-
Lee et al. (2025) A Spatially Masked Adaptive Gated Network for multimodal post-flood water extent mapping using SAR and incomplete multispectral data
[Information unavailable due to unreadable paper text.]
-
Sasaki et al. (2025) Environmental Influences on Deep Convective Upscale Growth Rate in Central Argentina From a Convection‐Permitting Simulation
This study investigates environmental conditions influencing the rate of deep convective upscale growth into mesoscale convective systems (MCSs) in central Argentina using a convection-permitting simulation, finding that rapid growth is associated with more favorable thermodynamic environments (higher moisture, instability) and more frequent low-level jets, with wind shear orientation also playing a role near complex terrain.
-
Chen et al. (2025) Hybrid Observing System Simulation Experiment for the FY-4 Geostationary Orbit Microwave Satellite Based on CMA-GFS
Not available in the provided text.
-
Yun et al. (2025) Addressing class imbalance extends the performance frontier of classification–regression satellite-gauge precipitation fusion
This study introduces ImbCRPF, a novel classification-regression framework for satellite-gauge precipitation fusion that explicitly addresses class imbalance, significantly improving the accuracy of precipitation estimates, particularly for heavy rainfall events.
-
Rensch et al. (2025) The Extent of El Niño and La Niña Influence on Australian Rainfall
This study details the complex influence of El Niño-Southern Oscillation (ENSO) on Australian monthly rainfall distributions, revealing that La Niña consistently intensifies rainfall, particularly extremes, while El Niño's impact is more limited spatially and temporally, even showing intensification during its mature phase.
-
Lamb et al. (2025) Perspectives on Systematic Cloud Microphysics Scheme Development With Machine Learning
This perspectives paper synthesizes recent progress and outlines challenges and opportunities for applying machine learning to improve cloud microphysics parameterizations, aiming to reduce significant parametric and structural uncertainties in weather and and climate models.
-
PANDYA et al. (2025) From raw to reliable: machine learning bias correction of reanalysis data for improved drought severity classification
This study develops a scalable machine learning bias correction approach for reanalysis data (ERA5, NASA POWER) to improve drought severity classification in vulnerable regions of India. It finds that Random Forest is the most reliable method for bias correction, significantly enhancing the accuracy of drought monitoring.
-
Kim et al. (2025) Ensemble artificial neural network and generalized additive model for data-scarce regional frequency analysis in design flood estimation
This study applied ensemble artificial neural networks (EANN) and generalized additive models (GAM) with canonical correlation analysis (CCA) for regional frequency analysis (RFA) to estimate design floods in data-scarce small streams in South Korea, finding that CCA-GAM outperformed EANN and river basin area was the most influential variable.
-
Liu et al. (2025) The impact of nonlinear surface energy partitioning on potential evapotranspiration: A machine learning study based on FLUXNET data
This study investigates the nonlinear relationship of the no-water-limited Bowen ratio (βNWL) with environmental factors using global FLUXNET data and machine learning, developing a new PET model (PETβNWL−RF) that significantly improves daily potential evapotranspiration estimation and drought monitoring accuracy.
-
Nikolov et al. (2025) How Complete Is Cloud Glaciation?
This study analyzes satellite observations of individual cloud tops to track their temporal phase evolution and quantify glaciation, finding that most glaciation events induce a sustained shift in cloud properties within the mixed-phase regime rather than complete freezing, and correlate with higher ice-nucleating particle concentrations.
-
Ye et al. (2025) Spherical Harmonic Fingerprints Characterize Moon‐Based Disk‐Integrated Earth's Emitted Radiation Signatures
This study simulates Moon-based disk-integrated Earth radiation to unravel the influence of orbital dynamics and clouds on planetary-scale radiation variations. It finds that orbital dynamics, particularly synodic and sidereal monthly cycles, dominate these variations, and while clouds systematically reduce radiation, they preserve these orbital-driven periodicities.
-
Zhao et al. (2025) Terrain-Aware Uncertainty Quantification and Cross-Sensor Consistency Analysis of Hyperspectral Surface Reflectance
Not available from the provided text.
-
Wang et al. (2025) Advancing Tropical Cyclone Rainfall Simulation and Projection With Eddy‐Resolving Climate Models
This study demonstrates that eddy-resolving high-resolution Community Earth System Model (CESM) simulations accurately capture historical Tropical Cyclone Rainfall (TCR) due to improved TC upward motion. Under the RCP8.5 warming scenario, HR CESM projects a significantly higher TCR increase rate (∼12.0% K⁻¹) exceeding Clausius-Clapeyron scaling, in contrast to non-eddy-resolving models which substantially underestimate both historical and projected TCR increases.
