes a deep learning model to integrate Landsat-8 and Sentinel-2 satellite images, aiming to improve spatiotemporal fusion networks for enhanced drought monitoring.
-
Gao et al. (2025) Dynamic difference between surging and normal glaciers in the context of climate change: Insights from multi-source remote sensing
This study investigates the contrasting dynamic responses of surge-type and normal glaciers to climate change in the Yunfeng Peak region of High Mountain Asia using multi-source remote sensing, revealing that glacier size and thermally-induced basal processes primarily drive these differential behaviors.
-
Shah et al. (2025) Global patterns of reservoir fullness and fluctuations during droughts
This study assesses the storage conditions and fluctuations of 6634 global reservoirs during major river basin-scale droughts from 1999 to 2018, revealing significant regional, functional, and socio-economic disparities in reservoir resilience and a strong link to large-scale climate variability.
-
Maia et al. (2025) Flood Detection in Optical Systems: A Novel Approach to Overcome Cloud Cover With DEM Data
Information not available from the provided text.
-
Lanteri et al. (2025) Seismic constraints on glacier density
This study combines densely sampled fiber-optic sensing data with Hamiltonian Monte Carlo sampling to directly constrain firn density to approximately 100 meters depth, revealing that commonly used seismic wave speed-to-density scaling relations introduce biases of about ±10 % and fail to capture detailed density structures.
-
Weng et al. (2025) Spatially coherent changes in Chinese annual flood peaks revealed by a consensus-based machine learning framework for regionalization
This study develops a consensus-based machine learning framework to identify homogeneous flood regions across China, revealing predominant trends of decreasing annual flood peak magnitudes and delayed occurrences in most regions, primarily driven by climate factors.
-
Tügel et al. (2025) Extreme precipitation and flooding in Berlin under climate change and effects of selected grey and blue-green measures
This study quantifies the projected increase in extreme precipitation in Berlin under climate change (RCP8.5) and its impact on urban flooding, demonstrating that a 46 % increase in 1 h 100-year rainfall leads to a 51 % increase in maximum water depth. It further assesses the effectiveness of grey infrastructure, infiltration, and retention roofs in mitigating these impacts, highlighting the non-linear relationship between rainfall and flooding and the need for combined adaptation strategies.
-
Song et al. (2025) Analysis study on the change of orchard area in Alar reclamation in the past 30 years
This study analyzed the spatiotemporal changes of orchard area in the Alar Reclamation Area, Xinjiang, from 1990 to 2019 using Landsat imagery and SVM classification. It revealed a significant continuous increase in orchard area from 417.57 km² to 1091.76 km², primarily converted from unused land, which intensifies water-use conflicts and soil salinization.
-
Zhang et al. (2025) Evaporation and transpiration processes and changes in their proportional relationships in cotton fields under varying degrees of film biodegradation
This study quantified the dynamic changes in evaporation and transpiration in cotton fields under varying biodegradable film degradation and irrigation depths, finding that an optimized irrigation depth of 495 mm can mitigate water stress from film degradation and increase cotton yield by 5.50–14.55%.
-
Rao et al. (2025) IOT-Based Smart Precision Farming: A Comprehensive Review of Enabling Technologies
This paper provides a comprehensive review of the core technologies and architectural frameworks essential for implementing robust Internet of Things (IoT)-based smart precision farming systems, aiming to enhance crop productivity, optimize resource usage, and promote sustainable agricultural practices.
-
Sharples et al. (2025) Australia’s changing hydroclimate extremes
This study examines past and future changes in Australia's hydroclimate extremes, revealing current drying trends in southern and central regions, an increasing link between natural disasters and compound events, and projections of more frequent 'hot and dry' and 'wet and windy' extremes across all regions, leading to heightened risks of droughts, fires, and floods.
-
Huimin et al. (2025) The response of meteorological drought to extreme climate in the water-receiving area of the Tao river diversion project in China
This study analyzed the spatiotemporal variations, interrelationships, and driving factors of meteorological drought and extreme climate events in the water-receiving area of the Tao River Diversion Project, China. It found a persistent drying trend since 1988, primarily driven by annual total precipitation, cold days, and summer days, with most extreme climate factors exhibiting complex nonlinear influences and critical thresholds on drought.
-
Chen et al. (2025) Spatiotemporal patterns of coexisting plant water uptake in ecologically vulnerable areas along the southeast margin of the Qinghai-Tibet Plateau
This study investigated plant water uptake patterns and the influence of belowground and aboveground stresses, including human activities like tunneling, across an elevation gradient in the Qinghai-Tibet Plateau. It revealed varied water uptake strategies among plant growth forms and highlighted the impact of tunneling on hydrological changes and plant water sources.
-
Liu et al. (2025) Rapid Shrinking of the Warming Hole Over the United States in ERA5 and SPEAR
This study reveals that the U.S. "warming hole," a summer cooling anomaly, has significantly shrunk and weakened in recent decades, with accelerated contraction during 2021–2024, driven by changes in atmospheric circulation and precipitation. It projects the warming hole's disappearance around 2050 under strong external forcing, emphasizing the role of both external forcing and internal variability.
-
Sakar et al. (2025) A physics-informed neural network workflow for forward and inverse modeling of unsaturated flow and root water uptake from hydrogeophysical data
This study introduces a Physics-Informed Neural Network (PINN) to infer the spatiotemporal dynamics of root water uptake (RWU) directly from hydrogeophysical data. The PINN successfully reconstructs high-resolution soil saturation fields and predicts unknown RWU distributions, with significant accuracy improvements when constrained by total daily transpiration.
-
Holwerda et al. (2025) A comparison of drought indices for crop yield loss detection: The role of green-up onset alignment and spatial resolution
This study compares six drought indices for detecting rainfed crop yield loss in the Central Plateau of Mexico, finding that aligning indices to satellite-derived green-up onset improves performance, with ALEXI-based Evaporative Stress Index and MODIS NDVI anomaly showing the strongest relationships with yield anomalies.
-
Boschat et al. (2025) Covariability in Large-Scale Climate Modes: Implications for Australian Precipitation Variability
This study applies principal component analysis to investigate the covariability of El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and Southern Annular Mode (SAM), quantifying their individual and collective impact on Australian seasonal precipitation, finding they explain up to 40%–45% of precipitation variance in parts of eastern Australia during spring.
-
Escudier (2025) Study of filter and predictor error in gaussian pairwise Markov models
[Information not extractable from the provided text.]
-
Shi et al. (2025) An Accelerated Discrete Ordinate Method (ADOM) Developed for Scalar Radiative Transfer by Merging Adjacent Clear‐Sky Atmospheric Layers: Forward and Jacobians Derivation
This study proposes an Accelerated Discrete Ordinate Method (ADOM) to significantly improve the computational efficiency of multi-layer radiative transfer simulations by applying the Discrete Ordinate Method only in scattering layers and merging adjacent clear-sky layers. ADOM maintains high accuracy, is applicable from visible to microwave spectra, and includes tangent linear and adjoint modules for accurate Jacobian computation in satellite radiance assimilation.
-
Chaaou et al. (2025) Mapping soil salinity using machine learning and remote sensing data in semi-arid croplands
This study evaluated machine learning algorithms combined with satellite-derived predictors for soil salinity mapping in semi-arid croplands of Morocco. The K-Nearest Neighbors (KNN) model achieved the highest accuracy (R² = 0.75; RMSE = 0.61 dS/m), demonstrating the reliability of this approach for monitoring soil salinity.
-
Gia et al. (2025) ThoR: A Motion-Dependent Physics-Informed Deep Learning Framework with Constraint-Centric Theory of Functional Connections for Rainfall Nowcasting
This paper introduces ThoR, a motion-dependent physics-informed deep learning framework with a Constraint-Centric Theory of Functional Connections (TFC) for rainfall nowcasting. ThoR integrates attention-centric spatiotemporal modeling with explicit physical constraints (advection-diffusion equation) to achieve superior deterministic precipitation forecasts, particularly for extreme weather events and longer lead times, outperforming existing methods on real-world radar datasets.
-
Alford et al. (2025) Dual‐Polarization Phased Array Radar Observations of Quasi‐Linear Convective System Tornadic Mesovortices
This study utilized a dual-polarization phased array radar to investigate three tornadic mesovortices within a Quasi-Linear Convective System, revealing pre-tornadogenesis downdrafts, enhanced gust front convergence, and associated dual-polarization signatures indicative of evaporation and precipitation.
-
Kroll et al. (2025) Parameterization adaption needed to unlock the benefits of increased resolution for the ITCZ in ICON
This study investigates the persistent double Inter-Tropical Convergence Zone (ITCZ) bias in climate models across a wide range of resolutions, demonstrating its persistence even with explicitly described deep convection. It identifies insufficient moisture transport from the subtropics to the inner tropics as a key driver of this bias, rather than solely resolution or deep-convective parameterizations.
-
Yang et al. (2025) Impact of Localized High Temperatures on Extreme Rainfall: Insights From the July 2023 Heavy Rainfall Event in the Beijing–Tianjin–Hebei Region Over the Taihang Mountains
This study demonstrates how antecedent high temperatures intensified moisture transport and convective activity, leading to a 22.2% increase in total rainfall during an extreme event in the Beijing–Tianjin–Hebei region, providing a new mechanism for such events.
-
Naud et al. (2025) A Map of Dominant Cloud‐Controlling Factors for Cloud Fraction and Total Liquid Water Path can Identify Marine Low‐Level Cloud Types
This study analyzes daily Moderate Resolution Imaging Spectroradiometer (MODIS) cloud fraction and multi-sensor total liquid water path (TLWP) over open oceans for a 5-year period to identify dominant cloud-controlling factors (DCCF). It presents the first global map of DCCF for TLWP and shows that pairing DCCF for cloud fraction and TLWP reveals known low-level cloud type regimes, aiding in understanding cloud feedbacks.
-
Zhou et al. (2025) Remote sensing meta modal representation for missing modality land cover mapping: From EarthMiss dataset to MetaRS method
This paper introduces EarthMiss, a high-resolution multimodal dataset for land cover mapping with missing modalities, and proposes MetaRS, a meta-modal learning framework that disentangles features to significantly improve performance in such challenging scenarios.
-
Young et al. (2025) Tracking vegetation phenology across diverse biomes using Version 3.0 of the PhenoCam Dataset
This paper introduces PhenoCam Dataset Version 3.0, a significantly expanded and updated dataset for tracking vegetation phenology across diverse biomes, which now includes a camera-derived Normalized Difference Vegetation Index (cameraNDVI) and simplified data products. It demonstrates that while cameraNDVI offers a complementary measure of canopy structure, the Green Chromatic Coordinate (GCC) generally provides a less noisy signal for phenological tracking.
-
Unknown (2025) No Title
N/A
-
Lehr et al. (2025) Technical note: An illustrative introduction to the domain dependence of spatial principal component patterns
This technical note introduces the concept of Domain Dependence (DD) in S-mode Principal Component Analysis (PCA) to the hydrological community, demonstrating how spatial PC patterns can be determined by the spatial extent and arrangement of data rather than hydrological functioning, and provides methods to detect and diminish this effect.
-
Pikeroen (2025) Modèles simplifiés de climat ˸ approche thermodynamique et approche dynamique
Information not extractable from the provided text.
-
Zheng et al. (2025) Deconstructing the Effects of Climate and Phenology on Hydrological Processes: A Case Study From an Inland Basin in Central Asia
This study quantified the individual contributions of temperature, precipitation, and phenological shifts to evapotranspiration and streamflow in the Kashi Basin, Central Asia, using an eco-hydrological model. It found that phenological changes, particularly a longer growing season, had impacts on hydrological processes comparable to those of temperature and precipitation changes, emphasizing their critical role under warming conditions.
-
Seager et al. (2025) Mediterranean Drying by a Positive North Atlantic Oscillation Trend over the Last 65 Years Is an Extreme Outlier in the CMIP6 Multimodel Ensemble
The study investigates the causes of observed Mediterranean cool season precipitation decline since the 1950s, finding it to be dynamically driven by a positive North Atlantic Oscillation trend, which current CMIP6 climate models largely fail to reproduce, suggesting either extreme natural variability or a missing forced response.
-
Zhang et al. (2025) Development of UI-WRF-Chem (v1.0) for the MAIA satellite mission: case demonstration
This paper presents the development of the Unified Inputs (of initial and boundary conditions) for WRF-Chem (UI-WRF-Chem) framework to support the Multi-Angle Imager for Aerosols (MAIA) satellite mission. Major updates include improving dust size distribution in chemical boundary conditions, updating land surface properties using recent satellite data, and enhancing the representation of soil NOₓ emissions, with subsequent model improvements demonstrated over several MAIA target areas.
-
Ren et al. (2025) Global land desertification risk assessment
This study conducted a global assessment of desertification risk in 2020 using an enhanced MEDALUS-ESA model, revealing that 43.09 % of the global land area faces high or extreme desertification risk, primarily driven by climate quality.
-
Unknown (2025) Mesoscale moisture convergence drives stronger rainfall extremes
High-resolution global climate simulations reveal that mesoscale moisture convergence, rather than thermodynamic effects alone, is the primary driver of projected extreme rainfall intensification under warming, significantly improving the robustness of future rainfall projections.
-
Rombeek et al. (2025) Torrential rainfall in Valencia, Spain, recorded by personal weather stations preceding and during the 29 October 2024 floods
This study quantifies the spatial and temporal structure of the torrential rainfall event in Valencia, Spain, on 29 October 2024, using high-density personal weather station (PWS) data. It demonstrates the significant potential of PWSs for real-time rainfall monitoring and flood early warning systems, complementing traditional rain gauge networks.
-
Nystrom et al. (2025) A Hybrid Four‐Dimensional Variational Data Assimilation System for the Model for Prediction Across Scales (MPAS‐Atmosphere): Leveraging the Joint Effort for Data Assimilation Integration (JEDI)
This study presents and evaluates a global Four-Dimensional Ensemble Variational (4DEnVar) data assimilation system for the Atmospheric component of the Model for Prediction Across Scales (MPAS-A) using the Joint Effort for Data assimilation Integration (JEDI), demonstrating improved meteorological and precipitation forecasts, especially with Hybrid-4DEnVar and all-sky assimilation.
-
Razavi-Termeh et al. (2025) Spatially explicit and interpretable GeoAI models for understanding factors controlling groundwater availability
This study develops an advanced GeoAI approach, optimizing the CatBoost algorithm with the Fruit Fly Optimization Algorithm (FOA) and using SHAP for interpretability, to accurately predict groundwater-prone areas in semi-arid regions. The model significantly improves prediction accuracy and identifies key environmental factors influencing groundwater availability.
-
Marrocu et al. (2025) Integrating deficit irrigation, crop modelling and Water–Energy–Food nexus to enhance durum wheat resilience in Mediterranean climate conditions
This study evaluates the impact of deficit irrigation on durum wheat yield and quality in southern Sardinia over two cropping seasons and two soil types, integrating agronomic monitoring, remote sensing, and crop modeling within a Water–Energy–Food (WEF) nexus framework. It found that moderate deficit irrigation (50% of plant water requirement) significantly improved grain yield and was comparable to full irrigation in efficiency, offering a sustainable strategy for food security in drought-prone Mediterranean regions.
-
Kneib et al. (2025) Topographically-controlled contribution of avalanches to glacier mass balance in the 21st century
This study quantifies the global contribution of avalanches to glacier snow accumulation and projects its impact on glacier evolution throughout the 21st century. It finds that avalanches provide a net 2% to global glacier accumulation, significantly altering mass balance patterns and prolonging the persistence of small glaciers under future warming scenarios.
-
Zhang et al. (2025) Studying the impacts of land use types and soil textures on agricultural drought in Ziya river basin using SWDI
This study investigates the impacts of land use types and soil textures on agricultural drought (AD) in the semi-arid Ziya River Basin (ZRB) using the Soil Water Deficit Index (SWDI). It finds that Forest and Grassland exhibit higher drought resistance, while Cropland, Waters, and Urban Land are more susceptible to severe droughts, and Clay soil texture shows better drought resistance compared to Sand and Loam.
-
Gu et al. (2025) Dynamics and Attribution of Land Use Change in China's Oases Using Multi-source Data
This paper describes a comprehensive multi-source dataset compiled to analyze the dynamics and attribution of land use change within China's oases over a 24-year period.
-
Li et al. (2025) Study on the Estimation of Greenhouse Sensible Heat Flux Based on the Surface Renewal Method: Validation and Calculation Results
This study evaluated the applicability of the surface renewal (SR) method for estimating sensible heat flux (H) in a Venlo-type greenhouse with tomato plants. It found that the Chen method, which incorporates friction velocity, significantly outperformed the traditional Snyder method under various weather and operating conditions, providing a reliable and accurate tool for greenhouse heat flux estimation.
-
Giannakopoulos et al. (2025) Examining the Characteristics and Evolution of Wintertime Temperature Whiplash Events in the U.S. Southern Plains
This study defines and characterizes rapid extreme temperature swings, termed "temperature whiplashes," in the U.S. southern plains during winter, identifying specific atmospheric and stratospheric precursors that offer opportunities for improved subseasonal to seasonal predictions.
-
Finlon et al. (2025) Influence of Cloud Microphysical Properties on Airborne Lidar Measurements: Results From the IMPACTS Field Campaign
This study utilized airborne lidar and in-situ measurements during the IMPACTS campaign to characterize ice and supercooled liquid water clouds. It established distinct lidar signatures (backscatter, color ratio, depolarization) for ice-dominated regions compared to supercooled liquid water, linking them to particle size and morphology.
-
Wang et al. (2025) Multivariate RVA Assessment of Hydrological Alterations: Huangshui River, Xining
This paper proposes a Comprehensive Degree (CD) index, integrating the Range of Variability Approach (RVA) with four additional statistical dimensions, to enhance the assessment of hydrologic alteration. Applying it to the Huangshui River, the study found a significantly higher degree of alteration compared to conventional RVA, improving diagnostic precision and ecological interpretability.
-
La et al. (2025) Cloud‐Top Entrainment Instability in Marine Stratocumulus Clouds: Observational Evidence From Collocated Microphysical, Turbulence, and Radiation Measurements
This study uses helicopter-borne observations to investigate the mechanisms governing the descent of entrainment-affected parcels in marine stratocumulus clouds (MSC), focusing on the relative roles of cloud-top entrainment instability (CTEI) and longwave radiative cooling (RC). It finds that sufficiently strong CTEI can dominate RC in driving the descent of diluted parcels, clarifying how CTEI, RC, and entrainment interfacial layer (EIL) thickness jointly shape MSC structure.
-
Thornton et al. (2025) The Vertical Development of Fog in the Presence of Turbulent Mixing and Low Stratus Cloud Using Infra-Red Imagery During the SOFOG3D Campaign
This study utilized infra-red camera observations from the SOFOG3D experiment to analyze the dynamics of radiation fog, revealing that turbulent mixing at the fog top is common and significantly influences both the vertical development of existing fog and the formation of very-low-stratus clouds.
-
Tye et al. (2025) Evaluating the time of occurrence of selected global warming levels for CMIP5 and CMIP6 models
[A VERY CONCISE 1-2 sentence summary of the paper's core objective and main finding.]
-
Alsumaiei (2025) Quantifying Irrigation Water Demand through Optimized Daily Vapor Pressure Deficit Forecasting Using LSTM and Metaheuristic Algorithms
## Identification - **Journal:** Journal of Computing in Civil Engineering - **Year:** 2025...
-
Chu et al. (2025) Evaluation of Tropical Cyclone Characteristics in the SPEAR Large Ensemble Simulations
This study evaluates tropical cyclone (TC) activity in the SPEAR model, finding that it generally captures key TC characteristics and their modulation by natural climate variability, despite exhibiting regional biases in track density, landfall frequency, and precipitation.
-
Neinavaz et al. (2025) Evaluating Airborne Thermal Infrared Hyperspectral Data for Leaf Area Index Retrieval in Temperate Forests
This study evaluates the reliability of retrieving Leaf Area Index (LAI) using in situ and airborne thermal infrared (TIR) hyperspectral data, moving beyond controlled laboratory conditions. It found that Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN) models, particularly with the Scaled Conjugate Gradient algorithm, effectively predict LAI, identifying specific TIR wavebands that are robust across varying environmental conditions.
-
Tafesa et al. (2025) Assessing groundwater and climate susceptibility in Masgeredo-Bulal catchment, Ethiopia
This study assesses groundwater potential and climate change impacts in Southern Ethiopia's Masgeredo-Bulal catchment using GIS, remote sensing, and climate modeling, revealing that 51.6% of the catchment has good groundwater potential while future projections indicate increased temperature and decreased precipitation.
-
Tang et al. (2025) Baseline temperature variability shapes the geographical distribution of future hot extremes under anthropogenic warming
This study identifies baseline temperature variability as a key factor shaping the global distribution of future hot extremes under anthropogenic warming, demonstrating that over 80% of the global increase in hot extremes is anticorrelated with this variability, a relationship anchored by persistent land-atmosphere coupling over century timescales.
-
Ndulue et al. (2025) Machine learning-based estimation of daily reference evapotranspiration across agro-ecological zones in Nigeria: comparative analysis and model ranking
This study evaluated five machine learning models for daily reference evapotranspiration (ETo) estimation across six agro-ecological zones in Nigeria under various data availability scenarios, demonstrating their potential for reliable ETo estimation with minimal inputs, particularly the Bagging model, to enhance water resource management.
-
Liu et al. (2025) A Nonstationary Probabilistic Approach for Probable Maximum Precipitation Estimation Based on Global Climate Model Large Ensembles
This study proposes a novel approach integrating a stochastic rainfall generator (StormLab) with a nonstationary generalized extreme value (GEV) model to estimate probable maximum precipitation (PMP) and probable maximum flood (PMF) under varying climate conditions. The approach projects significant increases in PMP (15%–25%) and PMF (35%–36%) by 2100 in the upper Red River basin, highlighting the impact of climate change on extreme events.
-
Ali (2025) Remote Sensing and GIS Applications in Vineyard Zoning and Yield Prediction
This paper reviews current remote sensing technologies and vegetation indices utilized in viticulture, detailing their underlying principles, platforms, and diverse applications for vineyard management and decision-making.
-
Iskaliyeva et al. (2025) Hydro-Climatic and Multi-Temporal Remote Analysis of Glacier and Moraine Lake Changes in the Ile-Alatau Mountains (1955–2024), Northern Tien Shan
This study investigated multi-decadal glacier and lake dynamics (1955–2024) in the Ile-Alatau range, finding significant warming, substantial glacier retreat (47.4% reduction), and an increase in moraine-dammed lakes, highlighting climate's dominant control and increasing glacial lake outburst flood risks.
-
Qian et al. (2025) Optimized decadal prediction of summer precipitation over eastern China
This study evaluates and optimizes decadal prediction skills of summer precipitation over eastern China using CMIP6 DCPP models and machine learning, identifying North Atlantic Subtropical and Subpolar Gyre sea surface temperatures as key predictability sources that significantly enhance forecast accuracy.
-
Chua et al. (2025) Impact of Future Methane Emission Trajectories on Atmospheric Composition and Climate in a Future Hydrogen Economy
This study uses an atmospheric chemistry-climate model to investigate the interactions between hypothetical future hydrogen (H2) and methane (CH4) emission trajectories, finding that H2's climate impact is largely independent of background CH4 levels, and that CH4 mitigation is crucial to maximize the climate and air quality benefits of a future H2 economy.
-
Wu et al. (2025) Multi-spatial scale assessment and multi-dataset fusion of global terrestrial evapotranspiration datasets
This study comprehensively evaluates 30 global terrestrial evapotranspiration (ET) datasets across multiple spatial scales and fuses them using a Bayesian model averaging (BMA) method to create a new, robust, and long-term ET dataset (BMA-ET) for 1980–2020, demonstrating improved accuracy and capturing a global increasing ET trend.
-
Nurmalitasari et al. (2025) Artificial intelligence-driven solar smart irrigation for sustainable agriculture: Trends, challenges, and SDG implications – A systematic review
This systematic literature review synthesizes 29 articles to examine the technological innovations, efficiency outcomes, adoption barriers, and sustainability impacts of AI-driven, solar-powered smart irrigation systems. It finds that these systems significantly improve water-use efficiency (up to 70 %), increase crop yields (15–40 %), and reduce energy consumption and greenhouse gas emissions, directly contributing to several Sustainable Development Goals despite persistent implementation challenges.
-
Lynne et al. (2025) Climatology, trends, and variability of cold-season northern hemisphere extratropical cyclone characteristics during 1950–2023 in ERA5
This study analyzes the climatology, trends, and variability of cold-season Northern Hemisphere extratropical cyclones (ETCs) from 1950 to 2023 using an impact-based tracking algorithm on ERA5 reanalysis data, revealing significant regional shifts and intensity changes in storm tracks, particularly a westward shift in the North Pacific and a poleward shift and strengthening in the North Atlantic.
-
Li et al. (2025) A New Approach to Sensible Heat Flux via CFD-Surface Renewal Integration
This study integrates surface renewal theory (SR) with computational fluid dynamics (CFD) and large eddy simulation (LES) to improve sensible heat flux estimation in tea plantations by proposing a new CFD-based method for determining the SR calibration coefficient (α) and demonstrating accurate temperature fluctuation simulations.
-
Hope et al. (2025) NESP CS Project 5.4 - Projection verification
[Unable to extract information from the provided garbled text.]
-
Li et al. (2025) NRT-GSF: A novel near-real-time ground-satellite fusion algorithm to retrieve daily green area index at field scale
This study developed the Near-Real-Time Ground-Satellite Fusion (NRT-GSF) algorithm, integrating Sentinel-2 imagery with IoTA system data to generate daily 10-meter Green Area Index (GAI) products for precision agriculture. The algorithm demonstrated enhanced spatiotemporal completeness and accuracy, offering a robust solution for near-real-time, high-resolution crop GAI mapping.
-
Zhu et al. (2025) A Novel Framework Based on Data Fusion and Machine Learning for Upscaling Evapotranspiration from Flux Towers to the Regional Scale
This study developed an integrated framework combining data fusion and machine learning to estimate spatiotemporally continuous evapotranspiration (ET) at a 30 m field scale, demonstrating high accuracy in both homogeneous and heterogeneous landscapes.
-
Buttafuocò et al. (2025) Mapping average annual precipitation accounting for location-dependent variations
This study demonstrates that a local geostatistics approach with varying variogram model parameters significantly improves the modeling of average annual precipitation in a mountainous Mediterranean region compared to a global variogram, leading to more accurate predictions, especially in orographically complex areas.
-
Arii (2025) Sensitivity Study of Variable Decomposition Using a General Volume Scattering Model for Seasonal Changes in Deciduous Forests
[No information available in the provided text.]
-
Michalezyk et al. (2025) Evaluation and Attribution of a Warm Winter Bias over Arctic Sea Ice in a Climate Model
This study evaluates near-surface air temperature biases in the IPSL-CM6A-LR climate model over Arctic sea ice, revealing a persistent winter warm bias ranging from +0.4 °C to +3.1 °C, primarily attributed to excessive poleward atmospheric heat transport and underestimated summertime sea ice cover in the coupled configuration.
-
Abdeyazdan et al. (2025) Projection of non-stationary compound droughts considering internal variability of a climate model over Iran
[Information not extractable due to unreadable paper text.]
-
Ren et al. (2025) Differential seasonal patterns and driving mechanisms of evapotranspiration components in a subtropical monsoon forest
This study quantified and partitioned evapotranspiration (ET) components (canopy rainfall interception evaporation (Ei), transpiration (Et), and soil evaporation (Es)) and their driving mechanisms in a subtropical monsoon forest over two years. It found that Et (46.2%) and Ei (43.9%) were the major components, with distinct seasonal patterns and drivers (precipitation characteristics for Ei, energy factors for Et, and a mix for Es), highlighting the significant role of Ei in wet-hot subtropical regions.
-
li (2025) Original Data
This study presents a hydrological process simulation model specifically developed for the Loess Plateau of China, designed to accurately simulate surface evapotranspiration under complex topography and dynamic underlying surface conditions.
-
Wang et al. (2025) Hydrothermal integration and synergy regulate carbon exchange in forest ecosystems of eastern China
This study investigated the influence of hydrothermal integration and synergy on carbon exchange across 16 forest ecosystems in eastern China, developing novel indices (TP and D) that more effectively explain the variability in gross primary productivity, ecosystem respiration, and net ecosystem productivity, with soil water content and atmospheric hydrothermal synergy identified as primary drivers.
-
Muzammal et al. (2025) Exploring the Links Between Variations in Snow Cover Area and Climatic Variables Across the Upper Indus Basin Under a Changing Climate
This study developed an ARIMA model to predict daily Snow Cover Area (SCA) in the Upper Indus Basin (UIB) using MODIS satellite data and correlated these predictions with future climate scenarios (SSP1-2.6, SSP5-8.5), revealing a significant decline in SCA, particularly under higher emission scenarios, with notable regional variations.
-
Singh et al. (2025) Integrated trend analysis and meteorological drought forecasting using ANN in the adjacent semi-arid and arid regions
This study integrated trend analysis and meteorological drought forecasting using an Artificial Neural Network (ANN) model in adjacent semi-arid and arid regions of Rajasthan, India, finding increasing precipitation trends in several periods and a decrease in drought severity with longer time scales.
-
Kulkarni et al. (2025) Near-global Agro-climatological Drought Monitoring Dataset
This study introduces the Near-global Combined Drought Monitoring (NEC-DROMO) dataset, integrating soil moisture, vegetation water content, rainfall, and temperature at a 0.25-degree monthly resolution from 2002-2021, demonstrating superior reliability in capturing global drought patterns compared to traditional indices.
-
Chen et al. (2025) Summer Surface Rainfall Deviations From Convective Cold‐Cloud Shields Over China
This study defines a novel index to quantify the spatial discrepancy between surface rainfall and cold-cloud shields in convective systems, finding that larger deviations are more prevalent in southern China and exhibit distinct spatial patterns linked to cloud tilting and wind shear.
-
Xu et al. (2025) Correcting Atmospheric Temperature and Vapor Density Profiles of Ground-Based Microwave Radiometer in Diverse Skies by Regression Model and Artificial Neural Network Methods
This study aims to improve ground-based microwave radiometer (MWR) temperature and vapor density retrieval accuracy by correcting deviations against radiosondes using regression and artificial neural network (ANN) models, finding that both models effectively reduce biases but do not significantly enhance retrieval consistency.
-
Sun et al. (2025) Canopy3D-Net: Semantic segmentation of fruit tree canopies based on 3D point clouds
This paper proposes Canopy3D-Net, a semantic segmentation network for 3D point clouds, to accurately delineate fruit tree canopies in complex agricultural environments. The network achieves high segmentation performance and strong generalization, offering an efficient solution for precision agriculture and forestry.
-
Xiong et al. (2025) Evaluating Terrestrial Water Storage, Fluxes, and Drivers in the Pearl River Basin from Downscaled GRACE/GFO and Hydrometeorological Data
This study develops and validates a downscaled terrestrial water storage anomaly (TWSA) product for the Pearl River Basin (PRB) by integrating GRACE/GRACE Follow-On observations with the WaterGap Global Hydrological Model (WGHM) via joint inversion, demonstrating enhanced spatiotemporal fidelity and providing actionable information for water management.
-
Mathbout et al. (2025) Europe’s Double Threat: Evolving patterns of compound heatwaves and droughts
This study quantifies the spatiotemporal evolution of Compound Hot and Dry Events (CHDEs) across Europe from 1980 to 2023, revealing a significant post-2000 intensification and northward/eastward expansion, primarily driven by heatwaves, with urban areas showing disproportionately higher increases.
-
Srivastava et al. (2025) Introducing Glaciohydrological Model Calibration Using Sentinel‐1 SAR Wet Snow Maps in the Himalaya‐Karakoram
This study introduces and validates a satellite-based calibration method for the SPHY glaciohydrological model using Sentinel-1 SAR wet snow maps and geodetic mass balance in the data-sparse Himalaya-Karakoram region, demonstrating its robustness for improving runoff estimates and understanding glaciohydrology.
-
İpek et al. (2025) Integrating Spatio-Probabilistic mapping and remote sensing for comprehensive drought risk assessment
This study developed a Spatio-Probabilistic Drought Mapping (SPDM) framework by integrating multiple drought indices with remote sensing and land cover analysis to assess drought dynamics and environmental impacts in the Küçük Menderes Water Basin. The research identified western regions as high-risk areas and quantified severe impacts on vegetation, agriculture, and forest fires during major drought episodes.
-
Wang et al. (2025) More Frequent and Intense Tropical Cyclone‐Heat Wave Compound Extremes Over the Coastal Regions of China in a Warmer Climate
This study projects future changes in tropical cyclone and heat wave (TC-HW) compound extreme events over the southeastern coast of China (SECC) using high-resolution CMIP6 simulations. It finds that TC-HW events are projected to become significantly more frequent and stronger under high-emission scenarios, primarily driven by enhanced heat wave activity.
-
Li et al. (2025) Wind Shaped Winter Snow Mass Balance at High Altitude: Insights From an Integrated Snow Observation System
This study develops an integrated observation system and a Gaussian kernel-based probabilistic classification method to quantify wind-driven snow events in the northeastern Tibetan Plateau, revealing that wind-driven processes account for 68.5% of snow mass changes and amplify sublimation above 8 m/s wind speeds.
-
Zhao et al. (2025) Integrating topographic characteristics to construct lake and catchment topology on the Tibetan Plateau
This study developed a novel lake-oriented algorithm to construct the first temporally consistent lake–catchment topological network for the Tibetan Plateau as of 2000, significantly improving accuracy by preserving true endorheic basins and ensuring data consistency between lake extents and topography.
-
Mashraqi et al. (2025) Hybrid deep learning and optimization-based land use and land cover classification for advancing sustainable agriculture in Najran city, Saudi Arabia
This paper develops and validates a hybrid deep learning model, integrating Convolutional Neural Networks (CNNs), Ant Colony Optimization (ACO), and Random Forest (RF), for accurate land-use/land-cover (LULC) classification in Najran, Saudi Arabia, using 2023 Landsat-8 imagery, achieving up to 97.56% overall accuracy to advance sustainable agriculture.
-
Yang et al. (2025) Aerosol emission reductions cause post-2011 rapid warming in the northwestern Pacific
This study identifies anthropogenic aerosol emission reductions as the primary driver of the anomalously rapid warming observed in the northwestern Pacific since 2011, attributing this warming to increased surface shortwave radiation resulting from decreased cloud cover. The research reveals a five-year lag in ocean warming, governed by a threshold-triggered nonlinear aerosol-cloud interaction.
-
Huang et al. (2025) Increasing Variability in Tropical Cyclone Lifetime Maximum Intensity Over the South China Sea
This study investigates Lifetime Maximum Intensity (LMI) trends in the South China Sea (SCS) since the 1980s, revealing a significant increase in LMI amplitude driven by opposing trends in local and migratory tropical cyclones, attributed to shifts in genesis, track, and environmental conditions.
-
Lee et al. (2025) Introducing NLCD-Imp: A QGIS plugin to better replicate urban characteristics in land use/cover maps for SWAT
This study developed NLCD-Imp, a QGIS plugin, to enhance National Land Cover Database (NLCD) maps with detailed urban characteristics for improved hydrological modeling. Applying NLCD-Imp to the Soil and Water Assessment Tool (SWAT) revealed increased surface runoff, reduced evapotranspiration, and a two-to fourfold increase in simulated nutrient loads in highly impervious urban areas, with a 2 % imperviousness threshold balancing model accuracy and complexity.
-
Ren et al. (2025) Two decades of vegetation productivity change shaped by water availability and human activity in northern China’s arid–semi-arid transition
This study analyzed vegetation net primary productivity (NPP) changes in northern China's arid-semi-arid transition zone from 2001 to 2020, revealing an overall NPP increase primarily driven by human activities (67.84%) and moisture-related climate factors, though a low Hurst index suggests these gains may not persist.
-
Han et al. (2025) Enhancing multi-step-ahead prediction of wave propagation with the CAE-LSTM model: a novel deep learning-based approach to flood dynamics
This paper introduces a novel Convolutional Autoencoder (CAE)-integrated Long Short-Term Memory (LSTM) model to enhance the learning ability and generalization of Physics-Informed Neural Networks (PINNs) for long-term wave propagation in flood dynamics, demonstrating superior accuracy and computational efficiency compared to traditional finite volume methods.
-
Zhao et al. (2025) Climate Surpasses Soil Texture in Driving Soil Salinization Alleviation in Arid Xinjiang
This study quantitatively assessed the spatiotemporal variations of soil salinization in southern Xinjiang from 2008 to 2023, revealing a significant overall decrease in salinity, and found that climatic factors consistently exerted a stronger influence on its evolution than soil texture.
-
Bai et al. (2025) Effect of natural and human factors on hydrological connectivity in the arid region: Application of water-ecological network and XGBoost model
This study developed a node-corridor water-ecological network in Altay Prefecture, China, to quantify structural and functional hydrological connectivity and used an XGBoost model to analyze the nonlinear impacts and critical thresholds of natural and human factors on connectivity in an arid region.
-
Portmann et al. (2025) ClimLoco1.0: CLimate variable confidence Interval of Multivariate Linear Observational COnstraint
This paper introduces ClimLoco1.0, a new statistical model that rigorously describes the confidence interval of a projected climate variable obtained using multivariate linear observational constraints, explicitly accounting for observational noise and estimator quality. It demonstrates that observational constraints correct the best guess and reduce uncertainty, with observational noise weakening this effect, and highlights the underestimation of uncertainty in existing methods that neglect estimator quality.
-
Koncsos et al. (2025) Correction: A multi-scenario multi-model analysis of regional climate projections in a Central–Eastern European agricultural region: assessing shallow groundwater table responses using an aggregated vertical hydrological model
This study conducts a multi-scenario, multi-model analysis of regional climate projections to assess the responses of shallow groundwater tables in a Central–Eastern European agricultural region, utilizing an aggregated vertical hydrological model.
-
Ha et al. (2025) Yield and Quality Prediction of Crops using a Deep Learning-Based Multimodal Data Integration Framework
[Information not extractable due to corrupted input.]
-
Pimentel et al. (2025) Asymmetry in snow-water nexus in mountain areas mainly governed by meteorological seasonal changes
This study analyzes 548 mountain catchments globally to quantify the nexus between snow cover and streamflow, revealing that only 5% of catchments show simultaneous significant trends in both variables. The findings highlight an asymmetric relationship where seasonal meteorological drivers, such as summer temperature increases or winter precipitation shifts, often decouple snow cover changes from annual water yield.
-
Gjerde et al. (2025) Seasonal drainage-system evolution beneath the Greenland Ice Sheet inferred from transient speed-up events
This study investigates the seasonal evolution of subglacial drainage beneath the western Greenland Ice Sheet by analyzing transient ice speed-up events using a Global Positioning System (GPS) array. It reveals that late-season melt events produce larger, more uniform velocity responses with less uplift compared to early-season lake drainages, suggesting a shift to a pervasive, cavity-dominated subglacial system with closed channels.
-
Fu et al. (2025) Influence of Climate Characteristics on Streamflow in the Murray–Darling Basin
This study assessed the relationship between streamflow and climate characteristics across 133 catchments in the Murray–Darling Basin, finding that annual streamflow is primarily driven by mean annual rainfall and potential evapotranspiration, with other rainfall characteristics influencing specific streamflow metrics.
-
Singha et al. (2025) Hybrid framework of physics-inspired optimization and explainable ensemble learning for irrigation classification mapping in Morocco
This study developed a novel integrated ensemble classification framework, leveraging remote sensing data, field surveys, and advanced machine learning, to generate high-resolution irrigation maps for the Moroccan region. The framework achieved high accuracy, particularly for drip irrigation, identified key hydro-meteorological features influencing classification, and revealed significant upstream irrigation expansion with implications for water sustainability.
-
Alifujiang et al. (2025) Spatiotemporal patterns and trends of meteorological drought in the Kaidu-Kongque River Basin
This study analyzed the spatiotemporal patterns and trends of meteorological drought in the Kaidu-Kongque River Basin from 1960 to 2023 using SPI, SPEI, Pettitt test, and Modified Innovative Trend Analysis (MITA). It revealed a significant "dry west and wet east" spatial divergence, increased drought persistence, and a critical shift in drought drivers from precipitation-dominated to water-heat coupling-dominated around 1999.
-
Li et al. (2025) Multi-Scale Remote Sensing Evaluation of Land Surface Thermal Contributions Based on Quality–Quantity Dimensions and Land Use–Geomorphology Coupling
This study develops an integrated assessment framework combining land use and geomorphology to analyze comprehensive thermal contributions in the Yellow River Basin, revealing that synergistic effects are crucial for accurate regional thermal assessments and that single-factor evaluations lead to biased results.
-
Silvestri et al. (2025) Smart Irrigation with Fuzzy Decision Support Systems in Trentino Vineyards
This study comparatively evaluates two fuzzy-logic-based irrigation decision support systems for vineyard management, revealing distinct strengths and trade-offs between water conservation and crop stress mitigation.
-
Scinocca et al. (2025) Runtime bias correction of regional climate model driving data and its continental-scale impacts
This study introduces and evaluates an Empirical Runtime Bias Correction (ERBC) method for Earth System Model (ESM) driving data, demonstrating its effectiveness in significantly reducing global model biases in regional climate model (RCM) downscaling products and inducing statistically significant changes in climate-change circulation responses.
-
Huanuqueño-Murillo et al. (2025) Comparative Analysis of Evapotranspiration from METRIC (Landsat 8/9), AquaCrop, and FAO-56 in a Hyper-Arid Olive Orchard, Southern Peru
This study compared evapotranspiration (ET) estimates from METRIC (Landsat 8/9), AquaCrop, and FAO-56 in a hyper-arid olive orchard in southern Peru over two contrasting seasons, finding that the integrated METRIC-AquaCrop framework provides robust, spatially explicit, and temporally continuous ET data crucial for precision irrigation management.
-
Dasari et al. (2025) A regionalization based machine learning framework for bias correction and downscaling of ESACCI soil moisture in data limited region: A case study over India
This study developed a regionalization-based machine learning framework for bias correction and downscaling of ESACCI soil moisture data in data-limited regions like India, demonstrating significant bias reduction (over 90%) and effective downscaling with high containment ratios (over 89%).
-
Rozante et al. (2025) Long-Term Temperature and Precipitation Trends Across South America, Urban Centers, and Brazilian Biomes
This study analyzed long-term trends in maximum and minimum near-surface air temperatures and precipitation across South America, its urban centers, and Brazilian biomes from 1979 to 2024 using ERA5 reanalysis. It found widespread, heterogeneous warming, with Tmax increasing faster than Tmin, and a meridional precipitation dipole, confirming the recent warming is an intensification of an externally forced secular climate change signal.
-
Fan et al. (2025) Applications of Attention‐Enhanced CNN Models to Regional Precipitation Downscaling
This study evaluates three attention-enhanced Convolutional Neural Networks (CNNs) for regional precipitation downscaling in the Middle Reaches of the Yellow River, China. The findings demonstrate that these models significantly improve spatio-temporal precipitation simulations and better capture extreme precipitation events compared to conventional CNNs, with the AttLap model showing the most notable improvements.
-
Xu et al. (2025) The applicability of statistical post-processing techniques for quantitative precipitation forecast in the Huaihe River Basin
This study evaluates seven post-processing methods for quantitative precipitation forecasts in the Huaihe River Basin, demonstrating that spatiotemporal deep learning models (specifically ConvLSTM) significantly outperform traditional statistical and time-series methods, particularly during flood seasons and in complex terrains.
-
Li et al. (2025) PhysWRNet: A physics-guided deep learning framework for flood inundation mapping with SAR and hydrodynamic simulations
This paper introduces PhysWRNet, a physics-guided deep learning framework that integrates Sentinel-1 SAR data and HEC-RAS flood probability maps to significantly improve flood inundation mapping accuracy and reduce errors compared to conventional deep learning methods. The framework achieves an overall accuracy of 90.2% and enhances boundary accuracy by 68.3%.
-
Ayar et al. (2025) Ensemble random forest for tropical cyclone tracking
This study develops and evaluates an Ensemble Random Forest (ERF) approach for tracking tropical cyclones (TCs) using a limited set of aggregated atmospheric variables. The ERF method demonstrates good performance in detecting TCs over the Eastern North Pacific and North Atlantic basins, achieving similar detection rates but significantly lower false alarm rates compared to a physics-based tracker.
-
Rakshit et al. (2025) Unprecedented winter precipitation over Delhi NCR, India (December 2024): a multi-Sensor perspective
This study comprehensively analyzes an unprecedented intense winter precipitation event over Delhi NCR on 27 December 2024 using multi-sensor observations and reanalysis, revealing it was driven by a synergy of synoptic forcing, mesoscale convergence, and dual-source moisture advection.
-
Boomsma et al. (2025) Leveraging Meteorological Reanalysis Models to Characterize Wintertime Cold Air Pool Events Across the Western United States from 2000 to 2022
This study develops and evaluates an automated method to classify wintertime Cold Air Pool (CAP) events using the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA) model outputs across the Western United States from 2000 to 2022. The results demonstrate that the ERA model, particularly when adjusted with surface observations, provides a reasonable and consistent estimate of CAP conditions, performing similarly to radiosonde observations for classification.
-
Abdel-Fattah (2025) GEE Script
This dataset provides remote sensing products, soil indicators, environmental layers, and an analytical Google Earth Engine (GEE) script to support studies on the integrated framework linking Normalized Difference Vegetation Index (NDVI), soil organic carbon (SOC), and environmental drivers in tropical agroecosystems.
-
Dandapat et al. (2025) Ensemble deep learning framework for groundwater storage forecasting under hydrological variability
This study develops an ensemble deep learning framework to forecast groundwater storage (GWS) in the Middle Mahanadi Basin, finding that the ensemble model significantly outperforms individual deep learning models in accuracy and reliability, particularly under hydrological variability and limited data conditions.
-
Yuan et al. (2025) A global drought dataset for Multivariate Composite Drought Index (MCDI) and its constituent drought indices
This study developed and validated a global, high-resolution (0.1°, monthly, 1980-2019) drought dataset based on the Multivariate Composite Drought Index (MCDI) and its constituent indices, demonstrating its effectiveness in characterizing comprehensive drought dynamics and ecosystem responses by accounting for time lag and cumulative effects.
-
Vishwakarma et al. (2025) Mapping of Vegetation Responses and Impacts on Groundwater in a Drought Afflicted Data-Scarce River Basin using Remotely Sensed Information
This study mapped vegetation responses and groundwater impacts in the drought-afflicted, data-scarce Bhadar River basin in India using an integrated approach of remote sensing and hydrological modeling. It successfully simulated drought propagation patterns, identified five major drought periods between 1996 and 2022, and quantified their varying effects on vegetation vigor and groundwater recharge.
-
Zhang et al. (2025) Physics-Informed Transformer Networks for Interpretable GNSS-R Wind Speed Retrieval
This study develops a novel Transformer-Graph Neural Network (GNN) model for Global Navigation Satellite System Reflectometry (GNSS-R) wind speed retrieval, addressing limitations in interpretability and accuracy during high wind conditions. The model significantly reduces wind speed RMSE and provides physically grounded interpretations of spatiotemporal influence propagation.
-
Hu (2025) Climate–Hydrology–Phenology Couplings across Diverse Watersheds
This study investigates hydroclimatic-phenological interactions across five diverse watersheds, ranging from humid to hyper-arid, utilizing advanced causal network analysis techniques.
-
Bobáľová et al. (2025) Improving Landsat land surface temperature estimation in Google Earth Engine using NDVI-based emissivity
This study developed and validated an improved Google Earth Engine (GEE) approach for Landsat Land Surface Temperature (LST) estimation, utilizing NDVI-based emissivity calculations combined with statistical mono-window and radiative transfer equation methods. The new method demonstrated higher accuracy and precision compared to the standard Landsat ST product and approaches relying on ASTER Global Emissivity Dataset (GED).
-
Kröger et al. (2025) EERIE: Ocean Eddy-rich Kilometer-scale Climate Simulation with ICON: Historical Simulation (Version 1)
This paper describes the generation and characteristics of a 65-year historical climate simulation dataset (1950-2014) using the ICON-Sapphire Earth System Model, which explicitly resolves ocean mesoscale dynamics at kilometer-scale resolution to improve long-term climate simulations.
-
Xu et al. (2025) Assessment of observation errors in AWS data assimilation: Application to a thunderstorm gale event forecast
This study assesses the impact of varying observation errors in Automatic Weather Station (AWS) data assimilation on the high-resolution simulation and forecast of a thunderstorm gale event in Beijing using the WRF model and 3DVAR system. It demonstrates that optimized observation error estimates significantly improve wind analysis and extreme wind speed forecasting, with a Desroziers-based method showing superior performance.
-
Han et al. (2025) Climate science data can be compressed efficiently by dual-stage extreme compression with a variational auto-encoder transformer
This paper introduces Aeolus, a deep learning framework utilizing Variational Auto-Encoder Transformer (VAEFormer) modules, to achieve extreme compression of large-scale atmospheric datasets. It successfully compresses the 400-terabyte ERA5 reanalysis dataset by a factor of 470x into a 0.85-terabyte dataset (CRA5) while maintaining high numerical accuracy and preserving critical climate patterns for scientific analysis and forecasting.
-
Sun et al. (2025) Spatiotemporal Evolution of Agricultural Land Systems in the North China Plain Over Two Decades (2002–2022)
This study analyzed agricultural land system transformations in the North China Plain (2002–2022) using fused satellite data, revealing significant shifts in crop distribution driven by environmental constraints, economic incentives, and policy interventions, with implications for water resource management.
-
Neethu et al. (2025) A model-based assessment of the regional safe aerosol boundary for the South Asian monsoon region
This study uses idealized climate model simulations to rigorously confirm proposed planetary boundary values for regional aerosol optical depth (AOD) in South Asia. It finds that an AOD of 0.25 can cause drought conditions (>10% precipitation reduction) in India, and an AOD of 0.5 reduces Indian summer monsoon precipitation by 19%.
-
Sodemann (2025) The Lagrangian moisture source and transport diagnostic WaterSip V3.2
This paper introduces WaterSip V3.2, a diagnostic software tool that identifies evaporation sources and transport pathways of precipitation or water vapour based on Lagrangian model output. It provides a comprehensive reference for the method's foundations, technical setup, and interpretation, illustrated with a case study, to facilitate its wider application and inter-comparison with other moisture source diagnostics.
-
Yang et al. (2025) Extreme multi-source forcings reshape three-dimensional circulation to drive the record-breaking Early-Autumn 2024 heat event over the Yangtze River Basin
This study investigates the record-breaking early-autumn 2024 heat event over the Yangtze River Basin, revealing it was driven by a synergistic combination of remote warm North Atlantic Sea Surface Temperature anomalies and intense South China Sea convection, which reshaped three-dimensional atmospheric circulation.
-
Kumar et al. (2025) Forecasting the Level of Groundwater in India Using a Machine Learning
This study develops and compares machine learning models to forecast yearly groundwater availability and extraction usage in India for 2025, finding that the Random Forest algorithm achieves the highest accuracy of 82%.
-
Lindersson et al. (2025) SHEDIS-Temperature: linking temperature-related disaster impacts to subnational data on meteorology and human exposure
This paper introduces SHEDIS-Temperature, an open-access dataset linking temperature-related disaster impacts from EM-DAT with subnational meteorological and population exposure data to enable comprehensive analysis of disaster drivers and outcomes. It reveals that more populated subnational areas tend to experience higher temperatures, highlighting critical risk hotspots.
-
Chen et al. (2025) A deep learning-based method for deep soil salinity prediction: considering the driving mechanisms of salinity profiles
This study investigated the transfer relationships and driving mechanisms of deep soil salinity using Hydrus-1D simulations and developed a Fully Connected Neural Network (FCNN) model to predict deep soil salinity from surface data and environmental factors, achieving R2 values from 0.44 to 0.79.
-
Zhong et al. (2025) Added Value of Assimilating FY-4B AGRI Water Vapor Radiances on Analyses and Forecasts for “23 · 7” Heavy Rainfall
This study evaluates the impact of assimilating Fengyun-4B (FY-4B) Advanced Geostationary Radiation Imager (AGRI) water vapor channels clear-sky data on heavy rainfall prediction using the WRFDA system, demonstrating significant improvements in analysis and forecast accuracy, particularly with the inclusion of a new channel 11.
-
Liu (2025) Data processing_5 ka event_Wang, Liu, et al.
This paper presents a processed time series dataset derived from seventeen high-quality paleoclimate proxy records, intended for identifying anomalous wet and dry climate conditions across West Asia, arid Central Asia, and north-central East Asia.
-
Liang (2025) Soil Moisture Prediction for Intelligent Irrigation: An XGBoost-based Model with Multi-Dimensional Feature Engineering
This study developed an Extreme Gradient Boosting (XGBoost)-based model with multi-dimensional feature engineering to predict 5 cm soil moisture using hourly meteorological data. The model achieved high-precision predictions (R² = 0.673) on a test set, significantly outperforming traditional linear models and providing reliable support for intelligent irrigation.
-
Flaounas et al. (2025) Dynamics, predictability, impacts and climate change considerations of the catastrophic Mediterranean Storm Daniel (2023)
This study comprehensively analyzes Storm Daniel, a catastrophic medicane in September 2023, linking its development, predictability, and extreme impacts (precipitation, floods, sea waves) in Greece and Libya to large-scale atmospheric circulation, anomalously warm sea surface temperatures, and potential climate change influences.
-
Belaid et al. (2025) Hemispherical imaging of canopy light interception: A ceptometer alternative for precision irrigation in orchards and vineyards
This study introduces a cost-effective hemispherical imaging method for rapidly and reliably assessing the diurnal pattern and daily fraction of Intercepted Photosynthetically Active Radiation (fIPAR) in orchards and vineyards. The proposed method, utilizing either still or action cameras, demonstrated high agreement with traditional ceptometer measurements (R² between 0.88–0.92) and offers a practical, scalable, and labor-efficient alternative for precision irrigation.
-
Zhang et al. (2025) Machine Learning Prediction of River Freeze-Up Dates Under Human Interventions: Insights from the Ningxia–Inner Mongolia Reach of the Yellow River
This study developed a systematic machine learning framework to predict river freeze-up dates in the Ningxia–Inner Mongolia reach of the Yellow River, explicitly incorporating stage-specific human interventions. It found that tailored predictor selection, hyperparameter optimization, and a stage-specific cumulative temperature predictor significantly improved accuracy, with XGBoost demonstrating the best overall performance (Mean Absolute Error = 2.95 days).
-
Sinore et al. (2025) Agricultural and meteorological drought variability assessment over the Rift Valley Lake Basin of Ethiopia
This study assessed the spatiotemporal variability of meteorological and agricultural droughts in Ethiopia's Rift Valley Lake Basin using multi-source remote sensing data and advanced statistical models. It revealed significant drought severity variations, with specific major events, and highlighted the compounded effects of thermal and moisture stress on vegetation.
-
Xiang et al. (2025) Unraveling the nonstationary effect of climate change and urbanization on summer drought-heatwave coupling degree in Beijing-Tianjin-Hebei Area, China
This study investigates the nonstationary coupling between summer drought and heatwaves in the Beijing-Tianjin-Hebei (BTH) area from 1970 to 2018, revealing a progressively intensifying relationship driven by the combined effects of climate change and urbanization. A novel Conditional Risk Sensitivity Indicator (CRSI)-based Sensitivity Enhancement Factor (SEF) is introduced to quantitatively attribute these impacts.
-
Singh et al. (2025) Mount Pinatubo's effect on the moisture-based drivers of plant productivity
This study investigates the impact of the 1991 Mt. Pinatubo eruption on moisture-based drivers of plant productivity using an Earth system model. It finds that up to 10%–15% of land regions exhibit statistically significant hydroclimate responses (wet/dry) as measured by Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI), providing a more robust understanding of agricultural impacts than precipitation alone, with regional variations in the dominant limiting factors.
-
Lefebvre (2025) Retrieval of satellite-based microphysical and dynamical properties of deep convection ˸ preparation of the C²OMODO mission
[A VERY CONCISE 1-2 sentence summary of the paper's core objective and main finding.]
-
Li et al. (2025) A Novel Fusion Method of Optical and Wide-Swath Interferometric Radar Altimeter Images for Enhanced Water Detection
This paper introduces a novel fusion method that combines optical and wide-swath interferometric radar altimeter images to enhance the detection of water bodies.
-
Kim et al. (2025) Improved Maritime Continent MJO Simulation in the KMA GloSea6 through Enhanced Thermodynamic Processes
This study evaluates the Madden–Julian oscillation (MJO) simulation and prediction skill in the Korea Meteorological Administration’s Global Seasonal Forecast System, version 6 (GloSea6), comparing it with its predecessor, GloSea5. GloSea6 demonstrates enhanced MJO propagation across the Maritime Continent (MC) due to improved thermodynamic processes and background state, leading to better prediction skill in that region.
-
Wang et al. (2025) Drivers and Future Regimes of Runoff and Hydrological Drought in a Critical Tributary of the Yellow River Under Climate Change
This study quantifies the contributions of climate change and human activities to runoff reduction and hydrological drought in China's Dahei River basin from 1983 to 2022, projecting future trends over the next 40 years. It finds that human activities are the dominant driver of runoff decline (61.4%) and hydrological drought, with future projections indicating more frequent and intense droughts.
-
Yan et al. (2025) Southward shift of winter wheat area intensifies regional water scarcity in the Beijing-Tianjin-Hebei region
This study analyzed winter wheat planting dynamics and water resource implications in the Beijing-Tianjin-Hebei (BTH) region from 1990-2019, revealing a 58 km southward shift of cultivation that intensified regional water scarcity, with a 2.6-fold higher unit-area water footprint increase in southern areas and a 68.7% surge in blue water requirement.
-
Varga et al. (2025) A 32-year species-specific live fuel moisture content dataset for southern California chaparral
This study developed a 32-year, species-specific Live Fuel Moisture Content (LFMC) dataset for southern California chaparral by training random forest models with observations and environmental predictors. The resulting dataset successfully captures the annual cycle, spatial heterogeneity, and interspecies differences of LFMC for four key fuel types, providing a valuable resource for wildfire research.
-
Mohammadi et al. (2025) Integrated Data Approaches in Crop Management: A Review on Advancing Productivity, Sustainability, and Climate Resilience
This review synthesizes 115 studies to evaluate how multi-source agricultural data improve productivity, climate resilience, and resource efficiency, finding that integrated data and machine learning enhance yield forecasting and stress detection, despite challenges in data interoperability and access.
-
Zolghadr‐Asli et al. (2025) Evaluating the potential of desalinated irrigation in water-stressed regions through optimized planting dates and irrigation strategies
This study evaluates the potential of desalinated irrigation in water-stressed regions by optimizing planting dates and irrigation strategies for tomato production in Chile's Atacama region. It demonstrates that adjusting planting dates combined with optimal deficit irrigation significantly improves water productivity and profit margins, potentially offsetting the costs of desalinated water, especially when leveraging economies of scale.
-
Yonaba et al. (2025) Hydrological evaluation of top-down and bottom-up rainfall products in West Africa: Model performance, parameter range and uncertainty propagation
This study evaluated the hydrological performance of four top-down and three bottom-up satellite rainfall products in three West African Sahelian river basins using the SWAT model. It found that model skill varied across basins, with some gridded products outperforming gauge observations, and demonstrated that carefully selected rainfall products can significantly enhance hydrological modeling and water resource planning in the region.
-
Kröger et al. (2025) EERIE: Ocean Eddy-rich Kilometer-scale Climate Simulation with ICON: SSP2-4.5 Scenario Simulation (Version 1)
This paper describes the generation of a kilometer-scale, eddy-rich global climate simulation dataset using the ICON Earth System Model for the SSP2-4.5 scenario pathway from 2015 to 2050, aiming to improve long-term climate projections by explicitly resolving ocean mesoscale dynamics.
-
Park et al. (2025) Climate change paradox: worsening droughts amidst increasing average precipitation across South Korea
This study reveals a climate change paradox in South Korea, demonstrating that droughts have worsened in frequency and intensity over the past century despite an overall increase in average precipitation, driven by enhanced meteorological variability and temperature-driven evapotranspiration, with strong implications for water resources.
-
Li et al. (2025) Climate change outweighs vegetation restoration in shaping water availability in humid karst watersheds
This study developed a new karst-based ecohydrological model to assess the impact of vegetation restoration on water availability in humid karst watersheds. It found that precipitation is the dominant driver of water availability, with vegetation restoration having a limited effect, suggesting afforestation is unlikely to cause water scarcity under current climatic conditions.
-
Lin et al. (2025) Cloud4D
[Information could not be extracted from the provided text.]
-
Yang et al. (2025) Lakes are experiencing more severe heatwaves than the atmosphere
This study globally compares lake and atmospheric heatwaves from 2000-2022, revealing that lake heatwaves are more severe, with longer durations and shorter reoccurrence periods, primarily driven by reduced wind speed.
-
Kusuma et al. (2025) TerraGrow: Integrated platform for real time plant monitoring and automated watering system with IoT and fuzzy Sugeno Algorithm
TerraGrow is an open-source, low-cost IoT platform for automated precision irrigation that integrates multi-sensor data (soil moisture, pH, air temperature, humidity) with a local Sugeno-type fuzzy controller on an ESP32, demonstrating stable moisture regulation and reduced unnecessary watering in bench and greenhouse tests.
-
Debrie et al. (2025) Hourly precipitation fields at 1 km resolution over Belgium from 1940 to 2016 based on the analog technique
This study develops and validates a high-resolution (1000 m, hourly) gridded precipitation dataset for Belgium from 1940 to 2016 using an analog technique, demonstrating that a median-based ensemble of 25 analogs provides optimal performance for precipitation estimation. The resulting dataset is publicly available for various hydrological and climate applications.
-
Somogyvári et al. (2025) Regional-scale groundwater analysis with dimensionality reduction
This paper proposes a novel methodology using dimensionality reduction on the misfits between modeled and observed groundwater levels to analyze regional-scale climate effects on groundwater changes. The approach successfully identifies regions with distinct climate-groundwater relations and vulnerabilities in the Berlin-Brandenburg area, demonstrating that linear models can capture monthly groundwater dynamics even under significant anthropogenic influence.
-
Kröger et al. (2025) EERIE: Ocean Eddy-rich Kilometer-scale Climate Simulation with ICON: Control Simulation (Version 1)
This paper describes the generation and characteristics of a 100-year global control simulation using the kilometer-scale ICON-Sapphire Earth System Model, explicitly resolving ocean mesoscale dynamics. The primary purpose of this simulation is to quantify model drift, providing a crucial baseline for future historical climate simulations within the EERIE project.
-
Yang et al. (2025) Seasonal drought during the soybean growth period and agricultural water stress in southern China eased in the context of global warming
This study investigated soybean water requirements and seasonal drought characteristics in the middle reaches of the Yangtze River Basin from 1961 to 2021. It revealed a decreasing trend in both drought severity and soybean water requirements amidst global warming, with net radiation and vapor pressure deficit identified as dominant drivers.
-
Zou et al. (2025) Nitrogen limitation reduces CO₂ emissions from land use change primarily by decreasing CO₂ and climate interactions
This study quantifies the impact of nitrogen (N) limitation on direct and indirect CO₂ emissions from land-use change using the CABLE model, revealing that N limitation significantly reduces total emissions primarily by weakening interactions between land-use change and atmospheric CO₂ and climate.
-
Park et al. (2025) Detecting Abandoned Cropland in Monsoon-Influenced Regions Using HLS Imagery and Interpretable Machine Learning
This study developed a robust framework combining Harmonized Landsat and Sentinel-2 (HLS) imagery with the XGBoost algorithm to accurately monitor abandoned cropland, achieving an accuracy of 0.84.
-
Han et al. (2025) Regional dynamics in the irrigation requirement of maize and wheat crops in relation to changes in the climate and cultivated area in the Shandong’s Yellow River Basin
This study analyzed the spatiotemporal variations and driving factors of irrigation water requirement (IWR) for maize and wheat in Shandong’s Yellow River Basin from 2003 to 2023. It revealed that a "warm-dry" climate trend combined with cultivated area expansion significantly increased IWR, with cultivated area being the dominant driver.
-
Resch et al. (2025) Land use evolution as an indicator of hydrological change: historical trajectory of the Bassée floodplain, France (1850–1987)
This paper investigates the historical land use evolution (1850–1987) of the Bassée floodplain, France, as an indicator of hydrological changes caused by hydraulic and navigation works, revealing three distinct stages of transformation from soil moisture control to floodplain disconnection.
-
Zhao et al. (2025) Watershed Runoff Simulation and Prediction Based on BMA Coupled SWAT-LSTM Model
This study develops and evaluates a SWAT-LSTM-BMA coupled model to improve runoff prediction accuracy, particularly in regions prone to extreme hydrological events, finding it to be the optimal model for the Zuli River Basin with significant accuracy improvements and predicting a future decrease in annual runoff.
-
Qiu et al. (2025) Analysis of spatiotemporal change characteristics of Poyang Lake from 1984 to 2021 based on GEE
This study analyzed the spatiotemporal changes of Poyang Lake's water area from 1984 to 2021 using satellite imagery and the Google Earth Engine platform, revealing complex interannual and seasonal fluctuations driven by both climate change and human activities.
-
Zhang et al. (2025) Intercomparison, Fusion and Application of FY-3E/WindRAD and HY-2B/SCA Ocean Surface Wind Products for Tropical Cyclone Monitoring
This study presents the first comprehensive intercomparison of Ku-band ocean surface wind vector products from FY-3E/WindRAD and HY-2B/SCA scatterometers, demonstrating strong inter-satellite consistency and the practical value of a multi-source fusion approach for tropical cyclone monitoring and wind radii estimation.
-
Kirono et al. (2025) NESP CS Project 2.8 - Extreme climate: dry, wet, hot-and-dry
Information not available due to corrupted input text.
-
Rotenberg et al. (2025) Evapotranspiration saturation amplifies climate sensitivity of terrestrial water yield
This study reveals that ecosystem evapotranspiration (ET) saturates at approximately 480 mm per year globally, which significantly amplifies the sensitivity of terrestrial water yield (WY) to precipitation variability, increasing climate change risks for ecosystems and society.
-
Xiao-meng et al. (2025) Regional Changes in Precipitation Concentration, Seasonality and Intermittency in China
This study assesses regional changes in precipitation concentration, seasonality, and intermittency across five climatic zones in mainland China from 1961 to 2020, linking these changes to large-scale atmospheric circulations. It reveals varying trends, including increased seasonality in some zones and reduced winter drought risk across all zones, providing insights for disaster mitigation.
-
Lu et al. (2025) A lake water level prediction method based on data augmentation and Physics-Informed Neural Networks with imbalanced data
This study proposes a novel Physics-Informed Neural Network (PINN) framework that integrates data augmentation and physically guided hyper-parameter selection to accurately predict lake water levels, specifically addressing challenges posed by imbalanced extreme event data and high computational costs. The framework demonstrates superior accuracy and efficiency compared to traditional models, achieving an RMSE of 0.021 m and requiring significantly less computational time.
-
Yu et al. (2025) Impacts of the mega cascade reservoirs on riverine hydrothermal regimes based on deep learning
This study investigates the impacts of four mega cascade reservoirs on the Lower Jinsha River's downstream hydrological and water temperature regimes using an LSTM-based hydro-thermal model, revealing significant alterations in flow, temperature, and their coupling, with implications for ecological risks.
-
Bokros et al. (2025) Homogenisation of Wind Data for the Period 1997–2023 in Hungary and Analysis of Different Methods Used for Deriving Daily Wind Data
This research homogenised automatic 10-minute wind speed measurements (1997–2023) from 40 Hungarian meteorological stations and compared them with simulated three-times-daily observations to quantify differences and enable integration with pre-1997 data for long-term climatological analyses.
-
Heidarian et al. (2025) Cross-Domain Land Surface Temperature Retrieval via Strategic Fine-Tuning-Based Transfer Learning: Application to GF5-02 VIMI Imagery
This study introduces a three-stage strategic fine-tuning-based transfer learning (SFTL) framework to retrieve Land Surface Temperature (LST) from GF5-02 VIMI imagery, demonstrating superior cross-site generalization (RMSE ≈ 2.89–3.34 K) compared to traditional methods by integrating large simulated datasets with limited in situ measurements and parameter-efficient fine-tuning strategies.
-
Wang et al. (2025) Simulation of Actual Evapotranspiration and Its Multiple-Timescale Attribution Analysis in the Upper Reaches of the Jinsha River, China
This study quantified the contributions of climatic variation and anthropogenic activities to actual evapotranspiration (AET) at multiple timescales in the Upper Reaches of the Jinsha River (URJR), finding that both climate and human activities are dominant factors, with their relative importance varying by hydrological station.
-
Gupta et al. (2025) Assessment of Flood Potential Through Rainfall Pattern Analysis
This study analyzes over a century of rainfall data across all Indian states using a Bidirectional Long Short-Term Memory (Bi-LSTM) model to understand and forecast flood potential, demonstrating high accuracy in identifying high-risk areas.
-
Smith et al. (2025) Strongly Heterogeneous Surface‐Water Warming Trends in High Mountain Asia
This study examines long-term (1994–2023) and high-resolution (30 m) changes in water-surface temperature across High Mountain Asia, revealing a significant increase in temperatures, particularly in snow-covered regions, with an acceleration in the last decade and surface water warming faster than land.
-
Wu et al. (2025) Response of Tropical Cyclone‐Related Precipitation During Boreal Summer Season Over East Asia to Pseudo‐Global‐Warming Climates
This study systematically investigates the evolution of tropical cyclone (TC)-related precipitation across East Asia during boreal summer under a global warming scenario, revealing a substantial 125.6% increase in total TC precipitation due to TC intensification, altered steering flows, and enhanced atmospheric moisture content.
-
Huang (2025) Actor–Critic Deep Reinforcement Learning for Multi-Objective Intelligent Irrigation Scheduling: Algorithm and Edge-Cloud Management System
This study developed an intelligent agricultural irrigation scheduling algorithm and management system based on a deep reinforcement learning model, demonstrating a 12.7% improvement in water resource utilization and an 8.3% gain in crop yield through field trials.
-
Ghosh et al. (2025) Water Withdrawal Trends Across Multiple UN Member Nations Using Time Series Forecasting
This study comprehensively analyzes historical freshwater withdrawal patterns in six UN member nations using the ARIMA time series model to identify driving factors and forecast future water demands, revealing significant inter-country differences and the need for proactive policy interventions.
-
Cho et al. (2025) On the dynamic link between summer inner-continental warming and the outer-continental weakened precipitation extreme ascent in East Asia
This study elucidates the mechanism by which Arctic-cooling-induced inner-continental warming and anthropogenic aerosols suppressed July extreme precipitation in East Asia (eCYK) from 2013 to 2019. It reveals that these factors disrupted moisture transport and convective cloud formation, leading to a weakening of extreme precipitation events despite a general increase in summer precipitation.
-
Jian et al. (2025) An advanced double-moment cloud microphysics scheme with explicit aerosol-cloud interactions and its performance in quantitative precipitation forecasting (QPF) in the CMA-MESO V5.0
This study develops an advanced double-moment cloud microphysics scheme with explicit aerosol-cloud interactions for the CMA-MESO V5.0 model, demonstrating its improved performance in quantitative precipitation forecasting, especially for extreme rainfall, under varying aerosol conditions.
-
Wang et al. (2025) Upper bound of pre-fill volume in cascade reservoirs considering forecast rainfall and its uncertainty
This study developed a risk-controlled framework to determine the upper bound of pre-fill volume (UBFV) for cascade reservoirs, explicitly linking forecast uncertainty to flood risk through probabilistic constraints to enhance floodwater utilization without increasing flood risk. It quantified UBFV for reservoir clusters and analyzed the impact of forecast uncertainty across spatial rainfall distribution, forecast lead time, and allowable flood high water level.
-
Sun et al. (2025) Study on the response mechanism of groundwater to rainfall in different geomorphic units of semi-arid regions
This study investigated groundwater level responses to precipitation across different geomorphic units and aquifer types in the Horqin Sandy Land from 2016 to 2023, revealing distinct groundwater dynamics, memory effects, and rainfall lag times primarily governed by soil permeability and groundwater depth.
-
Li et al. (2025) Integrating GNSS 3D deformation and GRACE/GRACE-FO gravity observations for terrestrial water storage changes and drought monitoring in Southwest China
This study pioneers the integration of GNSS 3D deformation with GRACE/GRACE-FO observations using three fusion methods to derive daily and monthly terrestrial water storage (TWS) changes in Southwest China, significantly improving TWS accuracy and enabling detailed drought monitoring.
-
Wang et al. (2025) Interpretable deep learning hybrid streamflow prediction modeling based on multi-source data fusion
This study introduces CICLAR, an enhanced interpretable deep learning hybrid model, for accurate daily streamflow and extreme flood prediction by fusing multi-source data and optimizing neural network hyperparameters. The CICLAR model significantly outperforms benchmark models, demonstrating improved accuracy in both general streamflow and extreme flood forecasting.
-
Zhang et al. (2025) Influence of drought identification methods on analyzing and assessing responses of water quality to droughts
This study compares fixed (FDT) and variable (VDT) drought identification methods to assess their influence on water quality responses in the Harp Lake catchment, Ontario. Findings reveal that the choice of drought identification method significantly impacts the assessment of water quality parameters (dissolved organic carbon, total nitrogen, total phosphorus) and their dynamics during drought events.
-
Can et al. (2025) Evaluating the impact of subsurface hydraulic barriers on Qanat flow rates using quantile regression forest
This study evaluated the impact of a subsurface dam on Qanat flow rates using machine learning models, finding that the dam significantly and positively influences discharge, with Quantile Regression Forest (QRF) demonstrating superior predictive performance (Nash–Sutcliffe Efficiency = 0.818).
-
Rana et al. (2025) Integrated Artificial Intelligence in Weather Forecasting for Agriculture: Opportunities, Challenges, and the Road Ahead
This review evaluates the application of Artificial Intelligence (AI) in agricultural weather forecasting, finding that AI, particularly hybrid AI-NWP models and multimodal data fusion, significantly improves prediction accuracy, reduces errors, and shortens lead times for various weather parameters and extreme events.
-
Sohrabi et al. (2025) Establishing coastal water level magnitude-duration-frequency curves for infrastructure design
This study introduces a framework for developing water level Magnitude-Duration-Frequency (MDF) curves using global tide gauge data to address the overlooked duration aspect of extreme sea level events. It finds that the average water level difference between 1-hour and 24-hour durations for similar return periods is 0.8 meters, with differences up to 3.63 meters for a 100-year event, and highlights the impact of event selection methods on these curves.
-
Wang et al. (2025) Analysis of Potato Growth, Water Consumption Characteristics and Irrigation Strategies in the Agro-Pastoral Ecotone of Northwest China
This study utilized the DSSAT model and two-year field observations to quantify potato water consumption and efficiency in the Yinshanbeilu agro-pastoral ecotone. It provided specific, validated irrigation recommendations to optimize potato yield or groundwater utilization, addressing challenges from the shift to irrigated agriculture.
-
Tang et al. (2025) Long‐Term Large‐Scale Atmospheric Forcing Data From Three‐Dimensional Constrained Variational Analysis for the ARM SGP Site
This study introduces VARANAL3D, a 15-year three-dimensional large-scale forcing data set, demonstrating that its incorporated spatial variability significantly improves the representation of clouds and precipitation in single column model simulations compared to domain-mean forcing.
-
Guzzon et al. (2025) Improving extreme precipitation forecasts in Catalonia (NE Iberian Peninsula) using analog methods: A comparison with the GFS model
This study evaluates novel analog-based methods (AMs) to enhance 24-hour extreme precipitation forecasts in Catalonia, aiming to support flood risk management. The findings demonstrate that AMs integrating Seasonal Standardization and the Perfect Prognosis framework significantly improve forecasts compared to the operational Global Forecast System (GFS), particularly in reproducing the intensity and spatial distribution of extreme events.
-
Prasad et al. (2025) The differential impact of global temperature trends on prolonged droughts in the Indian Monsoon realm during the past five millennia
This study provides a comprehensive hydroclimate overview of the past 5 millennia in the Indian Summer Monsoon realm (ISMr) by integrating existing records with new data from Manasbal Lake. It identifies two major reorganizations of seasonal precipitation pathways linked to high-latitude and tropical temperature trends, which triggered asynchronous prolonged droughts across the ISMr.
-
Rodrigo et al. (2025) Mechanisms of the QBO Influence on the Tropical Troposphere: Climatological SST Conditions
This study isolates the tropospheric impact of the Quasi-Biennial Oscillation (QBO) using an atmosphere-only experiment, revealing its seasonal modulation of deep tropical convection, Walker and Hadley circulations, particularly over the Indo-Pacific region, primarily driven by changes in static stability.
-
Shalwee et al. (2025) Multi-dimensional assessment of drought vulnerability with composite drought index and principal component analysis for socioeconomic development in Semi-Arid regions
This study developed a Composite Drought Index (CDI) using Principal Component Analysis (PCA) to integrate meteorological and agricultural drought indices, providing a more accurate and multi-dimensional assessment of drought vulnerability in semi-arid regions. The CDI revealed significantly higher instances of extreme and severe drought compared to individual indices, offering enhanced insights for drought management.
-
Zhang et al. (2025) EEMD disentangles climate-NPP dynamics in Southwest China’s karst area: Soil moisture overtakes precipitation as a key driver of afforestation-induced carbon sinks
This study used Ensemble Empirical Mode Decomposition (EEMD) to analyze Net Primary Productivity (NPP) and climate data in Southwest China's karst area (1981-2019), revealing that afforestation after 2001 significantly increased NPP, especially in new forests, and shifted the primary climate driver of NPP from precipitation to soil moisture.
-
Gao et al. (2025) A Review of Urban Flood Disaster Chain Research: Causes, Identification, and Assessment
This review synthesizes existing research on urban flood disaster chains, focusing on their formation mechanisms, identification methods, and risk assessment approaches. It highlights the need for unified quantitative frameworks and integrated models to enhance urban resilience against cascading flood impacts.
-
Chen et al. (2025) Causes of an extreme rainfall in Dubai on April 16th, 2024
This study investigates the causes of the extreme rainfall event in Dubai on April 16th, 2024, identifying anomalous vertical convection, driven by strong updrafts and a specific atmospheric circulation pattern, as the primary mechanism, termed the "Chimney effect."
-
Luo et al. (2025) Surface Soil Moisture Retrieval over Winter Wheat Fields Based on Fused Multispectral and L-Band MiniSAR Data
This study proposes a high-accuracy surface soil moisture (SSM) retrieval method for winter wheat fields by fusing Sentinel-2 satellite and UAV multispectral data with L-band MiniSAR observations. The results demonstrate that multi-platform data fusion combined with machine learning significantly outperforms single-satellite approaches, particularly at shallow soil depths.
-
Hannan et al. (2025) Regional heterogeneity in groundwater response driven by land-use transitions across Pakistan
This study investigated the relationship between land use and land cover (LULC) changes and groundwater storage (GWS) dynamics in Pakistan from 2003 to 2023. It found a national average GWS decline of −5.2 ± 0.7 mm per year, totaling approximately 104 mm over two decades, primarily driven by cropland expansion and reductions in forest, wetland, and open water cover.
-
Mihret et al. (2025) Hybrid GR4J-LSTM modeling for streamflow prediction of extreme events in data-scarce regions: Upper Blue Nile Basin, Ethiopia
This study develops and evaluates DeepGR4J, a hybrid rainfall-runoff model combining the GR4J conceptual framework with a Long Short-Term Memory (LSTM) neural network, for streamflow prediction and extreme event simulation in data-scarce regions of the Upper Blue Nile Basin, Ethiopia. The model demonstrates superior performance and transferability compared to standalone models, effectively predicting streamflow and extreme events like floods and droughts.
-
Guo et al. (2025) What roles do dynamic and thermodynamic water vapor transport processes play in extreme precipitation over the Asian monsoon region?
This study investigates the roles of dynamic and thermodynamic water vapor transport processes in extreme precipitation over the Asian monsoon region, revealing that while thermodynamic processes provide a robust basis, dynamic processes act as the direct trigger.
-
Ma et al. (2025) Severe 2023/2024 Winter Subseasonal Weather Extremes Over Eastern China: Two Pathways of ENSO Impacts
The study attributes the 2023/2024 winter cold extremes, heavy snowfall, and freezing rain in eastern China to two distinct pathways of El Niño's influence, modulated by enhanced atmospheric intraseasonal oscillation. These pathways involve El Niño's impact on the North Atlantic jet stream and the western North Pacific anticyclone, reinforcing cold-air circulation and enhancing moisture transport, respectively.
-
Onishi et al. (2025) How the hydrothermal regime differs between artificially planted coniferous and secondary deciduous forests
This study compared the hydrothermal regimes of paired coniferous and deciduous forest catchments using observational data and a two-heat source mixing model, revealing that deciduous catchments exhibit a larger groundwater contribution and a smaller seasonal stream temperature amplitude compared to coniferous catchments.
-
Kabtih et al. (2025) Risk of successive hot-pluvial extremes on crop yield loss over global breadbasket regions
This study investigates the risk of successive hot-pluvial extremes (SHPEs) on crop yield loss in global breadbasket regions, revealing an increasing trend in SHPE occurrences from 1979 to 2024 and a significant association with synchronized low yields for maize, rice, soybean, and wheat. Using machine learning models, the research quantifies yield sensitivity and predicts yield responses to these compound extreme events.
-
Yang et al. (2025) Agroforestry buffers drought stress by enhancing hydrological redistribution in dryland apple orchards
This study investigated the hydrological benefits of apple tree–oil crop (ATOC) intercropping systems under varying drought severities on China’s Loess Plateau, combining a four-year rainfall exclusion experiment with MAESPA modeling. It found that agroforestry enhances water-use efficiency and buffers moderate drought stress by promoting deeper water uptake and optimizing water partitioning, but its buffering capacity is overwhelmed under severe drought.
-
Cai et al. (2025) A Normalization-Calibration Model for Multi-Source Ground-Based FPAR Observations in Mountainous Forests
This study developed and validated a normalization-calibration model for multi-source ground-based FPAR observations in mountainous forests, using FPARnet as a reference, to overcome systematic biases and enhance spatial representativeness for remote sensing product validation. The model significantly improved consistency among FPAR data from various instruments, particularly for LAI-NOS, LAI-2200, and DHP.
-
Vasudeva et al. (2025) Forecasting Future Water Requirements and Assessing Storage Capacities in Reservoirs
This study develops predictive models using data science techniques, time-series forecasting, machine learning, and optimization strategies to forecast future water requirements and assess reservoir storage capacities for sustainable water management.
-
Chen et al. (2025) Improving estuarine discharge forecasting with a KAN-augmented LSTM model: A case study of the Yangtze River Estuary
This paper proposes a novel KAN-augmented LSTM (LSTM-KAN) hybrid deep learning model to enhance estuarine discharge forecasting in the Yangtze River Estuary, demonstrating significantly improved accuracy across short-, medium-, and long-term horizons compared to traditional and state-of-the-art methods.
-
Melón-Nava (2025) Patterns of snow cover distribution in the Cantabrian Mountains (NW Spain)
The study evaluates the performance of the ISBA land surface model and the mHM hydrological model across metropolitan France to improve the national hydrometeorological reanalysis. It demonstrates that while mHM excels in river discharge simulation due to its multiscale parameterization, ISBA provides a more comprehensive representation of surface energy fluxes.
-
Valk et al. (2025) Evaporation measurements using commercial microwave links as scintillometers
This study proposes and evaluates a novel, opportunistic method to estimate evaporation using commercial microwave links (CMLs) as scintillometers. It demonstrates the feasibility of using CMLs to estimate 30 min latent heat fluxes and daily evaporation, with the energy-balance method showing promising performance, though highly dependent on the quality of net radiation estimates.
-
Awoleye et al. (2025) Moisture and dust in motion: the dual role of integrated vapour transport over West Africa
This study comprehensively evaluates Integrated Vapour Transports (IVTs) over West Africa during the 2024 monsoon season, revealing their dual role as significant drivers of rainfall patterns and key regulators of atmospheric dust dynamics through wet scavenging.
-
Zhou et al. (2025) FoScenes: A high-fidelity, large-scale 3D forest plant area density product derived from open-access airborne lidar data
This study develops LS-PVlad, a novel workflow for large-scale 3D forest reconstruction from airborne lidar data, and introduces FoScenes, a high-fidelity plant area density product comprising 40 seamless scenes from 28 diverse forest sites, validated against field measurements and satellite products.
-
Zhang et al. (2025) Characterizing cold surge induced storm surge in the northern East China Sea: A 60-year hindcast reveals paradoxical trends in surge heights and return levels
This study conducted a 60-year hindcast of 780 cold surge-induced storm events in the northern East China Sea, revealing a paradoxical trend of declining storm surge frequency and magnitude but increasing return levels due to shifts in the surge height distribution.
-
Chen et al. (2025) Spatiotemporal propagation dynamics of multiple droughts in Central Asia: A three-dimensional perspective
This study employed a three-dimensional framework integrated with extreme gradient boosting and SHAP to systematically investigate the spatiotemporal dynamics, propagation characteristics, and dominant drivers of meteorological, hydrological, and agricultural droughts in Central Asia. The findings provide a scientific basis for developing early warning systems for drought-induced disaster chains in arid and semi-arid regions.
-
Gao et al. (2025) Land quality degradation exacerbates the impact of drought on vegetation in Northeast China
This study quantifies the coupled effects of drought and land degradation on the vulnerability, resistance, and recovery of forest, crop, and grassland vegetation in Northeast China from 2001 to 2020, revealing that land degradation significantly amplifies drought impacts, increasing vulnerability and reducing resistance, particularly for forests under severe combined stress.
-
Lu et al. (2025) Postprocessing for Fine Classification of Crops in UAV Hyperspectral Imagery
This paper focuses on developing postprocessing techniques to enhance the fine classification of crops using hyperspectral imagery acquired by Unmanned Aerial Vehicles (UAVs).
-
Tai et al. (2025) Declining sensitivity of transpiration fraction to vegetation greening in China’s arid grasslands: Role of soil moisture and vapor pressure deficit
This study investigated how vegetation greening affects the land-atmosphere water cycle in China's arid grasslands under coupled soil moisture (SM) and vapor pressure deficit (VPD). It found a widespread decline in the sensitivity of the transpiration-to-evapotranspiration ratio (T:ET) to Leaf Area Index (LAI) changes, primarily driven by boundary damping effects in relatively humid regions and increasing VPD in drier regions.
-
Zhao (2025) Daily All-Weather 30 m Land Surface Temperature over the Wanglang National Nature Reserve in 2022
This dataset provides daily, all-weather land surface temperature (LST) at 30 m spatial resolution for the Wanglang National Nature Reserve in 2022, generated through an integrated reconstruction–downscaling framework to mitigate cloud contamination and coarse thermal infrared resolution.
-
Lu et al. (2025) Mountain-plain solenoid over a deep basin modified by the Tibetan Plateau terrain effects on synoptic circulations
This study investigates how the Tibetan Plateau's topography modifies the Mountain-Plain Solenoid (MPS) circulation over the Sichuan Basin under different synoptic wind patterns, identifying two distinct MPS types (Westerly-type and Easterly-type) with varying vertical structures and intensities.
-
Cui et al. (2025) Optimizing deficit mulched drip irrigation improves grain crop yield and water productivity in global cropland
This global meta-analysis of 1071 field observations reveals that optimizing deficit mulched drip irrigation (DMDI) can reduce grain crop yield reduction risk and significantly increase water productivity by 8.5% compared to full mulched drip irrigation, with specific management practices and environmental conditions identified as key drivers for improved performance.
-
Li et al. (2025) Possible increase of tropical cyclone genesis frequency over the Northwest Pacific induced by the Tambora eruption in 1815
This study investigates the impact of the 1815 Tambora and 1991 Pinatubo tropical volcanic eruptions on tropical cyclone (TC) activity in the western North Pacific (WNP). Using high-resolution simulations and reanalysis data, the authors find that both eruptions significantly increased WNP TC genesis frequency and track density, primarily driven by dynamic factors like equatorial westerly wind anomalies despite general global cooling.
-
Bourbour et al. (2025) Pre-harvest forecasting of rainfed wheat yield in Iran using multi-source remote sensing and machine learning
This study developed and compared machine learning models integrating multi-source remote sensing and meteorological data to forecast rainfed wheat yield across 22 Iranian provinces from 2001 to 2021. The XGBoost algorithm achieved superior accuracy (R²=0.64, MAE=0.25 t/ha) two months pre-harvest, outperforming Random Forest and Support Vector Regression.
-
Morchid et al. (2025) Innovative applications of internet of things and machine learning in sustainable agricultural irrigation management: Benefits and challenges
This systematic review analyzes the integration of Internet of Things (IoT) and Machine Learning (ML) in smart agricultural irrigation, identifying their benefits in water conservation, irrigation efficiency, and productivity, alongside associated challenges and future research directions.
-
Lin et al. (2025) The impact of polar lows on the underlying ocean varies significantly by location
This study assesses the global impact of Polar Lows (PLs) on the underlying ocean using sensitivity experiments, revealing that their effects vary significantly by region depending on PL frequency and ocean structure, with PL-associated wind and precipitation often having distinct and opposing influences.
-
July-Wormit (2025) Amélioration de la prévision du givrage par eau liquide surfondue
Information not extractable from the provided text.
-
Kuma et al. (2025) Ship‐Based Lidar Evaluation of Southern Ocean Low Clouds in the Storm‐Resolving General Circulation Model ICON and the ERA5 and MERRA‐2 Reanalyses
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
-
Kar et al. (2025) Rebuilding soil hydrological functioning by adopting conservation agriculture in the degraded lands of India’s North-West Himalayan region
This study investigates the impact of land use transition from forest to various agricultural practices (conventional, reduced, and zero tillage) on soil hydraulic conductivity and dominant flow paths in the North-West Indian Himalayas. It concludes that adopting conservation agriculture can effectively restore soil hydrological functioning by diminishing infiltration-excess overland flow and enhancing subsurface flow and deep percolation.
-
Xue et al. (2025) Climate–human interactions influence widespread peatland subsidence and soil carbon stock vulnerability in China
This study provides the first national-scale assessment of peatland subsidence across China by integrating satellite radar observations with advanced modeling. It reveals widespread subsidence, particularly in hotspots like the Zoigê Plateau, driven by a combination of climate variability, drought, peat depth, and human pressures, projecting that over 65% of China's peatland carbon stock will be vulnerable under future high-emission scenarios.
-
McErlich et al. (2025) Description and evaluation of airborne microplastics in the United Kingdom Earth System Model (UKESM1.1) using GLOMAP-mode
This study describes the integration and evaluation of airborne microplastics into the United Kingdom Earth System Model (UKESM1.1) using its GLOMAP aerosol scheme. The model simulates global transport and deposition, revealing that smaller, hydrophilic microplastics have longer atmospheric lifetimes and can reach remote regions and the lower stratosphere, though their current contribution to total aerosol burden is minor.
-
Silber-Coats et al. (2025) Beyond scarcity: Science-based solutions for water and agriculture in the Western United States
This editorial synthesizes 14 research contributions focusing on science-based demand management strategies for sustainable agriculture in the water-scarce Western United States, demonstrating that agricultural productivity, environmental sustainability, and economic resilience are mutually compatible goals.