-
Wang et al. (2025) Dynamic Downscaling Resolution Affects Hot Extreme Processes: A Case Study of 2013 Summer Extreme Hot Event Over Central and Eastern China
This study investigated the 2013 extreme hot event (EHE) in central and eastern China using the Weather Research and Forecasting (WRF) model at 25 km and 9 km resolutions, finding that the higher resolution (9 km) model significantly improved the simulation by better capturing land-atmosphere feedback and diabatic heating processes.
-
McInerney et al. (2025) Tailored calibration of stochastic weather generators for enhanced hydrological system evaluation
Tailored calibration of stochastic weather generators (SWGs) using the Simulated Method of Moments (SMM) significantly improves the capture of critical climate attributes and hydrological responses compared to conventional methods, with the Robust Gauss-Newton (RGN) algorithm proving essential for accurate and efficient optimization in both historical and future climate assessments.
-
Yu et al. (2025) A dual-method, multi-scale causal framework reveals seasonal shifts in hydrological causality of a headwater catchment
This study develops a robust, dual-method, multi-scale causal inference framework to investigate hydrological interactions in a headwater catchment, revealing scale-dependent causal pathways and significant seasonal shifts in runoff generation mechanisms.
-
Ma et al. (2025) Temperature Is Surpassing Precipitation as the Dominant Driver of Flash Drought Acceleration Under Climate Warming
This study quantifies the relative contributions of various drivers to flash droughts, revealing that while precipitation has historically been dominant, temperature is projected to become the primary driver in most regions under climate warming.
-
Zhang et al. (2025) Remote Impact of Preceding Soil Moisture on the May—June Precipitation of Huang‐Huai Wheat Region in China
This study identifies March-April soil moisture in the north Black Sea and Caspian Sea (NBC) region as a key precursor for May-June precipitation anomalies in China's Huang-Huai Plain, revealing a significant positive correlation driven by a Rossby wave train mechanism.
-
Wang et al. (2025) Influence of Tidal Inundation and Salinity on the Generalized Complementary Relationship for Evaporation in a Mangrove Ecosystem
This study evaluated the Sigmoid Generalized Complementary (SGC) equation for estimating evaporation in a subtropical mangrove forest, finding it accurately captures evaporation dynamics and revealing how tidal inundation influences surface moisture parameters, while salinity negatively correlates with monthly evaporation at higher concentrations.
-
Helili et al. (2025) Assessment and intercomparison of 23 global satellite and model-based soil moisture products using cosmic ray neutron sensing observations over Europe
This study systematically evaluated 23 global satellite and model-based soil moisture products using 68 Cosmic Ray Neutron Sensing (CRNS) observations across Europe, finding that SMAP-INRAE-BORDEAUX (SMAP-IB) retrievals showed the superior consistency with CRNS measurements.
-
Bayati et al. (2025) Evaluating the Functional Realism of Deep Learning Rainfall‐Runoff Models Using Catchment Hydrology Principles
This study introduces a hydrology-specific Explainable AI (XAI) framework to evaluate the functional realism of Long-Short-Term-Memory (LSTM) networks in rainfall-runoff modeling. It reveals that despite high predictive accuracy, LSTMs often exhibit hydrologically implausible internal reasoning, particularly under varying climatic conditions.
-
Justino et al. (2025) Atmospheric rivers as mediators between climate teleconnections and burned area variability in North America
This study identifies atmospheric rivers (ARs) as key mediators linking large-scale climate teleconnections (ENSO, PNA, AO) to variations in vegetation activity (NDVI) and burned area (BA) across North America. The findings highlight the central role of ARs in shaping regional fire regimes and improving prospects for seasonal fire prediction.
-
Roksvåg et al. (2025) An LSTM network for joint modeling of streamflow and hydropower generation for run-of-river plants
This study develops a novel Long Short-Term Memory (LSTM) network for jointly estimating historical daily streamflow and hydropower generation for run-of-river plants in Norway, demonstrating superior performance compared to traditional hydrological models in both gauged and ungauged catchments.
-
Anwari (2025) Monitoring of Soil Moisture Influenced by Crop Choice and Management Practice in Morocco’s Rainfed Drylands Using Sentinel-1 SAR Timeseries Data
[Information not extractable from the provided garbled text.]
-
Sun et al. (2025) Fusion of multi-source precipitation records via coordinate-based generative models
The study introduces PRIMER, a coordinate-based diffusion model framework that fuses heterogeneous precipitation data from gauges, satellites, and reanalysis. The model successfully overcomes the trade-offs between spatial coverage and local accuracy, providing a unified tool for bias correction, downscaling, and optimal interpolation.
-
Jia et al. (2025) A Novel Algorithm for Optimizing Structure of In-Situ Supplementary Irrigation Device for Afforestation in Dryland
This study developed and validated a novel algorithm to optimize the structural parameters (rated area, capacity, and flow) of in-situ supplementary irrigation devices (SIDs) for afforestation in drylands, demonstrating a significant increase in sapling survival rate (83.6%) compared to traditional methods (46.9%) in a Lhasa case study.