-
Shi et al. (2025) Ai-driven spatiotemporal modelling of agricultural biomass residues in Northern Italy: A data-fusion and explainable-AI framework
This study presents an AI-based framework integrating Feedforward Neural Networks and XGBoost with remote sensing and explainable AI to assess and forecast agricultural biomass residue potential across 47 provinces in Northern Italy from 2002 to 2031. It finds that maize and wheat dominate residue supply, contributing over 100 million metric tonnes of dry biomass annually, but forecasts a decline in productivity after 2026 due to rising temperatures, increased water deficits, and land-use change.
-
Chen et al. (2025) Lateral groundwater-river exchanges estimation via actively heated fiber optics based thermal response test
This study introduces a novel data assimilation framework combining Actively Heated Fiber Optics Based Thermal Response Testing (ATRT) with Normal Score Ensemble Smoother with Multiple Data Assimilation (NS-ES-MDA) to accurately quantify lateral groundwater-surface water exchange fluxes by characterizing non-Gaussian hydraulic conductivity fields. The framework effectively resolves aquifer heterogeneity and localizes flux variations, showing enhanced accuracy under rapid flow reversal conditions.
-
Dong et al. (2025) Author Correction: Intensification of extreme cold events in East Asia in response to global mean sea-level rise
This paper investigates the potential intensification of extreme cold events in East Asia as a consequence of global mean sea-level rise.
-
Shen et al. (2025) An effective gauge-satellite fusion approach for daily precipitation bias correction based on multi-dimensional precipitation feature space (BCFS)
This paper proposes a novel daily precipitation bias-correction approach, BCFS, which integrates spatial patterns with temporal attributes into a unified precipitation feature space for gauge-satellite fusion. The method significantly improves the accuracy of daily precipitation estimates, especially in gauge-sparse regions, by effectively reducing biases across various precipitation intensities.
-
Ma et al. (2025) Characteristics and Mechanisms of the Dipole Precipitation Pattern in “Westerlies Asia” over the Past Millennium Based on PMIP4 Simulation
This study investigates hydroclimate variability in Westerlies Asia over the past millennium using PMIP4 multi-model simulations, revealing a persistent dipole precipitation pattern between arid Central Asia (ACA) and arid West Asia (AWA) driven by seasonal North Atlantic Oscillation (NAO) phases.
-
Grünig et al. (2025) Data from: Climate change will increase forest disturbances in Europe throughout the 21st century
This study simulates future forest disturbance regimes across Europe using a deep learning-based framework, projecting a more than doubling of disturbed forest area by the end of the 21st century, primarily driven by wildfires, which will significantly alter Europe's forest demography.
-
Nagashree et al. (2025) Evaluating sectoral water use and precipitation variability on blue water scarcity under historical and future climate conditions in the Mahi River Basin
This study quantifies the blue water scarcity index (WSIbw) in the Mahi River Basin, India, by integrating sectoral water withdrawals and precipitation variability under historical and future climate conditions. It finds that agriculture is the primary driver of scarcity, and future dry years are projected to intensify water stress by over 60% in some upper-basin districts.
-
Liu et al. (2025) Global warming intensifies extreme day-to-day temperature changes in mid–low latitudes
Global warming is intensifying extreme day-to-day temperature changes (DTDTs) in mid-low latitudes, a distinct and largely ignored extreme weather event, posing substantial risks to human health and ecosystems, with climate models projecting further amplification by 2100.
-
Hossan et al. (2025) Evaluation of wet snow dielectric mixing models for L-band radiometric measurement of liquid water content in Greenland's percolation zone
This study compares ten microwave dielectric mixing models for estimating liquid water amount (LWA) in wet snow and firn in Greenland's percolation zone using L-band radiometry, finding substantial differences in model performance and LWA retrievals, with power law-based empirical models generally performing better against surface energy and mass balance models.
-
Cremer et al. (2025) Atmospheric Correction Inter-Comparison eXercise, ACIX-III Land: An Assessment of Atmospheric Correction Processors for EnMAP and PRISMA over Land
This study extends the ACIX benchmark to comprehensively assess atmospheric correction processors for EnMAP and PRISMA imaging spectroscopy missions over land, evaluating their accuracy, precision, and uncertainty in retrieving aerosol optical depth, water vapour, and surface reflectance against ground truth data.
-
Cheng et al. (2025) Decadal Climatology and Trends in Oceanic Precipitation from Multiple Satellite and Reanalysis Datasets
This study examines climatologies and trends in global oceanic precipitation using 27 satellite and reanalysis datasets from 2001 to 2020. It finds that latest-version satellite products generally show an upward ocean-mean trend and better align with the "wet gets wetter, dry gets drier" hypothesis compared to reanalysis datasets, which often suggest declining trends.
-
Yaoyao et al. (2025) Rainfall determines the temporal stability of soil water content following the conversion of deep rooted to shallow rooted vegetation in arid regions
This study evaluated the spatiotemporal dynamics of soil water content (SWC) and its influencing factors after converting deep-rooted to shallow-rooted vegetation in arid regions. It found that the temporal stability of SWC increased with the duration of conversion, with rainfall being the primary influencing factor.
-
Zhu et al. (2025) Do Models Mis-Represent Evaporative Regimes in Arid and Semi-Arid Regions?
Not applicable - no scientific paper content provided.
-
González-Ramón et al. (2025) Integration of 3d geological models and groundwater flow models for the improvement of the management of complex multilayer aquifers under intensive exploitation. The case of the Loma de Úbeda (Southern Spain)
This study integrates 3D geological and numerical groundwater flow models to improve the management of the complex, intensively exploited multilayer aquifer system of Loma de Úbeda, Southern Spain. The research successfully corroborates the conceptual hydrogeological model and provides a validated tool for sustainable water resource management, highlighting the shift in water storage and flow dynamics due to prolonged pumping.
-
Chakrabortty et al. (2025) Urban Flood Susceptibility Assessment in Arid Environment Using a Novel Hybrid Deep Learning Approach
This study develops Hydro-TransformerNet, a novel hybrid deep learning framework for urban flood susceptibility mapping in data-scarce arid environments, demonstrating strong predictive performance (AUC of 0.945) by integrating spatial, temporal, and hydrologically guided attention mechanisms. The model effectively identifies flood-prone areas in Sharjah, UAE, using remote sensing data and synthetic flood masks, providing a scalable and interpretable tool for urban planning and disaster mitigation.
-
Ferdinand et al. (2025) Spatio-temporal variability of flooded areas in the Ouémé floodplain (Benin, West Africa) from 2015 to 2023
This study assessed the spatio-temporal variability of flooded areas in the Ou´em´e floodplain (Benin) from 2015 to 2023 using remote sensing and in-situ data, revealing a significant upward trend in flood extent driven by cumulative rainfall and river water surface elevation thresholds.
-
Westen et al. (2025) Changing European hydroclimate under a collapsed AMOC in the Community Earth System Model
This study investigates the impact of Atlantic Meridional Overturning Circulation (AMOC) collapse, combined with climate change scenarios, on the European hydroclimate using the Community Earth System Model (CESM). It finds that AMOC collapse exacerbates projected drying trends and drought extremes across Europe, primarily through reduced precipitation and increased potential evapotranspiration under higher radiative forcing.
-
He et al. (2025) Stratospheric aerosol injection does not cause stronger Asian monsoon drying than greenhouse gas mitigation
This study investigates the impact of Stratospheric Aerosol Injection (SAI) on Asian Monsoon (ASM) rainfall, finding that equatorial SAI causes no additional drying compared to greenhouse gas (GHG) mitigation for the same global mean surface temperature (GMST) cooling, and suggests that well-designed SAI strategies can minimize monsoon failure risk.
-
Kim et al. (2025) An Airborne G-Band Water Vapor Radiometer and Dropsonde Validation of Reanalysis and NWP Precipitable Water Vapor over the Korean Peninsula
This study conducted a unique airborne validation of hourly Precipitable Water Vapor (PWV) from local Numerical Weather Prediction (NWP) models and global reanalysis datasets over the Korean Peninsula, revealing that ERA5 provides the most accurate representation while local models exhibit significant dry biases, especially in moist and cloudy conditions.
-
Clement et al. (2025) A Signal-to-Noise Problem in Model Simulation of Decadal Climate Modes
This study investigates the influence of external radiative forcing on three major regional decadal climate modes (AMV, NAO, PDO) using large ensembles of over 50 climate models. It finds that radiative forcing is an important component of their observed behavior, but climate models significantly underestimate the amplitude of this forced signal, leading to an erroneous "signal-to-noise problem" that obscures predictable trends.
-
Peng et al. (2025) Ocean-driven shifts in circulation regime frequency modulate South China rainfall
This study reveals that the interannual variability of South China's rainy-season precipitation is primarily driven by shifts in the frequency of daily atmospheric circulation regimes, rather than changes in rainfall intensity. These regime frequency shifts are modulated by remote sea surface temperature (SST) anomalies in the Indian, Pacific, and Atlantic Oceans, which trigger large-scale atmospheric responses over the western North Pacific.
-
Heinz et al. (2025) From Soil to Stream: Modeling the Catchment-Scale Hydrological Effects of Increased Soil Organic Carbon
This study modeled the catchment-scale hydrological effects of increased soil organic carbon (SOC) in an agriculturally dominated catchment in Switzerland, finding that while SOC enhancement benefits soil water content and attenuates peak flows, it may also reduce groundwater recharge and downstream water availability.
-
Chakraborty et al. (2025) The mirage of the silver bullet: Exploring the limitations of high-resolution data in flood model validation
This study explores the limitations of high-resolution data in flood model validation, demonstrating that while beneficial, it does not resolve all discrepancies, which often stem from a complex interplay of observed data limitations, model uncertainties, and structural differences between datasets.
-
Qiu et al. (2025) Large contribution of antecedent climate to ecosystem productivity anomalies during extreme events
This study quantifies the significant contribution of antecedent climate conditions to ecosystem productivity anomalies during extreme events using an interpretable machine-learning framework, revealing that memory effects, particularly from precipitation, temperature, and vapour pressure deficit, substantially influence ecosystem responses, especially in semi-arid regions.
-
Gong et al. (2025) Exploring the impacts of assimilating AMSR2 and GNSS PWV data on rainfall prediction in South China
This study investigates the impact of assimilating AMSR2 and GNSS Precipitable Water Vapor (PWV) data into the WRF model using 3DVAR on rainfall prediction for extreme events in South China, demonstrating significant improvements in both forecast scores and spatial patterns.
-
Pfeifer et al. (2025) Efficient ice multiplication from freezing raindrop fragmentation
This study quantifies secondary ice production (SIP) from freezing raindrop fragmentation (DFF) during a refreezing rain event using combined in situ and remote sensing observations, estimating 1.2 to 6.1 secondary ice crystals produced per drop, with drops of 5.0 x 10^-4 m to 1.0 x 10^-3 m diameter being most prone to breakup.
-
Xu et al. (2025) Spatiotemporal Reconstruction of FY-3B Soil Moisture Using a Hybrid Attention and Partial Convolution Neural Network
This paper focuses on the spatiotemporal reconstruction of FY-3B satellite soil moisture data using a novel deep learning architecture.
-
Akhtar et al. (2025) Integrated use of meteorological and hydrological indices for drought early warning in the mountainous catchments of the Himalaya-Karakorum-Hindukush region
This study investigates the relationship between meteorological and hydrological droughts in the Upper Indus catchments of Pakistan using SPEI and SSI. It finds strong lagged cross-correlations between these indices in early Kharif months for Chenab, Jhelum, and Kabul catchments, which can be utilized for operational drought early warning and reservoir planning.
-
Kabe et al. (2025) Impact of hydrogeological regime changes on the Bakhtegan-Tashk lake system under groundwater overextraction
This study investigated the impact of human-induced hydrogeological regime changes on the desiccation of the Bakhtegan-Tashk Lake (BTL) system in southern Iran. It revealed a reversal in the natural groundwater-surface water flow, with the lake now losing approximately 10.5 million cubic meters of water annually to overexploited adjacent aquifers, accelerating its desiccation.
-
Morgan et al. (2025) Co-regulation of water use and canopy temperature in desert trees
This study assessed how desert trees co-regulate water use and canopy temperature under varying hydroclimatic stress using UAV-based remote sensing, finding species-specific water-use strategies but similar thermal responses, with a decoupling of water and temperature regulation under hot, wet conditions.
-
Bartsch et al. (2025) Soil laboratory and satellite spectral data filtering: A Spectral Quality Protocol (SQuaP)
This study introduces the Spectral Quality Protocol (SQuaP) to filter laboratory and satellite soil spectral data, significantly improving the reliability and predictive performance of soil property models for clay and soil organic carbon.
-
Li et al. (2025) Societal and environmental interconnections: future directions for flood inundation models
This review synthesizes the evolution of flood inundation models from 1970 to 2023, highlighting the transition toward large-scale simulations and identifying eight interdisciplinary frontiers for future research.
-
Cenobio-Cruz et al. (2025) Uncertainty propagation from gridded precipitation datasets to streamflow simulations: application to the Reno River basin (Italy)
[Information not extractable from the provided corrupted text.]
-
Talbot et al. (2025) Enhancing physically based and distributed hydrological model calibration through internal state variable constraints
This study evaluates the impact of incorporating groundwater recharge constraints into the calibration of the physically-based Water Balance Simulation Model (WaSiM). It finds that while streamflow-only calibration yields higher Kling-Gupta Efficiency, adding groundwater recharge constraints improves the representation of internal hydrological processes and seasonal runoff patterns.
-
Xie et al. (2025) Assessing the impact of green infrastructure spatial distributions on hydrological connectivity and runoff reduction using landscape metrics
This study systematically evaluates how three green infrastructure (GI) spatial strategies (uniform, random, and junction-based) affect runoff reduction, hydrological connectivity (HC), and landscape integrity in an urban watershed. It finds that spatial configuration, beyond total GI quantity, drives multifunctional performance, with trade-offs where intermediate-sized GI maximizes runoff reduction, while larger, interspersed GI patches better preserve HC.
-
Germeç et al. (2025) Modeling lake-groundwater interactions under climatic and anthropogenic stressors in a mediterranean closed basin: Burdur Lake, Türkiye
This study developed an integrated lake-groundwater model for Burdur Lake, Türkiye, to assess the long-term impacts of climate change and anthropogenic stressors. It found that surface water regulation by upstream reservoirs is the dominant driver of lake-level decline, with restoration of natural inflows potentially leading to a 3 meter recovery despite climate stress.
-
Cai et al. (2025) Impacts of Local and Remote Sub‐Grid Terrain Solar Radiative Effect on the Meiyu Rainfall Forecast in the Yangtze‐Huaihe River Basin, China
This study systematically explores the relative impacts of local (Yangtze-Huaihe River Basin) and remote (Tibetan Plateau) sub-grid terrain solar radiative effects (STSRE) on Meiyu rainfall forecasts in East Asia. It finds that local STSRE significantly improves forecast skill more than remote STSRE, primarily by reducing the overestimation of rainfall.
-
Wang et al. (2025) Spatiotemporal correction of decision variables using XGBoost for multi-objective intelligent scheduling rule extraction model in reservoir-lake flood control systems
This study introduces a Spatiotemporal Correction using XGBoost (SC-XGB) technique to extract intelligent multi-objective scheduling rules for reservoir-lake flood control systems, addressing challenges in reducing spatiotemporal errors and improving Pareto frontier simulation quality. The SC-XGB model demonstrates enhanced accuracy and generalization in the Chaohu Basin, significantly improving outflow prediction, reducing water balance errors, and decreasing relative hypervolume error compared to the standard XGB model.
-
Xu et al. (2025) Assessing the compound impacts of urbanization and climate change on flood hazards in rapid urbanized basin: A case study of Chebei River Basin, South China
This study quantifies the compound impacts of urbanization and climate change on flood hazards in the Chebei River Basin (1980-2050) using a coupled hydrodynamic model and interpretable AI, revealing a critical urbanization threshold of approximately 65% that fundamentally shifts the hydrological response to rainfall.
-
Nigro et al. (2025) Controls on Western U.S. Interior Water Cycling Over the Holocene: Investigating Water Isotope Trends in iCESM and Ferricrete Proxy Data
[Information not provided in the text, as the provided content is not a scientific paper.]
-
Li et al. (2025) Atmospheric dryness effects on canopy chlorophyll fluorescence and Gross Primary Production (GPP) in a deciduous forest during heat waves
This study investigated how atmospheric dryness during heat waves affects Sun-Induced chlorophyll Fluorescence (SIF), LED-induced chlorophyll fluorescence (FyieldLIF), and Gross Primary Production (GPP) in a temperate deciduous forest. It found that FyieldLIF provided a more robust and physiologically sensitive proxy for GPP and photosynthetic capacity (Amax) under high atmospheric dryness conditions compared to SIF, particularly at daily temporal scales.
-
Zhou et al. (2025) Nonstationary groundwater level responses to coupled human–natural drivers in the Baiyangdian Watershed
This study develops an integrated framework to quantify direct and indirect effects of coupled human-natural drivers on groundwater level (GWL) variations in the Baiyangdian Basin, revealing a transition in GWL driving mechanisms from natural-anthropogenic synergy to human activity dominance and hydrological regulation over different periods.
-
Zhao et al. (2025) Contrasting influences of cloud properties and environmental factors on potential cloud precipitation capacity over China: A wet vs. dry years analysis
This study analyzes the influence of environmental conditions and cloud properties on Potential Cloud Precipitation Capacity (PCPA) over China, revealing distinct cloud-precipitation interactions and their differential dependence between dry and wet years across various regions.
-
Zhang et al. (2025) Quantifying the contributions of isolated and connected pores to soil permeability in alpine meadow soils
This study developed the DHC-FVAM model to quantify the distinct contributions of connected and isolated pores to soil permeability in alpine meadow soils during frozen soil thawing. It found that connected pores dominate water transport, with their contribution significantly increasing as thawing progresses due to enhanced connectivity and pore transformation.
-
García‐García et al. (2025) Intercomparison of Earth Observation products for hyper-resolution hydrological modelling over Europe
This study comprehensively evaluated high-resolution Earth Observation (EO) products for precipitation, snow cover area, surface soil moisture, and evapotranspiration over Europe against observational references. It identified specific merged precipitation, MODIS/Sentinel-2 snow, and NSIDC SMAP soil moisture products as best performing for hyper-resolution hydrological modeling, while evapotranspiration products showed similar overall performance.
-
Xie et al. (2025) The impact of compound droughts and heatwaves on ecosystem carbon-water dynamics in Eurasia
This study investigated the spatiotemporal patterns of compound droughts and heatwaves (CDHW) in Eurasia from 1984 to 2018 and their impacts on ecosystem carbon-water dynamics (Net Ecosystem Carbon Exchange and water fluxes) across different land cover types. It found an increasing CDHW frequency and a general negative impact on carbon uptake and water fluxes, with grasslands being most affected in terms of carbon uptake.
-
Tomanek et al. (2025) Observations and Simulation of a High-Precipitation Texas Supercell: ICECHIP
N/A - Information not provided in the text.
-
Jalilvand et al. (2025) Characterization of irrigation timing using thermal satellite observations, a data-driven approach
This study presents a data-driven framework using thermal satellite observations and change point detection to estimate irrigation timing and individual events. By comparing cropland land surface temperature (LST) with nearby natural vegetation, the method accurately identifies irrigation schedules in diverse agricultural regions.
-
Prange et al. (2025) Elucidating the loose tie between precipitation and streamflow sensitivities to warming across the contiguous United States
This study uses a moderately high-resolution global climate model in a counter-factual warming scenario to elucidate the hydrological processes driving regional streamflow sensitivities to warming across the contiguous United States. It finds that while the West Coast and eastern US experience increased high-flows driven by atmospheric rivers, the mountainous western US sees dwindling streamflows due to snow loss fueling evapotranspiration at double the rate of precipitation changes.
-
Bowen et al. (2025) Water demand and management impacts on streamflow under the prior appropriation doctrine in the bighorn basin
This study evaluates how the spatial and temporal distribution of water rights and associated management decisions, under the prior appropriation doctrine, affect streamflow in the over-appropriated Bighorn Basin. It demonstrates that water right network structure and seniority strongly shape streamflow, particularly at mid-elevations, and that improving irrigation efficiencies paradoxically reduces streamflow by increasing overall consumptive use.
-
Pashinov et al. (2025) Channels near 22.235 GHz band improve the accuracy of water vapor profile measurements by microwave sounders
This study demonstrates that incorporating additional radiometric channels near the 22.235 GHz water vapor absorption line significantly improves the accuracy of space-borne microwave sounder measurements for atmospheric water vapor profiles, particularly in the lower troposphere (0-4 km), by enhancing sensitivity and information content. Both modeling and real satellite data confirm a substantial reduction in retrieval errors in this critical altitude range.
-
Pellicone et al. (2025) Assessment of Multiple Satellite Precipitation Products over Italy
This study evaluated five satellite precipitation products (CHIRPS, GPM, HSAF, PDIRNOW, SM2RAIN) against high-resolution ground data in Italy to address rainfall estimation uncertainties. It found that no single product performs optimally across all metrics, with GPM showing the most balanced performance, and product suitability depending on the intended hydrological application.
-
Zhang et al. (2025) Evaluation and statistical bias correction of ERA5-Land meteorological variables for a humid river basin in Southwest China
This study evaluates the performance of the ERA5-Land reanalysis dataset for key meteorological variables in the Lower Jinsha River Basin, China, and develops a statistical bias correction procedure. The correction significantly reduces systematic biases and improves the accuracy of precipitation, wind speed, air temperature, and solar radiation, providing a more reliable dataset for clean energy planning in data-scarce regions.
-
Mei et al. (2025) Soil moisture variability of typical mixed forests in the north-south climatic transitional zone of China
This study identified dominant drivers of soil moisture and developed a CEEMDAN-LSTM simulation framework for different mixed forests in China's north-south climatic transitional zone, finding that conifer-broadleaved mixed forests (CBMF) significantly enhance soil moisture and exhibit the highest hydrological conservation capacity under precipitation variation.
-
Préaux et al. (2025) On the proper use of screen-level temperature measurements in weather forecasting models over mountains
This study investigates how structural inhomogeneities in mountain observational networks, particularly varied sensor height, affect near-surface air temperature representation and assimilation in the Arome-France numerical weather prediction system. It reveals that neglecting sensor height differences significantly degrades model evaluation and assimilation performance, especially at night in high-altitude regions.
-
Bruna et al. (2025) Mikroklimatická mapa Průhonického parku
This study generated a set of eight high-resolution (5 m) microclimate maps for Průhonický Park, covering both forested and open areas, revealing significant microclimatic heterogeneity influenced by topography and vegetation structure. The maps provide essential data for park management, conservation, and ecological research.
-
Zareian et al. (2025) Adapting to dryness: Two decades of agricultural transformation in Iran’s arid zone through the water-energy-food-carbon lens
This study analyzed agricultural transformations in Iran's Isfahan Province (2004–2023) using the Water-Energy-Food-Carbon (WEFC) Nexus framework, revealing that declining groundwater availability, rather than meteorological drought, drove shifts towards water-efficient, high-value crops, improving water productivity and reducing environmental footprints despite a decrease in total cultivated area.
-
Roohinia et al. (2025) Spatiotemporal connections in high precipitation events in Iran: Application of complex networks
This study applies Complex Networks Theory, Event Synchronization (ES), and Event Coincidence Analysis (EC) to investigate synchronous and lagged teleconnections of high precipitation events in Iran and surrounding regions from 1980 to 2019, revealing shifts in critical moisture pathways and offering opportunities for seasonal forecasting.
-
Pan et al. (2025) Impact of permafrost degradation on alpine grasslands in the Three-Rivers Headwater Region
This study investigated the impact of permafrost degradation, measured by active layer thickness (ALT) and soil non-frozen period (NFP), on alpine grassland growth in the Three-Rivers Headwater Region (TRHR) from 2000 to 2020 using satellite data and statistical methods. It found significant permafrost degradation and grassland greening, with ALT and NFP having complex, spatially varying effects on grassland growth, suggesting potential future instability in warmer regions.
-
Diao et al. (2025) Vertical Wind Velocity Data from NSF and NASA Flight Campaigns
This paper presents a unified, quality-controlled dataset of near-global, in-situ atmospheric vertical velocity measurements at 1-second frequency, compiled from 12 NSF and NASA airborne campaigns conducted from 2008 to 2016. The dataset is structured to support research on atmospheric turbulence, vertical velocity trends, and cloud processes.
-
Jia et al. (2025) Evapotranspiration estimation at different land surface scales in semi-arid areas using gene expression programming and the FAO56 Penman-Monteith model
This study developed a GEP-PM model by integrating Gene Expression Programming (GEP) with the FAO56 Penman-Monteith (PM) model to improve actual evapotranspiration (ETa) estimation in semi-arid regions. The model accurately predicts ETa across diverse land surfaces (sand dunes and meadows) at a 30-minute temporal scale, demonstrating superior performance compared to traditional methods.
-
Brickner et al. (2025) Field crop mapping using machine learning and multi-sensor satellite fusion: toward dynamic agricultural monitoring
This study developed a novel hierarchical machine learning framework integrating Sentinel-1 SAR and Sentinel-2 multispectral data to generate high-resolution, multi-season crop type maps in dryland agricultural regions. The framework achieved high accuracy in classifying agricultural land cover and specific crop types, enabling dynamic monitoring of crop rotation, land-use intensity, and climate-driven phenological gradients.
-
Robelin et al. (2025) RECHARGE, a model of potential recharge of aquifers applied to mainland France
The study introduces the RECHARGE model, a simplified soil water balance approach designed to estimate potential groundwater recharge across mainland France by correlating effective precipitation with a cartographic infiltration index (IDPR). The model provides a robust, large-scale estimation of renewable groundwater resources, validated against observed river flows and the physically-based SURFEX model.
-
Sushmitha et al. (2025) Spatiotemporal trends and drivers of evapotranspiration across india’s diverse climatic zones
This review comprehensively analyzes spatiotemporal evapotranspiration (ET) trends and their drivers across India's diverse climatic zones, revealing complex regional variations with both increasing and decreasing ET influenced by factors like temperature, solar radiation, and land-use changes. It highlights the critical implications of these trends for water resource management, agricultural productivity, and climate modeling, emphasizing the need for advanced monitoring and region-specific adaptation strategies.
-
Alimohammadi et al. (2025) Reliability analysis of dam spillway capacity under extreme flood uncertainty: Boostan Dam, Iran
This study developed an integrated analytical-numerical framework to quantify uncertainties in Probable Maximum Precipitation (PMP), Probable Maximum Flood (PMF), and peak discharge quantiles for the Boostan Dam, Iran, and assessed spillway reliability. It found that the existing spillway has negligible reliability for PMF and requires significant widening (50-80 m) to achieve 95% reliability for 1,000-10,000 year floods.
-
Qiao et al. (2025) Particle Size as a Key Driver of Black Carbon Wet Removal: Advances and Insights
This paper reviews recent research on the size dependence of black carbon (BC) wet removal, highlighting that particle size is a critical driver influencing its atmospheric lifetime and climate impact, with significant uncertainties remaining in observational coverage and model parameterization.
-
Wang et al. (2025) Recent south-central Andes water crisis driven by Antarctic amplification is unprecedented over the last eight centuries
This study reconstructs 827 years of Negro River streamflow in northern Patagonia using tree-ring records, revealing an unprecedented decline in recent decades. This decline is primarily driven by Antarctic amplification, which exacerbates temperature rise and disrupts circulation patterns, intensifying regional aridity.
-
Sanda et al. (2025) Assessing the adequacy of three-component hydrograph separation for runoff partitioning in a small forest catchment
This study assessed the adequacy of three-component hydrograph separation for runoff partitioning in a small forest catchment, focusing on the magnitude and persistence of tracer-based uncertainties throughout rainfall-runoff events. It revealed that significant and sometimes physically implausible uncertainty can persist when end-member tracer signatures are similar, emphasizing the need for comprehensive uncertainty evaluation across full event hydrographs.
-
Gnanasekaran et al. (2025) Agronomic and environmental dimensions of large-scale irrigation projects for sustainable agriculture
This review synthesizes the agronomic and environmental dimensions of large-scale irrigation projects (LSIPs) globally, highlighting their crucial role in food security and water management while addressing challenges posed by climate change and proposing integrated, climate-resilient strategies. It emphasizes the necessity of combining adaptive management, community engagement, modern technologies, and hydrological models for sustainable agricultural productivity and water security.
-
Lu et al. (2025) Vegetation evolution and water sensitivity analysis in the source region of the Yangtze River and the Yellow River under the combined drive of energy-temperature-water
This study analyzed the temporal and spatial evolution of vegetation in the source region of the Yangtze and Yellow Rivers, revealing that 82.26% of the regional vegetation significantly improved from 1982 to 2020. It found that temperature and energy primarily influenced vegetation indirectly through water sources, while precipitation and shallow soil water were the most significant direct water source factors.
-
Yaseen et al. (2025) Quantitative assessment of best management practices for soil and water conservation: A case study from the Tarquinia plain
This study utilized the Soil and Water Assessment Tool (SWAT) to quantitatively evaluate individual and combined Best Management Practices (BMPs) in the Tarquinia plain, Italy, demonstrating that combined BMPs significantly reduce river sediment load by up to 33.9 %, total nitrogen by 27 %, and total phosphorus by 27.5 %.
-
Guo et al. (2025) An improved ROBust OpTimization-based (iROBOT) fusion model for reliable spatiotemporal seamless remote sensing data reconstruction
This study introduces iROBOT, an improved spatiotemporal fusion model that addresses block artifacts and cloud contamination in remote sensing data reconstruction by employing object-level processing and an adaptive gap-filling strategy. Experiments demonstrate iROBOT's superior accuracy and robustness compared to existing methods, particularly in cloud-prone environments.
-
Bongiovanni et al. (2025) Mapping soil water properties using soil samples and satellite images in the irrigated area of Biota (Spain)
This study developed and assessed quantitative and qualitative Total Available Water (TAW) maps for a shallow, stony irrigated area in Spain using Sentinel-2 imagery and field data. The quantitative maps, derived from multi-temporal satellite data, provided finer spatial detail and correlated with crop yield, offering enhanced value for irrigation management compared to traditional qualitative approaches.
-
Yin et al. (2025) Validation of the MODIS Clumping Index: A Case Study in Saihanba National Forest Park
This study developed a multi-scale validation framework for MODIS Clumping Index (CI) products using field measurements, UAV, and Landsat 8 data, revealing that MODIS CIs are generally reliable but subject to significant uncertainties due to scale issues and subpixel heterogeneity.
-
Liang et al. (2025) Quantifying anthropogenic drivers of water storage decline to support sustainable water management in a coal-mining semi-arid region
This study quantifies the anthropogenic drivers of terrestrial and groundwater storage decline in China's Mu Us Sandyland from 2003 to 2020 using a water balance framework, finding that ecological restoration and irrigation are the primary drivers, with coal mining also significant in energy-intensive areas, and proposes spatially differentiated management strategies for future sustainability.
-
Kim et al. (2025) An integrated approach for characterizing and selecting climate change scenarios based on variability and extremeness
This study proposes a novel integrated approach for selecting optimal Global Climate Model (GCM) and Shared Socioeconomic Pathway (SSP) combinations for aquatic environment impact assessments by quantifying and integrating climate change variability and extremeness into a single metric. The method effectively captures the full range of climate scenarios with a minimal number of combinations, demonstrating that expected trends in variability and extremeness under severe warming are not always consistent across all GCMs.
-
Arifin et al. (2025) Plausibility Criteria for GRACE‐Derived Groundwater Storage Changes From Aquifers Globally
This paper introduces a framework to evaluate the physical plausibility of groundwater storage change (ΔGWS) estimates derived from GRACE satellite data across 37 large global aquifer systems. The study demonstrates that excluding implausible estimates significantly improves the correlation of ΔGWS with total water storage and in situ observations, enhancing the reliability of groundwater monitoring.
-
Kumar et al. (2025) An Innovative Agrivoltaic System for Desert Climates with Anti-Soiling, Irradiance Control, and Water Management
This paper introduces GROOViD, an innovative agrivoltaic system designed for desert climates, integrating solar tracking, dual-use water systems for irrigation and module cleaning, and anti-soiling capabilities. Preliminary findings after six months demonstrate promising performance and reliability, along with benefits for photosynthetically active radiation (PAR), crop evapotranspiration, and anti-soiling.
-
Singh et al. (2025) Agricultural catchments exhibit enhanced climate and drought resilience compared to forested catchments in Peninsular India
This study assesses the drought and climate resilience of 116 catchments across 14 basins in Peninsular India, revealing that agricultural catchments with higher crop fraction and irrigation exhibit greater resilience compared to forested catchments, challenging traditional hypotheses.
-
Ge et al. (2025) Preliminary application of Chinese high-resolution small SAR satellites in large-scale monitoring of the middle route of the South-to-North Water Diversion Project
This study evaluates the preliminary application of Chinese high-resolution small SAR satellites ("Fucheng-1" and "Shenqi" series) for large-scale ground deformation monitoring along the Tianjin section of the South-to-North Water Diversion Project (SNWDP), demonstrating their capability and consistency with Sentinel-1A.
-
Fawcett et al. (2025) Hyperspectral indicators of vegetation vitality across scales: From trees to forests
This study assesses how hyperspectral vegetation indices (VIs) resolve vitality-related differences between tree crowns across very high (0.1 m), high (1 m), and moderate (30 m) spatial resolutions. It finds that most VIs effectively resolve crown variations at 1 m resolution, and at 30 m, VIs sensitive to photochemistry, chlorophyll, and water content are responsive to available water capacity, though significantly influenced by vegetation structure and functional type.
-
Silva et al. (2025) Rainfall Patterns and Trends on São Miguel Island (Azores, Portugal): A Hierarchical Clustering and Trend Analysis Approach
This study identified four distinct rainfall clusters on São Miguel Island, revealing a strong altitude-rainfall correlation but also significant recent declines in annual and seasonal rainfall, particularly in autumn and winter, which may deviate from global climate change model projections.
-
Jeon et al. (2025) C4MIP-based projections of moisture convergence and extreme precipitation risks in East Asia
This study evaluates the performance of the Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) in simulating vertically integrated moisture flux convergence (VIMFC) and projecting extreme precipitation risks over East Asia. It finds that C4MIP offers improved skill in capturing VIMFC patterns and projects intensified moisture convergence and elevated extreme precipitation risk over southeastern China and North Korea under a high-emission scenario.
-
Garrote et al. (2025) From global climate models to local water stress: A framework for estimating future water availability in the Mediterranean
This study developed a framework to estimate future water availability in Mediterranean basins under climate change, projecting reductions of up to 26% in annual flows and 41% in potential availability by 2100 under high-emission scenarios. It highlights the critical role of reservoirs in buffering variability and shows that increasing climate variability and population growth will exacerbate water stress in the region.
-
Liu et al. (2025) Enhanced evapotranspiration prediction by incorporating plant stomatal-hydraulic co-regulation into hydrological model
This study enhances hydrological predictions by integrating a plant stomatal-hydraulic co-regulation scheme into the SWAT model. The modified model significantly improves the simulation of runoff, soil moisture, and evapotranspiration, particularly during drought conditions, by accurately representing vegetation transpiration dynamics.
-
Li et al. (2025) Quantification of water uptake by winter wheat roots under different dripline burial depths using hydrogen and oxygen stable isotopes
This two-year field study investigated the optimal dripline burial depth for winter wheat under drip irrigation in the Guanzhong Plain using stable isotopes. It found that a 20 cm subsurface dripline depth significantly enhanced root water uptake efficiency and water use efficiency by optimizing root-zone water availability and promoting dynamic water uptake shifts.
-
Lubis et al. (2025) Projected changes in cross-equatorial northerly surges and their hydrological impacts in the near future
This study projects a significant increase in the regional hydrological impacts of cross-equatorial northerly surges (CENS) on extreme precipitation in Southeast Asia and northwestern Australia by 2030–2050, despite no change in CENS characteristics. This intensification is attributed to enhanced moistening efficiency and moist static instability in a warmer, more humid environment, leading to more intense CENS convection.
-
Nikoo et al. (2025) Assessing the fidelity of multi-satellite precipitation estimates for drought monitoring in a mountain water tower to arid basin system
This study evaluates the fidelity of four multi-satellite precipitation products (SPPs) for drought monitoring in the topographically complex Isfahan region, Iran, finding IMERG-V07 to be the most dependable for accurately capturing precipitation patterns and drought events.
-
Majid et al. (2025) Virtual water gauge from the Synthetic Aperture Radar (SAR) altimeters for small reservoirs in tropical regions
This study evaluates the effectiveness of Sentinel-3 SAR altimetry for monitoring water surface elevation in small tropical reservoirs in Malaysia. The research demonstrates that SAR altimetry can achieve high correlations (>0.95) with in-situ gauges, providing a viable "virtual gauge" for complex tropical landscapes.
-
Teng et al. (2025) Incorporating heat and water stress into BIOME-BGC to simulate the impact of extreme climate events on subtropical coniferous forest NEP
This study enhances the BIOME-BGC model by incorporating dynamic heat and water stress mechanisms to better simulate the impact of extreme climate events on subtropical coniferous forest Net Ecosystem Productivity (NEP). The improved model demonstrates significantly higher accuracy in NEP simulation and reveals an increasing NEP trend from 1981 to 2019, with drought identified as the strongest negative driver.
-
Abolafia‐Rosenzweig et al. (2025) Snow Cover Plays a Non‐Dominant Role in WRF/Noah‐MP Simulated Surface Air Temperature Cold Biases Over the Western U.S.
This study evaluates whether snow cover errors are the primary cause of persistent 2-meter air temperature cold biases in WRF/Noah-MP simulations across the western U.S. during snow seasons, concluding that while snow cover errors contribute modestly, they are not the main driver.
-
Ji et al. (2025) Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions
This study developed a multi-stage framework leveraging PRISMA spaceborne and NEON AOP hyperspectral data to investigate the seasonal dynamics of four key plant traits across diverse U.S. forest types, demonstrating PRISMA's capability to reliably track these traits and identifying their environmental drivers.
-
Lan et al. (2025) Linkage between the Tibetan Plateau summer rainfall and direct moisture supplies from the key low-latitude seas
This study quantifies the contributions of oceanic moisture export (OME) from the Arabian Sea, Bay of Bengal, and South China Sea to Tibetan Plateau (TP) summer precipitation over 35 years, revealing distinct regional impacts and contributions to the observed "south drying-north wetting" trend.
-
Tadayon et al. (2025) Enhancing long-lead rainfall forecasting in data-scarce large watersheds using multi-model fusion
This study developed a comprehensive multi-model fusion framework to enhance long-term monthly precipitation forecasts in data-scarce large watersheds. It demonstrated that integrating bias-corrected numerical weather prediction (NWP) models using machine learning techniques significantly outperforms individual NWP models, improving forecast accuracy and reliability.
-
Almeida et al. (2025) Evaluation of eight satellite and reanalysis precipitation products over Angola: The value of targeted regional assessment for water management
This study systematically evaluates eight satellite and reanalysis daily rainfall datasets over Angola from 2011 to 2023. It finds that daily precipitation is generally poorly captured by all datasets, with MSWEP v2.8 and IMERG v06B outperforming others, particularly in the arid southwest, underscoring the importance of targeted regional assessments for water management.
-
Chen et al. (2025) A global long-term (2002–2022) C-band vegetation optical depth record retrieved after merging AMSR-E, AMSR2 and WindSat
This study developed a global, long-term (2002–2022) C-band Vegetation Optical Depth (C-VOD) dataset by merging observations from AMSR-E, AMSR2, and WindSat sensors using a combined inter-calibration method. The resulting merged C-VOD exhibited substantially improved temporal consistency across sensors, reducing global discrepancies between AMSR-E and AMSR2 from 6.20 % to 0.34 %.
-
Richter et al. (2025) Overconsumption gravely threatens water security in the binational Rio Grande-Bravo basin
This study provides the first comprehensive accounting of consumptive water uses and losses in the binational Rio Grande-Bravo basin, revealing that 52% of direct water consumption is unsustainable, primarily driven by irrigated agriculture, leading to severe depletion of reservoirs, aquifers, and river flows.
-
Wahiduzzaman et al. (2025) Modelling of typhoon activities over the Western North Pacific using a generalised additive model and deep Q-learning model
This study developed a statistical and deep Q-learning model framework, incorporating kernel density estimation and a generalized additive model (GAM), to predict typhoon genesis and trajectories over the Western North Pacific, demonstrating significant competency (73-76%) in replicating observed landfall trajectories.
-
Shen et al. (2025) Soil moisture retrieval under different land cover conditions based on Sentinel-1 SAR
This study systematically evaluates the performance of optical and radar-derived Vegetation Indices (VIs) in the WCM-Oh coupling model for soil moisture retrieval across five land cover types using Sentinel-1 SAR, proposing a new radar-based VI (SVIdp) and demonstrating improved accuracy in vegetated areas.
-
Qiao et al. (2025) Improving the accuracy of gridded snow depth estimation through multi-source data and a machine learning fusion model
This study developed a Random Forest (RF) machine learning fusion method to improve the accuracy of gridded snow depth (SD) estimations over China from 2014 to 2018 by integrating multi-source SD data (ground-based, satellite-derived, reanalysis) and various environmental ancillary information. The fusion model significantly enhanced SD estimation accuracy, achieving a higher Kling-Gupta efficiency (KGE) and lower Root Mean Squared Error (RMSE) compared to individual input products.
-
Yang et al. (2025) Particle Swarm Optimization-Enhanced Fuzzy Control for Electrical Conductivity Regulation in Integrated Water–Fertilizer Irrigation Systems
This study developed an IoT-integrated, particle swarm optimization (PSO)-optimized fuzzy Proportional-Integral-Derivative (PID) controller for water-fertilizer integration, demonstrating significantly improved electrical conductivity (EC) control precision and response time in both simulations and field experiments on winter wheat.
-
García‐Pereira et al. (2025) Permafrost sensitivity to soil hydro-thermodynamics in historical and scenario simulations with the MPI-ESM
This work shows that changing the hydrological state of permafrost produces differences of up to 3 °C in the annual ground temperature, 1–2 m in the active layer thickness, and 5 million km² in the permafrost extent. Including a deeper vertical thermal scheme reduces the extent decline by more than 2 million km² in the highest radiative emission scenario, demonstrated for the first time in fully-coupled Earth System Model experiments.
-
Lin et al. (2025) Pluvial flood warnings in a tidally influenced basin enhanced by rainfall–tide compound indicators: Taipei case study
This study developed and evaluated rainfall-tide compound indicators using a copula-based statistical framework to enhance pluvial flood warnings in the tidally influenced Taipei metropolitan area. The compound indicators significantly improved flood detection rates (up to 84.3%) compared to rainfall-only thresholds, highlighting the critical role of tidal interaction in urban flood dynamics.
-
Yang et al. (2025) Synergistic effects of precipitation and phase changes intensify future rain-on-snow events in the Tianshan and Pamir regions, Central Asia
This study utilized WRF dynamically downscaled simulations driven by bias-corrected CMIP6 data to investigate future rain-on-snow (ROS) events in the Tianshan and Pamir regions. It projects significant increases in ROS frequency and intensity by mid-century under warming climate scenarios, leading to intensified flood potential and altered regional hydrological regimes.
-
Τολίκα et al. (2025) Can we predict the extreme? Assessing the skill of seasonal forecasts in capturing the 2024 European heatwave
This study analyzed the pronounced summer 2024 warming and heatwaves in Eastern Europe, finding average maximum temperature anomalies exceeding 3.3 °C. It evaluated seasonal forecasting systems (GCFS, SEAS5, Météo-France), which successfully predicted regional warming and large-scale circulation patterns, but showed limitations in accurately forecasting the precise timing and location of heatwaves and midlatitude blocking events.
-
Ng et al. (2025) Evaluation of Bias Correction Methods for Coupled Model Intercomparison Project Phase 6 Model and Future Rainfall Projections over Muda River Basin
This study evaluates three bias correction methods for CMIP6 rainfall projections in the Muda River Basin, finding that Local Intensity Scaling (LOCI) significantly improves accuracy and reveals a nonlinear relationship between future rainfall and emission scenarios, with increased annual rainfall under higher emissions by the century's end.
-
Huang et al. (2025) Differential sensitivities of three types of compound drought and heatwave events to human-induced climate change across the globe
This study quantifies the differential influences of human-induced climate change on three types of compound drought and heatwave (CDHW) events (precipitation-based, runoff-based, and soil-moisture-based) using CMIP6 simulations, revealing greenhouse gas forcing as the dominant driver of global CDHW intensification, particularly for soil-moisture-based events, and projecting significant future severity growth and population exposure under high-emission pathways.
-
Leivadiotis et al. (2025) Understanding Flash Droughts in Greece: Implications for Sustainable Water and Agricultural Management
This study investigates the spatiotemporal variability of flash droughts in Greece from 1990 to 2024 using ERA5-Land root-zone soil moisture data. It reveals distinct regional patterns in flash drought characteristics, including frequency, duration, and recovery, providing a data-driven framework for water management and adaptation strategies in Mediterranean agriculture.
-
Jeong et al. (2025) An integrated watershed modeling approach using soil and water assessment tool and graph convolutional long short-term memory
This study proposes an integrated watershed modeling approach combining the process-based Soil and Water Assessment Tool (SWAT) with the graph-based Graph Convolutional Long Short-Term Memory (GCLSTM) model to simulate streamflow and Total phosphorus (TP) load. The integrated approach significantly improved simulation accuracy compared to calibrated SWAT, demonstrating the GCLSTM's ability to capture complex spatiotemporal dependencies and aggregate upstream signals.
-
Díaz et al. (2025) Water balance analysis in Iowa using satellite-based evapotranspiration, precipitation, and streamflow data
This study analyzes Iowa's water balance from 2000 to 2023 using satellite-based evapotranspiration, gridded precipitation, and streamflow data, identifying areas of groundwater-dependent evapotranspiration, validating the Budyko framework for runoff estimation, and quantifying that historical land cover change from native prairie to cropland increased annual runoff by 23%.
-
Feng et al. (2025) A high-resolution (0.05°) global seamless continuity record (2002–2023) of near-surface soil freeze-thaw states via passive microwave and optical satellite data
This study developed a novel downscaling method integrating passive microwave and optical satellite data to create a global, high-resolution (0.05°), daily seamless record of near-surface soil freeze-thaw states from 2002 to 2023, which achieved an overall accuracy of 83.78% comparable to coarse-resolution products but with enhanced spatial detail.
-
Rateb et al. (2025) Dynamics and Couplings of Terrestrial Water Storage Extremes From GRACE and GRACE‐FO Missions During 2002–2024
This study evaluates global Terrestrial Water Storage (TWS) extremeness and its climate linkages using GRACE and GRACE-FO data from 2002 to 2024, revealing that TWS extremes are governed by a 2–3 year El Niño–Southern Oscillation-linked cycle and a weaker quasi-decadal cycle, with a shift around 2011–2012 where dry extremes became dominant despite no significant global trends in intensity.
-
Wang et al. (2025) Parsimonious analytical modelling of rainwater harvesting systems’ performance under climate change in six Chinese cities
This study proposed a novel quantitative assessment for data-scarce regions by integrating daily rainfall event reconstruction with an analytical probabilistic model (APM) to evaluate climate change impacts on rainwater harvesting (RWH) systems’ performance. Results in six Chinese cities reveal climate change necessitates larger RWH storage capacities to mitigate future urban flooding risk and optimize rainwater utilization, despite a trade-off where water supply reliability generally increases while stormwater control efficacy decreases.
-
Zhao et al. (2025) Evaluating the impact of canopy spatial heterogeneity on Solar-Induced chlorophyll fluorescence and Model-Based quantification
This study quantifies the impact of canopy spatial heterogeneity on Solar-Induced chlorophyll fluorescence (SIF) using a 3D radiative transfer model. It develops a correction function based on vegetation index-derived coefficients of variation (CVs) to convert homogeneous-canopy SIF to heterogeneous-canopy conditions, thereby improving SIF estimation accuracy.
-
Lynne et al. (2025) Connections of cold-season northern hemisphere extratropical cyclone characteristics to common climate modes during 1950–2023
This study investigates the connections between six cold-season Northern Hemisphere extratropical cyclone (ETC) characteristics and five common climate modes (PNA, ENSO, IPO, NAO, and decadal NAO) from 1950 to 2023, revealing similarities and key differences in their influences across interannual and decadal timescales through correlative and composite analyses.
-
Hameed et al. (2025) Groundwater storage changes in the United States using baseflow recession method: Comparison with GRACE and well observations
This study quantifies long-term groundwater storage changes in over 1000 minimally disturbed watersheds across the contiguous United States using a novel event-based baseflow recession algorithm, demonstrating its reliability by comparing estimates with GRACE-DA and well observations.
-
Wang et al. (2025) Water balance of three plantations in hilly areas of South China
This study assessed the water balance of three plantations (exotic *Acacia mangium*, native *Schima wallichii*, and native *Cunninghamia lanceolata*) in hilly South China to inform tree species selection for vegetation restoration. It found that native *S. wallichii* exhibited hydrological advantages, while exotic *A. mangium* was more water-limited in dry seasons and native conifer *C. lanceolata* posed risks due to high surface runoff and inefficient water use.
-
Liu et al. (2025) Projecting compound flood hazards induced by tropical cyclones in Southeast China using MRI-AGCM3-2-S climate model
This study developed an integrated framework to project tropical cyclone-induced compound flood hazards in 60 cities across Southeast China, finding that over half of these cities are projected to experience more severe flooding under future climate scenarios.
-
Sezen et al. (2025) A multi-hybrid model approach optimizing discharge forecasts in karst catchment under climate change
This study developed a novel multiple hybrid hydrological model (TUW-CemaNeige GR5J-SR-GP) to enhance daily rainfall-runoff simulations in the complex karst Ljubljanica River catchment, demonstrating significant improvements in simulating extreme flows under both observed and future climate change scenarios.
-
Devi et al. (2025) A framework for the evaluation of flood inundation predictions over extensive benchmark databases
This paper introduces FIMeval, an open-source framework for automated, large-scale Flood Inundation Mapping (FIM) evaluation, integrating pixel-based and impact-based assessments. It demonstrates FIMeval's utility across diverse flood scenarios and benchmark datasets, highlighting the sensitivity of evaluation outcomes to flood extent delineation methods.
-
Ferreira et al. (2025) Enhancing PROSAIL Inversion: Key Considerations and Practical Insights
[Information not available in provided text]
-
Zhang et al. (2025) 3D surface displacement modeling in Lorca, Spain, using dual-orbit MT-InSAR and multiple prior constraints
This study proposes a novel method to model three-dimensional (3D) surface displacement by integrating dual-orbit Multi-Temporal InSAR (MT-InSAR) measurements with multiple prior deformation models. Applied in the Lorca basin, Spain, the approach successfully reconstructed high-precision 3D cumulative displacement fields, revealing significant horizontal movements and improving accuracy over conventional methods without requiring ground-based observations.
-
Bai et al. (2025) Real-time atmospheric precipitable water retrieval performance evaluation based on satellite-based precise point positioning
This study systematically evaluates the real-time Zenith Tropospheric Delay (ZTD) and Precipitable Water Vapor (PWV) retrieval performance of mainstream satellite-based precise point positioning (PPP) services (BDS-3 PPP-B2b, Galileo HAS, and QZSS MADOCA-PPP) using Multi-GNSS Experiment (MGEX) data, concluding that MADOCA-PPP offers the best overall real-time performance and meets numerical weather prediction (NWP) accuracy requirements.
-
Prashanth et al. (2025) Unveiling hidden perspectives: Examining COVID-19 impact on non-perennial river flow across ungauged river segments and anthropogenic footprint on the hydrological cycle
This study investigates the impact of reduced human interference during the COVID-19 pandemic on the rejuvenation potential of the ungauged lower Pennar River, India. It found that the river's rejuvenation potential significantly increased during the lockdown due to decreased groundwater extraction, highlighting human interventions as the primary driver of groundwater drought in the region.
-
Chaudhary et al. (2025) A comprehensive water balance approach for improved assimilation of evapotranspiration estimates derived from soil moisture
This study develops and evaluates a Comprehensive Water Balance (CWB) model, coupled with an ensemble Kalman filter, to improve daily evapotranspiration (ET) estimation by explicitly accounting for ET-driven percolation, demonstrating superior performance over a Simple Water Balance (SWB) model, especially in coarse-textured soils.
-
Cruz et al. (2025) Long-term basin trends confirm a record 2022–2024 hydrological drought and water-storage losses in western Amazonia
This study quantifies long-term hydrological trends (1981-2024) in Western Amazonia and diagnoses the unprecedented 2022-2024 hydrological drought, revealing significant delays in high-runoff season onset, decreased low-flow discharge, and record-low terrestrial water storage. The findings underscore the region's increasing vulnerability and the urgent need for adaptive water resource management.
-
Ahmed et al. (2025) Efficient Hybrid Anomaly Detection in Environmental Data
This study develops an efficient hybrid anomaly detection model by combining K-Nearest Neighbors (KNN) and Isolation Forests (IF) to identify unusual patterns in environmental monitoring system data. Applied to a dataset of 56,996 points, the model identified 2,872 anomalous patterns with an approximate detection accuracy of 5.44%.
-
Bhosale et al. (2025) A Hybrid Framework for Forest Fire Detection and Severity Prediction using Sequential Deep Learning on Multitemporal Satellite Imagery
This study aims to detect and predict forest fires using a deep learning-based hybrid approach applied to multi-temporal satellite images. The proposed model, combining change detection, LSTM, and attention mechanisms, demonstrates high accuracy in identifying fire-prone zones and providing early warnings, particularly during the pre-monsoon period and in protected areas.
-
Yang et al. (2025) Balancing yield and water productivity in wheat: A meta-analysis of irrigation, soil, and climate interactions
This meta-analysis quantified the interactive effects of irrigation, climate, soil, and fertilizer management on wheat yield, yield components, grain protein content, and water productivity across China, revealing that irrigation significantly increased yield and water productivity, with optimal strategies varying based on environmental and management conditions.
-
Liu et al. (2025) Agricultural rapid-onset droughts in southern China’s grain-producing regions: Spatiotemporal evolution and potential drought-crop risks
This study investigated the spatiotemporal evolution and seasonal characteristics of agricultural rapid-onset droughts and their coupling with critical crop growth stages across southern China's major grain-producing regions from 1950 to 2022. It revealed significant spatial heterogeneity in drought characteristics and identified critical crop-drought coupling risks during specific phenological windows, emphasizing the need for phenology-aligned drought management.
-
Zhu et al. (2025) Ensemble forecast of precipitation enhancement potential using multiple microphysics parameterizations
This study developed an 18-member ensemble forecast system within the WRF model to investigate the impact of microphysics schemes and initial/boundary conditions on uncertainties in cloud seeding simulations for precipitation enhancement. It found that both factors comparably influence supercooled liquid water and precipitation, with microphysics schemes having a greater impact on seeding-induced changes.
-
Zhao et al. (2025) UAV multi-source data fusion with super-resolution for accurate soybean leaf area index estimation
This study developed a UAV multi-source data fusion framework with super-resolution to accurately estimate soybean Leaf Area Index (LAI) across varying flight altitudes. It demonstrated that combining super-resolution-enhanced RGB and multispectral data significantly improves LAI estimation accuracy, mitigating the negative impact of higher flight altitudes.
-
Shi et al. (2025) Spatiotemporal evolution pattern of water yield service of ecosystems in the Shule River Basin, Northwest China, integrating future climate and land use changes
This study projected future water yield in the Shule River Basin under various climate and land-use scenarios for 2030 and 2050 using InVEST, FLUS, and Geodetector models, finding that water yield generally increases, primarily driven by precipitation and digital elevation model (DEM), with significant spatial heterogeneity.
-
Li et al. (2025) Multi-source data fusion for estimating potato transpiration under water stress using machine learning models
This study developed a multi-source data fusion framework to estimate daily cumulative potato transpiration under varying water stress by integrating image-derived canopy indices (Crop Water Stress Index and Relative Leaf Area Index) with meteorological measurements, demonstrating that this integration significantly enhances model performance and that optimal model choice depends on environmental stability.
-
Ruiz Nunez et al. (2025) Analyzing Hurricane Maria’s Change in Track, Intensity and Duration in a Future Weather Scenario
N/A (The provided text is not a scientific paper and does not contain a summary of research.)
-
Li et al. (2025) Estimating soil water content of cotton fields using UAV-based multi-source remote sensing data fusion
This study aimed to improve soil water content (SWC) estimation in cotton fields by fusing UAV-based multi-source remote sensing and meteorological data with machine learning. It found that the CatBoost model, integrating multidimensional indices, achieved superior SWC estimation accuracy (R² = 0.762 ± 0.026) and robustness across different growth stages and irrigation levels.
-
Osei et al. (2025) An analysis of the long-term trend of evaporative water loss in Lake Hefner (Oklahoma) for sustainable water management
This study analyzed long-term evaporation trends at Lake Hefner, Oklahoma, from 1985 to 2018, revealing a significant increase primarily driven by air temperature, which poses a substantial and unmanaged water loss for municipal supply, largely overlooked in current water management policies.
-
Chagas et al. (2025) Coupling hydrological modelling and remote sensing for soil moisture assessment in an experimental Brazilian semi-arid catchment
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2025...
-
Al-Mulla et al. (2025) AI Driven Impact Assessment of Shaheen Tropical Cyclone Using Very High-Resolution Satellite Data
This study quantifies the impact of Tropical Cyclone Shaheen (STC) in Oman using very high-resolution satellite imagery and multiple deep learning models, revealing significant damage to vegetation and buildings, alongside drastic increases in water bodies. It provides detailed, object-specific damage assessments and identifies flood-prone zones to support climate resilience strategies.
-
Li et al. (2025) Magnitude and impacts of non-rainfall water inputs and nocturnal evapotranspiration on temperate grassland ecosystems
This study investigated the spatial variability and environmental drivers of non-rainfall water (NRW) inputs and nocturnal evapotranspiration (ETnight) across nine temperate grasslands, revealing that while NRW inputs were low and did not benefit early morning CO2 exchange, increasing ETnight losses pose additional challenges for grasslands in the future.
-
Cherie et al. (2025) Agricultural drought dynamics in East Gojjam: Insights on soil moisture, drought indices, and crop sustainability
This study investigates agricultural drought dynamics in East Gojjam, Ethiopia, from 2013 to 2024 by integrating remote sensing indices (NDVI_Max, SWDI, SMA, SPEI) and rainfall data with machine learning models. It found significant interannual drought variability, identifying 2022 as a critical year, and highlighted Soil Water Deficit Index (SWDI) and Normalized Difference Vegetation Index (NDVI_Max) as the most influential predictors of vegetation response.
-
Deng et al. (2025) Enhanced water stress on vegetation productivity with climate warming over the Northern Hemisphere
This study investigates the inter-annual changes in gross primary productivity (GPP) in the Northern Hemisphere from 1982 to 2018, revealing that GPP trends stalled after 1998 due to enhanced atmospheric dryness (vapor pressure deficit, VPD) and that dynamic global vegetation models (DGVMs) fail to accurately capture these changes.
-
Chen et al. (2025) Contrasting mechanisms of cross-regional heavy precipitation induced by an eastward-moving Tibetan Plateau vortex: Dynamical dominance versus thermodynamic maintenance
This study investigates the contrasting dynamical and thermodynamic mechanisms of heavy precipitation induced by an eastward-moving Tibetan Plateau Vortex (TPV) across the Eastern Slope of the TP (ESTP) and the North China Plain (NCP). It reveals that ESTP precipitation is driven by a coupled dynamical-thermodynamic mechanism, while NCP precipitation is dominated by dynamical forcing, highlighting an asymmetrical coupling framework.
-
Zhu et al. (2025) Giant Cloud Condensation Nuclei Facilitate Drizzle Formation in Stratocumulus—Insights From a Combined Observation‐Modeling Framework
This study investigates the hypothesis that Giant Cloud Condensation Nuclei (GCCN) initiate drizzle drops to trigger warm rain formation. It finds that GCCN efficiently generate drizzle drops and broad droplet size distributions, which accelerate collision-coalescence, with model simulations matching radar observations.
-
Ghodgaonkar et al. (2025) Realizing low-energy drip irrigation via a 1-dimensional model of low-pressure drip emitters
This paper develops a rapid 1-dimensional (1D) model of low-pressure drip emitter (LPE) physics, significantly accelerating design cycles from hundreds of hours to minutes. Using this model, the authors designed LPEs with 50-63% lower activation pressure than conventional emitters, which could reduce agricultural energy consumption by 18-23% in developing markets.
-
Lache et al. (2025) Performance of controlled drainage in tile-drained agricultural fields: An exploratory scenario analysis with the soil-plant model SWAP
This study used the SWAP model to conduct an in silico scenario analysis comparing regular drainage (RD) with manual controlled drainage (MCD) and climate-adaptive drainage (CAD) over 30 years in maize cultivation across different soil textures and management practices. It found that MCD significantly increases groundwater recharge (28-45%) and reduces drainage flux (17-25%) compared to RD, with minimal and soil-dependent effects on maize transpiration, while CAD offered fewer benefits than MCD.
-
Cambra et al. (2025) Edge-Computing Smart Irrigation Controller Using LoRaWAN and LSTM for Predictive Controlled Deficit Irrigation
This study presents an IoT-enabled edge computing model utilizing hybrid machine learning to predict soil moisture and manage Controlled Deficit Irrigation (CDI) strategies in high-density almond fields, achieving a 35% reduction in crop evapotranspiration (ETc) and enabling real-time water management without cloud dependency.
-
Guo et al. (2025) A new precipitation index for summer precipitation in eastern China
This study develops a novel Precipitation Index (PI) for summer precipitation in eastern China, specifically designed to capture the coordinated variability between North and South China. The PI demonstrates superior performance over existing indices in representing synchronous precipitation anomalies and reveals the underlying atmospheric dynamic and thermodynamic mechanisms driving this coherence.
-
Mirzakhani et al. (2025) Dendrohydrological analysis of flood rings in Quercus lyrata Trees: Unveiling Southeast USA flood history
This study reconstructs multi-century flood histories in the southeastern United States using anatomical flood rings in *Quercus lyrata* trees, validating these records against instrumental data. It reveals that flood-ring chronologies extend observed flood variability back to the early 1700s, capturing major 19th-century events, and highlights distinct flood dynamics between large, regulated river systems and smaller, free-flowing ones.
-
Chen et al. (2025) Locating the missing absorption enhancement due to multi‒core black carbon aerosols
This study reveals that multi-core black carbon (BC) aerosols, particularly prevalent in wildfire smoke, significantly enhance light absorption (up to 1.81 times) compared to single-core assumptions, leading to a global 19% increase in BC absorption aerosol optical depth and highlighting the need for model revisions.
-
Xu et al. (2025) Dataset for ‘UCL-CA: A Deep Learning-driven Sub-pixel Cellular Automaton for Land Use/Land Cover Change Simulation’
This paper introduces UCL-CA, a novel deep learning-driven sub-pixel cellular automaton, to simulate land use/land cover changes with enhanced spatial detail.
-
Jia et al. (2025) Ocean Wave Slope Effects on Global Air‐Sea Turbulent Heat Flux
This study introduces a geometric correction to standard ocean surface turbulent heat flux algorithms to account for the enhanced surface area due to ocean wave slopes. It finds that the effective air-sea interface is enhanced by approximately 2% on average, leading to mean corrections of 0.29 W/m² for sensible heat flux and 2.34 W/m² for latent heat flux, with statistically significant increasing trends over recent decades.
-
Zhang et al. (2025) Spatiotemporal evolution characteristics and driving forces of natural lakes in Qingtongxia irrigation area, China
This study investigated the spatiotemporal evolution and driving forces of natural lakes in the Qingtongxia Irrigation Area, China, from 1984 to 2022, revealing an average annual lake surface area expansion of 2.10 km² predominantly driven by human activities in low-lying lakes and hydro-meteorological conditions in wetland lakes.
-
Fullhart et al. (2025) Climate adaptation in the southwest US: The SWPar4.5 parameter set for stochastic weather generators
This paper introduces Southwest Parameter Set 4.5 (SWPar4.5), a framework for generating probable historical and future point-scale climate time series at approximately 800 m resolution for the southwestern US, revealing increases in local precipitation intensity with implications for environmental indicators.
-
Cruz-Perez et al. (2025) Impact of Central and Eastern Atlantic Niño on SST and precipitation patterns in reanalysis and Earth System Models
[Not available from the provided text, which is a website footer/navigation and not a scientific paper.]
-
Tuftedal et al. (2025) CROCUS Tipping Bucket Rain Gauge Data at Argonne National Laboratory Prairie Site
This paper describes a dataset of 1-minute precipitation accumulation measurements from two heated Tipping Bucket Rain Gauges (TBRGs) collected at the Argonne Testbed for Multiscale Observational Science (ATMOS), providing all-season data for hydrological studies and sensor validation.
-
Goodling et al. (2025) Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model
This paper introduces a low-cost, non-contact method for monitoring relative streamflow dynamics in small headwater streams using time-lapse imagery and a deep learning model (SRE) trained on human-annotated image pairs. The method successfully characterized relative streamflow dynamics, with modeled hydrographs closely matching observed ones (median Kendall’s Tau of 0.75 during the annotation period).
-
Furian (2025) Future glacial lake risk in High Mountain Asia
Unable to extract from provided text due to severe corruption.
-
Zhao et al. (2025) Spatiotemporal evolution of compound soil drought-heat events and their impact mechanisms on ecosystem productivity across China
This study developed a method to identify compound soil drought-heat events, revealing their increasing spatiotemporal evolution and impact mechanisms on Gross Primary Productivity (GPP) across China, highlighting regional variations in ecosystem response and recovery.
-
Lundquist et al. (2025) Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Years 2024 and 2025
This paper presents a comprehensive dataset of time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature collected across the East River basin from October 2023 to August 2025 to support studies of surface climate and hydrologic processes in complex terrain.
-
Damor et al. (2025) Data-Driven Modeling of FAO-56 Penman–Monteith Reference Evapotranspiration Using Limited Meteorological Parameters through Artificial Neural Networks
This study developed and evaluated Artificial Neural Network (ANN) models to accurately estimate daily reference evapotranspiration (ET0) using limited meteorological inputs, demonstrating that 3-4 input variables provide optimal accuracy and efficiency for data-scarce regions.
-
Steyaert et al. (2025) Data derived reservoir operations simulated in a global hydrologic model
This study develops a workflow to implement data-derived reservoir operations in global hydrologic models using satellite altimetry and machine learning. It demonstrates that this approach significantly improves the accuracy of simulated global reservoir storage compared to generic operations, which tend to overestimate storage and water availability, while having modest impacts on downstream streamflow.
-
Franze et al. (2025) Braided Rivers From SWOT: Water Surface Dynamics Over the Multi‐Channel Brahmaputra River
This study presents a method to integrate high-resolution SWOT pixel cloud data with Sentinel-2 imagery to generate dynamic river centerlines, significantly improving estimates of surface water dynamics in braided rivers compared to the standard SWOT RiverSP product.
-
Salazar-Martínez et al. (2025) Influence of vineyard row orientation on evapotranspiration estimates from a satellite-based two-source energy balance model
This study quantified the influence of vineyard row orientation (Southwest-Northeast vs. Northwest-Southeast) on evapotranspiration (ET) using a satellite-based Two-Source Energy Balance (TSEB) model. It found that Southwest-Northeast oriented vineyard blocks consistently exhibited higher ET rates (average 1.32 mm day⁻¹) during the growing season compared to Northwest-Southeast blocks, a difference primarily driven by variations in land surface temperature and albedo related to canopy radiation interception and shadowing.
-
Lv et al. (2025) Decadal-Scale Warming Signals in Antarctic Ice Sheet Interior Revealed by L-Band Passive Microwave Observations from 2015 to 2025
This study analyzed ten years of SMAP satellite brightness temperature (TB) data across Antarctica, revealing a significant warming trend of over 1.5 K per decade in West Antarctica, primarily correlated with internal ice temperatures at 500–2000 m depth, but not originating from increasing internal ice shelf temperatures.
-
Xie et al. (2025) Changing Northern Hemisphere weather linked to warming amplification in High Mountain Asia
Amplified warming in High Mountain Asia (HMA) has significantly altered Northern Hemisphere synoptic temperature variability (STV) from 1940–2022, enhancing summer STV in Canada and Russia while reducing winter STV in Eastern Europe and the Nordic Seas. These changes are primarily driven by modified high-frequency temperature advection through hemispheric teleconnections.
-
Madhavan et al. (2025) Quantitative assessment of cotton evapotranspiration, irrigation requirements and water productivity from weather-based estimations
This study quantitatively assessed cotton water use and productivity in a semi-arid region, demonstrating that locally calibrated crop coefficients improve evapotranspiration estimation, leading to enhanced yield and water use efficiency through precise irrigation scheduling.
-
Alioua et al. (2025) Quantitative Analysis of Urban Heat Island and ISA Density Effects on Land Surface Temperature: A Remote Sensing Study Using Local Morphological Density Analysis and Hybrid Optimization Model
This study developed a novel hybrid optimization model (Genetic Algorithm-Simulated Annealing with Generalized Additive Models) to quantify the nonlinear relationship between impervious surface area (ISA) density and Land Surface Temperature (LST) and identify Urban Heat Island (UHI) thresholds in Mediterranean cities, revealing a strong positive correlation between ISA density and LST and a mitigating effect of green spaces.
-
Van et al. (2025) Tree-Based Regressor Comparison for Burn Severity Mapping: Spatially Blocked Validation Within and Across Fires
This study benchmarks six tree-based machine learning models for predicting post-fire burn severity from satellite data, evaluating their generalization capabilities both within and across ten U.S. wildfires to provide practical recommendations for rapid severity mapping.
-
Jie et al. (2025) Study on Lag Law of Irrigation Return Flow Based on Unit Hydrograph and Hydrus
This study investigated the lag characteristics of irrigation return flow in the Jingdian Irrigation District using the unit hydrograph method and Hydrus-2D simulations. It found that return flow lag times range from 0 to 2.3 months in the Hongbiliang Basin and 0 to 5 months in the Nanshahe Basin, with the unit hydrograph model demonstrating high predictive accuracy.
-
Zhao et al. (2025) TS-SatFire: A Multi-Task Satellite Image Time-Series Dataset for Wildfire Detection and Prediction
This paper introduces TS-SatFire, a comprehensive multi-temporal remote sensing dataset designed for integrated wildfire monitoring and prediction using deep learning models. It provides benchmarks for active fire detection, daily burned area mapping, and next-day wildfire progression prediction across the contiguous U.S.
-
Amiri et al. (2025) Modeling root water uptake of landscape groundcovers with HYDRUS-1D and particle swarm optimization
This study coupled HYDRUS-1D with Particle Swarm Optimization (PSO) to calibrate root water uptake (RWU) parameters for four groundcover species, demonstrating that simultaneous optimization of RWU and soil hydraulic parameters significantly enhances the accuracy of soil water content (SWC) simulations compared to a two-step approach.
-
Gontara et al. (2025) Multivariate hydrological risk under heterogeneous conditions
This study proposes a framework for estimating multivariate quantile curves (MQCs) by directly integrating change-points (CPs) using mixture models for marginal distributions and copulas. It demonstrates that marginal CPs displace the curve, while dependence CPs alter its shape, with combined effects amplifying the impact on hydrological risk assessment.
-
Zhang et al. (2025) A multi-model based dataset of global atmospheric moisture source-sink relationships and atmospheric basins
This study presents AMSSRAB, a global, multi-model based dataset of atmospheric moisture source-sink relationships (AMSSRs) and derived atmospheric basins (ABs) at 1° resolution over 40 years (1979–2018). It integrates three atmospheric moisture tracking models to provide a robust ensemble representation of the atmospheric hydrological cycle, enabling detailed analysis of regional moisture recycling and climate responses.
-
Romero-Puig et al. (2025) Crop Classification With Multifrequency Multitemporal Polarimetric and Interferometric SAR Data
Information not available in the provided text.
-
Haiqing (2025) Climate-dominated, LUCC-enhanced water–ecosystem responses in the humid temperate Tumen River Basin
This study investigates the water-ecosystem responses to climate variability and land-use/land-cover change in the humid temperate Tumen River Basin, finding that climate is the dominant driver while land-use/land-cover change enhances these responses.
-
Dengate et al. (2025) Sensitivity Tests of the P3 Microphysical Scheme: Modifying Ice Fall Speeds in CM1
Not available from the provided text, as the provided text is organizational information, not a scientific paper.
-
Madaula et al. (2025) Hypersaline recharge in Mediterranean coastal aquifers: The role of aquifer–lagoon connectivity
This study investigates seasonal salinity variations in the shallow aquifer of the La Pletera salt marsh using time-lapse electrical resistivity tomography (ERT) and continuous monitoring. It reveals that hypersaline lagoons are a primary source of aquifer salinization, particularly after rainfall events, often exceeding the influence of seawater intrusion.
-
Мүтәліпқызы et al. (2025) Анализ Эффективности Цифровых Технологий Точного Земледелия Для Оптимизации Агропредприятий
This study analyzes the effectiveness of digital precision farming technologies in optimizing agricultural enterprise operations amidst increasing competition, resource appreciation, and climate change, demonstrating their role in resource efficiency, cost reduction, and improved management. It highlights how continuous monitoring and data-driven planning lead to more sustainable agricultural practices.
-
Imroz et al. (2025) Integrated assessment of urban flooding and heat island interactions: A systematic review of geospatial technologies, machine learning approaches, and microclimate dynamics
This systematic review synthesizes geospatial technologies and machine learning approaches used to assess the integrated dynamics of urban flooding and Urban Heat Island (UHI) interactions, identifying critical methodological gaps and proposing a unified data-driven framework for enhanced urban climate resilience.
-
Wu et al. (2025) Effect of slope gradient on drought propagation in different climate zones and under different climate-change backgrounds
## Identification - **Journal:** Geomatics Natural Hazards and Risk - **Year:** 2025...
-
Mitrowska et al. (2025) Modeling Solar Radiation Data for Reference Evapotranspiration Estimation at a Daily Time Step for Poland
This study calibrated Angström–Prescott (A-P) and Hargreaves–Sammani (H-S) solar radiation models for Poland and assessed their impact on Penman–Monteith (P-M) reference evapotranspiration (ET0) estimations, finding that local calibration generally improved accuracy, but the method of radiation determination had a significant and sometimes unexpected impact on ET0 values.
-
Sathian et al. (2025) Assessment of Impact of Climate Change Towards Agricultural Drought Using Remote Sensing Indices in Semi-Arid and Humid Regions of South India
This study assessed agricultural drought dynamics and its impact in the semi-arid Thamirabarani and humid Keecheri river basins of South India from 2014 to 2023 using remote sensing indices, finding that over 60% of both areas experienced mild drought, exacerbated by deficit rainfall in 2016 and 2019.
-
Holtanová et al. (2025) Correction to: Scenarios of Köppen-Trewartha climate types in Europe based on GCM-RCM combined projections
This paper serves as a correction notice for a previously published article, specifically addressing and rectifying an error in the author list by removing an incorrectly added name.
-
Li et al. (2025) The Spatial and Temporal Variability of Soil-Water Evaporation as Influenced by Near-Surface Soil Porosities
This study investigated the specific mechanisms by which soil porosity influences the spatial and temporal variability of soil water evaporation under transient field conditions. It found that higher porosity leads to earlier and greater daily peak evaporation, with a 7% increase in porosity resulting in an 18% increase in cumulative evaporation.
-
Shang et al. (2025) High-Resolution Mapping and Spatiotemporal Dynamics of Cropland Soil Temperature in the Huang-Huai-Hai Plain, China (2003–2020)
This study developed a high-resolution (1 km) monthly cropland soil temperature dataset for the Huang-Huai-Hai Plain (2003–2020) at 0–5 cm and 5–15 cm depths using a Random Forest model with recursive feature elimination and cross-validation, revealing complex spatiotemporal dynamics and a shift from cooling to rapid warming after 2012.
-
Peng et al. (2025) Assessing seasonal prediction of DGPI over the Western North Pacific by the Climate Forecast System and its improvement using deep learning
This study evaluates the Climate Forecast System's (CFS) seasonal prediction skill for the Dynamic Genesis Potential Index (DGPI) in the Western North Pacific, finding limited skill in operational forecasts, and then significantly improves this prediction using Convolutional Neural Network (CNN) models trained with CMIP6 and reanalysis data.
-
Soni et al. (2025) Impact of Climate Variability on Future Water Availability
This paper conducts a bibliometric analysis of 582 research papers from Scopus and Google Scholar to synthesize existing knowledge on the impact of climate variability on water availability and quality, considering population growth, and to identify future research directions.
-
Zhang et al. (2025) Analysis of Monsoon Characteristics in China Based on Precipitable Water Vapor Derived From GNSS and ERA5 Over 2016–2020
This study characterized the Asian Summer Monsoon (ASM) over China by retrieving precipitable water vapor (PWV) grid products from GNSS data and ERA5 reanalysis for 2016-2020. It found the average monsoon onset from late May to mid-June and retreat in September, advancing unevenly from southeast to northwest across China.
-
Kilpys et al. (2025) Climate Impact on the Seasonal and Interannual Variation in NDVI and GPP in Mongolia
This study investigated the impact of climate variability on vegetation dynamics in Mongolia from 2000 to 2024, revealing significant warming and increased precipitation that correlate with enhanced vegetation productivity, particularly in spring (temperature-driven) and summer (precipitation-driven).
-
Davoli et al. (2025) Observed Mesoscale Wind Response to Sea Surface Temperature Patterns: Modulation by Large-Scale Physical Conditions
This study uses high-resolution (10 km) MetOp A satellite observations to observationally verify the dependence of sea surface temperature (SST)-wind coupling on large-scale wind and atmospheric stability, confirming theoretical understanding but revealing significant discrepancies in global numerical models under stable atmospheric conditions.
-
Uliana et al. (2025) Estimation of Global Solar Radiation in Unmonitored Areas of Brazil Using ERA5 Reanalysis and Artificial Neural Networks
This study developed and trained an artificial neural network (ANN) model using ERA5 reanalysis data to accurately estimate and map global radiation (GR) distribution, demonstrating its efficacy in areas with sparse sensor networks.
-
Henz et al. (2025) Alps-wide high-resolution 3D modelling reconstruction of glacier geometry and climatic conditions for the Little Ice Age
This study presents the first Alps-wide, three-dimensional, model-derived reconstruction of glacier surfaces during the Little Ice Age (LIA) at 50 m resolution, using the Instructed Glacier Model (IGM) to match empirically mapped LIA glacier extents. It reveals a total ice volume of 283 ± 42 km³ and regional/local patterns of equilibrium line altitudes (ELAs) influenced by climatic and topographic factors.
-
Xue et al. (2025) Extreme Lake Level Rise in the Zaysan Basin Driven by Intense Snowmelt Runoff
This study reconstructed water level changes for Lake Zaysan and Lake Ulungur in Central Asia from 2003 to 2024 using satellite altimetry data, revealing significant fluctuations primarily controlled by runoff processes and highlighting their sensitivity to climate change.
-
Chen et al. (2025) Emerging Patterns of Changes in Intra‐Annual Variability of Streamflow
This study presents the first large-scale analysis of spatial patterns in long-term trends of intra-annual streamflow variability from 1970 to 2022 across 12,311 streamflow stations. It found that approximately 23% of stations exhibit significant trends, with increasing variability dominating in tropical regions due to declining low flows, and decreasing variability prevailing in cold regions due to rising low flows, indicating widespread non-precipitation-led flow regime changes.
-
Wang et al. (2025) A cross-city transferable convolutional neural network framework for assessing street-scale flood risks in urban networks
This study develops an AI-driven convolutional neural network (CNN) framework for street-scale flood risk assessment by integrating hydrometeorological, topographic, and urban morphological data. The model, trained on Shenzhen data and applied to Hong Kong, demonstrates strong spatial transferability and identifies critical flood-prone areas and high-risk road segments under various rainfall scenarios.
-
Afridi (2025) Real-Time Monitoring and Prediction of Evapotranspiration and Organic Carbon in Soil using IoT Sensors
This research develops a cost-effective, IoT-enabled system integrating smart sensors and machine learning for real-time monitoring and prediction of evapotranspiration and soil organic carbon. The system demonstrates high accuracy in field deployments, offering scalable solutions for precision agriculture and sustainable water resource management.
-
Zhang et al. (2025) Unraveling threshold effects and feature contributions in hydrological-soil-vegetation response to Budyko parameters: A data-driven perspective
This study investigated the threshold effects and feature contributions of hydrological, soil, and vegetation elements on watershed ecohydrological processes in a sandy region using a data-driven approach. It identified critical thresholds for key ecohydrological variables within the Budyko framework, providing a quantitative basis for ecosystem restoration.
-
Chen et al. (2025) An interpretable LAI time series prediction model of subtropic forests using a ConvLSTM coupling spatiotemporal attention mechanism model
This study developed an interpretable ConvLSTM model with a Spatio-Temporal Attention Mechanism (ConvLSTM-STAM) to predict forest Leaf Area Index (LAI) in subtropical forests of Zhejiang Province (2013–2018). The model achieved high accuracy (R² = 0.887, RMSE = 0.349 m²/m²) and provided mechanistic insights into seasonal LAI drivers through SHAP analysis.
-
Gao et al. (2025) Quantifying controls on rapid and delayed runoff response in double-peak hydrographs using ensemble rainfall-runoff analysis (ERRA)
This study quantifies the controls of precipitation intensity and antecedent wetness on double-peak runoff generation in the Weierbach catchment using Ensemble Rainfall-Runoff Analysis (ERRA), demonstrating distinct thresholds for the rapid near-surface and delayed groundwater-mediated runoff components.
-
Beber et al. (2025) Super Resolution of Satellite-Based Land Surface Temperature Through Airborne Thermal Imaging
This study introduces the Dilated Spatio-Temporal U-Net (DST-UNet), a novel deep learning approach designed to bridge the resolution gap between low-resolution satellite thermal imagery and high-resolution optical data, enabling the generation of detailed, high-frequency urban thermal maps.
-
Li et al. (2025) A Class-Aware Unsupervised Domain Adaptation Framework for Cross-Continental Crop Classification with Sentinel-2 Time Series
This study proposes PLCM, an unsupervised domain adaptation framework, to overcome domain shift challenges in cross-continental crop classification using Sentinel-2 satellite time series, achieving robust and balanced high-accuracy mapping, particularly for difficult-to-adapt crop categories.
-
Perin et al. (2025) Assessing the cumulative impact of on-farm reservoirs on modeled surface hydrology
This study developed a novel framework integrating remote sensing data with a hydrological model (SWAT+) to assess the cumulative spatial and temporal impacts of on-farm reservoirs (OFRs) on surface hydrology in eastern Arkansas, finding significant reductions in annual flow (14%–24%) and peak flow (43%–60%).
-
Mishra et al. (2025) Artificial intelligence and soil conservation: An overview
This review article synthesizes the transformative role of Artificial Intelligence (AI) in soil conservation, detailing its evolution from traditional methods to advanced data-driven solutions for soil health assessment, degradation monitoring, and fertility prediction, while also addressing associated challenges and future prospects.
-
Park et al. (2025) AI-Based Time-Series Ensemble Approach Coupled with a Hydrological Model for Reservoir Storage Prediction in Korea
This study developed an AI-based time-series ensemble framework coupled with a hydrological model to accurately predict reservoir storage rates in South Korea, especially for reservoirs lacking inflow/outflow data. The framework achieved high accuracy, with Mean Absolute Errors of 0.820%p, 1.339%p, and 1.766%p for 1, 2, and 3-day ahead predictions, respectively, outperforming individual models.
-
Khoso et al. (2025) Impacts of global climate change on floods in Pakistan with current trends challenges and future perspectives
This comprehensive review examines the impacts of global climate change on floods in Pakistan, identifying key drivers, socioeconomic vulnerabilities, and proposing a flood process typology. The study concludes that climate change is intensifying flood frequency and severity in Pakistan, necessitating improved data, forecasting, and robust mitigation strategies aligned with sustainable development goals.
-
Otoro et al. (2025) Integration of Machine Learning and Remote Sensing to Evaluate the Effects of Soil Salinity, Nitrate, and Moisture on Crop Yields and Economic Returns in the Semi-Arid Region of Ethiopia
This study integrated machine learning and remote sensing to evaluate the combined effects of soil salinity, nitrate, and moisture on crop yields and economic returns for banana, cotton, and maize in semi-arid Ethiopia. It found that soil salinity was the most critical factor reducing crop yields and economic profitability, with Random Forest models demonstrating high predictive accuracy for these outcomes.
-
Chang et al. (2025) Future extreme precipitation amplified by intensified mesoscale moisture convergence
This study uses high-resolution climate simulations to demonstrate that future daily extreme precipitation over land could increase by approximately 41% by 2100, primarily due to intensified mesoscale moisture convergence, a dynamic contribution underestimated by low-resolution models.
-
Bayar et al. (2025) Artificial intelligence of things (AIoT) for precision agriculture: applications in smart irrigation, nutrient and disease management
This review comprehensively examines the applications of Artificial Intelligence of Things (AIoT) in smart irrigation, nutrient, and disease management within precision agriculture, highlighting its transformative potential, current challenges, and future opportunities for sustainable and climate-smart agricultural practices.
-
Chauhan et al. (2025) Snow cover analysis using NDSI and SWI indices in Pindari-Kafni Glacier valleys, Kumaon Himalaya
This study analyzed seasonal snow cover area (SCA) dynamics in the Pindari and Kafni glacier valleys, Kumaon Himalaya, over two decades using Landsat imagery and comparing NDSI and the newly applied SWI, revealing an overall increasing SCA trend and superior performance of SWI in cloud and water discrimination.
-
Rosso et al. (2025) Drought hazard assessment across Sweden's diverse hydro-climatic regimes
This study assesses meteorological, agricultural, and hydrological drought hazard across Sweden using multiple standardized indicators and hydrological model simulations. It reveals distinct regional drought patterns, with central-eastern and south-eastern Sweden experiencing increasing dry conditions, while northern and western Sweden show wetting trends.
-
Ioniță et al. (2025) Multi-Indicator Drought Variability in Europe (1766–2018)
This study compares three long-term European drought reconstructions (PDSI, SPEI, and SMI) from 1766 to 2018, finding that the identification of "extreme" drought years and decades varies significantly depending on the indicator used. While the 2015–2018 event was exceptional in several metrics, its "unprecedented" status is indicator- and region-dependent, though all indicators consistently link drought to large-scale atmospheric blocking.
-
Magazin et al. (2025) Wheat Production Transition Towards Digital Agriculture Technologies: A Review
This systematic literature review analyzes the global distribution and core digital technologies in wheat production, revealing an uneven adoption of digitalization worldwide, particularly highlighting the dominance of remote sensing and the critical need for improved implementation in less developed countries to enhance food security.
-
Zhou et al. (2025) Future exposure to moist heat extremes linked to soil dryness
This study investigates the global impact of soil moisture on moist heat stress using CMIP6 models, revealing that soil moisture dynamics significantly amplify moist heat extremes and increase global population exposure to wet-hot conditions, particularly in mid-latitudes, with regional variations in coupling strength.
-
Shehaj et al. (2025) A feasibility study to reconstruct atmospheric rivers using space- and ground-based GNSS observations
This study investigates the minimum number of Low Earth Orbit (LEO) Global Navigation Satellite Systems (GNSS) radio occultation (RO) satellites required to accurately characterize the morphology of Atmospheric Rivers (ARs) using machine learning. It concludes that at least 36 satellites are needed for refractivity mapping and 48 for continuous column-integrated water vapor (IWV) mapping, with ground-based GNSS data significantly improving land-based AR mapping.
-
Zhao et al. (2025) A new pattern expanding current temperature models: A negative correlation between soil respiration and temperature in cold environments
This study investigates the relationship between soil respiration (Rs) and temperature across China, revealing a novel U-shaped annual Rs-temperature relationship with a negative correlation in cold regions (mean annual temperature < 3.75 °C) due to moisture-mediated suppression. This finding highlights that traditional models significantly underestimate Rs in these cold environments and underscores the importance of scale-dependent modeling for accurate carbon flux predictions.
-
Balasubramaniam et al. (2025) The Impact on Triple/N-Way Collocation-Based Validation of Remote Sensing Products Due to Non-Ideal Error Statistics
This paper investigates the impact of violating statistical assumptions in triple/N-way collocation on error variance estimates using a numerical simulator and introduces a new, more general version of the collocation analysis tool.
-
Ma et al. (2025) Soil oxygen dynamics: a key mediator of tile drainage impacts on coupled hydrological, biogeochemical, and crop systems
This study used the ecosys model to quantify the integrated impacts of tile drainage on hydrology, biogeochemistry, and crop growth, revealing that soil oxygen dynamics mediate these interactions. It found that tile drainage enhances soil oxygenation, alleviating crop oxygen stress and promoting root growth, which increases crop yield by approximately 6% and improves resilience to climate change.
-
Lombardi et al. (2025) Testing Machine Learning and Traditional Models for Tree-Ring-Based scPDSI Streamflow Reconstruction: A 1500-Year Record of the French Broad River, Tennessee, USA
This study reconstructed 1500 years of streamflow for the French Broad River using dendrochronological tools, identifying a significant hydrologic regime change in 1271 CE and an emerging trend of higher average flow with severe single-year droughts.
-
Biget et al. (2025) Brief communication: Sharp precipitation gradient on the southern edge of the Tibetan Plateau during cold season
This study documents a sharp, order-of-magnitude precipitation gradient over a 10 km distance at the southern edge of the Tibetan Plateau during the cold season, using novel in-situ lake water pressure measurements, automatic weather station data, and atmospheric models. It reveals that precipitation totals can vary by a factor of ten over a short distance, marking the transition between the Great Himalayas and the Tibetan Plateau.
-
Chen et al. (2025) Radiative Response to Large Decline in Anthropogenic Emissions From China Between 2008 and 2016 Is Modified by Simultaneous Biomass Burning Emission Changes
This study quantifies the instantaneous radiative forcing (IRF) from changes in anthropogenic and biomass burning aerosol emissions over 2008–2016 using the UK Earth System Model, revealing significant regional and global IRF shifts due to Chinese anthropogenic emission reductions and a notable role for biomass burning, particularly in the Arctic.
-
Janicka-Kubiak (2025) Hydrological drought trends and seasonality in selected Polish catchments between 1993 and 2022 using a threshold based approach
This study investigates long-term trends and seasonality of hydrological droughts in selected Polish lowland catchments from 1993 to 2022, revealing a significant increase in summer and autumn low-flow events and a strong correlation between drought intensification and land use changes, particularly urbanisation.
-
Kadam et al. (2025) Comparison of Satellite Based Models for Estimating Evapotranspiration of Soybean Crop
N/A
-
Zhang et al. (2025) Unraveling the anisotropic pattern of track uncertainty in tropical cyclones
This study quantifies the global pattern of tropical cyclone track uncertainty, revealing a strong latitudinal variation in the dominant direction of uncertainty (along-track vs. cross-track) and its correlation with translation speed.
-
Gong et al. (2025) Spatiotemporal Variability of Vegetation Productivity Responses to Meteorological Factors in China's Drylands Over Two Decades
This study developed a machine learning model to attribute gross primary productivity (GPP) trends in China's drylands from 2001 to 2020 to meteorological factors, revealing that declining solar radiation caused a GPP decrease largely offset by increased precipitation, with minor temperature effects due to seasonal compensations, leading to a slight overall GPP decline.