-
Li et al. (2025) A Comparative Study of Urban Pluvial Flood Susceptibility Assessment Based on Multi-Machine Learning Algorithm
This study developed and benchmarked a multi-machine learning framework for urban pluvial flood susceptibility assessment in Wuxi, China, finding that the Particle Swarm Optimization-optimized eXtreme Gradient Boosting (PSO-XGB) model achieved superior predictive performance and spatial delineation compared to other models.
-
Shiogama et al. (2025) Emergent constraints on future change projections of mean and extreme temperature and precipitation in the global maize harvesting area
This study investigates whether 'hot' Earth system models (ESMs) overestimate future temperature and precipitation changes in global maize harvesting areas, finding that these models do overestimate changes in mean and extreme temperature and extreme precipitation, with emergent constraints significantly reducing projection uncertainties.
-
Duffy et al. (2025) Is the High ECS in CESM2 Degrading Transient Climate Change Projections Over the 21st Century?
This paper evaluates the fitness of CESM2 for various applications, concluding that despite a high equilibrium climate sensitivity, it accurately simulates 20th and 21st-century transient climates but is unreliable for extreme paleoclimates or long-term projections beyond the 21st century.
-
Wang et al. (2025) Joint Learning for Feature Reconstruction and Prediction in Agricultural Semantic Segmentation From Incomplete Satellite Image Time Series
[Information not available in the provided text.]
-
Lan et al. (2025) The GLACE-Hydrology Experiment: Effects of Land–Atmosphere Coupling on Precipitation Change and Monsoonal Circulations
This study investigates the impact of land-atmosphere interactions on rainfall and monsoon intensity using coupled and uncoupled CESM simulations. It finds that while most land regions show higher temperatures and reduced precipitation in coupled simulations, monsoon regions experience increased precipitation due to enhanced vertical motion and strengthened monsoonal circulation.
-
Amma et al. (2025) Balancing yield and water productivity in sweetpotato through crop evapotranspiration–based irrigation scheduling across seasons
This study evaluated crop evapotranspiration (ETc)-based irrigation scheduling for sweetpotato over three seasons in South India, comparing drip, sprinkler, and furrow methods. It found that irrigation at 100–112% ETc maximized tuber yield, while moderate deficit irrigation (63–75% ETc) optimized water productivity, highlighting a critical trade-off for growers.
-
Angulo‐Umana et al. (2025) Multiscale Convective Circulations and Scale Interactions in a Global Storm‐Resolving Model
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
-
Salau et al. (2025) A Novel Index for Integrative Drought Assessment in Agricultural Reservoirs
This study developed a Multivariate Drought Index (MDI) using Principal Component Analysis (PCA) to integrate precipitation, inflow, and reservoir storage for comprehensive hydrometeorological drought assessment in 404 agricultural reservoirs across five major watersheds in South Korea, demonstrating its superior ability to detect earlier and longer droughts compared to existing indices like SPEI.
-
Baishya et al. (2025) An Easy-to-Apply Machine Learning Framework for Hydrologic Evaluation of Ungauged Catchments
This study developed a novel machine learning framework for streamflow regionalization in ungauged catchments by integrating Curve Number (CN) and specific discharge normalization, demonstrating superior performance over a conventional SWAT parameter-transfer method in Northeast India. The LightGBM model, incorporating dynamic CN and specific discharge scaling, achieved significantly higher accuracy and lower bias in the ungauged target basin.
-
Liu et al. (2025) Young Versus Old: Does Forest Age Regulate Water and Dissolved Carbon Processes Belowground?
This study investigates how hydrological flow paths and dissolved carbon processes belowground differ between young and old forests. It reveals that young forests exhibit lower streamflow and deep groundwater contributions, along with distinct dissolved organic and inorganic carbon cycling patterns compared to old forests, highlighting the significant role of forest age in subsurface carbon dynamics.
-
Zhang et al. (2025) A Comprehensive Radiative Transfer Model for Vegetated Land Surfaces Incorporating Multiple Volume–Surface Scattering Coupling Effects
This paper introduces a comprehensive radiative transfer model specifically designed for vegetated land surfaces, with a key focus on integrating multiple volume–surface scattering coupling effects.
-
Battaglioli et al. (2025) Contrasting trends in very large hail events and related economic losses across the globe
This study develops a global climatology of very large hail (≥5 cm) events from 1950 to 2023 and examines trends in their frequency and related economic impacts. It finds contrasting regional trends, with Europe experiencing a sharp rise in very large hail frequency linked to atmospheric instability, while the Southern Hemisphere sees declines, and attributes increasing economic losses in Europe to more frequent events, whereas in the USA and Australia, increasing exposure and vulnerability are the primary drivers.