-
Soltani et al. (2025) Enhancing Flood Forecasting with Machine Learning Informed by Integrated ParFlow-CLM Hydrological Modeling
This study integrates a fully coupled hydrological model (ParFlow/CLM) with a Gated Recurrent Unit (GRU) Convolutional machine learning model to enhance flood forecasting. It demonstrates that incorporating physically-derived soil water content (SWC) significantly improves the accuracy of river discharge predictions, outperforming standalone AI and hydrological models.
-
Huang et al. (2025) A GCN-Attention Model for Precision Irrigation Evaluation
This paper proposes UFOGCN-SPANet, a novel and computationally efficient GCN-attention model for precision irrigation evaluation, which integrates a linear-complexity Vision Transformer, Graph Convolutional Networks, and a Salient Positions-based Attention Network to overcome limitations of traditional irrigation methods and improve performance in resource-constrained agricultural settings.
-
Takong et al. (2025) Simulating Widespread Extreme Rainfall Events Over the Drakensberg using the WRF and MPAS Models
This study investigates the characteristics of widespread extreme rainfall events (WEREs) over the Drakensberg Mountain Range and evaluates the performance of the WRF and MPAS climate models in simulating these events and their associated synoptic features. The models realistically simulate rainfall patterns and synoptic drivers, identifying the eastern slopes as hotspots for intense WEREs primarily due to moisture transport from the Indian Ocean and Mozambique Channel.
-
Li et al. (2025) Estimating and Mapping Aboveground Biomass of Vegetation in Typical Lake Flooding Wetland Based on MODIS and Landsat Images Fusion
This study simulated aboveground biomass (AGB) of multi-community wetland plants in Poyang Lake using long-term high-resolution NDVI data, revealing spatial distribution patterns and their strong relationship with wetland surface elevation and water level dynamics.
-
Yin et al. (2025) Widespread weakening of soil-atmosphere thermal coupling and its response to climate warming on the Qinghai-Tibetan plateau
This study quantifies the spatiotemporal dynamics of soil-atmosphere thermal coupling (β) at 0.1 m depth on the Qinghai-Tibetan Plateau (QTP) from 1980 to 2020, revealing a widespread weakening of coupling, particularly in seasonal frost zones, driven by a complex interplay of atmospheric warming and soil moisture changes.
-
Katsora et al. (2025) Flash Drought Assessment: Insights from a Selection of Mediterranean Islands, Greece
This study assesses historical flash drought (FD) events in the Northeastern Aegean islands, Greece, using ERA5 soil moisture data and the Standardized Precipitation Evapotranspiration Index (SPEI) to characterize their spatio-temporal distribution and identify vulnerable "hotspot" areas. The findings reveal significant spatial and seasonal variability in FD characteristics, with northern islands generally experiencing higher frequency and longer durations, while southern islands show greater severity.
-
Ali (2025) Water Use Efficiency and Irrigation Innovations in Crop Systems
This study reviews Precision Irrigation Water-Saving Systems (PISs), examining their development, application, and beneficial effects on sustainable water management, concluding that PISs are crucial for optimizing water use and efficiency amidst increasing water scarcity and climate change.
-
Jong et al. (2025) A computationally efficient method to model similar and alternate stratospheric aerosol injection experiments using prescribed aerosols in a lower-complexity version of the same model: a case study using CESM(CAM) and CESM(WACCM)
This study develops a computationally efficient method using pattern-scaling to prescribe stratospheric aerosol injection (SAI) forcing in lower-complexity climate models (CESM(CAM)) based on data from full-complexity models (CESM(WACCM)). The method successfully replicates tropospheric climate responses to SAI across various scenarios and model configurations, significantly reducing computational costs.
-
Tian et al. (2025) Global Patterns of Increasing Interannual Variability of Surface Air Temperature throughout the Holocene
This study examines the interannual variability of surface air temperature throughout the Holocene using new transient simulations, revealing a global increase in variability since 11.5 ka primarily driven by orbital forcing and energy balance changes, with regional influences from ice sheet retreat and ocean-atmosphere interactions.
-
Joshi et al. (2025) Assessment of precipitation and its extreme precipitation changes over the Himalayan Bhilangana River Basin, India
This study assessed historical and projected changes in extreme precipitation over the Himalayan Bhilangana River Basin, India, by first developing a high-resolution bias-corrected precipitation dataset and then analyzing CMIP6 model outputs. The findings indicate consistent declines in short-duration extreme precipitation events (Rx1day, Rx5day), fewer consecutive dry days, and a marked increase in consecutive wet days, particularly in high-altitude regions.
-
Xing et al. (2025) Responses of Soil Water Infiltration and Recharge to Irrigation Methods in the Semi‐Arid Farming–Pastoral Ecotone of Northern China
This study investigated soil water transport mechanisms in maize fields under drip and flood irrigation in a farming-pastoral ecotone of northwest China. It found that drip irrigation significantly reduces water loss and improves water use efficiency compared to conventional flood irrigation, and identified optimal groundwater depths for maximizing recharge efficiency under both regimes.
-
Lopez et al. (2025) Evaluating a Simple Algorithm for an Evapotranspiration Retrieval Energy Balance Model in Mediterranean Citrus Orchards
This study evaluates the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model's ability to estimate actual evapotranspiration in a Mediterranean citrus orchard using Sentinel-2 imagery and Eddy Covariance data. The findings indicate that while the model is highly accurate during wet seasons, its performance declines during dry periods due to its limited sensitivity to plant physiological water stress.
-
Ashraf et al. (2025) Geospatial Investigation of Cryosphere-Fed Irrigation System in High-Altitude Hunza Basin, Pakistan
This study evaluates the existing status and characteristics of cryosphere-fed kuhl irrigation systems in the high-altitude Hunza Basin, Pakistan, using geospatial techniques and ground information, revealing their diverse lengths, primary water sources, and multi-purpose applications.
-
You et al. (2025) Growth in agricultural water demand aggravates water supply-demand risk in arid Northwest China: more a result of anthropogenic activities than climate change
This study quantifies water supply-demand risks in the arid Tailan River Basin under 24 climate-land change scenarios, revealing that continuous agricultural expansion and the resulting surge in irrigation demand are the primary drivers of increasing water scarcity and risk, rather than climate change.
-
Aviv et al. (2025) The Relation between Jet Meandering and Storm Intensity in an Idealized Aquaplanet GCM
This study uses an idealized moist global circulation model to investigate the quantitative link between eddy-driven jet meandering and storm development under climate change. It demonstrates that Arctic amplification leads to a flattening of the mid-atmospheric meridional temperature gradient, which increases jet meandering, and that these greater meanders are associated with the development of more intense storms.
-
Mahmoudi et al. (2025) Decoding Iran’s Drought Drivers: An Explainable AI Approach to Unraveling Global Teleconnection Impacts
This study utilized an Explainable Artificial Intelligence (XAI) approach, combining Random Forest and SHAP, to decode the complex, nonlinear, and lagged impacts of 36 global teleconnection indices on monthly meteorological droughts across Iran, identifying both known and lesser-known key regional drivers.
-
Ayala et al. (2025) Less water from glaciers during future megadroughts in the Southern Andes
This study assesses the hydrological response of Southern Andes glaciers to the current Chilean megadrought (2010-2019) and projected end-of-century megadroughts using glacio-hydrological simulations. It finds that while glaciers buffered water supply during the current drought by losing 10% of their volume, their buffering capacity will significantly decline in future megadroughts due to massive ice loss, leading to substantial reductions in glacier runoff.
-
Xu et al. (2025) A hybrid approach for regionalization of precipitation based on maximal discrete wavelet transform and growing neural gas network clustering
This study developed a hybrid methodology combining Maximal Overlap Discrete Wavelet Transform (MODWT) and Growing Neural Gas (GNG) clustering to regionalize precipitation patterns in China using 45 years of monthly data from 123 stations. The approach successfully identified 12 homogeneous precipitation clusters, demonstrating improved accuracy and robustness in capturing multiscale temporal variability for water resource planning.
-
Li et al. (2025) A new approach for joint assimilation of cosmic-ray neutron soil moisture and groundwater level data into an integrated terrestrial model
This study developed a novel weakly coupled multivariate data assimilation framework to jointly assimilate cosmic-ray neutron soil moisture and groundwater level data into an integrated terrestrial model (TSMP). The new approach significantly improved the accuracy of both soil moisture and groundwater level predictions, outperforming single-variable assimilation and conventional fully coupled methods.
-
Yan et al. (2025) An Onboard Calibration Technique for Synthetic Aperture Radar Interferometry Using Multisource Data
## Identification - **Journal:** IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - **Year:** 2025...
-
P. et al. (2025) Integration of Soil Moisture and Meteorological Data Using Deep Learning for Flash Drought Detection in Northeastern Brazil
This study developed and validated a deep learning U-Net model to integrate meteorological and satellite-derived soil moisture data for detecting flash drought events in Northeastern Brazil (NEB) from 2015–2023. The model accurately reproduced flash drought frequency and duration, demonstrating its potential for high-resolution monitoring and improving early-warning systems in data-scarce regions.
-
Wang et al. (2025) Decoupling anthropogenic and climate impacts on vegetation dynamics in China’s Huaihe River Basin using geodetector
This study developed a novel hybrid framework combining AR1 modeling and spatial autocorrelation analysis with Geodetector to decouple anthropogenic and climate impacts on vegetation dynamics in China's Huaihe River Basin (HRB) from 2000 to 2022. It found a significant basin-wide greening trend (0.00152 yr⁻¹ NDVI increase), with land use type being the dominant spatial driver and extreme climatic events governing temporal anomalies, highlighting complex nonlinear interactions between drivers.
-
Zhang et al. (2025) Contrasting Effect of Spring Snowmelt on Early Summer Surface Air Temperature Over Western and Central Tibetan Plateau
This study investigates the causes of interannual variability in early summer surface air temperature (SAT) over the Tibetan Plateau (TP), revealing an opposite effect of spring snowmelt on local SAT in western versus central TP due to distinct snow-atmosphere coupling mechanisms.
-
(NCPA) (2025) Flood in Albania (2025-11-18)
The provided paper text is unreadable, preventing the extraction of its core objective and main findings.
-
Deng (2025) Nonlinear influence of coexisting CO 2 and anthropogenic aerosols on East Asian summer precipitation through a mid-high-latitude pathway
This study investigates the nonlinear effects of coexisting increased carbon dioxide (CO₂) and anthropogenic aerosols (AAs) on summer precipitation over East Asia, revealing that their combined forcing leads to a nonlinear increase in North China precipitation, driven by remote nonlinear climate responses originating from the Arctic and North Atlantic via Rossby wave propagation.
-
Garouani et al. (2025) Earth observation open data in Google Earth Engine for water resource management in the Saïss Plain, Morocco
This study utilizes Google Earth Engine (GEE) and open Earth observation data to analyze water balance and monitor drought conditions in Morocco's Saïss Plain, revealing seasonal water deficits in summer/autumn and identifying vulnerable areas prone to recurring water scarcity.
-
Cao et al. (2025) Increasing Frequency of Very Severe Cyclonic Storms Over the Northern Indian Ocean Driven by Anthropogenic Greenhouse Gas Forcing
This study identifies a significant increasing trend in the frequency of very severe cyclonic storms over the northern Indian Ocean since 1979, attributing this trend primarily to greenhouse gas emissions, with anthropogenic aerosols having a dampening effect.
-
Abadefar (2025) Assessment of land suitability and water availability for surface irrigation using fuzzy logic algorithm in GIS, in the case of Upper Awash Basin, Ethiopia
This study identified suitable potential zones for surface irrigation in the Keleta watershed, Ethiopia, using a fuzzy logic algorithm integrated with GIS. It found that approximately 52% of the watershed is suitable based on surface water sources and 20.34% (highly and moderately suitable) based on groundwater sources, providing a comprehensive assessment for irrigation development.
-
Lu et al. (2025) Uncertainty Mixture of Experts Model for Long Tail Crop Type Mapping
This paper proposes the Difficulty-based Mixture of Experts Vision Transformer (DMoE-ViT) framework to address challenges in global crop type mapping, specifically intra-class variability and imbalanced training samples, achieving superior classification accuracy and robustness in complex agricultural environments.
-
Başkesen et al. (2025) Comparative analysis of interpolation methods for missing daily precipitation data by suggesting an alternative inverse distance weighted model
This study compares various interpolation methods for estimating missing daily precipitation data in Istanbul, proposing an Alternative Inverse Distance Weighting (AIDW) model. The AIDW model consistently performs comparably to or slightly better than established methods like IDW and MNR-T, offering a reliable solution for data infilling.
-
Jó et al. (2025) Surface Velocity and Dynamics of the Southern Patagonian Icefield Using Feature and Speckle Tracking Methods on Sentinel-1 SAR Images During 2019–2020
This study investigated the surface velocity and dynamics of 64 glaciers in the Southern Patagonian Icefield (SPI) during 2019–2020 using Sentinel-1 SAR images, revealing an unstable and rapidly changing state primarily influenced by calving events.
-
Feng et al. (2025) Inferring River Channel Geometry Based on Multi-Satellite Datasets and Hydraulic Modeling
This study proposes an innovative method integrating multi-source satellite data (Sentinel-2 and ICESat-2) and hydraulic modeling to derive accurate channel geometry for rivers in data-scarce areas, demonstrating its effectiveness in simulating 1D and 2D flows.
-
Nguyen‐Duy et al. (2025) Performance and added value of a high-resolution (2 km) rainfall product based on WRF-downscaled ERA5 for Ho Chi Minh City, Vietnam
This study dynamically downscaled ERA5 reanalysis data using the WRF model and applied bias correction to generate a 2-km resolution rainfall product for Ho Chi Minh City, Vietnam. The bias-corrected product (WRFC-HCM) significantly improved daily rainfall accuracy and representation of extreme events compared to original reanalysis datasets, despite limited improvement at the monthly scale.
-
Takahashi et al. (2025) Evaluation of the flagGraupelHail Product from Dual-Frequency Precipitation Radar Onboard the Global Precipitation Measurement Core Observatory Using Multi-Parameter Phased Array Weather Radar
This study evaluates the GPM/DPR flagGraupelHail product in a humid convective environment over Tokyo, Japan, using high-resolution Multi-Parameter Phased Array Weather Radar (MP-PAWR) data and a novel volume-matching method. It demonstrates that incorporating storm-top height information significantly improves the accuracy of DPR's graupel/hail detection.
-
Zhang et al. (2025) Topographic Algebraic Rossby Solitary Waves: A Study Using Physics-Informed Neural Networks
This study investigates the influence of topography on Rossby solitary waves by deriving and numerically solving the Benjamin–Davis–Ono (BDO) equation using Physics-Informed Neural Networks (PINNs), revealing that topographic variations significantly alter the amplitude of these waves.
-
Jiang et al. (2025) Improving sugar beet canopy mapping through UAV image analysis
This study evaluated 18 image segmentation methods for estimating sugar beet fractional vegetation cover (FVC) from Unmanned Aerial Vehicle (UAV) RGB imagery. It found that the Excess Green (ExG) index combined with Otsu or Ridler–Calvard (RC) thresholding provided the most accurate FVC estimations, significantly outperforming other combinations.
-
Wiuff (2025) Probability of Climate Records and Their Likely Magnitudes Given Climate Data with an Idealized Linear Trend
This study develops a simple stochastic model, incorporating a linear trend and a normal stochastic component, to analyze historical climate data and estimate the probabilities and magnitudes of climate extremes. It finds that these probabilities and magnitudes depend on the time scale (ratio of standard deviation to trend slope) and the number of observational years, stabilizing when the observational period exceeds approximately twice the time scale, and suggests climate change is accelerating based on a global temperature case study.
-
Wang et al. (2025) Acceleration of diverging runoff trends on the Third Pole
This study quantifies long-term (1960-2016) divergent runoff trends in the Third Pole's major rivers, revealing significant increases in westerlies-dominated rivers and insignificant declines in monsoon-dominated rivers, with these contrasting changes remarkably accelerating post-1997 due to atmospheric circulation shifts and cryospheric melt.
-
Caicedo et al. (2025) Enhanced Boundary Layer Height Detection Using Ceilometer, Surface Meteorology, and Radiation Products With a Random Forest Ensemble Method
This study develops and evaluates a Random Forest (RF) model for estimating planetary boundary layer height (PBLH) using diverse atmospheric measurements, demonstrating its superior accuracy and robustness compared to traditional methods, particularly during daytime and transition periods.
-
Zhang et al. (2025) A strong stratospheric harbinger for cold extremes: Weak polar vortex transition from displacement to split pattern
This study introduces a novel clustering method to identify "mixed-type" weak stratospheric polar vortex (WSPV) events, characterized by a transition from displaced to split patterns. These mixed-type events are found to induce more persistent and stronger negative Arctic Oscillation-like surface signatures, leading to robust cold anomalies over northern Eurasia and the central U.S. 10–39 days after onset, driven by synergistic tropospheric planetary wavenumbers 1 and 2.
-
Zhao et al. (2025) Super-resolve satellite imagery to perform on par with UAV-borne hyperspectral imagery in predicting spring wheat physiological parameters using transformer models
This study developed a deep learning-based super-resolution model to fuse UAV-borne RGB imagery with Sentinel-2 satellite data, creating high spatial and spectral resolution (HRS2S) images. These HRS2S images, combined with a novel transformer model (ResTrans21), accurately predicted spring wheat physiological parameters (dry matter, nitrogen content, nitrogen uptake), demonstrating performance comparable to costly UAV-borne hyperspectral imagery and superior to classical machine learning.
-
Salamon et al. (2025) Satellite-derived flood depth maps for Europe
Information not extractable due to corrupted input.
-
Leandro et al. (2025) Requirements for Flood-Driven Forecasting Systems for Small and Medium-Sized Catchments in Germany
This study discusses the capabilities of six state-of-the-art flood forecasting centres in Germany, identifying challenges and deriving key requirements for improving flood forecasting in small to medium-sized catchment areas.
-
Chen et al. (2025) El Niño-like warming underestimated in a warmer climate due to ENSO rectification effect
This study reveals that traditional climate models underestimate El Niño-like warming in a warmer climate by 14.5% ± 11.9% due to a weakening El Niño–Southern Oscillation (ENSO) asymmetry-related rectification effect. By employing a normalized mean state framework, the research clarifies how ENSO nonlinearity shapes tropical Pacific warming patterns and provides an improved basis for climate projections.
-
Xu et al. (2025) Objectivization of an expert assessment framework for drought monitoring
The study develops a Comprehensive Drought Monitoring Model (CDMM) that objectivizes the expert-based U.S. Drought Monitor (USDM) framework using the Random Forest algorithm. The model successfully reproduces USDM drought categories and demonstrates high transferability by effectively capturing regional drought dynamics across China.
-
Athar et al. (2025) Phenology-aware in-season crop yield estimation through UAV multispectral imagery and deep neural networks
This study introduces a novel phenology-aware framework for in-season crop yield estimation using high-resolution UAV multispectral imagery and deep neural networks, demonstrating significantly improved accuracy (R² of 0.89) by integrating temporal phenological features with structural head metrics.
-
Ahmed et al. (2025) Drought Dynamics From Meteorological Stress to Agricultural Impacts Using Physically‐Based Remote Sensing Indices in the Horn of Africa
This study analyzed drought propagation, its impacts on vegetation and crop productivity, and recovery dynamics in the Horn of Africa (HOA) from 2000 to 2022 using a novel integrated framework of remote sensing indices. It identified eastern and southeastern HOA as severe drought hotspots with significant agricultural losses and prolonged recovery times, offering recommendations for mitigation.
-
Sjursen et al. (2025) Machine learning improves seasonal mass balance prediction for unmonitored glaciers
This study introduces the Mass Balance Machine (MBM), an XGBoost-based machine learning model, to provide accurate, high spatio-temporal resolution regional-scale reconstructions of glacier mass balance, demonstrating superior performance over traditional models for seasonal predictions on unmonitored glaciers.
-
Wang et al. (2025) Moist orographic gravity wave drag parameterization reduces the bias of summer rainfall over the Tibetan Plateau
This study revises the orographic gravity wave drag (OGWD) parameterization in the Weather Research and Forecasting (WRF) model by incorporating moisture effects. Seasonal simulations demonstrate that this modification significantly reduces the persistent wet biases in summer precipitation over the southeastern Tibetan Plateau by altering monsoon circulation and water vapor transport.
-
Dehghani et al. (2025) Classifying Drought Severity in Northern Iran Using Machine Learning and Integrated Climate Indices
This study assessed the effectiveness of machine learning models (Random Forest, AdaBoost, Decision Tree, Transformer) for drought classification in the northern Iranian provinces, finding that Random Forest consistently outperformed other models across all regions.
-
Bouregaa (2025) Comparative evaluation of machine learning models for regional agricultural drought prediction in Algeria using SHAP analysis
This study comparatively evaluated eight machine learning models for regional agricultural drought prediction in Algeria, finding that optimal model performance is highly dependent on region and timescale, and that efficient feature selection can maintain accuracy while SHAP analysis reveals key climate drivers.
-
Huang et al. (2025) Simulating precipitation-induced karst-stream interactions using a coupled Darcy–Brinkman–Stokes model
This study developed a coupled Darcy–Brinkman–Stokes model to simulate precipitation-induced karst-stream interactions, integrating water-air two-phase flow and variably saturated conditions. It found that rainfall intensity is the dominant driver, leading to complex multi-media interactions and shifting discharge contributions, with groundwater stored in porous media significantly influencing subsequent stream levels.
-
Zhang et al. (2025) Impact of Spring Soil Moisture Over the Eastern Iranian Plateau on Interannual Variability of Persistent Spring‐Summer Dryness/Wetness in Yunnan, China
This study investigates the first leading mode of spring-summer dryness/wetness in Yunnan, China, revealing an in-phase variation driven by spring soil moisture anomalies over the Eastern Iranian Plateau through a series of atmospheric teleconnections that lead to consistent dry conditions in Yunnan.
-
Tang et al. (2025) Reversed tropical-Arctic teleconnection under climate change
This study reveals a reversal in the interannual relationship between winter El Niño-Southern Oscillation (ENSO) and subsequent spring Arctic surface air temperature (SAT) in recent decades, attributing this shift to changes in high-latitude atmospheric circulation and Rossby wave propagation patterns modulated by climate-change induced alterations in the westerly jet.
-
Zhang et al. (2025) An Integrated Feature Framework for Wetland Mapping Using Multi-Source Imagery
This paper proposes an integrated framework combining knowledge-driven and data-driven features from multi-source imagery into a Random Forest classifier for wetland mapping, achieving superior classification performance, enhanced robustness, and improved interpretability across different study areas.
-
Ungermann et al. (2025) JuWavelet – continuous wavelet transform and S transform for wave analysis
This paper describes JuWavelet, an open-source Python package that implements 1-D, 2-D, and 3-D continuous wavelet transforms (CWT) using the Morlet wavelet and the S transform, providing a comprehensive tool for analyzing and reconstructing localized wave-like phenomena in geosciences.
-
Weng et al. (2025) Seychelles Dome Simulated in the CMIP6 Models
This study evaluates the ability of CMIP6 models to reproduce key characteristics of the Seychelles Dome (SD), finding that while the multimodel ensemble mean captures its semiannual oscillation and ENSO relationship, individual models exhibit discrepancies linked to biases in the Intertropical Convergence Zone (ITCZ) and ENSO-driven atmospheric teleconnections, despite overall improvements over previous CMIP phases.
-
Lyu et al. (2025) An indicator framework for assessing forest ecosystem productivity resilience and transition risks under climate change
This study develops a composite indicator framework using critical slowing down (CSD) metrics from gross primary production (GPP) to quantify forest ecosystem productivity resilience (EPR) and identify state transitions under climate change across China. It reveals that over half of China's forests experienced EPR decline, primarily due to climatic water availability, with a significant portion transitioning to unstable multistability.
-
Zhu (2025) Snow depth
A long-term daily snow depth dataset for the Northern Hemisphere was prepared by combining multi-source data and machine learning, which was then used to analyze the spatiotemporal characteristics of average and maximum snow depth on the Qinghai-Tibet Plateau from 1980 to 2019.
-
Zhu et al. (2025) Drought identification using standardized evaporative fraction: Perspective from surface energy partitioning
This study introduces the Standardized Evaporative Fraction (SEF) as a new drought index, derived from surface energy partitioning, to better incorporate land-atmosphere interactions in drought identification. The SEF is shown to be effective globally from 1960–2022, demonstrating good consistency with existing indices and revealing increasing drought trends in several global hotspots.
-
Wu et al. (2025) Spatiotemporal variation and propagation process of snow drought over the Tibetan Plateau
This study systematically investigates the spatiotemporal variations of snow drought (SD) and its propagation to soil-moisture drought (SMD) on the Tibetan Plateau, revealing the dominant SD types, their trends under climate change, and the varying propagation times.
-
Dorthe et al. (2025) The thermal future of a regulated river: spatiotemporal dynamics of stream temperature under climate change in a peri-Alpine catchment
This study investigates the future thermal regime of a peri-Alpine regulated river under climate change using a high-resolution process-based model. Projections indicate mean annual water temperatures may rise by up to 4 °C by 2080–2090 under RCP 8.5, with river regulation introducing distinct spatial and seasonal warming patterns, particularly in autumn and winter due to reservoir thermal inertia.
-
Yuliana et al. (2025) The influence of root-crack dynamics on soil water infiltration across seasonal variations
This study investigated the combined influence of root growth and soil desiccation cracks on soil water infiltration rates across seasonal wetting-drying cycles in bare and vegetated (vetiver grass) zones. It found that both root dynamics (growth and decay) and crack formation significantly regulate infiltration, with vegetated zones showing higher rates due to macropore creation and drying-wetting cycles enhancing overall infiltration capacity.
-
Yiou et al. (2025) Using artificial intelligence to identify CMIP6 models from daily SLP maps
This study employs a neural network to classify CMIP6 climate models from single daily sea-level pressure (SLP) maps over the North Atlantic, revealing high model identifiability in summer and enabling the identification of model families and the assessment of climate change impacts on SLP patterns.
-
Yan et al. (2025) Improved Near-Real-Time Precipitation Estimation From Himawari-8 Data and Gauge Observations in the Xiangjiang River Basin Using a Three-Stage Machine Learning Framework
This paper aims to improve near-real-time precipitation estimation in the Xiangjiang River Basin by developing a three-stage machine learning framework that integrates Himawari-8 satellite data with ground-based gauge observations.
-
Boardman et al. (2025) Improving model calibrations in a changing world: controlling for nonstationarity after mega disturbance reduces hydrological uncertainty
This study demonstrates how equifinality in hydrological models leads to significant uncertainty in predicting post-disturbance streamflow changes after a megafire. It introduces a novel metamodel framework that controls for non-stationary model error (bias shift) to drastically reduce this uncertainty, providing more robust estimates of hydrological responses to environmental disturbances.
-
Lesiv et al. (2025) A global reference data set for land cover mapping at 10 m resolution
This paper presents a unique global reference land cover dataset for 2015, featuring over 16.5 million 10 m resolution records across 12 land cover classes, collected through expert visual interpretation to support high-resolution land cover mapping.
-
Aryal et al. (2025) Dynamics of Meteorological and Agricultural Drought in the Karnali River Basin, Nepal
This study provides a multidimensional drought analysis for the Karnali River Basin (Nepal) using 30 years of observational and satellite data, revealing a long-term greening trend despite a significant increase in meteorological drought severity, highlighting the complex interplay of climatic and anthropogenic factors.
-
Duchemin et al. (2025) Data-driven estimation of the hydrologic response using generalized additive models
This paper introduces GAMCR, a novel data-driven approach employing Generalized Additive Models (GAM) to estimate time-dependent Catchment Responses (CR) from rainfall-runoff data. GAMCR successfully estimates hydrologic response functions, showing consistency with an alternative data-driven approach (ERRA) and physical catchment properties across synthetic and six diverse Swiss basins.
-
Zhao et al. (2025) Evaluating drought conditions and predicting return periods with the standard soil moisture index: a three-threshold run theory and GH copula approach
This study develops an integrated methodology using the Standard Soil Moisture Index (SSMI), a three-threshold run theory, and the GH copula function to identify agricultural drought processes and predict their return periods. The approach, validated in the Huai River Basin, demonstrates high consistency with observational data and effectively quantifies drought duration and severity for improved risk assessment.
-
Fu et al. (2025) Spatial-temporal patterns of concurrent droughts and heatwaves in the Qaidam Basin, China
This study investigated the spatiotemporal patterns and synergistic mechanisms of extreme heatwave–drought compound events in the Qaidam Basin from 1990 to 2020, revealing increasing heatwave trends, contrasting monthly moistening versus seasonal aridification, and identifying teleconnections with western Pacific subtropical high dynamics.
-
Masoud et al. (2025) Groundwater Recharge Estimation Based on Environmental Isotopes, Chloride Mass Balance and SWAT Model in Arid Lands, Southwestern Saudi Arabia
This study aimed to determine the origin and quantify the recharge rate of a coastal aquifer in Saudi Arabia using environmental isotopes, the chloride mass balance (CMB) method, and a SWAT model. The findings indicate that groundwater recharge originates from precipitation, with recharge rates estimated at 3.57% of rainfall by CMB and an average of 8.75 mm/year by the SWAT model.
-
Xu et al. (2025) Joint estimation of global daily 1 km surface radiation budget components from MODIS observations (2000−2023) using conservation-constrained deep neural networks
This study developed conservation-constrained deep neural network models to jointly estimate global daily surface radiation budget (SRB) components at 1 km resolution from MODIS observations (2000–2023). The method significantly improves the accuracy and conservation of SRB component retrievals compared to existing products, facilitating a better understanding of their coordinated variation.
-
Han et al. (2025) Spatiotemporal changes in agricultural planting structure in the Turpan–Hami Basin, Xinjiang, China: Remote sensing monitoring from 1990 to 2023
This study developed a remote sensing method using long-term Landsat imagery and crop phenology to monitor spatiotemporal changes in agricultural planting structure in the Turpan-Hami Basin from 1990 to 2023, revealing a significant expansion of total cultivated area and a distinct shift towards high-value economic crops.
-
Peleg et al. (2025) Hotter summers, heavier showers: Global warming and its impact on Swiss short-duration rainfall extremes
This study quantifies future changes in Swiss sub-daily extreme rainfall using the physically-based TENAX model and Klima CH2025 climate projections. It finds that a 3 °C global warming could increase 10-minute rainfall return levels by up to 40% and hourly extremes by approximately 20%, with greater intensification in high-altitude regions despite an overall reduction in summer rainfall event frequency.
-
Comeau et al. (2025) From mild to extreme heatwaves: Examining trends in North America
This study quantifies the intensity and duration trends of North American heatwaves from 1940 to 2019, introducing a novel severity metric to differentiate between mild, moderate, and extreme events. It finds that while heatwave temperatures have increased due to climate warming, the temperature anomalies relative to a changing threshold have remained largely stable, with higher severity heatwaves exhibiting more spatially coherent trends influenced by changes in both the mean and standard deviation of the temperature distribution.
-
Liu et al. (2025) High-resolution soil salinity mapping and driving factor analysis at regional scale using multi-source remote sensing data
This study mapped high-resolution soil salinity and analyzed driving factors across 15 oasis irrigation districts in southern Xinjiang, evaluating five machine learning models with diverse predictor sets and identifying quantile random forest as the best performer, with wind-related variables being crucial.
-
Zhe et al. (2025) Area dynamics of alpine lakes in the Yamzhog Yumco Basin: Optimized water indices reveal spatiotemporal patterns and key drivers
This study evaluated and optimized water indices for accurate lake extraction in the Yamzhog Yumco Basin, reconstructing five-decade spatiotemporal lake area dynamics and identifying key climatic and anthropogenic drivers. It revealed that intense anthropogenic activities, particularly pumped-storage operations, can override climate-driven hydrological regimes in connected lakes, while isolated lakes follow regional climate trends.
-
Boettcher et al. (2025) Strategies for Statistical‐Dynamical Downscaling to Urban Climate Using Global Data
This study evaluates different statistical-dynamical downscaling strategies for urban climate modeling, comparing nested domain and non-uniform grid approaches using the METRAS model for Hamburg, Germany. It finds that the non-uniform grid method is more computationally efficient while achieving similar or slightly better performance, particularly for summer climate, suggesting its utility for very local scale downscaling.
-
Febrianti et al. (2025) A New Approach to Peatland Groundwater Level Estimation: Leveraging Remote Sensing and Field Data for Fire Risk Mitigation
This study developed an accurate groundwater level estimation model for Indonesian peatlands by integrating remote sensing drought indices and field data, demonstrating that maintaining groundwater levels above 66 cm is crucial for mitigating peatland fire risk.
-
Liu et al. (2025) Reconstruction of SMAP Soil Moisture Data Based on Residual Autoencoder Network with Convolutional Feature Extraction
This study introduces TsSMNet, a residual autoencoder model that combines multi-source remote sensing inputs with statistical time-series features to reconstruct gap-free surface soil moisture (SSM) estimates, demonstrating superior performance and improved spatial coverage compared to existing models.
-
Munir et al. (2025) Understanding Changing Trends in Extreme Rainfall in Saudi Arabia: Trend Detection and Automated EVT-Based Threshold Estimation
This study analyzed daily rainfall data (1985-2023) from 26 stations in Saudi Arabia to detect long-term trends, characterize annual cycles, and establish objective extreme rainfall thresholds, revealing considerable spatial variability and a higher likelihood of intense, infrequent events in the future.
-
Zheng et al. (2025) The characteristics of heavy rainfall during the rainy season over North China and prediction skill of NCEP S2S
This study characterizes two distinct types of heavy rainfall during the North China rainy season and evaluates the prediction skill of the NCEP S2S model for these events, attributing forecast discrepancies to the model's ability to predict key atmospheric circulation patterns.
-
Shi et al. (2025) Spatial heterogeneity of agricultural drought drivers in irrigation district: A causal inference framework bridging covariation and structural equation modeling
This study developed a novel causal framework combining causal covariation and Structural Equation Modeling (SEM) to analyze the spatially heterogeneous drivers of agricultural drought in China's Hetao Irrigation District, revealing that temperature-related factors consistently dominate drought severity while other factors exhibit significant spatial variability influenced by elevation and drainage density.
-
O et al. (2025) Development and Validation of a Flutter-Based Android Application for Near Real-Time Reference Evapotranspiration Estimation Using the FAO-56 Penman–Monteith Model in Nigeria
This study developed and validated a Flutter-based Android application for near-real-time estimation of reference evapotranspiration (ETo) using the FAO-56 Penman–Monteith model and OpenWeatherMap data. The application demonstrated high accuracy and reliability when validated against NASA POWER data, providing a portable and cost-effective solution for water management in data-scarce tropical regions.
-
Wang et al. (2025) Projected decline in glacier runoff contribution during drought periods across the Qinghai-Tibetan Plateau
This study developed a new glacier-hydrology coupled model to quantify the evolving regulatory function of Qinghai-Tibetan Plateau glaciers under climate change, particularly during drought periods. It projects a significant decline (25–38 %) in glacier runoff contributions during future droughts by the late 21st century, weakening their buffering role for regional water security.
-
Shah et al. (2025) Western Disturbances: A Comprehensive Review of Modeling Approaches and Research Directions
This paper provides a comprehensive review of Western Disturbances (WDs), synthesizing current understanding of their dynamics and impacts, and critically evaluating existing modeling approaches from mesoscale to global scales. It highlights that while higher resolution models improve WD representation, they face biases and subgrid-scale challenges, with satellite data integration showing promise for enhanced accuracy.
-
Meng et al. (2025) Can machine-learning methods better characterize the relationships between crop yields and water disaster intensity at different growth stages?
This study investigates whether machine learning methods (Random Forest and XGBoost) can better characterize the nonlinear relationships between major crop yields and water disaster intensities across different growth stages compared to multiple linear regression. The findings demonstrate that machine learning models significantly outperform linear models in accuracy, especially for individual growth stages and severe disaster scenarios, offering improved insights for agricultural disaster assessment.
-
Moussa et al. (2025) Climate Changes in Algerian Sahara from Ancient Times - an Approach to Human Interactions with Their Environment
This study challenges colonial historiography by revealing significant paleoclimatic shifts and human-environment interactions in the Algerian Sahara from prehistory to the present, demonstrating a stark "adaptation asymmetry" between resilient prehistoric communities and modern vulnerabilities to drought.
-
Jia et al. (2025) Sensitivity of thermal evapotranspiration models to surface and atmospheric drivers across ecosystems and aridity
This study investigates the sensitivity of thermal-based evapotranspiration (ET) models to surface and atmospheric drivers across diverse ecosystems and aridity levels. It reveals a global transition in the dominant ET driver from soil dryness in water-limited regimes to downward solar radiation in energy-limited regimes, and quantifies the differential impacts of soil dryness and vapor pressure deficit on ET partitioning under drought conditions.
-
Mishra et al. (2025) Improving the Prediction of Land Surface Temperature Using Hyperparameter-Tuned Machine Learning Algorithms
This study developed a machine learning framework to predict Land Surface Temperature (LST) at a 10 m spatial resolution by leveraging Sentinel-2 spectral indices and Landsat 8-derived LST data, demonstrating improved accuracy for urban thermal dynamics monitoring.
-
Miller et al. (2025) Compounding preconditions of wildfires vary in time and space within Europe
This study analyzes hydro-meteorological and land-surface drivers of wildfires across eight European climate regions from 2001 to 2020, revealing that drought conditions and vapor pressure deficit are the dominant drivers, with their influence varying by season and region.
-
Ge et al. (2025) High-resolution GRACE-based assessment of hydrological drought patterns and recovery dynamics across the Nile Basin countries (2003–2023)
This study developed a comprehensive framework combining high-resolution GRACE terrestrial water storage (TWS) downscaling with advanced spatiotemporal pattern recognition to monitor hydrological droughts across the Nile Basin countries from 2003 to 2023. It identified seven distinct drought hotspots, quantified pixel-level drought characteristics and recovery dynamics, and revealed varied country-scale water security trends, highlighting significant improvements in some nations post-2017 while others face persistent challenges.
-
Fagan et al. (2025) Upper Troposphere Lower Stratosphere Composition Change in Tropical Cyclones: Assessments From 17 Years of Satellite Observations
This study investigates the impact of tropical cyclones (TCs) on upper troposphere and lower stratosphere (UTLS) ozone and water vapor composition using 17 years of satellite observations, revealing that composition changes are highly sensitive to TC intensity, distance from the center, and environmental wind shear.
-
Cheng et al. (2025) Quantifying Global Climate Change Impacts on Daily Record‐Breaking Temperature Events in China Over the Past Six Decades
This study statistically analyzes record-breaking daily surface air temperature extremes across China from 1960 to 2023, revealing that summer high-temperature records occur more frequently than predicted while winter low-temperature records occur less frequently. It quantifies that climate-driven trends account for 10%–30% of these events, providing a reference for attributing changes in record-breaking events under non-stationary climate conditions.
-
Tong et al. (2025) Changes in persistent anticyclonic circulation across Eurasian continent and its linkage with extreme heatwaves
This study develops an objective framework to identify Persistent Anticyclonic Circulation (PAC) events across Eurasia from 1979 to 2023, revealing that while overall PAC frequency shows no trend, long-lived PAC events (≥7 days) have significantly intensified and expanded, exhibiting heterogeneous impacts on extreme heatwaves across different latitudes.
-
Al-Taher et al. (2025) Optimizing cotton green water footprint prediction using hybrid machine learning algorithms: a case study of Al-Gezira state, Sudan
This study optimizes cotton green water footprint (GWFP) prediction in Al-Gezira state, Sudan, using hybrid machine learning algorithms (RF, XGBoost, SVR) with climatic and remote sensing data from 2001-2020, demonstrating that hybrid models significantly outperform single models in accuracy and error reduction.
-
Serpa-Usta et al. (2025) Hybrid Deep Learning Models for Predicting Meteorological Variables Associated with Santa Ana Wind Conditions in the Guadalupe Basin
This study explored the predictive capability of hybrid deep learning architectures to model the temporal evolution of key atmospheric variables during Santa Ana wind events in the U.S.-Mexico border region. The Bidirectional LSTM with Attention (BiLSTM–Attention) model achieved the best overall performance, demonstrating high accuracy for temperature and relative humidity.
-
Guan et al. (2025) Anthropogenic enhancement of subsurface soil moisture droughts
This study introduces a Lagrangian four-dimensional tracking framework to identify "deep droughts" (more extensive moisture deficits in deep than surface soils) and reveals their increasing duration and intensity globally over the past four decades due to anthropogenic climate change, with projections for further exacerbation under higher-emission scenarios.
-
Lei et al. (2025) Ecohydrological responses of vegetation changes to artificial water allocation schemes in the Heihe River Basin over the past 40 years
This study investigates the ecohydrological responses of vegetation to artificial water allocation schemes and natural factors in the Heihe River Basin (China) over 40 years (1982-2022). It found a significant increasing trend in vegetation cover, primarily driven by soil moisture, with the lower reaches benefiting most from water allocations, often with a 1-year lag.
-
Kaur et al. (2025) Scatterometer for the monitoring of seasonal dynamics of snow cover melt
This chapter introduces the critical importance of monitoring seasonal snow cover melt in the Himalayan region due to climate change impacts, highlighting the role of scatterometers for precise classification and tracking to support hazard analysis and water resource management.
-
Mohammed et al. (2025) Agricultural soil moisture monitoring using scatterometers
This chapter introduction emphasizes the critical role of real-time soil moisture (SM) monitoring, particularly using scatterometers, for sustainable agriculture, efficient water management, and mitigating the impacts of water scarcity and global climate change. It highlights SM's fundamental importance for plant health, agricultural productivity, and hydrological processes.
-
Zhou et al. (2025) A novel Hankel spectrum analysis filtering for reducing North-South stripes in GRACE gravity solutions
This study introduces the Hankel Spectrum Analysis Filtering (HSAF) framework to effectively reduce North-South striping noise in GRACE-derived terrestrial water storage anomalies. HSAF significantly improves the accuracy of water storage estimates by preserving hydrological signals and outperforming conventional filters across global and basin scales.
-
Kang et al. (2025) Estimating Tropical Upper‐Level Cloud Feedback Based on Radiative‐Convective Equilibrium Framework
This study investigates tropical upper-level cloud (TUC) feedback to sea surface temperature (SST) warming using a radiative–convective equilibrium (RCE) model. It finds that the TUC feedback parameter is more negative when constrained by observations compared to CMIP6 models, suggesting that climate models may underestimate this crucial negative feedback.
-
Unknown (2025) Emerging applications of scatterometers
This study proposes an AI-based framework for real-time analysis of snow cover over the Western Himalayas, leveraging scatterometer datasets to enhance monitoring capabilities.
-
Wendt et al. (2025) Controls on the southwest USA hydroclimate over the last six glacial-interglacial cycles
This study uses an absolute-dated speleothem record from Devils Hole cave 2 and Earth system simulations to identify the primary drivers of hydroclimate and vegetation changes in the southwest USA over the last 580,000 years, finding that temperature-related mechanisms primarily control δ18O variability, with secondary influences from North American ice sheets, while vegetation density is forced by Northern Hemisphere summer intensity.
-
Joseph et al. (2025) Subseasonal prediction of Indian summer monsoon variability and extreme weather events
This chapter introduces the complex subseasonal variability of the Indian Summer Monsoon (ISM), highlighting the role of intraseasonal oscillations in driving active and break spells and their impact on rainfall distribution and extreme weather events across the Indian subcontinent.
-
Legasa et al. (2025) Strengths and Limitations of Statistical and Dynamical Downscaling for the Representation of Compound Dry and Hot Events Over Spain
This study evaluates the performance of statistical and dynamical downscaling approaches in reproducing compound dry-hot events over Spain and the Balearic Islands. It finds that while both approaches perform well for individual variables, their performance declines for compound extremes, with neither consistently outperforming the other, highlighting the need for more advanced model development.
-
Grégoire (2025) CPCMATEX-2025_SAFIRE-ATR42_SAFIRE_RADIATION Radiation data
[Information not available in the provided text.]
-
Sun et al. (2025) All-sky AMSU-A radiance data assimilation using the gain-form of Local Ensemble Transform Kalman filter within MPAS-JEDI-2.1.0: implementation, tuning, and evaluation
This study implements and evaluates the Gain-form of Local Ensemble Transform Kalman Filter (LGETKF) within the MPAS-JEDI system for global all-sky Advanced Microwave Sounding Unit-A (AMSU-A) radiance assimilation. It demonstrates that an optimized LGETKF configuration significantly improves forecasts of moisture, wind, clouds, and precipitation, particularly in tropical regions, for up to 7 days.
-
Buizza (2025) The THORPEX interactive grand global ensemble and subseasonal-to-seasonal ensembles
This chapter defines key terms and concepts related to global medium-range, subseasonal, and seasonal prediction, emphasizing the use of Earth-system models and ensembles for reliable and accurate forecasts.
-
Guo et al. (2025) A three-decade lake dataset on the Mongolian plateau tracking water area and quality dynamics (1990–2020)
This study developed the Mongolian Plateau Lake Dataset (MPLD), the first open-access, long-term (1990-2020) dataset of lake area and water quality (Secchi Disk Depth, Total Suspended Matter, and Forel–Ule Index) for 1,161 lakes (> 1 km²) on the Mongolian Plateau, revealing initial shrinkage followed by partial recovery and widespread eutrophication.
-
Han et al. (2025) SAR-Conditioned Consistency Model for Effective Cloud Removal in Remote Sensing Images
This paper proposes CM-CR, a fast-sampling SAR-conditioned consistency model, to simultaneously enhance reconstruction quality and sampling efficiency for cloud removal in optical remote sensing imagery. The method achieves state-of-the-art performance in image quality and delivers up to a 40-fold acceleration in inference speed.
-
Yin et al. (2025) Spatiotemporal Patterns of Climate-Vegetation Regulation of Soil Moisture with Phenological Feedback Effects Using Satellite Data
This study comprehensively analyzes global patterns and drivers of rootzone and surface soil moisture and leaf area index (LAI) across different seasons and climate zones from 1982 to 2020. It reveals a shift from soil drying to wetting trends from 2000-2020, with LAI inducing moistening, while climatic factors like solar radiation and precipitation show varying dominance across depths and seasons.
-
Wang et al. (2025) Hidden deep soil moisture droughts
Anthropogenic climate change exacerbates global soil moisture droughts, which are now revealed to also occur in deeper layers, and are projected to become longer lasting and more severe in a warming climate.
-
Vitart et al. (2025) Subseasonal-to-seasonal prediction of weather extremes
This chapter introduces the critical importance of subseasonal-to-seasonal (S2S) prediction for mitigating the impacts of extreme weather events, highlighting the significant economic and social damages caused by such phenomena. It positions S2S prediction as a primary duty for developing early warning systems and improving preparedness against weather extremes.
-
Woolnough et al. (2025) The Madden–Julian oscillation
This chapter synthesizes the current understanding of the Madden–Julian oscillation (MJO), describing it as the primary mode of intraseasonal tropical variability, a planetary-scale eastward-moving disturbance with a 40–50 day period that modulates tropical convection and influences global weather systems.
-
Environment (2025) Storm Claudia in Ireland (2025-11-14)
[No discernible information in the provided text.]
-
Beauchamp et al. (2025) Multiscale neural assimilation scheme for high-resolution sea surface temperature reconstruction from satellite observations
This study develops an enhanced multiscale neural assimilation scheme, 4DVarNet, incorporating a variational autoencoder (VAE) for probabilistic sea surface temperature (SST) reconstruction in the North and Baltic Seas. The method significantly improves accuracy and resolves finer spatial scales (33–45 km) compared to traditional optimal interpolation (59–69 km), while also providing robust uncertainty quantification.
-
Kaur et al. (2025) Enhanced resolution cryosphere analysis with pan-sharpening of scatterometer dataset
This study aims to enhance the resolution of scatterometer datasets using pan-sharpening techniques to improve cryosphere analysis, specifically for accurate snow cover tracking and classification in regions like the Himalayas, thereby aiding natural hazard prediction.
-
Damiani et al. (2025) Spatially generalizable bias correction of satellite solar radiation for regional climate assessment—a case study in Japan
This study develops a physics-informed eXtreme Gradient Boosting (XGBoost) model to bias-correct Himawari satellite surface solar radiation (SSR) estimates over Japan, achieving significant improvements in accuracy and spatial consistency, particularly over snow-covered and complex terrain, and uses the corrected data to evaluate a regional reanalysis.
-
Mohanty et al. (2025) Machine Learning Insights for Fire Impacts on Snow Disappearance Predictability in Northern California
This study investigates the impact of wildfires on snow disappearance predictability in Northern California using machine learning models. It finds that wildfires significantly alter snow dynamics, and including post-fire landscapes in model training data is crucial for improving snow disappearance predictions in burned areas.
-
Dutt et al. (2025) Comparative analysis of C-Band and Ku-band-based scatterometers
This introductory text sets the stage for a chapter focused on the comparative analysis of C-band and Ku-band scatterometers, highlighting their role as active remote sensing tools for various Earth observation applications. The primary objective of the full chapter is to compare these two scatterometer types.
-
Arora et al. (2025) Systematic survey on the global level applications of scatterometers
This chapter provides a systematic survey of the global-level applications of scatterometers, highlighting their operational principles and diverse utility in remote sensing for environmental monitoring and hazard tracking. It details how these active microwave instruments contribute to measuring key geophysical parameters under various conditions.
-
Jiménez et al. (2025) Automatic optical depth parametrization in radiative transfer model RTTOV v13 via LASSO-induced sparsity
This study introduces a novel methodology for automatic and sparse parametrization of atmospheric optical depths within the Radiative Transfer for TOVS (RTTOV) version 13 model, utilizing statistical thresholds and LASSO regression. The approach significantly reduces computational costs and parameter counts while maintaining accuracy, making it highly efficient for satellite data assimilation.
-
Toth et al. (2025) Weather forecasting: what sets the forecast skill horizon?
This paper examines the evolution of weather forecasting skill over the past 50 years, identifying the current practical limits of predictability for instantaneous weather and the transition to subseasonal-to-seasonal forecasting for weather statistics.
-
Bagheri et al. (2025) Artificial intelligence for the water sector/industry
This chapter comprehensively reviews the diverse applications of artificial intelligence (AI) techniques across critical domains of the water sector, emphasizing their potential to enhance efficiency and sustainability, while also addressing associated challenges and future directions.
-
Leger et al. (2025) The Greenland-Ice-Sheet evolution over the last 24 000 years: insights from model simulations evaluated against ice-extent markers
This study simulates the Greenland Ice Sheet's evolution over the last 24,000 years using an ensemble of 100 high-resolution ice-sheet models, quantitatively evaluating them against empirical ice-margin extent data. The findings reveal the dynamics, drivers, and spatial heterogeneities of the ice sheet's past evolution, indicating a larger Last Glacial Maximum extent and faster deglacial mass loss than previously estimated, while highlighting persistent regional model-data misfits.
-
Wu et al. (2025) Cascading effects of cross-year droughts on flow-sediment dynamics across distinct drought types
This study investigates the cascading effects of cross-year meteorological and hydrological droughts on flow-sediment dynamics in seven Loess Plateau tributaries. It reveals that hydrological droughts induce significantly greater reductions in sediment transport rates and larger post-drought surges compared to meteorological droughts, with afforestation further reducing sediment supply efficiency.
-
Ershadfath et al. (2025) Selecting CMIP6 precipitation models by integrating relative importance metrics, compromise programming index, and Jenks optimized classification
This study introduces a novel framework combining Relative Importance Metrics (RIMs), Compromise Programming Index (CPI), and Jenks Optimized Classification (JOC) to evaluate 14 CMIP6 General Circulation Models (GCMs) for projecting precipitation across Iran. MPI-ESM1–2-LR was identified as the top-ranked model, projecting significant precipitation declines from February to September, especially in the far future under high emission scenarios.
-
Deser et al. (2025) Northern Hemisphere Wintertime Teleconnections from the 2023–24 El Niño Offset by Background SST Trends
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
-
Routray et al. (2025) Observational and numerical simulation study of tropical cyclones using oceansat-3 scatterometer derived surface winds
This study aims to enhance tropical cyclone (TC) predictions in the North Indian Ocean by improving the representation of initial vortex location and structure. It proposes to achieve this through observational and numerical simulation, specifically utilizing Oceansat-3 scatterometer-derived surface winds for assimilation into high-resolution numerical weather prediction models.
-
Ghil et al. (2025) Extratropical subseasonal-to-seasonal oscillations and multiple regimes: the dynamical systems view
This chapter introduces the challenges of subseasonal-to-seasonal (S2S) prediction, framing extratropical oscillations and multiple regimes from a dynamical systems perspective, and highlighting S2S as the most difficult prediction problem.
-
Dutt et al. (2025) AI-Based framework for real-time snow-cover analysis over western himalayas using scatterometer dataset
This paper proposes an AI-based framework for real-time snow-cover analysis over the Western Himalayas using scatterometer datasets to improve monitoring of water resources and mitigate natural disaster risks in the context of climate change.
-
Doshi et al. (2025) A Framework for Minimizing the Impact of Wet Antenna Attenuation on Rainfall Estimates Provided by Commercial Microwave Links
This paper proposes a framework to minimize the impact of wet antenna attenuation, thereby improving the accuracy of rainfall estimates derived from commercial microwave links.
-
Lehmann et al. (2025) Soil and tree stem xylem water isotope data from two pan-European sampling campaigns
This paper presents a pan-European dataset of stable hydrogen (δ2H) and oxygen (δ18O) isotope ratios in soil and tree stem xylem water from 40 forest sites, collected during spring and summer 2023, revealing spatial and seasonal variations in isotopic signatures and species-specific differences in water uptake.
-
Singh et al. (2025) Evolution of scatterometers in earth observations: A tutorial on products, applications and algorithms
This tutorial paper provides a comprehensive overview of scatterometers, detailing their evolution, operational principles, data products (like sigma-nought), and diverse applications across oceanography, hydrology, and cryosphere studies.
-
Tripathy et al. (2025) Lagged Soil Moisture Controls on the Persistence of Drought and Heatwaves in the United States
## Identification - **Journal:** Geophysical Research Letters - **Year:** 2025...
-
Murmu et al. (2025) Scatterometers for leaf area index estimation: A review
This review paper synthesizes the current understanding and applications of scatterometers, active microwave sensors, for estimating Leaf Area Index (LAI), a critical parameter for understanding plant growth, ecosystem functioning, and climate regulation. It highlights the utility of scatterometers in various Earth observation tasks, particularly for LAI estimation in agriculture, ecology, and climate studies.
-
Dirmeyer et al. (2025) Land surface processes relevant to subseasonal-to-seasonal prediction
This chapter provides a comprehensive background on the theories and physical processes linking land-surface states, particularly soil moisture and snowpack, to subseasonal-to-seasonal (S2S) weather and climate predictability, highlighting their impact between 1 week and 2 months after forecast initialization.
-
Portmann (2025) Développement de nouveaux algorithmes d’apprentissage statistique pour coupler projections climatiques et observations passées en vue de réduire les incertitudes du changement climatique à venir
[N/A - Information not extractable from the provided text.]
-
Yuan et al. (2025) Detectable ship tracks account for just 5% of aerosol indirect forcing from ship emissions
This study reconciles a long-standing discrepancy in estimates of aerosol indirect forcing from ship emissions (AIF-ship), revealing that visible ship tracks account for less than 5% of the total forcing, with the majority stemming from diffused, undetected aerosol plumes.
-
Lei et al. (2025) Balancing Accuracy and Efficiency: HWBENet for Water Body Extraction in Complex Rural Landscapes
This paper introduces HWBENet, a novel hybrid deep learning network designed for efficient and accurate extraction of water bodies from high-resolution remote sensing imagery, particularly in complex rural landscapes, by balancing computational cost with segmentation precision.
-
Lin et al. (2025) Tropical-extratropical interactions and teleconnections
This chapter introduces tropical-extratropical interactions and atmospheric teleconnection patterns, emphasizing their crucial role in subseasonal-to-seasonal predictability and the significant tropical origins of extratropical atmospheric variability.
-
Islam et al. (2025) Variations of urban water balances considering subsurface sewer fluxes: a hydrologic modeling study
This study developed an integrated hydrologic model to evaluate how sewer-mediated subsurface fluxes respond to variations in environmental and structural factors in urban water balances. Findings reveal that sewer-mediated fluxes are primarily sensitive to water table depth and pipe defect size, while natural fluxes (surface runoff, evapotranspiration) are more sensitive to native soil and trench backfill types; backfilling trenches with native soils reduces sewer fluxes and enhances natural outflows.
-
Susiluoto et al. (2025) Improved Atmospheric Correction for Remote Imaging Spectroscopy Missions with Accelerated Optimal Estimation
This paper introduces Accelerated Optimal Estimation (AOE), a Bayesian algorithm that significantly speeds up hyperspectral surface reflectance retrieval and improves convergence compared to standard Optimal Estimation (OE), while also validating the accuracy of Gaussian uncertainty estimates from OE-type algorithms using Markov Chain Monte Carlo (MCMC).
-
Usman et al. (2025) Leveraging Limited ISMN Soil Moisture Measurements to Develop the HYDRUS-1D Model and Explore the Potential of Remotely Sensed Precipitation for Soil Moisture Estimates in the Northern Territory, Australia
This study developed and validated a HYDRUS-1D numerical model to estimate long-term soil moisture in the data-scarce Northern Territory, Australia, demonstrating good performance and identifying CHRS-CCS as the most effective remote sensing precipitation product for this application.
-
Fan et al. (2025) An Improved Neural Network‐Based Scale‐Adaptive Cloud Fraction Scheme: Incorporation of Atmospheric Stability
This study enhances a neural network-based cloud fraction scheme for general circulation models by incorporating atmospheric stability and predicting cloud volume fraction. The improved scheme significantly boosts prediction accuracy and scale adaptability for cloud area fraction, especially for low-level clouds, and effectively predicts cloud volume fraction.
-
Gong et al. (2025) Spatiotemporal dynamics and influencing factors of precipitation and soil water use efficiency on the Chinese Loess Plateau
This study investigated the spatiotemporal dynamics and driving mechanisms of precipitation use efficiency (PUE) and soil water use efficiency (SWUE) on the Chinese Loess Plateau from 2001 to 2023 using an improved two-leaf model, finding that both efficiencies increased, primarily influenced by human activities, precipitation, and soil moisture.
-
Hirons et al. (2025) Coproducing reliable, actionable subseasonal-to-seasonal climate services across Africa
This paper advocates for the coproduction of reliable, actionable subseasonal-to-seasonal climate services across Africa, arguing that integrating subseasonal forecasting with participatory approaches can significantly enhance the continent's climate resilience.
-
Li et al. (2025) Human influence on the unprecedented 2022 extreme dragon boat water event in South China: Insights from historical and projected perspectives
This study quantifies the influence of human-induced climate change on the unprecedented 2022 extreme dragon boat water event in South China and projects the future likelihood of similar events. It finds that anthropogenic forcing increased the event's probability by approximately 64%, with future projections indicating up to an 11-fold increase by the end of the 21st century under high-emission scenarios.
-
Pattanaik et al. (2025) Weather prediction models for scatterometer satellite data
This paper highlights the critical role of scatterometer satellite data in enhancing weather prediction models by providing accurate ocean surface wind speed and direction, thereby improving the performance of numerical weather prediction systems.
-
Kaur et al. (2025) Machine learning models for crop monitoring from optical and microwave remote sensing
This chapter reviews the application of machine learning models with optical and microwave remote sensing data for crop monitoring, emphasizing the critical role of soil moisture for agricultural sustainability and outlining various sensor technologies.
-
Vitart et al. (2025) Introduction: why subseasonal-to-seasonal prediction?
This introductory chapter defines subseasonal-to-seasonal (S2S) prediction as the timescale beyond 2 weeks but less than a season, emphasizing its role in bridging the historical divide between weather and climate forecasting for a seamless prediction approach.
-
Balmaseda et al. (2025) The role of the ocean in subseasonal-to-seasonal predictability and prediction
This chapter introduces the critical role of ocean-atmosphere coupling in subseasonal-to-seasonal (S2S) predictability and prediction, emphasizing its necessity for tropical intraseasonal oscillations and posing questions about its importance in midlatitudes.
-
Kumar et al. (2025) Role of scatterometers in tracking and monitoring tropical cyclones
This chapter highlights the critical role of scatterometers in tracking and monitoring tropical cyclones in the Indian Ocean, a region prone to frequent cyclogenesis influenced by factors like the Indian Ocean Dipole and Madden-Julian Oscillation. It emphasizes the need for high-resolution scatterometer wind data to improve tropical cyclone forecasting and detection.
-
Zheng et al. (2025) Corrigendum to “Coupling differentiable tau-omega model with Kolmogorov–Arnold network for soil moisture estimation over the Tibetan Plateau” [J. Hydrol. 662(Part B) (2025) 133940]
This corrigendum rectifies an error in the author affiliation order for the original article titled "Coupling differentiable tau-omega model with Kolmogorov–Arnold network for soil moisture estimation over the Tibetan Plateau."
-
Gauch et al. (2025) How to deal w___ missing input data
This paper addresses the critical challenge of missing input data in operational deep learning hydrologic models by introducing and comparing three strategies: input replacing, masked mean, and attention. The study concludes that the masked mean approach generally performs best across various missing data scenarios, offering a robust solution for real-world applications.
-
Gauvrit (2025) Applications de la Wavelet Scattering Transform à la caractérisation multi-échelle de signaux turbulents : de l'astrophysique à la couche limite atmosphérique marine
[Information not extractable from the provided garbled text.]
-
Zhou et al. (2025) MSFlood-Net: A physically informed deep learning model integrating multi-source data for flood inundation mapping
This study introduces MSFlood-Net, an enhanced U-Net-based deep learning model that integrates multi-source remote sensing and topographic data with physical information for accurate flood inundation mapping. The model achieves 97.187 % accuracy and a 96.756 % F1 score, demonstrating superior robustness in complex environments compared to baseline models.
-
Ekatpure et al. (2025) Advancing Crop Cultivation Estimation with Aerial Imaging and Artificial Intelligence: A Comprehensive Review
This review paper comprehensively analyzes current methods and applications of Artificial Intelligence (AI) in crop cultivation estimation using aerial imagery, demonstrating its effectiveness in various agricultural fields for observation, yield prediction, and decision support.
-
Elias et al. (2025) Drought feature assessment unravels how temperature increase has enhanced earlier and more severe drought in Lebanon over the last 60 years
This study investigated how climate change from 1960 to 2020 affected various drought facets in Lebanon using the DFEAT tool, which analyzes daily soil moisture. It revealed a significant shift towards drier conditions, characterized by an earlier drought onset (up to 17 days) and a delayed offset (up to 5 days), primarily driven by rising temperatures despite stable annual precipitation.
-
Wang et al. (2025) A 40-year dataset of soil salinity dynamics (1985–2024) at 100 m resolution in the Western Songnen Plain, China
This study developed and validated a 40-year (1985–2024) high-resolution (100 m) dataset of soil salinity dynamics for the Western Songnen Plain, China, using remote sensing, extensive field data, and machine learning, revealing significant spatiotemporal variability in salinization trends. The resulting dataset provides crucial information for improved monitoring and sustainable land management in salinized regions.
-
Mouassom et al. (2025) Hydrodynamics of rainfall peaks in homogeneous regions clustered using the K-means algorithm in Central Africa
This study identifies three homogeneous rainfall subregions in Central Africa using K-means clustering on 1984–2023 daily reanalysis data, revealing distinct rainfall peak patterns and their underlying hydrodynamic and thermodynamic mechanisms.
-
Tian et al. (2025) The Change of Cloud Base Height Over East Asia During 2010–2019
## Identification - **Journal:** IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - **Year:** 2025...
-
Unknown (2025) Volcano mega-eruptions lead to parched times
This study used a climate model to investigate the effects of large volcanic eruptions beyond global cooling, finding that while cooling plateaus with increasing eruption size, the reduction in global rainfall continues to intensify, leading to severe drought.
-
Makwana et al. (2025) Spatially Inhomogeneous Phase Change, and Its Effect on Cloud Turbulence
This study demonstrates that small-scale turbulence in clouds is significantly enhanced by inertial droplets, a phenomenon driven by a mismatch in time scales between vortex evacuation and condensation, which generates baroclinic torque and could accelerate raindrop growth.
-
Swinbourne et al. (2025) Trade-off between water-saving and cooling objectives: restricting irrigation increases the number of hot days
This paper investigates the trade-off between urban cooling and water conservation by simulating various irrigation scenarios in Melbourne, Australia, finding that increased irrigation significantly reduces heat stress, with quantifiable benefits for reducing heat stress days and mean air temperature.
-
Hu et al. (2025) Evaluation of UAV and satellite platforms for gross primary production monitoring under different model frameworks in agroecosystems
This study evaluated the performance of Unmanned Aerial Vehicle (UAV) and satellite platforms (Sentinel-2, MODIS) for monitoring gross primary production (GPP) in agroecosystems under various model frameworks, finding that UAVs provide more accurate GPP estimates, particularly with simpler models.
-
Hadei et al. (2025) Three Decades of Climate Change in Iran: Spatiotemporal Evidence from National-Scale Meteorological Indicators
This study assessed climate trends in Iran over three decades (1993–2022) using national meteorological data, revealing widespread warming, heterogeneous precipitation changes, and complex wind and dew point patterns with significant regional variability. These observed shifts indicate a transition towards a warmer, drier, and more hydrologically variable climate, necessitating targeted adaptation strategies.
-
Singh et al. (2025) Smart Irrigation and AI-Based Water Management in Climate-Stressed Regions: A Systematic Review
N/A (Paper content unreadable)
-
Latif et al. (2025) Transition of the Karakoram anomaly under emerging hydroclimatic trends
This study examines the amplitude and temporal evolution of the Karakoram Anomaly (KA) by analyzing hydroclimatic trends in the Hunza River Basin (HRB) and develops an Artificial Neural Network for runoff simulation.
-
Feng et al. (2025) Research on Optimizing Rainfall Interpolation Methods for Distributed Hydrological Models in Sparsely Networked Rainfall Stations of Watershed
This study optimizes rainfall interpolation methods for distributed hydrological models in sparsely networked rainfall stations of watersheds. It found that the Inverse Distance Weighting (IDW) method significantly outperforms Thiessen Polygon Interpolation (THI) and Trend Surface Interpolation (TSI) for flood forecasting in such conditions, demonstrating superior stability and accuracy.
-
Ashok et al. (2025) Impact of improved initial condition in a medium range forecast of super cyclonic storm Kyarr over Arabian sea using high resolution WRF and its data assimilation technique
This study investigates the impact of assimilating conventional and non-conventional observations on improving the initial conditions and medium-range forecasts of Super Cyclonic Storm (Su-CS) Kyarr over the Arabian Sea using the WRF-3DVAR technique. The results demonstrate that data assimilation significantly enhances the accuracy of track, intensity, and rainfall predictions, with a cold start assimilation mode proving most beneficial.
-
Preeti et al. (2025) Smart Solar-Irrigo System
This paper presents an Advanced Solar Tracking and Automatic Sprinkler Irrigation System that integrates single-axis solar tracking, real-time environmental sensing, and mobile communication to optimize solar energy generation and water usage in agriculture. The system demonstrates improved power generation (25-35% over static panels) and efficient water management by autonomously adjusting irrigation based on sensor data.
-
Dong et al. (2025) ENSO Influence on Subsequent Early‐Summer North American Atmospheric Circulation: Role of Land‐Atmosphere Interaction
This study identifies an ocean-land-atmosphere relay mechanism where El Niño-induced sea surface temperature anomalies enhance moisture transport into North America, leading to land temperature anomalies that feedback on the atmosphere, weakening the westerly jet and extending ENSO's influence into early summer.
-
Tang et al. (2025) Reproductive stage superiority in irrigation scheduling: UAV spectral mechanisms validated by field canopy architecture for soybean yield prediction
This study aimed to improve soybean yield prediction accuracy for precision irrigation management using UAV multispectral imaging. It found that the full pod stage (R4) is the most sensitive window for prediction, and a multi-source data fusion framework with XGBoost achieved high accuracy (R² = 0.83) by combining vegetation indices, texture features, and texture indices.
-
Jääskeläinen et al. (2025) High-resolution soil moisture mapping in northern boreal forests using SMAP data and downscaling techniques
This study develops a machine-learning-based downscaling model to estimate soil moisture at 1 km and 250 m spatial resolutions across northern boreal forests. By integrating SMAP satellite data with vegetation and weather parameters, the model improves soil moisture prediction accuracy over forested sites compared to original coarse-resolution products.
-
Yu et al. (2025) Estimating Winter Wheat Leaf Water Content by Combining UAV Spectral and Texture Features with Stacking Ensemble Learning
This study developed a stacking ensemble learning model integrating UAV multispectral and texture features to accurately estimate winter wheat leaf water content (LWC). The model achieved significantly improved estimation accuracy (R² = 0.865, rRMSE = 16.3%) compared to single-feature or single-model approaches, demonstrating the effectiveness of multi-source feature fusion for precision agricultural water monitoring.
-
Marín-Martín et al. (2025) A five-century tree-ring record from Spain reveals recent intensification of western Mediterranean precipitation extremes
This study reconstructs 520 years of quantitative precipitation in the Iberian Range, eastern Spain, using tree-ring data, revealing an unprecedented intensification in the frequency and intensity of hydroclimatic extremes during the late 20th and early 21st centuries compared to previous centuries.
-
Shi et al. (2025) Amplified Global Seasonality in Water Availability over Land in Recent Decades
This study quantified global seasonal precipitation minus evapotranspiration (P-E) shifts from 2000 to 2020 using observational data, revealing a significant increase in seasonal P-E range driven primarily by a global decrease in minimum P-E values, largely attributed to increased evapotranspiration.
-
Veness et al. (2025) User priorities for hydrological monitoring infrastructures supporting research and innovation
This study identifies end-user priorities for the UK’s new GBP 38 million Floods and Droughts Research Infrastructure (FDRI), revealing that value is maximized when infrastructures move beyond simple data provision to actively enable decentralized data collection and foster collaborative research communities.
-
Unknown (2025) Geo-Insights for a Greener Tomorrow: Remote Sensing and GIS for Sustainable Agriculture and Climate Resilience
This study integrates remote sensing and GIS to assess hydroclimatic variability impacts on agricultural sustainability and climate resilience. It reveals significant crop health variations, land-use changes (agricultural expansion at the expense of natural habitats), and predicted crop yield declines of up to 15% by 2035, particularly in rainfed areas.
-
Bryndal et al. (2025) Effects of Severe Hydro-Meteorological Events on the Functioning of Mountain Environments in the Ochotnica Catchment (Outer Carpathians, Poland) and Recommendations for Adaptation Strategies
This study comprehensively evaluates the multi-year environmental and economic impacts of a severe flash flood event in the Ochotnica catchment (Outer Carpathians, Poland) and proposes adaptation strategies, emphasizing the critical role of Maximum Probable Flood (MPF) hazard zone delineation for effective flood risk management.
-
Zeng et al. (2025) Stratified layering for soil profile: Dynamic short field Mamba network
This paper proposes a Dynamic Short Field Mamba (DSFM) network, built upon the Vision Mamba UNet, for accurate stratification of soil profiles, achieving significant improvements in accuracy by optimizing spatial feature extraction and handling blurred inter-layer transitions.
-
Rolbiecki et al. (2025) Forecasted Yield Responses of Carrot, Celeriac and Red Beet to Sprinkler Irrigation Under Climate Change in a Highly Water-Deficient Area of Central Poland
This study forecasts the yield responses of carrot, celeriac, and red beet to sprinkler irrigation under two climate change scenarios (RCP 4.5 and RCP 8.5) for 2021–2100 in central Poland. It found that irrigation consistently enhances yields, especially in very dry years, but its relative benefits may decline under more severe climate change (RCP 8.5).
-
Gohil et al. (2025) Global Simulations Suggest Biomass Burning Aerosol Emissions From Grassland Fires Could Be Important Ice Nucleating Particles
This study investigates the global importance of biomass burning aerosols (BBA) as immersion-mode ice nucleating particles (INP) using a global aerosol-climate model. It finds that BBA can be a more significant INP source than mineral dust and marine INP in specific atmospheric regions and seasons, particularly over the Southern Hemisphere during June-September.
-
Fair et al. (2025) Review article: using spaceborne lidar for snow depth retrievals: recent findings and utility for hydrologic applications
This review synthesizes the current status of spaceborne lidar for snow depth retrieval, focusing on the ICESat-2 mission, and evaluates its utility for hydrologic applications. It concludes that while ICESat-2 can achieve centimeter-level accuracy under ideal conditions, challenges persist in complex terrain and with current temporal revisit limitations, necessitating integration with hydrologic models and improved snow-off digital elevation models.
-
Wang et al. (2025) Decadal Responses of Global Land Monsoon to Two Surface Thermal Modes in the Last Millennium
This study investigates the influence of global mean surface temperature (GMST) and tropical Pacific temperature gradient (TPTG) on global land monsoon variability over the last millennium (950–1850). It finds that cool-GMST primarily suppresses summer precipitation thermodynamically, while weak-TPTG drives a summer interhemispheric dipole dynamically, with compound conditions largely showing a linear superposition of these distinct effects.
-
Patil et al. (2025) Investigating firn structure and density in the accumulation area of the Grosser Aletschgletscher using ground-penetrating radar
This study investigates firn structure, density, and accumulation history in the Grosser Aletschgletscher using ground-penetrating radar (GPR) and glaciological methods, demonstrating the potential to enhance glacier mass balance estimations by providing spatially distributed firn properties and accumulation records over the past 10-14 years.
-
Pan et al. (2025) Why Hydrological Memory Dominates in Low‐Latitude Highlands: A Mechanistic Shift in Ecosystem Response to Extremes
This study investigates how the pre-stress state of ecosystems determines their response to compound extreme events, revealing a shift in land-atmosphere interaction paradigms (from water-limited to energy-governed) driven by antecedent root zone soil moisture.
-
Wu et al. (2025) Thermodynamic and Dynamic Effects of Parametric Uncertainties on the Simulated Interannual‐To‐Interdecadal Variability of Summertime Precipitation Over China
This study quantifies uncertainties in simulated precipitation variability over China, attributing them primarily to the thermodynamic component, though the dynamic component's uncertainty is comparable, especially interannually. It identifies convective precipitation evaporation as a major source of interannual uncertainty, demonstrating its complex role in enhancing large-scale precipitation despite local suppression.
-
Fang et al. (2025) IMPMCT: a dataset of Integrated Multi-source Polar Mesoscale Cyclone Tracks in the Nordic Seas
The Integrated Multi-source Polar Mesoscale Cyclone Tracks (IMPMCT) dataset provides a comprehensive 24-year (2001–2024) record of wintertime Polar Mesoscale Cyclone (PMC) tracks in the Nordic Seas, integrating ERA5 reanalysis, AVHRR infrared imagery, and scatterometer wind data. This dataset, containing 1110 tracks, 16 001 cloud features, and 4472 wind records, aims to advance the understanding of PMC genesis and intensification mechanisms.
-
Cui et al. (2025) Multi-model coupled climate-land use-runoff feedback mechanism: analysis and prediction of spatial and temporal heterogeneity in the transboundary watershed of the Tumen River
This study developed a multi-model coupled climate-land use-runoff feedback mechanism (M-S-C) to predict annual runoff in the Tumen River Basin from 2025 to 2070, analyzing the spatial and temporal heterogeneity of impacts from climate and land use change, and introducing the Contribution of Transboundary River Volume (CTRV) concept. It found that climate change is a more substantial driver of runoff changes than land use change, with forest and cultivated land being key influencing factors, and revealed divergent runoff contributions between China (decreasing) and North Korea (increasing) due to differing land use policies.
-
Luo et al. (2025) Coupling strategies for precipitation nowcasting in China
[Information not extractable due to unreadable paper text.]
-
Biella et al. (2025) The 2022 drought needs to be a turning point for European drought risk management
This study analyzes the 2022 European drought by linking climate indices with a continent-wide survey of 481 water managers to evaluate sectoral impacts and management effectiveness. The findings reveal that while drought risk is perceived to be increasing across Europe, current management remains largely reactive and fragmented, prompting a call for a legally binding European Drought Directive.
-
Han et al. (2025) Optimization of ecological restoration efficiency in Qinghai-Tibet Plateau using the Cubist regression tree model: A study of environmental adaptability models
This study develops an integrated Cubist-BiGRU-SA regression tree model to enhance the prediction accuracy and environmental adaptability of ecological restoration efficiency on the Qinghai-Tibet Plateau (QTP). The model achieves high accuracy (over 96%) in predicting vegetation restoration rates and soil quality improvements, providing quantitative guidelines for manual intervention measures.
-
Kumar et al. (2025) Rainfall variability for crop water management under changing climate in Himachal Pradesh
This study analyzed spatial and temporal rainfall variability and water balance across three agro-climatic zones of Himachal Pradesh, India, from 1974-2021, revealing decreasing annual and seasonal rainfall trends and shifts in water surplus/deficit patterns that significantly impact crop water management and agricultural productivity.
-
Phogat et al. (2025) Regional dynamics in evapotranspiration components, crop coefficients, and water productivity of vineyards
This study estimated water balance components, crop coefficients, and water productivity for Shiraz vineyards across 48 locations in Barossa, South Australia, over three seasons using the FAO-56 dual crop coefficient approach, revealing significant spatial and temporal variability in these parameters and highlighting the need for site-specific irrigation management.
-
Yılmaz (2025) From Past to Future: Uzbekistan’s Climate Signals Through Time
This study analyzed historical trends (1995-2024) and forecasted future trajectories (2025-2050) of key climate variables in Uzbekistan using multi-source satellite data and reanalysis. Results indicate a significant warming and drying trend, with projected increases in land surface temperature, evapotranspiration, and vegetation, alongside a possible decline in soil moisture.
-
He et al. (2025) Benchmarking and evaluating the NASA Land Information System (version 7.5.2) coupled with the refactored Noah-MP land surface model (version 5.0)
This study integrates the refactored Noah-MP version 5.0 land surface model with the NASA Land Information System (LIS) version 7.5.2 to enhance interoperability and evaluates its global and regional performance against the previous LIS/Noah-MPv4.0.1 for key land surface variables. The results show that LIS/Noah-MPv5.0 generally performs similarly or better than its predecessor, with slight degradations in simulated surface soil moisture and snow water equivalent.
-
Zhang et al. (2025) A new experimental methodology to determine the soil saturated hydraulic conductivity in homogeneous soil
This study developed a new mathematics experiment method (MEM) for the rapid determination of soil saturated hydraulic conductivity (Ks) in homogeneous soils, utilizing both horizontal and vertical infiltration rates. The MEM demonstrated strong agreement with established constant head and two-ponding depths methods, offering a robust and efficient measurement technique.
-
Carletti et al. (2025) Multitemporal analysis of Sentinel-1 backscatter during snowmelt using high-resolution field measurements and radiative transfer modelling
This study presents a unique high-resolution dataset of wet-snow properties to validate Sentinel-1 backscatter links to snowmelt stages and investigate scattering mechanisms using a radiative transfer model, revealing the dominant influences of liquid water content and surface roughness.
-
Nguyen et al. (2025) The Collaborative Border Fuzzy Clustering Approach Based on the Semi-supervised Technique for Land Cover Classification
This paper introduces a novel semi-supervised border fuzzy clustering approach (B-S²CFC) that combines border fuzzy c-means and semi-supervised collaborative fuzzy clustering for land cover classification from remote sensing imagery, demonstrating superior performance in results and computation time while effectively handling vague cluster boundaries and noise.
-
Bartolomé et al. (2025) Records of past flood in caves of the Central Pyrenees
This paper advocates for and demonstrates an integrated approach to reconstruct and quantify past flood events using sedimentary evidence from caves, particularly stalagmites and clastic infills, combined with karst hydraulic modeling and monitoring. The study highlights the method's potential to extend flood records beyond instrumental data, improving long-term flood risk assessment in the Central Pyrenees under climate change.
-
Niu et al. (2025) Long-Term Trends and Seasonally Resolved Drivers of Surface Albedo Across China Using GTWR
This study analyzed the spatiotemporal variations and long-term trends of surface albedo across China from 2001 to 2020, revealing an overall decline primarily driven by the Normalized Difference Vegetation Index (NDVI) (approximately 48%), with air temperature and precipitation also playing significant, seasonally varying roles.
-
Xu et al. (2025) Vegetation Phenological Responses to Multi-Factor Climate Forcing on the Tibetan Plateau: Nonlinear and Spatially Heterogeneous Mechanisms
This study quantifies the independent and interactive effects of multiple climate factors on vegetation phenology on the Tibetan Plateau, revealing a significant increase in growing season length (0.24 days per year) primarily due to earlier spring onset, with spatially heterogeneous and ecosystem-specific responses.
-
Hierro (2025) Observational Evidence of Vertical Drying Over Deforested Amazonia From GPS ‐ RO Data
This study investigates the impact of deforestation on the vertical humidity structure in Amazonia, revealing significant drying, particularly in the lower troposphere, in highly deforested regions, which intensifies during the dry season.
-
Cheng et al. (2025) A Multi-Stage Deep Learning Framework for Multi-Source Cloud Top Height Retrieval from FY-4A/AGRI Data
This study proposes a multi-stage deep learning framework to enhance the accuracy of Cloud Top Height (CTH) retrieval from Fengyun-4A (FY-4A) satellite data. The model significantly improves CTH retrieval accuracy, reducing the Mean Absolute Error by 49.12% to 2.03 km compared to the official FY-4A product, and successfully characterizes CTH spatial distribution in complex weather systems.
-
Liu et al. (2025) Soil hydrological processes and drought response of typical vegetation in arid regions under long-term climate patterns
This study integrated field data and Hydrus-1D modeling to analyze long-term soil water dynamics and water-use strategies of five land types in China's arid Mu Us Sandy Land (1980–2022), revealing that *Caragana korshinskii* (CK) exhibits the strongest drought resistance and water conservation, while bare land (BL) is crucial for groundwater recharge.
-
Yakubu et al. (2025) Global Bias-Corrected CORDEX Datasets at Half Degree Resolution
This paper presents GloBCORD-HD, a new quasi-global dataset of bias-corrected Coordinated Regional Climate Downscaling Experiments (CORDEX) at 0.5° spatial and daily temporal resolution for historical (1950–2019) and future (2020–2099) periods under three Representative Concentration Pathways (RCPs). Comprehensive validation demonstrates that GloBCORD-HD significantly reduces biases, improves regional extremes representation, and enhances climate signal consistency, enabling robust global impact assessments.
-
López-Martí et al. (2025) Can data-driven weather models accurately forecast atmospheric rivers?
This study assesses the performance of leading data-driven weather models (GraphCast, Pangu-Weather) against a physics-based model (IFS-HRES) in forecasting integrated vapour transport (IVT) and atmospheric rivers (ARs). While data-driven models show comparable IVT skill, they struggle with higher IVT quantiles and geometrically stricter AR detection, suggesting physics-based models may retain advantages for complex derived features.
-
Long et al. (2025) Assessing the Consistency Among Three Mascon Solutions and COST-G-Based Grid Products for Characterizing Antarctic Ice Sheet Mass Change
This study comprehensively assesses the consistency of three GRACE/GRACE-FO mascon solutions (CSR, JPL, GSFC) and COST-G-based grid products for characterizing Antarctic Ice Sheet (AIS) mass change from 2003 to 2023, revealing overall good agreement but significant discrepancies in specific regions like the Antarctic Peninsula and during the late GRACE mission period.
-
Samuel et al. (2025) Assessment of Historical and Future Mean and Extreme Precipitation Over Sub‐Saharan Africa Using NEX ‐ GDDP ‐ CMIP6 : Part II —Future Changes
[Information not extractable from the provided corrupted text.]
-
Deng et al. (2025) Enhancing Forecasting Capabilities Through Data Assimilation: Investigating the Core Role of WRF 4D-Var in Multidimensional Meteorological Fields
This study systematically evaluated the WRF 4D-Var data assimilation system, implementing a novel two-layer nested "assimilation-forecast" workflow, and found significant improvements in multidimensional meteorological forecasts across different seasons and underlying surface types, with benefits persisting for approximately 12 hours.
-
Thiam (2025) Paramétrisation du soulèvement de poussières au Sahel par les poches froides
[Information not extractable from provided text due to corrupted input]
-
Ilyas et al. (2025) Modeling meteorological drought across scales with regional and global climate indicators
This study introduces a novel Hybrid Convolutional Bi-Kernel Ensemble (HCBKE) model for multiscale meteorological drought prediction using the standardized precipitation index (SPI) at 3, 6, and 12-month timescales. Evaluated in Ankara province, Turkey, the HCBKE model consistently outperformed traditional and deep learning models, demonstrating superior accuracy and robustness for operational drought monitoring.
-
Qin et al. (2025) Sensitivity Differences Between 118 GHz and 183 GHz Radiance in All‐Sky Assimilation With Hydrometeor Control Variables and the Impact on a Typhoon Structure Forecast
This study investigates the comparative impacts of separate and joint assimilation of 118 GHz and 183 GHz Microwave Humidity Sounder-II (MWHS-II) channels on hydrometeor analysis and forecasting of Typhoon Lekima (2019). Joint assimilation significantly improves the representation of temperature, humidity, and hydrometeor distribution, enhancing typhoon intensity forecasting and understanding of its structure.
-
Shabbir et al. (2025) Hybrid method for river inflow prediction: an integration of Hampel filter, decomposition techniques, and support vector machine
This study develops a novel hybrid model (HEVS: Hampel filter, EEMD, VMD, SVM) for daily river inflow prediction, demonstrating significantly improved accuracy over existing models by effectively addressing outliers, noise, randomness, and multi-scale characteristics in hydrological time series within the Indus River Basin.
-
Islam et al. (2025) From Traditional Machine Learning to Fine-Tuning Large Language Models: A Review for Sensors-Based Soil Moisture Forecasting
This paper proposes a novel taxonomy for soil moisture (SM) forecasting and provides a comprehensive review of 68 peer-reviewed studies published between 2017 and 2025, covering traditional machine learning, deep learning, and hybrid models, while identifying future research directions.
-
Matthews et al. (2025) Error-correction across gauged and ungauged locations: A data assimilation-inspired approach to post-processing river discharge forecasts
This study presents a novel data-assimilation-inspired post-processing method to error-correct river discharge ensemble forecasts across both gauged and ungauged locations. The method successfully improves the skill of the ensemble mean and maintains spatial and temporal consistency in the corrected forecasts.
-
Werapitiya et al. (2025) Extratropical Cloud Feedback Constrained by Cloud Sources and Sinks in Cyclones
This study constrains Southern Ocean liquid water path (LWP) response and shortwave cloud feedback (SW FB) in global climate models using observational data and a perturbed parameter ensemble, finding that observations suggest a more positive SW FB and do not reject high climate sensitivity models.
-
Yasin et al. (2025) Spatially integrated standardized relative humidity index: A principal component analysis-based approach for regional drought assessment
This study introduces the Multivariate Standardized Relative Humidity Index (MSRHI), a novel drought assessment tool that integrates relative humidity data from multiple stations using Principal Component Analysis (PCA). The index provides a more stable and spatially coherent representation of regional drought conditions across Pakistan's diverse climatic zones compared to traditional station-based univariate indices.
-
Sseguya et al. (2025) Deep Reinforcement Learning for Optimized Reservoir Operation and Flood Risk Mitigation
This study applies deep reinforcement learning (DRL) models (DQN, PPO, DDPG) to optimize reservoir operations at the Soyang River Dam, South Korea, using 30 years of daily hydrometeorological data. The DRL framework effectively balances flood risk mitigation and water supply, with models like PPO and DQN demonstrating superior performance over observed operations during high-inflow periods by increasing storage buffers and reducing peak discharge.
-
Bhat et al. (2025) Forecasting the Future of Food: An Integrated Review of Crop and Climate Simulation Models
This review synthesizes the structure, applications, and limitations of prominent crop and climate simulation models (DSSAT, APSIM, Aqua Crop, WOFOST, InfoCrop) to inform climate-resilient agricultural planning and discusses their integration with climate projection tools.
-
Huang et al. (2025) Seasonal and Spatial Variations of Vertical Profile Heating (VPH) Latent Heat Over Northern East Asia Based on GPM Observations
This study quantifies the three-dimensional structure of latent heating (LH) from precipitation over northern East Asia using GPM observations, documenting its seasonal, meridional, and land-sea contrasts. It reveals that summer precipitation exhibits stronger and higher LH, with a poleward decrease in LH heights and coastal amplification, providing a crucial midlatitude LH climatology.
-
Beyene et al. (2025) Comparison of bias correction methods to enhance CHIRP rainfall estimates for improved streamflow simulation at Ziway-Shalla catchment, Ethiopia
This study compares five bias correction methods for the CHIRP satellite rainfall product in Ethiopia's Ziway-Shalla catchment, finding that Quantile Mapping based on Gamma distribution (QMG) and Power Transformation (PT) significantly improve streamflow simulations by the HBV model compared to raw CHIRP data.
-
Abdulahi et al. (2025) Impact of Climate Change on Drought Dynamics in the Ganale Dawa River Basin, Ethiopia
This study assessed the impact of climate change on agricultural and hydrological drought dynamics in Ethiopia's Ganale Dawa Basin using machine learning-enhanced CMIP6 projections and satellite-based indices. Findings reveal increasing variability in agricultural drought and continued recurrence of hydrological drought, especially under high-emission scenarios.
-
Gupta (2025) Ai-Based Spatio-Temporal Analysis for Predicting Climate-Resilient Crop Yields in Indian Agricultural Systems
[Information could not be extracted due to unreadable input text.]
-
Sarah et al. (2025) Earth observations for climate adaptation: tracking progress towards the Global Goal on Adaptation through satellite-derived indicators
This Perspective paper explores how space-based Earth Observation (EO) data can support tracking progress towards the Paris Agreement's Global Goal on Adaptation (GGA), focusing on agriculture, biodiversity, extreme events, and health. It highlights EO's strengths and challenges, offering recommendations for integrating EO into the development of standardized, operational adaptation indicators.
-
Patil et al. (2025) Over 100 global climate sensitive rivers are experiencing large and severe changes in streamflow volume and timing
This study analyzed streamflow volume and timing changes in 812 climate-sensitive rivers globally from 1950 to 2022, finding increasing streamflow and earlier timing in over half of the sites, largely driven by precipitation changes, with significant implications for river health and water management.
-
Lin (2025) Daily meteorological data for a typical meteorological year
This entry describes a dataset providing daily meteorological parameters for a typical meteorological year, intended for general use in meteorological studies.
-
Faquseh et al. (2025) Long-Term Drought Analysis in Dura City, Palestine, Using the Standardized Precipitation Index (SPI)
This study assessed long-term drought in Dura City, Palestine (2000-2023) using the Standardized Precipitation Index, revealing high short- and medium-term drought variability despite stable annual precipitation, primarily driven by a significant increase in yearly average temperature.
-
Zhu et al. (2025) Attention enhanced ResNet for ocean surface wind speed retrieval using CYGNSS observables
This study proposes an Attention-enhanced Residual Network (Att-ResNet) to improve ocean surface wind speed retrieval using Cyclone Global Navigation Satellite System (CYGNSS) bistatic radar data. The Att-ResNet model achieved high accuracy, demonstrating root mean square errors of approximately 1.38 m/s when validated against ERA5 and CCMP wind products.