-
Eriskin et al. (2025) A Horizon-Adaptive Benchmarking Framework for Long-Term Reservoir Storage Forecasting Using Physics-Informed Transformers and Machine Learning
This study develops a horizon-adaptive benchmarking framework for 12-month reservoir storage forecasting using physics-informed transformers and machine learning models. It demonstrates that optimal model selection varies significantly across different forecast horizons, highlighting the need for a dynamic, horizon-specific approach for robust water management in semi-arid regions.
-
Liu et al. (2025) Strong Wind‐Driven Oceanic Forcing on Decadal SST Variability Over the Global Ocean
This study dynamically validates a previous statistical assessment, confirming that wind-driven oceanic forcing is the dominant driver of decadal sea surface temperature variability in mid-latitudes.
-
Andreasen et al. (2025) Independent Short‐ and Longwave Pathways for a Zonally Asymmetric Northern Hemisphere Temperature Response to Tropical Volcanic Eruptions
This study investigates the independent and distinct impacts of shortwave reflection and longwave absorption properties of stratospheric sulfate aerosols from tropical volcanic eruptions on Northern Hemisphere extra-tropical winter temperatures, revealing fundamentally different dynamical responses.
-
Wang et al. (2025) Recent accelerated drying in southwest China dominated by anthropogenic aerosol forcing
This study investigates the drivers of the recent drying trend in Southwest China, revealing that anthropogenic aerosol forcing, particularly from European reductions and Chinese increases, is the dominant factor, accounting for 77% of the total forcing.
-
Groom et al. (2025) Entropic Learning Enables Skilful Forecasts of ENSO Phase at up to 2 Years Lead Time
This paper extends the entropy-optimal Sparse Probabilistic Approximation (eSPA) algorithm with an ensemble meta-learning strategy to predict ENSO phase using only satellite-era observational data. The enhanced eSPA model achieves probabilistic forecast skill comparable to the IRI plume, extends accurate lead times up to 24 months, and operates at a significantly lower computational cost.
-
Akinsoji et al. (2025) Ensemble Machine Learning-Based Feature Selection for Flood Susceptibility Mapping Under Climate and Land Use Change Scenarios
This study compares feature selection techniques with ensemble machine learning algorithms for flood susceptibility mapping in South Korea, integrating historical data, future climate projections (CMIP5/CMIP6), and land use change scenarios. It found that the Variance Inflation Factor (VIF) combined with Gradient Boosting (GB) achieved the highest accuracy (ROC-AUC: 0.93) and predicted increased flood exposure in urbanized, low-lying areas under future conditions.
-
Xu et al. (2025) Quantifying the Impact of Rainfall Spatial Heterogeneity and Patterns on Urban Flooding by Integrating Machine Learning Algorithm and Hydrodynamic–Hydrological Modeling
This study developed a machine learning-based model to generate spatially nonuniform rainfall scenarios and integrated it with a hydrodynamic–hydrological model to quantify the impact of rainfall spatial heterogeneity and patterns on urban flooding. The findings reveal that neglecting rainfall spatial heterogeneity systematically underestimates urban flooding, with underestimation intensifying with higher rainfall peak coefficients.
-
Köhler et al. (2025) Dense stands of aquatic plants retain water in lowland rivers and in adjacent floodplain aquifers
Dense stands of aquatic plants in a lowland river effectively compensate for declining discharge by elevating water levels, significantly increasing water storage in both the river channel and the adjacent floodplain aquifer, with weed cutting offering only a temporary reduction in this beneficial water retention.
-
Sahu et al. (2025) Optimizing Irrigation Scheduling with a Hybrid Transformer-GRU Model and Reinforcement Learning in Smart Agriculture
This research proposes an intelligent irrigation scheduling framework that integrates multi-source environmental data with a hybrid Transformer-GRU model and reinforcement learning to enhance water usage efficiency and crop yield while reducing drought risk in smart agriculture.
-
Palumbo et al. (2025) Precipitation, moderated by spring temperature and vegetation, drives runoff efficiency in the Upper Colorado River Basin, USA
This study uses causal inference with historical data to identify drivers of surface runoff efficiency in the Upper Colorado River Basin. It finds that runoff efficiency is primarily driven by precipitation and snow accumulation, moderated by spring temperature and vegetation phenology, with summer temperature not emerging as a statistically significant direct driver.
-
Xiao-ya et al. (2025) Quantifying Individual Contribution of Human Activities on Long-Term Streamflow Change in a Large Climate-Sensitive River Basin
This study developed a Variable Infiltration Capacity (VIC) model-based framework to quantify the individual contributions of climate change and human activities to long-term streamflow changes in the Songhua River Basin, finding that water withdrawal was the dominant driver, explaining 48.30% of streamflow changes between 1994 and 2017.
-
Kareem et al. (2025) Improving River Flood Mapping with Adaptive Sampling and Artificial Intelligence Techniques for Enhanced Flood Risk Assessment
This study develops and evaluates AI-assisted adaptive sampling techniques, driven by precipitation and topographic factors, to enhance river flood mapping and risk assessment. It demonstrates that elevation-adapted sampling significantly improves the accuracy of AI models, particularly Random Forest, in delineating flood extents and identifying vulnerable assets for 200-year and 500-year flood events.