-
Aarich et al. (2025) Ensemble Stacking Learning Approach for Forest Fire Prediction in Satellite Dataset
This study proposes and evaluates an ensemble stacking learning approach for forest fire prediction using MODIS satellite imagery, comparing its performance against individual supervised machine learning models.
-
Haraz et al. (2025) Towards Smart Irrigation: Architecture of an Irrigation System Based on Artificial Intelligence
This paper proposes an innovative four-layer architecture for an intelligent irrigation system, leveraging Internet of Things (IoT) and Artificial Intelligence (AI) technologies to optimize water resource management and maximize agricultural yield in drylands, particularly in Morocco, amidst climate change and water scarcity.
-
J et al. (2025) Can discharge be used to inversely correct precipitation?
This study investigates the feasibility of using observed streamflow to inversely correct catchment-average precipitation from reanalysis products using LSTM networks. It demonstrates that discharge significantly improves precipitation estimates, especially for high-magnitude events, and enhances subsequent hydrological forward modeling of streamflow and soil moisture.
-
Pardo et al. (2025) Dynamics of Downdrafts Around a Growing Convective Cloud: A Numerical Study
This study investigates the dynamics of cloud-edge downdrafts in growing isolated cumuli, revealing that the most intense downdrafts are predominantly driven by dynamic pressure accelerations rather than buoyancy, and are stronger than predicted by simple vortex models.
-
Patel et al. (2025) QuSrO-MEnDRN: data assimilation with quest search optimization enabled multihead error minimum learning approach for rainfall prediction
This study introduces QuSrO-MEnDRN, a novel deep learning model integrating quest search optimization and a modified deep spatial transformer U-Net for data assimilation, to enhance rainfall prediction accuracy and mitigate overfitting. The model achieves superior performance with minimal error rates compared to conventional methods.
-
Zhao et al. (2025) A systematic review of methods for identifying drought-flood abrupt alternation: advances and future directions
This systematic review synthesizes 55 publications on Drought-Flood Abrupt Alternation (DFAA) event identification, proposing a unified definition framework, evaluating methodological advances, and outlining future directions to improve DFAA identification under climate change. It finds that while traditional indices are common, advanced methods incorporating transition characteristics are emerging, and future research needs multi-source data integration and dynamic time windows for better accuracy and policy alignment.
-
Bauer et al. (2025) An Analytical Model for the Influence of Soil Moisture on Temperature Extremes in the Midlatitudes
This paper develops a theoretical framework and analytical models to demonstrate that low soil moisture nonlinearly controls the frequency of midlatitude temperature extremes by slowly altering the land surface climate state, rather than directly modifying atmospheric variability.
-
Raghuvanshi et al. (2025) An Hourly Dataset of Moisture Budget Components Over the Indian Subcontinent (1940–2024)
This paper introduces ERA5moistIN, a high-resolution (0.25° spatial, hourly temporal) dataset of atmospheric moisture budget components over the Indian subcontinent from 1940 to 2024, derived from ERA5 reanalysis. The dataset's physical consistency and reliability are validated against native ERA5 diagnostics, demonstrating its capability to accurately capture moisture transport dynamics, even during extreme events.
-
Xiang et al. (2025) Contribution of lower stratospheric ozone to long-term trends in summer precipitation over Mongolia-Northeastern China
This study investigates the interdecadal fluctuations of summer precipitation in Mongolia-Northeastern China, revealing that variations in lower stratospheric ozone significantly contribute to observed declines (1982-2002) and subsequent increases (2002-2022) by altering atmospheric circulation.
-
Coquereau et al. (2025) Increase in ENSO Frequency and Intensity Under 20th and 21st Century Warming: Insights From CMIP6 Large Ensembles
This study analyzes CMIP6 large-ensemble simulations to demonstrate that anthropogenic warming increases El Niño-Southern Oscillation (ENSO) frequency and strengthens its intensity, primarily driven by a shift towards Eastern Pacific El Niño and an overall increase in both EP and CP variability.
-
Hunt et al. (2025) Wetter, Weaker, and More Frequent Monsoon Low Pressure Systems in CMIP6 Future Scenarios
This study synthesizes future characteristics of South Asian monsoon low pressure systems (LPSs) using CMIP6 models, projecting that LPSs will become more frequent and deliver increased precipitation per event despite dynamic weakening, leading to an expanded risk of extreme rainfall and flooding deeper into the Indian subcontinent.
-
Ehmimed et al. (2025) AI for Climate Change Resilience in Water Management
This paper provides an in-depth, state-of-the-art analysis of Artificial Intelligence (AI) applications in water management for climate change resilience, evaluating their effectiveness, limitations, and future opportunities. It highlights the critical role of AI in improving water security, provided challenges like data scarcity, interpretability, and ethical frameworks are addressed.
-
wu (2025) Annual Precipitation Raster Data of Xiluodu Reservoir Area (2001-2020)
[Information not extractable due to corrupted input.]
-
Salazar et al. (2025) Spatio-Temporal Evaluation of MSWEP, CHIRPS and ERA5-Land Reveals Regional-Specific Responses Across Complex Topography in Bolivia
This study evaluated three gridded precipitation datasets (MSWEP V2.2, CHIRPS V2, and ERA5-Land) against ground-based observations in Bolivia from 1980-2022, finding MSWEP V2.2 to be the most reliable across most regions and identifying significant precipitation declines in the Llanos.
-
Renou et al. (2025) Innovative validation of Sentinel-3 SAR altimetry measurements over rivers for the evaluation of hydrology-dedicated retrackers
This study introduces an innovative "off-nadir" validation method for Sentinel-3 SAR altimetry over rivers, which significantly reduces the impact of river slope on Water Surface Height (WSH) estimates. Applying this method to French rivers, the research statistically evaluates hydrology-dedicated retrackers, finding a WSH bias of approximately 0.04 m for the sinc2 retracker (reducible to near zero in favorable conditions) and 0.22 m for the OCOG retracker.
-
Wang et al. (2025) Subsurface constraints amplify vegetation stress during extreme heat and drought in a Mediterranean forest
This study investigated how subsurface conditions interact with climate stress to influence forest vulnerability and die-off at landscape scales. It revealed that vegetation die-off in the Jarrah Forest during the 2023–2024 summer resulted from the interplay of heat, water stress, and subsurface factors, with shallow soils and limited subsurface water predisposing vulnerability.
-
Wang et al. (2025) Generalized Variational Retrieval of Full Field-of-View Cloud Fraction and Precipitable Water Vapor from FY-4A/GIIRS Observations
This study proposes a generalized variational retrieval framework to estimate full field-of-view (FOV) cloud fraction and precipitable water vapor from Fengyun-4A/Geostationary Interferometric Infrared Sounder (FY-4A/GIIRS) observations, demonstrating improved brightness temperature simulations in cloudy regions and better indication of high-impact weather events.
-
Xu et al. (2025) Elevating predictive reliability: time-varying parameter bayesian deep learning techniques for flood probability forecasting
This study introduces a Fourier-based Time-Varying Parameter Bayesian Long Short-Term Memory (F-TV-BLSTM) model, integrating FourCastNet precipitation forecasts, to enhance multi-step-ahead probabilistic flood forecasting reliability in non-stationary environments. Applied to the Yalong River Basin, the model demonstrates superior performance in accuracy and dependability, particularly for extreme flood events, by dynamically adjusting parameters.
-
AKSOY (2025) Prediction of LAI in Scots pine forests of Türkiye using UAV and Sentinel 2 Images
This dataset provides ground-measured Leaf Area Index (LAI) values alongside features derived from Unmanned Aerial Vehicle (UAV) and Sentinel-2 imagery, intended for the prediction of LAI in Scots pine forests of Türkiye.
-
Zhao et al. (2025) Correction–fusion of NWP precipitation conditioned by rainfall stations and multivariate environmental information
This study proposes a novel correction-fusion method for numerical weather prediction (NWP) precipitation, integrating station observations and multivariate environmental information to generate high-resolution, high-precision precipitation products. The merged precipitation significantly improves accuracy, precipitation detection, and hydrological utility compared to raw NWP model output.
-
Lee et al. (2025) Enhancement of hydrologic model optimization with single-step reinforcement learning
This study proposes a single-step Reinforcement Learning (PPO-1) approach for efficient calibration of hydrological models with static parameters. It demonstrates that PPO-1 achieves better or comparable calibration accuracy with significantly reduced computational resources compared to traditional methods.
-
Beshir et al. (2025) Climate change projections using CMIP6 GCMs and downscaling approaches in the Upper Wabe Shebele Basin, Ethiopia
This study projected future precipitation and temperature changes in the Upper Wabe-Shebele River Basin, Ethiopia, using CMIP6 GCMs and downscaling techniques. Findings indicate a significant decline in precipitation (up to 50.33%) and a substantial increase in temperature (up to 3.6 °C) by the 2070s, threatening water availability and agricultural productivity.
-
Pang et al. (2025) A skillful self-evolving deep-learning framework for pluvial flood process forecasting in urban areas
This paper presents a self-evolving deep-learning framework, based on two autoregressive convolutional neural networks, for real-time pluvial flood process forecasting. The framework accurately predicts flood depth and velocity fields with significantly reduced computational time compared to conventional hydrodynamic models and improved accuracy over existing end-to-end surrogate models.
-
Yang et al. (2025) Modeling Tropical Cyclone Boundary Layer Wind Fields over Ocean and Land: A Comparative Assessment
This study evaluates four tropical cyclone boundary layer models under idealized and real-case conditions, demonstrating that incorporating nonlinear vertical advection and spatially varying surface roughness significantly improves the accuracy of wind field simulations, particularly near landfall.
-
Pacheco‐Labrador et al. (2025) BOSSE v1.0: the Biodiversity Observing System Simulation Experiment
This paper introduces BOSSE v1.0, a novel Observing System Simulation Experiment (OSSE) designed to simulate synthetic landscapes featuring diverse vegetation, associated remote sensing signals, and ecosystem functions. BOSSE aims to address the critical lack of consistent global ground diversity measurements, enabling the benchmarking and development of remote sensing methodologies for estimating plant functional diversity (PFD) and biodiversity-ecosystem function (BEF) relationships.
-
Mastir et al. (2025) Integrating Artificial Intelligence into the Morocco Flood and Drought Monitor: A Framework for Sustainable and Resilient Disaster Management
This paper proposes an Artificial Intelligence (AI)-integrated framework to enhance the Morocco Flood and Drought Monitor (MFDM), aiming to improve disaster preparedness, response, and prevention through predictive analytics, real-time monitoring, and decision support systems.
-
Yang et al. (2025) Effect of Sowing Time Variations and Irrigation Water Levels on Growth, Yield of Wheat, and Water Footprints
This field experiment investigated optimal sowing times and irrigation levels for wheat, concluding that normal sowing with a 15–30% irrigation deficit significantly enhances water productivity and water use efficiency with minimal yield and economic losses, offering a climate-adaptive approach.
-
Hasan (2025) Soil Moisture SK4
This paper presents the Soil Moisture SK4 dataset, an 8-year, high-resolution in-situ record of multi-sensor soil moisture, temperature, and collocated meteorological variables from Saskatchewan, Canada, along with a detailed preprocessing workflow for its use in soil moisture prediction research.
-
Dalle Vaglie (2025) Eslr
This study maps exposure to Extreme Sea Level Rise (ESLR) across Europe and North Africa through 2100, combining JRC global projections with high-resolution terrain data using a cost-distance/topographic connectivity model.
-
Tielidze et al. (2025) Post-Little Ice Age Shrinkage of the Tsaneri–Nageba Glacier System and Recent Proglacial Lake Evolution in the Georgian Caucasus
This study reconstructs the post-Little Ice Age evolution of Tsaneri–Nageba Glacier in the Georgian Caucasus and documents the development of its newly formed proglacial lake, revealing significant glacier shrinkage and the rapid expansion of a lake prone to glacial lake outburst floods.
-
Ansari et al. (2025) Global assessment of aerosol radiative effects: New insights from observations, reanalysis, and climate models
This study provides a global assessment of aerosol direct radiative effects (DRE) using multi-source data, revealing that DRE and atmospheric heating rates are highest over South Asia due to high aerosol optical depth and low single scattering albedo, while also quantifying significant biases in reanalysis and climate models.
-
Zhi-jie et al. (2025) Nonstationary Spatiotemporal Projection of Drought Across Seven Climate Regions of China in the 21st Century Based on a Novel Drought Index
This study projects the spatiotemporal evolution of drought across seven climate regions of China in the 21st century using a novel CO2-aware standardized moisture anomaly index (SZI[CO2]) and nonstationary Copula-based approaches. It finds a wetting trend in Northern and Western China, while Central and Southern China are projected to experience drying, with drought characteristics exhibiting strong nonstationarity and higher joint probabilities under high-emission scenarios.
-
Köcher et al. (2025) The spatial distribution of convective precipitation – an evaluation of cloud microphysics schemes with polarimetric radar observations
This study statistically evaluates five cloud microphysics schemes in the WRF model for simulating convective precipitation events over a 30-day dataset, revealing that the choice of scheme significantly impacts the distribution of precipitation into convective and stratiform regions and their microphysical properties, mainly due to differences in simulated rain drop size distributions.
-
Pan et al. (2025) SCFNet: A Swin-CNN Synergistic Fusion Network for Urban and Rural Water Extraction in Remote Sensing Images
This paper introduces SCFNet, a novel Swin-CNN synergistic fusion network, designed to enhance the accuracy and robustness of water body extraction from remote sensing images across both urban and rural landscapes.
-
Wei et al. (2025) Time fractional Saint Venant equations reveal the physical basis of hydrograph retardation through model comparison and field data
This study introduces novel fractional-order Saint-Venant equations (FSVEs) for simulating river flow dynamics, demonstrating their superior accuracy in capturing hydrograph retardation, peak attenuation, and tailing behavior compared to traditional models and machine learning approaches, particularly in data-sparse conditions.
-
Park et al. (2025) Assessing the Applicability of the LTSF Algorithm for Streamflow Time Series Prediction: Case Studies of Dam Basins in South Korea
This study systematically assessed the applicability of two Long-Term Time Series Forecasting (LTSF) linear models, NLinear and DLinear, for hydrological inflow prediction at eight major dams in South Korea, comparing their performance against conventional AI models like LSTM and XGBoost. While LSTM generally achieved the highest R2 and lowest NRMSE, DLinear minimized NMSE, and NLinear showed superior hydrological consistency, demonstrating the potential of LTSF models for this domain but highlighting site-specific performance variations.
-
Feng et al. (2025) Enhanced Detection of Drought Events in California’s Central Valley Basin Using Rauch–Tung–Striebel Smoothed GRACE Level-2 Data: Mechanistic Insights from Climate–Hydrology Interactions
This study develops a state-space model to mitigate GRACE north-south strip errors, applying it to estimate the GRACE Groundwater Drought Index (GGDI) in the California Central Valley. The model effectively quantifies groundwater changes, revealing significant depletion during droughts and recharge during floods, with precipitation and runoff identified as primary drivers influenced by ENSO.
-
Boukdire et al. (2025) Interpolation and Machine Learning Methods for Sub-Hourly Missing Rainfall Data Imputation in a Data-Scarce Environment: One- and Two-Step Approaches
This study develops and evaluates machine learning and interpolation approaches for imputing missing 10-minute rainfall data, demonstrating that a two-step machine learning approach, which first classifies rain/no-rain periods, consistently outperforms direct methods and traditional interpolation techniques.
-
Sundaram et al. (2025) Identifying and Correcting for Residual Biases in the ASCAT and ERA5 Wind Speed Products
[The provided text only contains the title of the paper. A concise summary cannot be generated without the full paper content.]
-
Wang et al. (2025) The effect of rainfall variability on Nitrogen dynamics in a small agricultural catchment
This study investigates the effect of inter-annual and intra-annual rainfall variability on nitrogen (N) dynamics and water quality in a small agricultural catchment in central Germany using a coupled hydrological and N transport model driven by a stochastic rainfall generator. It finds that higher annual precipitation enhances N transformation and transport, while lower annual precipitation promotes N retention, with vegetation health critically influencing N dynamics during extreme droughts and rewetting periods.
-
Subhasree et al. (2025) Performance Assessment of Soil Moisture Sensors for Precision Irrigation Scheduling
This study evaluated the performance of electrical resistance and capacitance soil moisture sensors for precision irrigation in bottle gourd, finding that capacitance sensors provided more consistent measurements and significantly improved crop yield by 21% and irrigation water use efficiency by 51% compared to manual irrigation.
-
Kumar et al. (2025) Doppler weather radar-based 5-year climatology of winter storms over the National Capital Region of India
This study presents the first Doppler Weather Radar-based 5-year climatology of winter storms over the National Capital Region of India, characterizing their life period, intensity, size, and movement. It reveals that most storms are short-lived and spatially confined, but a subset of intense, long-lived storms occurs more frequently in polluted and urban-influenced regions, predominantly moving northeastward.
-
Liu et al. (2025) Hydrological drought dynamic using copula functions and drought center migration in the Ganjiang river basin
This study investigated the spatiotemporal evolution and risk of hydrological drought in China's Ganjiang River Basin (1959–2019) using observed and SWAT-simulated runoff data, the Standardized Runoff Index (SRI), run theory, a gravity center model, and Copula functions. The findings revealed an intensification of droughts after the 1990s, a concentration of drought centers in the central basin with prominent north-south migration, and the Gumbel copula as the best model for interdependent drought characteristics (duration, severity, and peak intensity) to enhance risk assessment.
-
Xiao et al. (2025) Research status of winter irrigation based on bibliometric analysis
This bibliometric analysis reviews 61 articles on winter irrigation from the Web of Science (2000–2024) to map research trends, hotspots, and key challenges, highlighting the increasing focus on soil water-salt dynamics and the need for integrated, region-specific strategies.
-
Wang et al. (2025) High-Resolution Crop Mapping and Suitability Assessment in China’s Three Northeastern Provinces (2000–2023): Implications for Optimizing Crop Layout
This study mapped the distribution of rice, maize, and soybean in Northeast China from 2000 to 2023 using satellite imagery and a Random Forest classifier, and assessed crop suitability with a multi-criteria evaluation and MaxEnt model, identifying significant shifts in cultivation and mismatches between actual distribution and suitability.
-
Elhaddad et al. (2025) Nile basin flow regimes under 21st century climate variability
This study assesses future flood risk in the Nile Basin's downstream countries using a calibrated, climate-driven SWAT+ model forced by bias-corrected CMIP6 models under SSP2-4.5 and SSP5-8.5 scenarios, projecting a significant increase in 100-year peak discharges (63% to 85%) and more frequent extreme floods by the 21st century.
-
Chadwick et al. (2025) Processes Controlling the South American Monsoon Response to Climate Change
Future projections consistently show early South American monsoon drying, primarily driven by sea surface temperature (SST) changes (uniform warming and patterned changes), with Atlantic SST gradients explaining over half of the intermodel uncertainty in November.
-
Zhang et al. (2025) Volcanic eruptions disrupt ENSO teleconnections with land summer temperature
This study uses observations to demonstrate that volcanic eruptions significantly disrupt global El Niño–Southern Oscillation (ENSO) teleconnections with land surface air temperature in boreal summer, a phenomenon largely missed by current Earth System Models. This disruption challenges the stationarity assumption in ENSO reconstructions and highlights limitations in climate modeling under external perturbations.
-
Zhang et al. (2025) First Global Retrievals of Solar Induced Chlorophyll Fluorescence From the SIFIS Instrument Onboard the Chinese Goumang Satellite
This paper introduces and validates the Solar-Induced Chlorophyll Fluorescence Imaging Spectrometer (SIFIS) on the Goumang satellite, demonstrating its capability to provide high-resolution SIF data (370 m × 800 m) with high accuracy, enabling fine-scale monitoring of terrestrial photosynthesis.
-
Sultana et al. (2025) ArcticNet for Semantic Segmentation of Meltpond Regions in the Arctic Sea Ice
This paper introduces ArcticNet, a novel deep learning architecture based on UNet with recurrent, residual, and attention operations, for semantic segmentation of meltpond regions in Arctic sea ice. ArcticNet demonstrates superior performance in accurately delineating meltponds, open water, and snow compared to existing state-of-the-art models.
-
Ashfaq et al. (2025) CMIP6-based Dynamically Downscaled Hydroclimate Projection over the Conterminous US
This study presents a dataset of dynamically downscaled hydro-climate projections for the conterminous United States (CONUS), generated by downscaling multiple CMIP6 Global Climate Models using the Regional Climate Model version 4 (RegCM4). The dataset covers baseline (1980-2019) and near-future (2020-2059) periods under the high-end SSP585 emission scenario.
-
Wu et al. (2025) Assessing the real impact of inter-provincial grain trade on water and land resources within China via a modified framework
This study developed a modified framework to assess the real impact of inter-provincial grain trade on water and land resources in China by incorporating irrigation's yield-enhancing effects. It found that existing frameworks significantly underestimate virtual blue water and arable land flows, leading to erroneous trade evaluations and policy guidance.
-
Shim et al. (2025) East Asian dust source region restructuring linked to recent extreme drying
This study reveals a significant northward expansion of East Asian dust source regions, particularly over Mongolia, driven by extreme regional drying and reduced summer precipitation over the past three decades, which has fundamentally altered soil conditions and nearly tripled dust outbreaks.
-
Murodov et al. (2025) Impacts of future climate change on runoff and its components for the Vanj river basin in the western Pamir
This study projects the impacts of future climate change on runoff and its components in the glacierized Vanj River Basin, Tajikistan, using the SPHY model under CMIP6 SSPs. Results indicate significant temperature increases, substantial glacier area loss, and a decline in annual runoff, alongside a shift in peak snowmelt runoff, highlighting the urgent need for adaptive water management.
-
Polonik et al. (2025) Emulation of the Climate Response to Greenhouse Gas and Aerosol Emissions From High‐ and Low‐Income Nations
This study uses a large ensemble climate model to identify spatiotemporal temperature changes due to greenhouse gases and aerosols from different economic regions, revealing that non-OECD aerosol emissions could mask over 20% of greenhouse gas-induced warming for a significant global population by 2050.
-
Peven et al. (2025) Climate controls on seasonal groundwater–stream connectivity in snow-dominated semi-arid headwaters
This study investigates how groundwater-stream connectivity in snow-dominated semi-arid headwaters responds to climate variability, particularly the 2021 extreme drought, using diel air–water temperature amplitude ratios. It found that groundwater connectivity can increase or remain stable during drought, buffering thermal extremes in some streams.
-
Kallush et al. (2025) Flash flood dynamics in arid areas at the Sub-Basin Scale: The Ze’elim Basin, Israel
This study quantitatively evaluates rainfall-runoff relationships at the sub-basin scale in the hyper-arid Ze’elim Basin, Israel, finding that rainfall depth reliably predicts runoff depth, and peak discharge correlates more strongly with rain-core coverage (spatial extent of intense rainfall) than with point-maximum rainfall intensity.
-
Jeon et al. (2025) Data Assimilation for a Simple Hydrological Partitioning Model Using Machine Learning
This study proposes an Artificial Intelligence Filter (AIF) that integrates machine learning into a data assimilation framework to improve streamflow prediction accuracy in hydrological models, demonstrating enhanced performance in four Korean dam basins.
-
Yang et al. (2025) Assessment of monthly vegetation response and ecosystem risks to drought-flood abrupt alternation using an integrated framework
This study investigates the distinct monthly vegetation response and ecosystem risks to drought-flood abrupt alternation (DFAA) modes (drought-to-flood and flood-to-drought) in the Hai River Basin, revealing a novel seasonal reversal in vegetation vulnerability and ecosystem risk patterns.
-
Na et al. (2025) Intensifying hydroclimatic swings under a warming climate: Disentangling anthropogenic climate change and internal variability in North America
This study investigates the relative contributions of anthropogenic forcing (ACC) and internal climate variability (ICV) to hydroclimatic swing events in North America. It finds that transitions between extreme drought and flood phases will become more abrupt and intense, primarily driven by ACC, with robust trends emerging under +4.0 °C warming and becoming discernible by +2.0 or +3.0 °C warming.
-
Gomes et al. (2025) A coupled Darcy–Richards framework for hydrological modeling of permeable pavements, green roofs, and bioretention systems
This study introduces a physics-based, open-source framework that couples a one-dimensional Richards equation solver with a conceptual rainfall–runoff model to simulate the long-term hydrological performance of Low-Impact Development (LID) practices. The framework demonstrates high accuracy against established models and field data, providing critical insights into runoff retention, evaporation efficiency, and flow duration patterns of LIDs across various climate conditions over decades.
-
Haseeb et al. (2025) Forecasting Rainfall IDF Curves Using Ground Data and Downscaled Climate Projections to Enhance Flood Management in Punjab, Pakistan
This study projects future Intensity–Duration–Frequency (IDF) curves for urban centers in Punjab, Pakistan, using downscaled satellite-derived precipitation data under CMIP6 scenarios. It reveals a substantial increase in extreme rainfall intensities, with 100-year return period rainfall intensities projected to rise by 30–55%, particularly under the SSP5-8.5 scenario.
-
Huang et al. (2025) Hydropower vulnerability to drought-flood abrupt alternation under climate change
This study quantifies the global impact of drought-flood abrupt alternation (DFAA) events on hydropower, revealing that these rapid transitions significantly reduce generation and that high reservoir regulation capacity is a key factor in mitigating these losses.
-
Disasa et al. (2025) Comprehensive review of drought characteristics and intensification under climate change: implications for agriculture and water resources
This review synthesizes the intensification of drought characteristics across meteorological, hydrological, and agricultural sectors under climate change. It highlights how global warming alters drought frequency and severity, leading to significant risks for water resources and crop yields.
-
Mu et al. (2025) Impacts of Northerly Low-Level Jets on Mesoscale Convective Systems East of the Andes
This study investigates the distinct impacts of three types of low-level jets (Central, Northern, and Andes) on Mesoscale Convective Systems (MCSs) across South America using a 4 km Weather Research and Forecasting (WRF) model simulation and satellite data. It finds that Central and Andes LLJs significantly enhance MCS activity and heavy precipitation over the La Plata Basin, while the Northern LLJ has a weaker, more scattered impact over the Amazon Basin, with stronger LLJs supporting larger, longer-lived MCSs modulated by ENSO.
-
Askari et al. (2025) A novel entropy-based machine learning frame work for flood risk mapping in Pakistan
This study develops the District-level Flood Risk Assessment Model (D-FRAM), a three-tiered framework integrating satellite data, national surveys, and machine learning to map spatial and temporal flood risk across Pakistan. It identifies critical hotspots and provides monthly flood risk profiles, with XGBoost proving most effective for susceptibility prediction and flood frequency as the primary risk determinant.
-
Wessinger et al. (2025) An Unexpected Peak in Daytime Convection Initiation Weakens Diurnal Amplitude of Tropical Oceanic Precipitation and Cloud Cover
This study investigates the diurnal cycle of deep convective system (DCS) initiation and subsequent rainfall over tropical oceans to address deficiencies in Earth system models. It identifies two peaks of DCS initiation—an overnight peak and an unexpected late morning peak—and demonstrates that daytime-initiated DCSs significantly contribute to afternoon rain and cloud cover, suggesting that Earth system models may underestimate these daytime initiations.
-
Yao et al. (2025) Increasing coupling of hot‒dry winds and drought across China: Observational evidence and future projection
This study investigates the historical and future dynamics of compound hot-dry wind and drought events (CHDWDs) across China using observations and CMIP6 simulations. It reveals an increasing coupling and likelihood of CHDWDs, particularly in drylands, with future intensification projected under higher emission scenarios and varying dominant drivers across climatic zones.
-
Kaur et al. (2025) Estimation of soil hydraulic parameters for fine-textured soils using HYDRUS-1D coupled with PEST
This study utilized HYDRUS-1D coupled with PEST to estimate van Genuchten-Mualem soil hydraulic parameters for a fine-textured soil in Manitoba over four growing seasons, revealing the non-uniqueness of solutions and identifying stable parameter ranges through sequential calibrations.
-
Ming et al. (2025) Environmental and Boundary Layer Characteristics Associated With Intensity Change of Tropical Cyclones Under High Fullness
This study investigates why high-fullness tropical cyclones (TCs) sometimes weaken, comparing the structural and environmental conditions of intensifying and weakening TCs using GPS dropsonde data to improve intensity change prediction.
-
Liu et al. (2025) A combined spatial interpolation method of co-Kriging with inverse distance weighting and random forest for soil water and salt in arid oasis
This study developed a combined spatial interpolation method integrating Co-Kriging (CK), Inverse Distance Weighting (IDW), and Random Forest (RF) to accurately characterize soil water and salt distribution in arid oasis regions. The novel combined method significantly improved prediction accuracy for both soil water content and total salt content compared to traditional methods, establishing a transferable framework for multi-method integration.
-
Zhao et al. (2025) A GNSS-based standardized index for near-real-time monitoring of the spatiotemporal evolution of droughts
This study introduces a novel GNSS-based Standardized Terrestrial Water Storage Anomaly Index (GNSS-STWSAI) that leverages daily vertical displacements from a dense GNSS network and a Gaussian Mixture Model to quantify hydrological droughts with high spatiotemporal resolution. The index provides near-real-time monitoring, revealing the lagged response of hydrological drought to meteorological forcing and enhancing understanding of monsoon-driven drought dynamics in Yunnan Province.
-
Zhou et al. (2025) Snow effects on altimeter waveforms over sea ice in the Weddell Sea — Part I: Radar waveform decomposition
This study analyzes Ku-band CryoSat-2 and Ka-band KAREN altimeter waveforms over the Weddell Sea to decompose scattering contributions from snow surface, snow volume, and ice surface. It finds that snow-volume scattering significantly contributes to Ku-band returns, often as much as or more than the snow-ice interface, while Ka-band is dominated by surface/near-surface snow scattering.
-
Xiong et al. (2025) Assessment of Object-Level Flood Impact Considering Pump Station Operations in Coastal Urban Areas
This study develops a high-resolution integrated modeling framework (HiPIMS-PSM-FIM) to simulate urban multi-source flooding with mobile pumping operations, demonstrating its effectiveness in reducing flood impacts and enhancing resilience in coastal cities.
-
Wang et al. (2025) Decreasing terrestrial water storage limited the increase of woody plant structure resulting from afforestation in the Loess Plateau, China
This study investigated the spatial-temporal patterns and interaction between vegetation canopy structure (LAI) and terrestrial water storage anomaly (TWSA) in the Loess Plateau from 2000 to 2022. It found that decreasing terrestrial water storage increasingly limited the growth of woody plant structure, highlighting the critical role of water availability in afforestation outcomes.
-
Zhang et al. (2025) From Depletion to Recovery: Tracking Water Storage Changes in the Semiarid Region of Inner Mongolia, China
This study evaluated spatiotemporal variations in terrestrial water storage (TWS) and groundwater storage (GWS) in semiarid Inner Mongolia from April 2002 to January 2025, revealing a long-term TWS and GWS depletion that notably reversed after 2022 due to policy interventions and precipitation changes, with significant regional differences in driving factors.
-
Kanno et al. (2025) Deep sowing accelerates rice emergence under water deficit: field experiments and model development
This study investigated the potential of deep sowing to accelerate rice emergence under water deficit, finding that sowing at 4 cm or deeper significantly advanced emergence in field experiments under drought. A novel process-based model was developed and validated, accurately predicting emergence dates based on sowing depth, soil temperature, and moisture, and suggesting optimal deep sowing depths to mitigate drought risk.
-
Schmidt et al. (2025) Datasets and protocols for including anomalous freshwater from melting ice sheets in climate simulations
This paper presents comprehensive data products and recommendations for incorporating anomalous freshwater fluxes from the Greenland and Antarctic ice sheets into climate model simulations, particularly for CMIP7, to improve the representation of ocean temperature, sea ice, and regional sea level trends.
-
Sindarov et al. (2025) Application of the AquaCrop model for cotton production under water scarce arid conditions
This study calibrated and validated the AquaCrop model for cotton production under arid conditions in Uzbekistan, identifying an optimal irrigation regime (FC 70-70-65%) that maximized yield and water productivity with high model accuracy.
-
Gregorio et al. (2025) Nonlinear Dynamic Aspects of Generalized Frosts in the Pampa Húmeda of Argentina
This study investigates the dynamical and physical mechanisms behind short-lived (0DP) generalized frosts in the Pampa Húmeda, revealing that nonlinear interactions, particularly the divergent term of the Rossby Wave Source (RWS) equation, are crucial for both their formation and rapid dissipation.
-
Rosquete-Estevez et al. (2025) North Atlantic SSS-Precipitation Links via Lagrangian Moisture Transport (1985–2014)
This paper describes a minimal dataset designed to ensure the basic reproducibility of analyses for a study investigating the link between sea surface salinity and precipitation through moisture transport in the North Atlantic.
-
Sebastian et al. (2025) Incorporating varying vegetation characteristics driven by Hydrometeorology in the land surface modeling by variable Infiltration Capacity model
This study demonstrates the critical role of dynamic vegetation in hydrological modeling, particularly for evapotranspiration in India, by integrating a machine learning model (LSTM) to simulate vegetation variability within the Variable Infiltration Capacity (VIC) model, revealing an 18% increase in annual evapotranspiration compared to static vegetation approaches.
-
Wang et al. (2025) Improvement of the hillslope-storage Boussinesq model by incorporating rainfall infiltration effects
This study develops a novel coupled hillslope-storage Boussinesq (hsB) and Green-Ampt (GA) infiltration model to improve the simulation of hillslope hydrological responses by incorporating rainfall infiltration, demonstrating superior performance over the conventional hsB model. The new model enables continuous simulation from rainfall onset through post-event drainage, capturing complex hydrodynamic features across various soil types and rainfall intensities.
-
Gaiolini et al. (2025) Salt migration and export via subsurface irrigation in a saline reclaimed landscape of the Po River lowland (Italy)
This study investigates the causes and quantifies the sources of dissolved salts in a saline reclaimed landscape of the Po River lowland, Italy, focusing on the impact of subsurface irrigation via tile drains. It reveals that sub-irrigation significantly accelerates salinization and salt export, with peaty lenses and decomposing halophytes acting as major salt sources, leading to high surface water salinity.
-
Cerato et al. (2025) Summer Drought Predictability in the Euro-Mediterranean Region in Seasonal Forecasts
This study evaluates the ability of state-of-the-art seasonal forecast systems to predict summer drought in Europe, finding that the Standardized Precipitation Evapotranspiration Index (SPEI-3) offers more spatially coherent and higher forecast skill than the Standardized Precipitation Index (SPI-3), particularly in southern Europe. The multimodel ensemble (MME) provides the most robust solution for early summer drought detection, demonstrating widespread significant skill up to a 1-month lead time.
-
Paramanik et al. (2025) Automated and continuous estimation of FAPAR from distributed wireless PAR sensor networks
This study evaluates the performance of two-flux (2f) and four-flux (4f) FAPAR measurement systems and digital hemispherical photography (DHP) across multiple vegetation types and temporal scales using automated wireless PAR sensor networks. It reveals strong agreement between 2f- and 4f-FAPAR (R² > 0.99, RMSE ≤ 0.04), suggesting that 2f systems are a reliable and cost-effective alternative, and underscores the importance of daily integrated FAPAR for long-term ecosystem monitoring.
-
Kanishkar et al. (2025) Enhancing temperature data analysis through Threshold-Optimized Ensemble Detection (TOED) approach of climate anomalies
This study introduces a Threshold-Optimized Ensemble Detection (TOED) approach for identifying climate temperature anomalies, demonstrating its superior effectiveness (92.1% AUC-ROC) compared to individual anomaly detection methods.
-
Poyam et al. (2025) Assessment of performance of conventional and machine learning methods for estimating missing precipitation data
This study assesses the performance of fifteen conventional and two machine learning (Artificial Neural Network and Long Short-Term Memory) methods for estimating missing precipitation data, finding that the Artificial Neural Network generally outperforms all conventional methods and LSTM for longer missing periods.
-
Zhang et al. (2025) Rainfall monitoring based on commercial microwave links in Jiangyin, China: Long-term performance analysis over 3 years
This study analyzes 3 years of data from 48 commercial microwave links (CMLs) in Jiangyin, China, to calibrate and validate CML-based rainfall inversion algorithms, demonstrating their effectiveness, particularly for summer, high-intensity, and convective rainfall.
-
Anderson et al. (2025) What is a drought-to-flood transition? Pitfalls and recommendations for defining consecutive hydrological extreme events
This study assesses the suitability and differences of various threshold-level methods for defining drought-to-flood transitions using eight case study catchments, revealing that methodological choices significantly alter detected event characteristics and often fail to capture historically impactful transitions.
-
Wu et al. (2025) Perspective on the shifting interannual variability of recent summer temperature modes in eastern China: Roles of Arctic sea-ice, Arctic Oscillation and Pakistan precipitation
This study reveals a fundamental reorganization of interannual summer temperature variability in eastern China, characterized by an enhancing south-north uniform warming (SNUW) mode and a disappearing south-north meridional dipole (SNMD) mode. These shifts are driven by increasing Pakistan convective precipitation and Arctic Oscillation variability for SNUW, and reduced Barents-Kara sea-ice variability for SNMD, with implications for future extreme heat events.
-
Wei et al. (2025) A time-varying weighted merging method for integrating multisource precipitation data considering error variations
This study developed a Time-varying Weighted (TVW) merging method to integrate multi-source precipitation data, demonstrating its superior performance over parent datasets and state-of-the-art products in mainland China, even with reduced station density.
-
Mahfouz et al. (2025) Prescribing the aerosol effective radiative forcing in the Simple Cloud-Resolving E3SM Atmosphere Model v1
This study assesses the sensitivity of aerosol effective radiative forcing (ERFaer) to anthropogenic aerosol changes in the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) v1 using a prescribed aerosol scheme. It finds that while the default scheme initially overestimates forcing, parameterization adjustments in aerosol activation enable SCREAM v1 to reproduce the reference model's ERFaer across resolutions.
-
Montpetit et al. (2025) Snow Water Equivalent from airborne Ku-band data: the Trail Valley Creek 2018/19 snow experiment
This study develops and validates a snow water equivalent (SWE) retrieval algorithm for a proposed Ku-band synthetic aperture radar (SAR) satellite mission by combining a priori snow conditions from a land surface model (SVS-2) with a Markov Chain Monte Carlo (MCMC) Bayesian model coupled with the Snow Microwave Radiative Transfer (SMRT) model, achieving a root-mean-square error (RMSE) of 15.8 mm (16.4 %) for SWE retrieval.
-
Guo et al. (2025) Modeling soil water–heat–salt transport and cotton growth under high salinity and seasonal freezing conditions in Northern Xinjiang, China
This study developed a coupled water–heat–salt transport and crop growth model (SWHS-C) to simulate annual cycles in salinized, seasonal frozen agricultural regions, revealing that salt stress primarily limits cotton transpiration and groundwater dynamics drive soil salinity evolution.
-
Ono et al. (2025) Estimating Maximum Precipitation for an Extreme Rainfall Event in Japan Using Lower-Level Shifts of Atmospheric Initial and Boundary Conditions
This study investigates the sensitivity of maximum precipitation (MP) estimates to different atmospheric initial and boundary condition (AIBC) shifting methods using a high-resolution weather model. It finds that a novel lower-level shift (LLS) method, focusing on lower-tropospheric moisture, significantly increases estimated 6-hour precipitation by 65% compared to traditional all-layer shifts (ALS), which showed only slight increases.
-
Anoop et al. (2025) Atmospheric aridity perturbs critical soil moisture thresholds of plant water stress over Indian biomes
This study quantifies critical soil moisture thresholds (θcrit) for Indian biomes using satellite data and two independent methodologies, revealing that atmospheric aridity (VPD) significantly perturbs θcrit, leading to seasonal and hydrological forcing-driven variations. The covariance-based method (Cov(GPP-VPD)-SM) is found to be more sensitive for assessing these dynamics.
-
Zhang et al. (2025) Coupled surface water-groundwater-crop model considering the impact of irrigation using different calibration targets
This study developed a coupled VIC-EPIC-HYDRUS (VEH) model to improve the simulation accuracy of hydrological processes on agricultural land, considering irrigation impacts, and demonstrated its superior performance through multi-objective parameter optimization and validation.
-
Alshamsi et al. (2025) Synoptic-Scale Forcing and Its Role in a Rare Severe Rainfall Event over the UAE: A Case Study of 15–16 April 2024
This study investigates the atmospheric conditions responsible for a rare severe rainfall event over the United Arab Emirates (UAE) on 15–16 April 2024, identifying a deep, negatively tilted cut-off low-pressure system (COL) as the primary driver, supported by subtropical jet (STJ) divergence and pre-monsoonal moisture influx.
-
Gačnik et al. (2025) Climate change reflected in 40-year isotopic composition trends of precipitation in Slovenia
This study analyzes 40-year stable isotope records of precipitation (δ18O, δ2H, d-excess) from Ljubljana, Slovenia, revealing significant increasing trends consistent with regional warming. The findings indicate an accelerating, exponential-like isotopic response to warming that has propagated into the water cycle, with implications for water resource management.
-
Fagundes et al. (2025) Global Warming Levels (1.5–4 °C) and Water Availability for Eucalyptus Plantations Based on High-resolution CMIP6 Models
This study globally assessed the impacts of four global warming levels (1.5 °C, 2 °C, 3 °C, 4 °C) on water availability for Eucalyptus plantations, finding increased water deficit and reduced water surplus, which could decrease economic viability by 30% to 89% in vulnerable regions.
-
Gebreegziabher et al. (2025) Projected future temperature, precipitation, and drought conditions in the Muger River sub-basin, central Ethiopia, using CMIP6 models
This study projects future temperature, precipitation, and drought conditions in the Muger River watershed, Ethiopia, using bias-corrected CMIP6 models under SSP2-4.5 and SSP5-8.5 scenarios, revealing significant warming, declining precipitation, and intensifying aridity by the end of the century.
-
Syed et al. (2025) Precision Agriculture with IoT: A Comprehensive Review of Innovations and Applications
This comprehensive review explores the innovations and applications of Internet of Things (IoT) technology in precision agriculture, highlighting its role in enhancing efficiency, sustainability, and resource management, while also addressing socio-economic challenges and proposing future solutions.
-
Hasan et al. (2025) Bangladesh IoT Environmental Dataset (BIED)
The Bangladesh IoT Environmental Dataset (BIED) is a curated collection of simulated IoT sensor readings representing diverse environmental conditions across Bangladesh, designed to support AI, IoT, and environmental research through analysis, prediction, and system validation.
-
Lu et al. (2025) Advances in Watershed Hydro-Environment Simulation: From Process Mechanisms to Sustainable Management
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
-
Gillo et al. (2025) Integrated assessment of meteorological, hydrological and agricultural drought in Abaya Chamo sub Basin, Ethiopia
This study comprehensively assessed meteorological, hydrological, and agricultural drought characteristics in Ethiopia's Abaya Chamo sub-basin from 1981-2021 using SPEI, SSI, and SSMI, revealing increasing aridity and severe to extreme drought intensities that varied spatially across catchments.
-
Puche (2025) Vers une modélisation hydrologique flexible et opérationnelle pour la simulation des débits : optimisation et comparaison des approches à base physique et de Deep Learning
Information not extractable from provided text.
-
Lumbrazo et al. (2025) Cle Elum Snow Pack Study, in Cle Elum Ridge Snow-On Lidar for Forest Management
This project focuses on utilizing snow-on lidar data for forest management applications within the Cle Elum Ridge area.
-
Hu et al. (2025) A novel framework for accurately quantifying wetland depression water storage capacity with coarse-resolution terrain data
This study developed a novel framework, WetlandSCB, to accurately quantify wetland depression water storage capacity (WDWSC) using coarse-resolution terrain data and multi-source remote sensing, overcoming limitations of high-resolution data scarcity and biases in global Digital Elevation Models (DEMs). The framework achieved WDWSC estimation with less than 10% relative error compared to field measurements, demonstrating its applicability for large-scale wetland water storage assessment.
-
Yu et al. (2025) An efficient and physics-informed regional maize yield estimation scheme by combining data assimilation and machine learning
This study developed an efficient, physics-informed regional maize yield estimation framework by integrating data assimilation (DA) with machine learning (ML), significantly reducing computational costs while maintaining accuracy in Shandong Province, China.
-
Alhat et al. (2025) Evaluating Land Use and Land Cover Transformation Through Remote Sensing: A Study of the Purandar Lift Irrigation Area
This study evaluates land use and land cover (LULC) transformations in the Purandar Lift Irrigation Area between 2005 and 2023 to assess the impact of the irrigation scheme. The analysis revealed significant increases in agricultural land, built-up areas, vegetation, and water bodies, coupled with a substantial decrease in barren land, indicating the project's success in improving water availability and promoting productive land use.
-
Bowen et al. (2025) The Role of Spatial Water Right Data in Understanding Anthropogenic Effects on the Water Balance
This study investigated the impact of anthropogenic water use, specifically water rights, on the water balance in two irrigated headwater watersheds in Wyoming using a hydrologic-allocation modeling framework. It found that full appropriative demand significantly decreases streamflow (54%) and watershed storage (6%) while increasing evapotranspiration (18%) during the growing season, with responses varying based on water right characteristics.
-
Revelo Aristizabal et al. (2025) Time-Frequency Analysis of Soil–Atmosphere Interactions
This dataset provides preprocessed time series of soil and atmospheric variables, along with results from Ensemble Empirical Mode Decomposition (EEMD) analysis, to facilitate time-frequency analysis of soil-atmosphere interactions across three study sites.