-
Li et al. (2025) Assessment of compound hot-dry events in lakes across global drylands
This study analyzed the frequency, dynamics, and drivers of hot, dry, and compound hot-dry events in 2,338 global dryland lakes from 1985 to 2020, revealing a significant increase in compound events in many lakes, predominantly driven by dry event frequency.
-
Kim et al. (2025) Multi‐Scale Decomposition for Skillful All‐Season MJO Prediction With Deep Learning
This study introduces a novel deep learning framework for Madden-Julian Oscillation (MJO) prediction that integrates background atmospheric fields alongside MJO anomalies, significantly enhancing prediction skill up to 29 days.
-
Liu et al. (2025) A tale of two towers: comparing NEON and AmeriFlux data streams at Bartlett Experimental Forest
This study compared parallel meteorological, phenological, and eddy covariance flux observations from co-located AmeriFlux and NEON towers at Bartlett Experimental Forest, finding excellent agreement for meteorology and phenology but significant annual-scale discrepancies in carbon dioxide and latent heat fluxes, highlighting challenges in merging long-term data from different platforms.
-
Chen et al. (2025) The Impact of Large‐Scale Land Surface Conditions on the South American Low‐Level Jet
This study investigates the influence of antecedent soil moisture on the dynamics of the South American low-level jet (SALLJ), specifically strong Chaco jets, finding that dry soil conditions significantly intensify these jets by enhancing land-atmosphere interactions.
-
Xu et al. (2025) Stochastic Resonance Elucidates the Emergence and Periodicity Transition of Glacial Cycles
This study proposes a stochastic resonance model to resolve the emergence and intensification of glacial cycles (41-kyr to 100-kyr periods) in the Pliocene-Pleistocene. It suggests that non-stationary greenhouse gas concentrations and a noise component modulated by orbital variations drive these transitions, with weakened noise favoring the 100-kyr cycle.
-
Williams et al. (2025) Mechanisms of Projected Changes in Thunderstorm Downburst Environments Across the United States
This study investigated the responses of downdraft convective available potential energy (DCAPE) to global warming using the CESM2 model under a high-emission scenario, projecting a 5%–12% average increase in DCAPE, with extreme values increasing at much faster rates and a significant poleward shift in winter, indicating an increased potential for downbursts.
-
Luschen et al. (2025) Stratiform and Anvil Cloud‐Radiative Forcing in Tropical Cyclogenesis
This study investigates the role of convective-scale cloud-radiative forcing (CRF) in tropical cyclone (TC) genesis, hypothesizing that CRF in stratiform clouds weakens downdrafts and moistens the environment. Using a convection-permitting WRF model, the research finds that CRF, particularly from stratiform and anvil clouds, reduces downdraft strength and number, increases humidity and moist entropy, suppresses ventilation, and ultimately accelerates TC development.
-
Hua et al. (2025) Divergent water controls on vegetation productivity across drylands of the contiguous United States
This study quantifies the relative importance and nonlinear impacts of soil moisture (SM) and vapor pressure deficit (VPD) on gross primary productivity (GPP) across drylands of the contiguous United States. It reveals that SM is a stronger control than VPD, with varying critical soil depths and threshold-like responses along aridity gradients.
-
Wang et al. (2025) Multi-scale shifts in rainfall patterns in a subtropical region (1981–2024): challenges and implications for water management
This study analyzed multi-scale rainfall pattern shifts in a subtropical region from 1981 to 2024, revealing increased temporal concentration, more intense sub-daily events, and longer dry spells, which decouple total rainfall volume from its hydrological utility and pose challenges for water management.
-
Chen et al. (2025) The trend and interannual variability in the global terrestrial evapotranspiration are respectively dominated by humid regions and drylands
This study reveals that the increasing trend in global terrestrial evapotranspiration (ET) is predominantly driven by humid regions, while its substantial interannual variability (IAV) is primarily controlled by drylands.
-
Yan et al. (2025) A Modified Hierarchical Vision Transformer for Soil Moisture Retrieval From CYGNSS Data
This research introduces a novel deep learning framework, multi‐head self‐attention‐aided vision Transformer (MSA‐ViT), for soil moisture retrieval using Cyclone Global Navigation Satellite System (CYGNSS) data. The MSA-ViT model integrates physical understanding with deep learning to capture nonlinear interactions, demonstrating superior performance over conventional and established deep learning models, and improving upon existing CYGNSS L3 products.
-
Bellos et al. (2025) An analytical methodology to assess epistemic uncertainty of 2D flood models under steady flow conditions
This study develops an analytical methodology to assess epistemic uncertainty in 2D flood models under steady flow conditions using an idealized benchmark setup. It proposes a new taxonomy of five uncertainty drivers (forcing, geometric, physical, computational, structural) and finds that the forcing driver has the most significant impact on model output, followed by geometric and physical drivers.
-
Sattar et al. (2025) A coupled land use change-ecohydrological model for multi-seasonal arid agricultural systems: an Egyptian case study
This study presents a novel coupled land-use change and ecohydrological model, linking SWAT+ and CRAFTY, to simulate agricultural production and water use in arid multi-seasonal systems under climate change scenarios. Applied to Egypt, the model projects varying crop yield responses and improved water use efficiency, particularly under high CO2 emission futures, demonstrating the value of integrated biophysical and socioeconomic feedback for adaptation.
-
Barthelemy et al. (2025) Future Atmospheric Rivers in Antarctica: Characteristics and Impacts With the IPSL Model
This study investigates the future impacts of atmospheric rivers (ARs) on the Antarctic surface mass balance (SMB) using 21st-century climate simulations, finding that ARs are projected to increase in frequency and severity, leading to a dominant increase in continental snow accumulation.
-
Sahu et al. (2025) Deep Learning and Remote Sensing for Crop Yield Prediction and Decision Support
This study proposes a deep learning framework integrating multispectral satellite imagery and environmental variables for improved crop yield prediction and decision support. The framework, utilizing cGANs for data augmentation and DARTS for architecture optimization, significantly reduces prediction errors and demonstrates the potential to increase yields by 27% and reduce resource costs by 22% compared to conventional practices.
-
Li et al. (2025) Mechanistic Insights Into Regional Air‐Sea Coupling Effects on East Asian Summer Climate: A Comparative Modeling Study With a New Regional Earth System Model
This study enhances the mechanistic understanding of regional air-sea coupling's influence on East Asian summer climate by comparing coupled (RIEMS) and uncoupled (WRF) regional models. It finds that RIEMS significantly improves simulations of air temperature and heat fluxes compared to WRF and global models, demonstrating the critical role of regional air-sea coupling.
-
Verma et al. (2025) Indian Summer Monsoon During the Medieval Climate Anomaly With a Steady Trend From 1140 to 1250 CE
This study reconstructs Indian Summer Monsoon (ISM) dynamics during the Medieval Climate Anomaly (∼722 to 1250 CE) using a high-resolution speleothem record from Meghalaya, revealing distinct wet and dry phases and attributing multidecadal variability to solar activity and subdecadal variability to El Niño-Southern Oscillation.
-
Xu et al. (2025) A global intercomparison of SWOT and traditional nadir radar altimetry for monitoring river water surface elevation
This study presents the first global-scale intercomparison between SWOT’s wide-swath Ka-band InSAR and traditional nadir radar altimetry (Sentinel-3 and Sentinel-6) for monitoring river water surface elevation. The research identifies that while high-quality SWOT data aligns well with traditional altimetry (RMSE = 0.80 m), factors such as river width, ice cover, and extreme backscatter significantly modulate data consistency.
-
Wen et al. (2025) Differences in Tropical Cyclone Tornado Activities and Key Tornadic Environments Between China and the United States
This study investigates why tornadic tropical cyclones (TCs) in China produce significantly fewer tornadoes than those in the U.S., revealing that differences in TC characteristics and a critical mismatch between favorable thermodynamic and kinematic environmental conditions in China are key factors.
-
Reddy et al. (2025) Exploring the Impact of Optimization Techniques on Streamflow Prediction Using XGBoost: A Comparative Analysis with Satellite and Reanalysis Precipitation Datasets
This study systematically compares the joint impact of eight precipitation datasets and five optimization techniques on the performance of an Extreme Gradient Boosting (XGBoost) model for one-day-ahead streamflow prediction in India's Godavari Basin. The research found that the combination of Simulated Annealing (SA) for hyperparameter tuning and the India Meteorological Department (IMD) precipitation dataset consistently yielded the most accurate and reliable streamflow forecasts.
-
Manco et al. (2025) Identifying recurring patterns of extreme daily precipitation using K-means algorithm: Uncovering spatial shift driven by climate change over the Italian Peninsula
This study applies k-means clustering to high-resolution climate projections (VHR-PRO_IT) to identify and characterize recurring spatial-temporal patterns of extreme daily precipitation over the Italian Peninsula, revealing significant shifts and increased variability under future climate scenarios (RCP4.5 and RCP8.5).
-
Li et al. (2025) Fine‐Scale Characteristics and Upper‐Level Forcing of Heavy Rainfall Over the Northeastern Tibetan Plateau
This study investigates the spatiotemporal evolution and anomalous upper-level circulation of heavy regional rainfall events (RREs) in summer over the southern Qilian Mountains, identifying two distinct types with different characteristics and underlying upper-level atmospheric disturbances.