-
Wang et al. (2025) The Emerging Precipitation Dipole Regime during the Tropical Asian Summer Monsoon Termination Phase
This study investigates the mechanisms behind the amplified east-west precipitation dipole observed during the delayed tropical Asian summer monsoon withdrawal since 2005/06. It reveals that multiscale interactions between intraseasonal (Madden–Julian oscillation) and low-frequency (negative Interdecadal Pacific Oscillation and southern Indian Ocean warming) processes drive this dipole, leading to increased extreme rainfall risks in the east and precipitation deficits in the west.
-
Karmokar (2025) Do morphometry and unit hydrograph consistently reflect the flood generation potential? Insights from the analysis of the Himalayan watersheds
This study investigates the consistency between morphometric parameters and unit hydrographs in assessing the flood generation potential of 30 Himalayan watersheds. It finds that a plot of Melton’s ruggedness number (MRn) versus log first order stream magnitude (F1m) provides 100% accuracy in categorizing watersheds by relative peak magnitude, showing 85% agreement with the CWC Synthetic Unit Hydrograph (SUH) model.
-
Deshpande et al. (2025) Remote Sensing and Precision Agronomy: A Comprehensive Review of Applications and Prospects
This review synthesizes the foundations, platforms, and analytical methods of remote sensing, connecting them to core agronomic decisions. It concludes that remote sensing is mature for many tasks and rapidly improving, enabling more precise, profitable, and sustainable agronomy when integrated into validated workflows.
-
Lubis et al. (2025) Cloud radiative effects significantly increase wintertime atmospheric blocking in the Euro-Atlantic sector
This study demonstrates that cloud radiative effects (CREs) significantly increase wintertime atmospheric blocking frequency in the Euro-Atlantic sector. This occurs by enhancing upstream diabatic wave activity sources, primarily through feedback on latent heating, which then promotes downstream wave activity convergence and blocking formation.
-
Vinayaka et al. (2025) Harnessing AI and Remote sensing for precision sugarcane farming: tackling water stress, salinity, and nitrogen challenges
This review synthesizes the application of artificial intelligence (AI) and remote sensing (RS) technologies for precision sugarcane farming, focusing on detecting and managing water stress, salinity, and nitrogen challenges to enhance crop productivity and environmental sustainability.
-
Tefera et al. (2025) Satellite-Based Machine Learning for Soil Moisture Prediction and Land Conservation Practice Assessment in West African Drylands
This study integrated remote sensing, in situ data, and machine learning to predict soil moisture and evaluate the impact of land conservation practices in northern Ghana, finding that stone bunds increased soil moisture by 4–6% compared to non-bunded fields.
-
Shan et al. (2025) Monitoring Long-Term Vegetation Dynamics in the Hulun Lake Basin of Northeastern China Through Greening and Browning Speeds from 1982 to 2015
This study investigated long-term vegetation dynamics in the Hulun Lake Basin (HLB) from 1982 to 2015 using NDVI and a novel Vegetation NDVI Change Rate (VNDVI) metric, revealing an overall greening trend, accelerated spring greening, and delayed autumn browning driven by distinct seasonal climatic factors.
-
Behura et al. (2025) Evaluation of Actual Evapotranspiration from Rice Fields of Odisha Using Remote Sensing Based Surface Energy Balance Approach
This research utilized the Surface Energy Balance Algorithm for Land (SEBAL) to evaluate actual evapotranspiration (AET) from rice fields in Odisha, India, integrating remote sensing and meteorological data. The study successfully quantified spatial and temporal AET variations across four crop growth stages, demonstrating SEBAL's strong performance and utility for efficient water resource management in rice-based agricultural systems.
-
wangyakai01 (2025) wangyakai01/DLEM-Ag-SIF: Integrating satellite SIF with agroecosystem modeling to constrain carbon-water coupling in Midwest U.S. croplands
This study integrates satellite-derived Solar-Induced Fluorescence (SIF) with agroecosystem models to improve the understanding and representation of carbon-water coupling dynamics in Midwest U.S. croplands.
-
Runge et al. (2025) Climate vulnerability of Earth’s terrestrial biomes
This study trains machine learning models on global biome distributions using soil and climate data to accurately capture contemporary biome-climate envelopes (BCEs). It then predicts how climate change scenarios (RCP 4.5 and 8.5) will alter these BCEs, finding significant shifts and uncertainties across the terrestrial surface by 2080, with poleward boundary movements, a shrinking tundra, and expanding drylands.
-
Mahmoud et al. (2025) Groundwater recharge dynamics to recent wetting in a cold region aquifer
This study quantified groundwater recharge to the Oakes aquifer in southeastern North Dakota for the 1991–2024 water years using field observations and process-based models, revealing that spring and summer rainfall is the primary recharge source, with snowmelt contributing variably based on frozen soil conditions and winter temperatures.
-
Zantout et al. (2025) Shifting dominant periods in extreme climate impacts under global warming
This study investigates how the regularity patterns (dominant periods) of extreme climate impacts (crop failure, heatwaves, wildfires) change under global warming. It finds that natural regularity, linked to climate oscillations, is increasingly replaced by monotonic growth and a shift towards shorter dominant periods in the Anthropocene, reducing predictability.
-
Bonassies et al. (2025) A comprehensive study of Surface Water and Ocean Topography (SWOT) Pixel Cloud data for flood extent extraction
This study comprehensively evaluates the capabilities and limitations of the Surface Water and Ocean Topography (SWOT) satellite's Ka-band Radar Interferometer (KaRIn) Pixel Cloud products for flood extent extraction across four major flood events, comparing its performance against Sentinel-1/2 data and its built-in classification. It demonstrates SWOT's potential for detecting floods in vegetated and urban areas while identifying sensitivities to high soil moisture and incidence angle.
-
Lee et al. (2025) An explainable AI-based approach for estimating potential evapotranspiration in ungauged areas
This study develops an explainable AI-based approach using Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) models, coupled with Shapley Additive Explanations (SHAP), to accurately estimate potential evapotranspiration (PET) in ungauged areas of South Korea with limited meteorological data. The approach demonstrates that PET can be effectively estimated using only key variables like maximum temperature and average wind speed, significantly enhancing the spatial resolution of PET in data-scarce environments.
-
Gou et al. (2025) Climate‐Dependent Mechanisms Accelerate Flash Droughts in Drylands and Humid Regions
This study globally assesses flash drought onset speed using satellite-based evaporative stress and machine learning interpretation to identify dominant hydrometeorological factors, revealing that while humid regions experience more frequent events, drylands intensify more rapidly due to climate-dependent energy and water constraints.
-
Ali (2025) Machine learning approaches for soil moisture prediction: enhancing agricultural water management with integrated data
This study evaluates the effectiveness of nine machine learning algorithms for predicting soil moisture at two depths in New South Wales, Australia, using integrated climate, soil, and vegetation data. The results demonstrate that ensemble models, particularly Random Forest and XGBoost, significantly outperform traditional linear models, providing a robust framework for precision irrigation management.
-
Wu et al. (2025) Automated drone-borne GPR mapping of root-zone soil moisture for precision irrigation
This study demonstrates the potential of drone-borne Ground-Penetrating Radar (GPR) to map spatial and temporal root-zone soil moisture dynamics across an agricultural field over an entire growing season. Using the gprSense® system with full-wave inversion, the research achieved precise and automated time-lapse soil moisture mapping, showing strong agreement with conventional methods and providing actionable insights for precision irrigation.
-
Ballinger et al. (2025) Importance of beginning industrial-era climate simulations in the eighteenth century
This study investigates early industrial climate changes (1750-1850) using Earth system model simulations, demonstrating that initializing simulations in 1750, rather than 1850, leads to more representative historical climate simulations with lasting effects into the 20th century due to better capture of early human influence and volcanic activity.
-
Hwang et al. (2025) Unfolding North American spring weather extremes along a scale ladder
This study delineates the multi-layered dynamics of North American spring weather extremes by identifying four leading subseasonal modes of variability. These modes are shown to modulate the occurrence frequencies of wet, dry, and wind extremes by up to twofold and exhibit distinct decadal-scale activity changes, providing a dynamics-based framework for understanding and predicting long-term extreme weather variability.
-
Lee et al. (2025) Influence of Baroclinic Eddies on the Hadley Cell Edge
This study refines the theoretical understanding of Hadley Cell (HC) extent by incorporating baroclinic eddy heat and momentum fluxes into the energy flux balance, demonstrating that these eddies significantly shift the HC edge.
-
Yao et al. (2025) Irrigation-induced land water depletion aggravated by climate change
This study uses seven Earth System Models to analyze the effects of historical irrigation expansion on global water fluxes and resources. It reveals that irrigation expansion significantly decreases the net water influx from the atmosphere to land, thereby exacerbating existing drying trends caused by climate change and leading to substantial terrestrial water storage depletion.
-
Thual et al. (2025) A fresh perspective on ENSO nonlinearity: the ENSO pattern continuum metric
This study introduces a "pattern continuum" metric, based on Warm Pool Edge Position (WPEP) quantiles, to synthesize and understand the spatial diversity, asymmetry, and nonlinearity of El Niño-Southern Oscillation (ENSO) sea surface temperature (SST) patterns. It demonstrates that ENSO's complex features can be effectively approximated by a simple "shifted-mean" framework, where a fixed SST structure shifts zonally, providing a novel interpretation for the quadratic relationship between SST principal components.
-
Qi et al. (2025) Retrieving fine-scale leaf and soil spectral properties from canopy reflectance with differentiable 3D radiative transfer
This study introduces 3D-Diff, a novel differentiable three-dimensional (3D) radiative transfer approach, to accurately retrieve fine-scale leaf and soil spectral properties from canopy reflectance using remote sensing imagery and known 3D canopy structures, demonstrating robust performance in both virtual and real-world validations.
-
Tuğrul et al. (2025) Hybrid Wavelet–ML models for regional drought forecasting in Norway
This study develops and evaluates hybrid wavelet-machine learning models for regional drought forecasting in Norway using the Effective Drought Index (EDI). The main finding is that Long Short-Term Memory (LSTM) networks enhanced by wavelet transformation (LSTMW) provide the best forecasts across the studied regions, significantly improving predictive accuracy.
-
Kuang et al. (2025) The U.S. DOE ARM User Facility Establishes a New Site for Studies of Land–Aerosol–Cloud Interactions in the Southeastern United States
The U.S. DOE ARM user facility has established a new observational site in the Bankhead National Forest, Alabama, to gather multiyear data on land-atmosphere-cloud-aerosol interactions at various scales, addressing challenges in Earth system models for the southeastern United States.
-
Wagner et al. (2025) The fully-automatic Sentinel-1 Global Flood Monitoring service: Scientific challenges and future directions
The Global Flood Monitoring (GFM) service, launched in 2021 as part of the Copernicus Emergency Management Service (CEMS), provides fully-automatic, near-real-time global flood maps using Sentinel-1 SAR imagery. This paper presents a comprehensive analysis of GFM's scientific achievements and challenges, demonstrating its rapid delivery and good accuracy for larger-scale floods while identifying limitations in coverage and detection in specific environments.
-
Robinson et al. (2025) The hydrological archetypes of wetlands
This study investigated the hydrological regimes of 43 Ramsar wetlands in Sweden using Sentinel-1 SAR imagery and a deep learning model (DeepAqua) to predict surface water extent. It identified five distinct hydrological archetypes, revealing how wetland water dynamics relate to their ecosystem services and providing a novel classification method.
-
Ouassanouan et al. (2025) Downscaled ERA5 Land addresses agrometeorological data scarcity in North African basins
This study evaluates the accuracy of original and MicroMet-downscaled ERA5_Land agrometeorological variables and their impact on reference evapotranspiration (ET0) estimation in a data-scarce North African basin, revealing that while downscaling improves some variables, it often degrades overall ET0 accuracy, yet ERA5_Land remains valuable for long-term ET0 trend analysis.
-
Kartal et al. (2025) Projected intensification of drought in the Kızılırmak Basin, Türkiye, under CMIP6 climate scenarios: comparative evaluation of multi-model and multi-index approaches
This study projects the future evolution of drought characteristics in the Kızılırmak Basin, Türkiye, under four CMIP6 Global Climate Models (SSP5-8.5 scenario) and three meteorological drought indices. The findings indicate a progressive intensification of droughts, with a higher frequency of extreme drought events, particularly in the mid- and far-future periods, emphasizing the critical role of temperature-driven evapotranspiration in exacerbating drought severity.
-
Min et al. (2025) Hydrological drought prediction and its influencing features analysis based on a machine learning model
This study develops an interpretable machine learning framework using XGBoost and SHAP to predict hydrological drought in the Huaihe River Basin, China, achieving 79.9% overall accuracy and identifying the Standard Precipitation Index (SPI) as the most influential feature.
-
Jiahan et al. (2025) Responses of Picea schrenkiana Tree-Ring Density to Climate Extremes at Different Elevations in the Kashi River Basin of the Western Tianshan Mountains
This study established earlywood and latewood density chronologies from *Picea schrenkiana* tree cores in the western Tianshan Mountains to analyze their correlation with extreme climate indices and altitudinal variation, revealing that extreme minimum temperature changes exacerbate drought stress in vulnerable mid-high-altitude forests.
-
McAdam et al. (2025) Feature selection for data-driven seasonal forecasts of European heatwaves
This study develops an inexpensive, purely data-driven machine learning approach for seasonal forecasting of European summer heatwaves, demonstrating skill comparable to, and in some regions outperforming, state-of-the-art dynamical multi-model products, while also identifying key predictors and their optimal time-lags.
-
Zheng et al. (2025) Modeling, prediction, and retrieval of surface soil moisture from InSAR closure phase
This study presents a discretized, multi-layer soil moisture model that links soil moisture variability to single-look SAR measurements and their closure phase. It introduces a scalable algorithm for retrieving a relative InSAR Soil Moisture Index, demonstrating its potential for large-scale soil moisture monitoring through validation against in situ and satellite data.
-
Chongbo et al. (2025) Extreme rainfall south of the Yangtze River in China during June 2024: Observational diagnosis and dynamical downscaling prediction
This study investigates the physical mechanisms behind the extreme rainfall south of the Yangtze River in June 2024 and evaluates the performance of dynamical downscaling for its prediction. It finds that combined external sea surface temperature forcing and internal atmospheric variability drove the record rainfall, and a regional climate model significantly improved spatial prediction by correcting global model biases, primarily through better representation of lower-tropospheric meridional wind.
-
Bastviken et al. (2025) Future methane emissions from lakes and reservoirs
This study presents data-driven, globally gridded modeling to project future methane emissions from lakes and reservoirs under various IPCC climate change scenarios. It predicts a 24–91% increase in total lake and reservoir CH4 emissions by 2080–2099, primarily driven by changes in temperature and seasonality, with area and nutrient load also contributing significantly to reservoir emissions.
-
Sandoval et al. (2025) Towards water resilience: A multi-stage calibration framework for large-scale integrated surface–subsurface hydrological models
This study presents a multi-stage calibration framework for large-scale, high-fidelity integrated surface water–groundwater models using sensitivity analysis and Gaussian Process Regression surrogates. The approach resulted in the first robustly calibrated integrated model of the Po River District (87,000 km²), effectively capturing complex 3D subsurface dynamics and river discharge.
-
Khan et al. (2025) Deep learning approach for vertical soil moisture profile estimation using hydrometeorological data
This study presents the evaluation of the eartH2Observe Tier-1 dataset, a global ensemble of ten hydrological and land surface models forced by a consistent atmospheric dataset. The research demonstrates that the ensemble mean generally provides a more reliable estimation of global water fluxes and storage than any individual model.
-
Zheng et al. (2025) Impact of Drought‐Flood Abrupt Alternation on Vegetation Productivity in Karst and Non‐Karst Regions of Southern China
This study identifies drought-flood abrupt alternation (DFAA) events using the Standardised Soil Moisture Index (SSMI) and quantifies their impact on vegetation productivity in southern China, revealing that flood-to-drought events intensify stress and slow vegetation recovery, particularly in vulnerable karst regions.
-
Shi et al. (2025) Towards an easy-to-use algorithm to estimate longwave cloud radiative forcing: algorithm development and preliminary evaluation
This study develops a lightweight algorithm to estimate surface longwave cloud radiative forcing (LWCRF) using only five readily available parameters, achieving a theoretical root-mean-square error (RMSE) of 5.47 W/m² and an RMSE of 12.03 W/m² against global satellite data.
-
Li et al. (2025) The Joint Occurrence Probability of Compound Drought and Heatwaves: A Copula‐Based Multivariate Analysis of Duration and Severity in China
This study develops a two-dimensional joint function integrating duration and severity to assess the comprehensive risk of Compound Drought and Heatwave (CDHW) events in China. It reveals that the impacts of duration and severity on CDHW joint occurrence probability vary significantly across different regions of China, with severity often having a greater influence on extreme events.
-
Vojtek et al. (2025) Dataset of physical-geographical predictors
This dataset provides a collection of seven high-resolution physical-geographical predictors derived from LiDAR DEM and orthophotos for a critical section of the Gidra River in Slovakia, intended for fluvial flood extent and flow depth modeling.
-
Ho et al. (2025) The impact of climate change on dam overtopping floods in Australia
This study projects changes in the exceedance probabilities of dam overtopping floods for 18 large Australian dams under various global warming scenarios. It finds that under 4 °C of global warming, the probability of overtopping floods increases by 2.4–17 times compared to historical conditions, primarily driven by increases in rainfall depth.
-
Mthembu et al. (2025) Enhancing AquaCrop model precision for accurate simulation of sweet potato and taro landraces
This study recalibrated and validated the AquaCrop model for orange-fleshed sweet potato (OFSP) and taro using multi-location datasets in South Africa. Recalibration significantly improved simulations of canopy cover, biomass, and yield, demonstrating AquaCrop's reliability for these neglected and underutilised crops under non-stressed conditions, though performance declined under severe water limitation.
-
Farag et al. (2025) Enhancing Water Use Efficiency in Subsurface Drip Irrigated Citrus Trees Using Wrapped Drip Irrigation Lines and Soil Covers
[Information not extractable from the provided text.]
-
Zhang et al. (2025) Spatiotemporal simulation and prediction of solar-induced chlorophyll fluorescence (SIF) across large-scale grasslands via multi-source data synergy: a case study of Northern China
Unable to extract from provided text.
-
Waltner et al. (2025) Meteorological data for Gödöllő (MATE SZIC)
This paper introduces a comprehensive, high-resolution dataset of meteorological and soil parameters collected at Gödöllő, Hungary, providing valuable environmental data for agricultural and environmental research.
-
Wen et al. (2025) Terrain-Driven Variability of Raindrop Size Distribution and Rainfall Kinetic Energy in Shaanxi, China, and Implications for Microphysics Estimation
This study investigated the spatial variability of raindrop size distribution (DSD) across terrain gradients in Shaanxi Province, China, using six years of disdrometer observations. It revealed a pronounced south-north DSD gradient, with larger raindrops and higher kinetic energy in the semiarid Loess Plateau compared to the humid Qinling–Daba Mountains, amplifying soil erosion risks, and developed robust Dm-based estimators for key microphysical quantities.
-
Shen et al. (2025) Unravelling the future role of internal variability in South Asian near-surface wind speed
This study quantifies the role of internal variability, specifically the Interdecadal Pacific Oscillation (IPO), in modulating near-surface wind speed (NSWS) projections over South Asia, finding that accounting for IPO reduces projection uncertainty by 8% in the near future and 15% in the far future.
-
Izadgoshasb et al. (2025) Comparison of a Semiempirical Algorithm and an Artificial Neural Network for Soil Moisture Retrieval Using CYGNSS Reflectometry Data
This study develops and compares a novel physically based algorithm and an Artificial Neural Network (ANN) for soil moisture estimation using CYGNSS Level 1B data over land, finding that the ANN generally outperforms the semiempirical model, though the latter shows greater stability in data-scarce conditions.
-
You et al. (2025) Sensitivity of Soil Moisture Simulations to Noah-MP Parameterization Schemes in a Semi-Arid Inland River Basin, China
This study evaluated the Noah-Multiparameterization Land Surface Model (Noah-MP)'s ability to simulate soil moisture in a semi-arid inland river basin, identifying key sensitive physical processes and quantifying their contributions to simulation uncertainty. The findings provide guidance for improving parameterization schemes to reduce model uncertainty in such regions.
-
Fu et al. (2025) Analysis of Spatial and Temporal Evolution Characteristics and Driving Forces of NDVI in Gansu Province from 2000 to 2022
This study analyzed MODIS NDVI data in Gansu Province from 2000 to 2022 to understand vegetation dynamics, finding a significant overall increase driven more by human activities than climate change, though their synergistic effect was dominant.
-
Bushra et al. (2025) CAMELS-NZ: hydrometeorological time series and landscape attributes for New Zealand
This paper introduces CAMELS-NZ, the first large-sample catchment hydrology dataset for New Zealand, providing hourly hydrometeorological time series and comprehensive landscape attributes for 369 catchments from 1972 to 2024. The dataset fills a critical gap in global hydrology by representing a Pacific Island environment with complex hydrological processes, supporting diverse research applications.
-
Yan et al. (2025) Streamflow and stage monitoring data along the lower Pajaro River, Central Coastal California
This repository provides streamflow and stage monitoring data from multiple locations along the lower Pajaro River in Central Coastal California, collected by various agencies from Water Year 2021 to Water Year 2024, to support hydrological research and water management.
-
Khan et al. (2025) Escalating Heat Stress And Outdoor Labor Productivity Loss In The Arabian Peninsula: A Five-Decade Analysis Of Climate Change Impacts
This study analyzes the spatiotemporal trends of Wet Bulb Globe Temperature (WBGT) and its impact on outdoor labor productivity across the Arabian Peninsula over five decades (1974–2023), revealing significant increases in heat stress and substantial productivity losses, particularly for heavy workloads in coastal and urban areas.
-
Baste et al. (2025) Unveiling the limits of deep learning models in hydrological extrapolation tasks
This study investigates the extrapolation capabilities of stand-alone Long Short-Term Memory (LSTM) networks in hydrological rainfall-runoff modeling under extreme, synthetic precipitation events, revealing their inability to predict discharge beyond a calculated theoretical limit and exhibiting physically unrealistic concave runoff responses, in contrast to a more robust hybrid model.
-
Wang et al. (2025) A UAV-based method for root zone soil moisture modeling of different farmland scale with grain and economic crops
This study developed an integrated UAV-based remote sensing and Remote Sensing-based Water Balance Assessment Tool (RWBAT) model to accurately estimate root zone soil moisture (RZSM) for four crop types in the Loess Plateau, demonstrating high simulation accuracy, especially at deeper soil depths.
-
Barriopedro et al. (2025) A Multimethod Attribution Analysis of Spain’s 2024 Extreme Precipitation Event
This study conducts a comprehensive attribution analysis to assess the influence of climate change on an extreme precipitation event in Spain (October-November 2024), finding that while unconditional probabilistic methods show no discernible anthropogenic influence, climate change signals emerge when atmospheric conditions are considered, highlighting the complex interplay of thermodynamic and dynamic factors.
-
Bazlen et al. (2025) Hydrograph Spread Increases as Snow Declines Across the Western U.S.
This study investigates the previously unexamined influence of changing snow characteristics on the spread of streamflow distribution throughout the water year. It finds that lower peak snow water equivalent (SWE) is significantly associated with wider streamflow distributions across the western United States.
-
Li et al. (2025) Research on the estimation method of crop net primary productivity based on improved CASA model
This research refines crop Net Primary Productivity (NPP) estimation by improving Fraction of Photosynthetically Active Radiation (FPAR) retrieval within the CASA model using a Convolutional Neural Network, significantly reducing FPAR Root Mean Square Error (RMSE) from 0.2040 to 0.0020 and NPP Mean Absolute Percentage Error (MAPE) from 28.92% to 20.31%.
-
Wen et al. (2025) Exploring causal pathways among soil moisture, climate and ocean–atmosphere teleconnection patterns over the drought-prone Greater Horn of Africa
This study employs a data-driven causal graph discovery algorithm (PCMCI+) to uncover causal relationships among ocean-atmosphere teleconnection patterns (ENSO, IOD), precipitation, temperature, and soil moisture over the drought-prone Greater Horn of Africa (GHOA) from 1980 to 2022, revealing that IOD generally exerts stronger causal effects on soil moisture, primarily mediated through precipitation.
-
Wu et al. (2025) Global patterns and drivers of canopy storage capacity in different biomes
This study compiled global canopy storage capacity (S) observations from 162 publications and used a boosted regression tree model to identify its biotic and climatic drivers. It revealed that global S ranges from 0.08 mm to 8.9 mm, with a median of 0.93 mm, and that biotic factors are the primary predictors of S variation.
-
Reddy et al. (2025) Exploring the Influence of Sea Surface Temperature Extremes on Precipitation Extremes Across India's Climate Zones: A Complex Network Approach
This study investigates the multiscale impact of Sea Surface Temperature (SST) extremes on precipitation extremes across India's climate regions from 1981 to 2020, revealing that proximal oceanic regions drive short-term precipitation extremes while remote SST influences dominate longer-term extremes through atmospheric teleconnections.
-
Ernst et al. (2025) Historical and Future Drought Intensification in the Pantanal Wetland: Evidence from Multi-Source Weather Data and CMIP6 Multi-Model Projections
This study assessed historical (1980-2024) and future (2026-2100) drought intensification in the Pantanal wetland using multi-source weather data and bias-corrected CMIP6 multi-model projections. It revealed a significant historical drying trend and projected intensified, severe multi-year droughts under future climate change scenarios, particularly when considering evaporative demand.
-
Zhang et al. (2025) Underestimated future flash drought risks in major grain-producing areas over China
This study assesses historical and future flash drought risks in major Chinese croplands by integrating crop phenology into the analysis framework. It reveals that a significant proportion of historical flash droughts occurred during critical crop growth phases, and future projections substantially underestimate risks if crop phenology is overlooked, particularly in northern regions.
-
Campoverde et al. (2025) Streamflow simulations for the extreme drought event of 2018 on the Rhine River Basin using WRF-Hydro
This study assessed the capability of WRF-Hydro to reproduce the 2018 extreme drought conditions in the Rhine River basin, finding that the model reasonably reproduced the observed variability and low water levels, indicating its suitability for future drought analyses.
-
Rhee et al. (2025) Topographic Setting Drives the Imprint of the Madden–Julian Oscillation ( MJO ) on Tree Growth in the Northern Sierra Nevada
This study investigates whether conifer tree growth in the northern Sierra Nevada, United States, can serve as a proxy for Madden–Julian Oscillation (MJO) variability. It provides the first documented evidence that *Pinus jeffreyi* tree-ring chronologies exhibit significant correlations with MJO indices, offering a novel tool for reconstructing past MJO dynamics.
-
Santos et al. (2025) Spatial and Temporal Patterns of Rainfall Anomalies and Their Relationship With Global Climate Indices in Rio Grande do Norte, Brazil
This study analyzed rainfall variability in Rio Grande do Norte, Brazil, using the Rainfall Anomaly Index (RAI) and its relationship with global climate indices (1963–2023). It identified homogeneous rainfall regions and found that teleconnections like AMO, TNA, and Niño indices directly influence rainfall, while LOTI and Solar Flux show an inverse relationship.
-
Ahamed et al. (2025) Identifying Baseflow Source Areas Using Remotely Sensed and Ground‐Based Hydrologic Data and Models
This study developed a data-driven approach using satellite and ground-based data to assess the spatial influence of rainfall and snowmelt on baseflow in California's Sierra Nevada. It revealed that snowmelt occurring in the 3000–3700 m elevation range is the most significant driver of baseflow, rather than areas with the highest annual rainfall or snowmelt rates.
-
Jaiswal et al. (2025) More accurate forecasting of drought indices using a decomposition-based hybrid machine learning model
This study develops a decomposition-based hybrid machine learning framework for more accurate forecasting of precipitation-based drought indices (Effective Drought Index (EDI), Standardized Precipitation Index (SPI) at 3- and 6-month scales) in two drought-prone districts of Maharashtra, India. The Ensemble Empirical Mode Decomposition-Time Delay Neural Network (EEMD-TDNN) hybrid model emerged as the most effective, achieving a 15–30% reduction in Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) compared to conventional and other hybrid models.
-
Fu et al. (2025) Intensifying Hydroclimate Whiplash From a 3D Perspective
This study investigates the spatiotemporal evolution of contiguous hydroclimate whiplash (HCW) extremes globally from 1982 to 2015 using a 3D scanning approach, revealing a significant underestimation of HCW frequency and affected areas by traditional pixel-level analyses.
-
Shizuo (2025) Snow cover forcing experiments
This dataset presents primary outputs from snow cover forcing experiments, designed to investigate the influence of snow cover variations on atmospheric and surface climate variables, supporting a manuscript currently under review.
-
Wang et al. (2025) Day‐to‐Day Temperature Variability in Meteorological Observations and Reanalysis Data Over China
This study quantifies day-to-day temperature variability (DTD) across China using observations and reanalysis data, revealing distinct spatiotemporal patterns and an earlier occurrence of extreme DTD events, thereby enhancing understanding of synoptic-scale temperature changes.
-
Peng et al. (2025) Beyond the Mean: Cold and Warm Tail Temperature Trends Reveal Asymmetric Controls on Snowpack Changes in the Northern Hemisphere
This study introduces a distributional diagnostic framework to decompose winter temperature trends into median, cold-tail, and warm-tail components across the Northern Hemisphere. It finds that mean and median winter temperature trends diverge significantly, and these asymmetric distributional changes, particularly tail behavior, are crucial for explaining March snow water equivalent trends, outperforming mean trends alone.
-
Shi et al. (2025) Runoff Response to Climate and Landscape Changes Under Variable Fraction of Snowfall in Precipitation
This study modified the snow-involved Budyko equation to quantify runoff elasticity to various factors in 552 US catchments, revealing that ignoring snowfall overestimates runoff sensitivity to precipitation and landscape, and that declining snow ratios due to warming will increase runoff sensitivity.
-
Hong et al. (2025) Quantifying Impacts of Precipitation and Evapotranspiration on Future Runoff in the Han River Basin Using the Budyko Framework
This study quantifies the relative impacts of precipitation and potential evapotranspiration on future runoff in the Han River basin using the Budyko framework. The findings reveal that precipitation is the dominant driver of projected runoff increases, contributing between 67% and 84% to the total change.
-
Anand et al. (2025) Balancing Productivity and Climate Impact: A Framework to Assess Climate‐Smart Irrigation
## Identification - **Journal:** Earth s Future - **Year:** 2025...
-
Feng et al. (2025) Spatiotemporal evolution and driving mechanisms of compound dry and hot events in the Yellow River Basin under climate warming
This study investigates the spatiotemporal evolution and driving mechanisms of compound dry and hot events (CDHEs) in the Yellow River Basin from 1960 to 2023, revealing a significant increase in extreme CDHEs since a 1996 climate regime shift, primarily driven by positive land-atmosphere feedbacks with spatially varying mechanisms.
-
Gao et al. (2025) Dynamics and Drivers of Suprapermafrost Groundwater on the Qinghai–Tibet Plateau Under Climate Change
This study investigated the seasonal dynamics, drivers, and future projections of suprapermafrost groundwater (SPG) in alpine meadow and alpine wet meadow ecosystems on the Qinghai–Tibet Plateau. It found that SPG fluctuations are primarily driven by thaw depth and rainfall infiltration, with climate warming projected to significantly deepen SPG tables by the 2090s.
-
Liang et al. (2025) Is There Sufficient Information to Reliably Estimate Return Periods for Very Rare Heat Extremes in Event Attribution?
This study evaluates methods for estimating return periods of hot extremes in event attribution, finding that multi-year block maxima improve accuracy for very rare events compared to annual maxima, which tend to overestimate return periods in the far right tail.
-
Gujree et al. (2025) Understanding flood behavior in the Kashmir Himalayas through copula-driven models
This study introduces a bivariate copula-based framework for flood frequency analysis at three gauging stations along the Jhelum River in Kashmir, India, to determine joint and conditional return periods for flood characteristics, identifying Asham as the most vulnerable station.
-
Wu et al. (2025) Graph Fourier Deep Learning for Spatiotemporal and Hydrogeological Interpretation of Groundwater Levels in the Yellow River Basin
This study proposes a novel Graph Fourier Network (GFN) model that integrates hydrogeological prior information for regional groundwater level prediction. The GFN model significantly outperforms baseline models, achieving high accuracy and enhanced extrapolation capability for lead times up to 25 days, effectively capturing the complex dynamics of groundwater systems.
-
Raghuvanshi et al. (2025) Complex network reveals propagation and moisture dynamics of Indian monsoon precipitation extremes
This study applies a complex network framework with nonlinear synchronization metrics to analyze the spatiotemporal organization, propagation, and drivers of extreme precipitation events (EPEs) during the Indian Summer Monsoon. It reveals distinct spatial communities influenced by synoptic systems and orography, identifying vertically integrated moisture convergence as the dominant driver of widespread EPEs, with the east coast and Bay of Bengal as major moisture sources and central and western India as primary sink zones.
-
Wang et al. (2025) Dominant role of climatic water availability in net ecosystem productivity in China's drylands: A comparison with atmospheric water demand and soil moisture
This study investigated the dominant water stress factors (climatic water availability, atmospheric water demand, and soil moisture) influencing net ecosystem productivity (NEP) in China's drylands over 40 years. It found that climatic water availability (SPEI) is the primary driver for 44.04% of natural vegetation, though its dominance shifts to atmospheric water demand (VPD) and soil moisture (SM) as aridity decreases, with specific aridity index thresholds.
-
Miazza et al. (2025) Limits to the Estimation of Old Streamwater in Catchments Using Environmental Tracers
This paper introduces a framework to systematically assess the "critical age" in water transit time distributions (TTDs), which defines the maximum water age reliably identifiable by tracers. It demonstrates that the critical age is often significantly lower than previously assumed, typically below 1 year for stable isotopes and rarely exceeding 5–6 years for tritium in streamflow dominated by younger waters.
-
Karimi et al. (2025) Robust assessment of snow persistence dynamics in Iran (2002–2024) using MODIS satellite imagery and multi-source evaluation
This study provides a comprehensive assessment of snow persistence (SP) variability across Iran from 2002 to 2024 using MODIS satellite imagery. It reveals a significant nationwide decline in SP, averaging −6.2% (approximately 23 snow-covered days), primarily driven by warming trends, with the most pronounced losses at mid-elevations.
-
Fiorese et al. (2025) Understanding the influence of Malta litho‐structural features on the dynamics of nitrate transport in the vadose zone
This study evaluates and compares the performance of the ISBA land surface model and the mHM hydrological model in simulating river discharge across 560 basins in France. The results demonstrate that mHM significantly outperforms ISBA in discharge simulation accuracy, while ISBA provides a more comprehensive representation of surface energy balance.
-
Yifan et al. (2025) Optimal allocation of brackish groundwater in arid inland area: A case study
This study developed a multi-objective water allocation model for Yingjisha County to optimize the utilization of groundwater with varying salinity levels, aiming to reduce overall irrigation water consumption and mitigate groundwater over-exploitation. The model successfully decreased annual irrigation water demand and enabled the use of brackish groundwater, significantly lowering the groundwater exploitation rate.
-
Vernier et al. (2025) A Soil–Plant–Atmosphere Continuum model coupled to CFD to simulate plant energy and water exchanges in heterogeneous microclimates
This study developed and validated a Soil–Plant–Atmosphere Continuum (SPAC) model coupled with Computational Fluid Dynamics (CFD) to simulate plant energy and water exchanges in heterogeneous microclimates. The coupled model accurately assesses energy exchanges with a relative error less than 20% and successfully predicts soil water content evolution over several days, demonstrating its utility for agrivoltaic, agroforestry, and urban environments.
-
Zhang et al. (2025) A generalized Complementary Principle (GCP) Model With Atmospheric Stability Correction for Estimating Sub‐Daily Evaporation
This study tested the validity of the Generalized Complementary Principle (GCP) for estimating land surface evaporation at sub-daily timescales, finding it to be accurate when combined with Monin-Obukhov similarity theory and atmospheric stability corrections.
-
Barrientos et al. (2025) Dynamic Water Storage Response During Meteorological Droughts in South‐Central Chile
This study investigates the spatiotemporal patterns of dynamic water storage during 13 extreme drought events across 43 watersheds in south-central Chile (1980–2020), revealing its rapid response to precipitation and decline during dry periods, with lower storage maxima linked to drier conditions and specific catchment characteristics.
-
Gupta et al. (2025) Finetuning AI Foundation Models to Develop Subgrid‐Scale Parameterizations: A Case Study on Atmospheric Gravity Waves
This study introduces a novel approach to developing machine learning parameterizations for small-scale climate processes by fine-tuning a pre-trained AI foundation model, demonstrating its superior performance in capturing atmospheric gravity wave effects for coarse-resolution climate models.
-
Ye et al. (2025) Quadratic Response and Adaption of Vegetation Growth to Hydrological Drought in Heterogeneous Lake Floodplain Wetlands
This study investigated the response of wetland vegetation growth to hydrological drought in China's Poyang Lake floodplain from 2000 to 2023, revealing a quadratic relationship between vegetation growth (EVI) and hydrological drought (SIAI) with significant time-lag and cumulative effects.
-
Yi et al. (2025) Flood changes and generating mechanisms in the Upper Rhine Basin under a warming climate
This study simulates runoff in the Upper Rhine Basin (URB) using the SPHY model to analyze flood characteristics and their underlying mechanisms from 1960 to 2019. It finds a general trend of increasing flood peaks and prolonged durations, primarily driven by rising temperatures altering hydroclimatology and enhancing snowmelt-driven runoff, especially for snowmelt-dominated events.
-
Murindangabo et al. (2025) Innovative analysis of half-century (1973–2022) spatial-temporal variability and trends in climate indices across the Czech Republic
This study assesses the spatio-temporal variability and trends of climate conditions across the Czech Republic using observations from nine stations over half a century (1973–2022). Results indicate predominantly moderate aridity in central and southwest regions, with increasing dryness in typically wet areas and decreasing aridity in some dry areas, providing critical insights for water-resources planning.
-
Manteaux et al. (2025) Evaluation of SWAT‐RIVE's Ability to Represent the Hydrobiogeochemical Dynamics in the Vienne Watershed
Not discernible from the provided corrupted text.
-
Patidar et al. (2025) Integrating Field Observations, Remote Sensing and Modelling for the Assessment of Bhilangana Lake, Central Himalaya, India
This study investigates the evolution of Bhilangana Lake and associated glacier changes in the Himalaya from 1968 to 2025, revealing significant lake expansion driven by rising temperatures and quantifying the high risk of a Glacial Lake Outburst Flood (GLOF) with substantial downstream impacts.
-
Zhang et al. (2025) Spatio-temporal heterogeneity of the propagation from meteorological to hydrological drought: A case study of the Luanhe River Basin
This study investigates the spatio-temporal heterogeneity of meteorological to hydrological drought propagation in the Luanhe River Basin, quantifying Drought Response Time (DRT) and identifying land use changes and reservoir operations as key anthropogenic factors prolonging drought propagation. The findings enhance understanding of drought mechanisms in regulated basins and support adaptive water management strategies.
-
Yan et al. (2025) Observational Evidences in the Effects of Large‐Scale Reforestation on Precipitation
This study statistically tests the effects of large-scale reforestation on downwind precipitation using observational data from China's Loess Plateau. It reveals a significantly positive relationship between increased vegetation cover (LAI) and regional precipitation during the growing season, providing observational evidence for this effect.
-
Bernard et al. (2025) Process‐Level Evaluation of the Land‐Atmosphere Interactions Within CNRM‐CM6‐1 Single‐Column Model Configuration
## Identification - **Journal:** Journal of Advances in Modeling Earth Systems - **Year:** 2025...
-
Li et al. (2025) Modeling Land Subsidence Under Future Water Stress: The Influence of Groundwater Exploitation, Climate Change, and Inter‐Basin Water Diversion
This study develops a coupled groundwater flow and land subsidence model to explore the co-evolution of groundwater levels and land subsidence in a key city along China's South-to-North Water Diversion route under future scenarios. It finds that future changes in groundwater levels and subsidence are primarily driven by water demand and diversion strategies, with climate change having a minor effect, and demonstrates that optimized water management can lead to significant subsidence recovery.
-
Srarfi et al. (2025) Investigation of soil salinity and environmental indicators by Google Earth Engine/Machine Learning in Northeast Tunisia under climate changes
This study mapped and monitored soil salinity in Northeast Tunisia over 24 years (2000–2023) using Google Earth Engine and machine learning, revealing strong spatio-temporal variability driven by climatic and anthropogenic factors, with remote sensing offering a reliable monitoring tool for sustainable land management.
-
Lee et al. (2025) Future variability in groundwater-surface water interactions: Implications for streamflow and its components using SWAT-MODFLOW 6 and CMIP 6 projections
This study investigates the future variability of groundwater-surface water interactions and their implications for streamflow and its components under climate change using integrated hydrological and climate models. Projections indicate an increase in average streamflow, a decrease in the baseflow index due to intensified surface runoff, and a higher probability of flooding under the SSP 5–8.5 scenario.
-
Lee et al. (2025) Large‐Scale Drought Forecasting in the U.S. Southern Plains Through a Hybrid Cluster‐Based Wavelet‐Machine Learning Approach
This study developed a novel hybrid clustering-based machine learning approach, combining Discrete Wavelet Transform (DWT) and Multilayer Perceptrons (MLPs), to forecast the gridded Standardized Precipitation-Evapotranspiration Index (SPEI) across the U.S. Southern Plains, demonstrating effective capture of drought spatial variability for early warning systems.
-
McEachran et al. (2025) Knowledge‐Guided Machine Learning for Operational Flood Forecasting
This study introduces a Factorized Hierarchical Neural Network (FHNN), a knowledge-guided machine learning framework for operational hydrologic forecasting at the catchment scale. The FHNN demonstrates superior streamflow prediction performance compared to expert human forecasters after the initial 12–18 hours, laying groundwork for AI-human collaboration in river forecasting.
-
Wang et al. (2025) Seasonal divergence in the sensitivity of carbon and water fluxes to climate variability in terrestrial ecosystems
This study quantifies the seasonal sensitivity of daily carbon and water fluxes (GPP, RE, NEP, ET) to hydroclimate factors across diverse biomes globally using eddy covariance observations and Earth System Models (ESMs). It reveals significant seasonal variations in these sensitivities, identifying dominant climate drivers for different fluxes in different seasons and an increasing water limitation for summer GPP.
-
Muñoz‐Vega et al. (2025) Influence of Hydraulic Conductivity Conceptualization and Unsaturated Flow Parameters for an Integrated Hydrological Model
This study investigates the sensitivity of integrated hydrologic model outputs to subsurface parameters, particularly hydraulic conductivity and van Genuchten parameters, in a headwater catchment. It finds that these parameters significantly influence streamflow, baseflow, peak flows, and soil moisture dynamics, especially during dry periods, providing crucial insights for model calibration.
-
Aguayo et al. (2025) Hybrid Glacio‐Hydrological Modeling Reveals Contrasting Runoff Changes in Western Patagonia Over the 21st Century
This study develops a novel hybrid modeling framework combining LSTM neural networks with ice-dynamical glacier modeling (OGGM) to simulate historical glacio-hydrology and project climate change impacts on freshwater resources across 2,236 catchments in Western Patagonia. The framework outperforms other models and projects significant runoff reductions in northern Western Patagonia, while southern glacierized basins are projected to experience runoff increases under a high-emission scenario.
-
Lin et al. (2025) Quantifying the Lifespan of 3D Flood Structures: Unlocking the Potential of Flood Detention Areas for Enhanced Flood Control in China
[Information not extractable due to corrupted input.]
-
Keskin et al. (2025) Correction: Streamflow Estimation for underground dams using machine learning and hydrological modeling: a case study of Bartın Bahçecik underground dam
This article is a correction notice for a previously published paper, "Streamflow Estimation for underground dams using machine learning and hydrological modeling: a case study of Bartın Bahçecik underground dam," replacing an erroneous figure and updating a reference.
-
Zhu et al. (2025) Cascading impacts of compound drought-heat extremes on global gross primary production
This study evaluates the probability of extreme negative anomalies in Gross Primary Production (GPP) for various vegetation types under different intensities of drought, heat, and compound drought-heat events, considering future warming levels of 2 °C, 3 °C, and 4 °C. It finds that compound drought-heat events significantly increase the likelihood of GPP reduction, particularly in low-latitude regions, with these impacts intensifying as global warming progresses.
-
Zhang et al. (2025) Thermodynamic and dynamic features of summer extreme heat events in the southwestern region of the Mongolian Plateau
This study investigates the thermodynamic and dynamic mechanisms of summer extreme heat waves in the southwestern Mongolian Plateau using JRA-55 reanalysis data, revealing a unique vertically tilted anticyclonic anomaly structure with dual-stratified wave activity peaks and distinct layer-specific heating pathways compared to Eastern China.
-
Workneh et al. (2025) Recent Trends and Variability in Climatic Water Balance: Implications for Forestry Development in Ethiopia
This study analyzed the climatology, trends, and variability of precipitation, reference evapotranspiration (ET₀), and climatic water balance (CWB) across Ethiopia and its 12 basins from 1980 to 2021, revealing significant spatial and temporal patterns with critical implications for forestry development.
-
Eiras‐Barca et al. (2025) Revisiting the Impact of Moisture Transport Deficit on Droughts: Prospective Climate Change Analysis and Emerging Hypotheses
This comprehensive review systematically examines the pivotal role of moisture transport deficits in the genesis and progression of droughts under climate change, confirming that these deficiencies amplify drought severity by reducing precipitation or intensifying evaporative demand.
-
Saavedra et al. (2025) From Soil Moisture Spatial Patterns to Catchment Nitrate Dynamics Using Explainable AI
This study developed a multi-branch Deep Learning framework, leveraging high-resolution satellite soil moisture data, to predict daily nitrate concentrations in streams across eight US catchments. The model successfully represents nitrate dynamics, demonstrating that spatial patterns of soil moisture are significant predictors and identifying near-stream hotspots as critical areas for nitrate export.