-
Simard et al. (2025) Delta-X: An airborne remote sensing framework to calibrate hydrodynamic and ecogeomorphic processes responsible for land building in coastal deltas
This paper presents the Delta-X framework, an airborne remote sensing and in situ data integration strategy used to calibrate and validate hydrodynamic and ecogeomorphic models. The framework was implemented in the Mississippi River Delta to quantify the processes of mineral sediment deposition and organic matter production that determine deltaic land sustainability.
-
Ji et al. (2025) In Situ Observations Reveal That the South Asian Summer Monsoon Weakens Aerosol Cloud Activation Over the Southern Tibetan Plateau
This study investigates the impact of the South Asian Summer Monsoon (SASM) on aerosol activation over the southern Tibetan Plateau using in situ measurements. It reveals a weak aerosol activation capacity and a significant negative correlation between SASM intensity and aerosol activation, attributed to enhanced wet scavenging during stronger monsoonal transport.
-
Subrahmanian et al. (2025) A Novel Approach To Integrate Low Impact Development (LID) Modules into the SWAT Model To Facilitate Sustainable Urban Drainage Planning at River Basin Scales
This study integrates novel physics-based Multi-Layer Green-Ampt (MLGA) infiltration subroutines for Low Impact Development (LID) measures into the Soil Water Assessment Tool (SWAT) model, enhancing its capability for sustainable urban drainage planning and flood mitigation at large river basin scales. The enhanced model demonstrates improved hydrological simulation accuracy and significant reductions in surface runoff and increases in aquifer recharge under various LID implementation scenarios.
-
Reich et al. (2025) Beyond optimality: Dryland ecosystems infrequently use water efficiently for carbon gain
This study investigates the applicability of optimality theory, which posits that plants maximize carbon gain per unit water lost (WUE scales with VPD^k, k=½), in dryland ecosystems. It reveals that dryland plant water-use efficiency often deviates from optimal behavior, particularly under arid conditions or low soil moisture, underscoring the necessity for dynamic representations of plant water-use strategies.
-
Kim et al. (2025) Geophysical and Remote Sensing Monitoring of a Snow Patch System in Barton Peninsula Shows Impacts of Warming on Low-Altitude Permafrost
This study monitored a snow patch system in Barton Peninsula using geophysical and remote sensing techniques, revealing the impacts of warming on low-altitude permafrost.
-
Singer et al. (2025) Southern Ocean Clear‐Sky Brightening From Sea Spray Aerosol Increase Drives Departure From Hemispheric Albedo Symmetry
This study investigates negative trends in reflected shortwave radiation over the 21st century, finding global darkening driven by clouds, while clear-sky signals show offsetting hemispheric trends. It reveals an unexpected widespread clear-sky brightening over the Southern Hemisphere, particularly the Southern Ocean, which is attributed to increased wind-driven sea spray aerosol emissions linked to rising near-surface winds.
-
Geng et al. (2025) Analyzing Pluvial Flooding Influenced by Urban Road Network Metrics Based on Hydrodynamic Simulation and SHAP Values
This study quantitatively investigates the influence of urban road network structure metrics on pluvial flooding using 2D hydrodynamic simulations, statistical analysis, and interpretable machine learning. It reveals that regular grid networks enhance drainage efficiency, while cul-de-sac types exacerbate flooding, with topography and network connectivity metrics exhibiting spatially heterogeneous impacts on flood depth and velocity.
-
Nie et al. (2025) Identifying Hydrological Analogous Year from Magnitude and Temporal Patterns
This study proposes a comprehensive Similarity Index (SI) that integrates six existing methodologies to identify hydrologically similar years by balancing magnitude consistency and temporal pattern alignment. The SI family demonstrates superior cross-station stability, while a hybrid Cosine Similarity + Euclidean Distance (CS + ED) method achieves the highest overall similarity score, both significantly improving runoff forecasting accuracy.
-
Vojtek et al. (2025) Fluvial Flood Inundation Modeling: a Comparative Assessment of 1D and 2D Hydraulic Approach Using MIKE+
This study compares 1D and 2D hydraulic modeling approaches using MIKE+ for fluvial flood inundation mapping across three flood scenarios (Q10, Q100, Q1000) in a narrow river valley in western Slovakia. The findings indicate that the 1D model significantly underestimates flood extents and flow depths compared to the 2D model and official 2D benchmark maps, concluding its unsuitability for such geomorphological settings.
-
Dai et al. (2025) Understanding the Climate Response to Different Vertical Patterns of Radiative Forcing
This study investigates how the vertical structure of radiative forcing influences climate response, finding that higher-altitude forcings lead to less global warming due to more negative cloud feedback, a phenomenon linked to sea-surface temperature patterns and tropospheric static stability. It highlights the critical need to accurately represent the vertical distribution of anthropogenic forcings for precise climate projections.
-
Kacimov (2025) Ephemeral reservoirs of low-head wadi dams drained by seepage, evaporation and pumping: the Pavlovsky/Polubarinova-Kochina/Abel analytical legacy redux
This paper develops analytical and numerical solutions, based on conformal mappings and ordinary differential equations, to model transient 2-D seepage and reservoir depletion in low-head wadi dams, facilitating optimal design for water management in arid regions.
-
Ren et al. (2025) From seasonal variability to long-term trends: a comprehensive analysis of reservoir-induced flow regime alterations
This study comprehensively assesses the seasonal variations and long-term trends (1980-2020) of reservoir-induced flow regime alterations across the Contiguous United States, revealing distinct intra-annual patterns and significant reductions in high flow impacts alongside increases in low flow impacts over recent decades.
-
Bai et al. (2025) Tracking drought-flood abrupt alternations: Event identification, path analysis, and ecological impacts on vegetation
This study introduces a novel three-dimensional method to identify drought-to-flood and flood-to-drought abrupt alternation events in the Pearl River Basin, quantifying their spatiotemporal characteristics and evaluating their distinct ecological impacts on vegetation.
-
Ning et al. (2025) Multi-model simulation performance of monthly water balance models for global catchments: Thresholds and structural sensitivity
This study evaluates the performance of 14 monthly water balance models across over 2,000 global catchments, revealing that while most models perform reasonably well, their accuracy significantly declines in high-latitude or snow-dominated regions and improves with the inclusion of non-linear snow modules and in more humid conditions.
-
Lee et al. (2025) Autocorrelation Structure of SPI and Its Implication for Drought Forecasting
This study investigates the autocorrelation function (ACF) structure of the Standardised Precipitation Index (SPI) to understand its inherent deterministic components. It finds that SPI possesses a deterministic structure, which implies that drought forecasting using SPI must be approached with caution due to this predictable component.
-
Zhang et al. (2025) Diurnal Propagation of Precipitation in Landfalling Tropical Cyclones
This study investigates the diurnal propagation of precipitation (DPP) in landfalling tropical cyclones (TCs) globally, revealing that these events account for approximately 30% of landfalling TC days and lead to wider and more extreme rainfall distributions. It identifies distinct initiation times and underlying mechanisms for overland DPP compared to open-ocean events.
-
Sreeraj et al. (2025) Data-driven parameterization of SWAT+ reservoir module without access to operation rules
This study developed a data-driven approach using simulated annealing to parameterize the SWAT+ reservoir module without requiring operational rules. The method significantly improved the accuracy of modeled reservoir outflow, storage, and evaporation, enabling reliable hydrologic simulations in data-scarce environments.
-
Gbodjo et al. (2025) Self-supervised representation learning for cloud detection using Sentinel-2 images
This study leverages self-supervised representation learning (Momentum Contrast and DeepCluster) for accurate cloud and cloud shadow detection in Sentinel-2 imagery, demonstrating that these methods outperform industry standards and several supervised approaches with significantly fewer labeled data.
-
Sun et al. (2025) Reducing Evapotranspiration Simulation and Forecast Uncertainties Due To Initial and Model Errors Over the Tibetan Plateau
This study introduces a combined approach using minimization, data assimilation, and the conditional nonlinear optimal parameter perturbation ensemble prediction (CNOP-PEP) method to reduce uncertainties in evapotranspiration (ET) simulations and predictions over the Tibetan Plateau. The proposed method significantly improves ET ensemble prediction accuracy compared to traditional methods by optimizing initial conditions and perturbing model parameters.
-
Terzi (2025) PyDRGHT: A comprehensive python package for drought analysis
This study introduces PyDRGHT, an open-source Python package for comprehensive drought analysis, providing a unified framework for computing univariate and multivariate drought indices, characterizing droughts, and conducting frequency analyses. Its utility is demonstrated using long-term precipitation and streamflow records from the Seyhan River Basin, Türkiye, showcasing robust drought detection and characterization.
-
Zhang et al. (2025) High-Resolution Mapping of Dry and Wet Snow Using Multisource Remote Sensing Data in Mountainous Regions
This study aims to achieve high-resolution mapping of dry and wet snow in mountainous regions by utilizing multisource remote sensing data.
-
Carter et al. (2025) Impact of drought on global food security by 2050
This study quantifies the impact of drought on global maize, soybean, rice, and wheat production by 2050 using a process-based crop model and an Earth system model, integrating socio-economic factors into a food insecurity index. It finds modest global average losses but significant country-level reductions (over 10% in 62 countries) and identifies regions most vulnerable to food insecurity.
-
Zhou et al. (2025) Shortened intensification duration offsets the increase of tropical cyclone lifetime maximum intensity
This study introduces a rate-duration framework to decompose tropical cyclone (TC) lifetime maximum intensity (LMI), revealing that a significant shortening of intensification duration, driven by poleward and landward shifts in TC genesis locations, has offset nearly half (48.7%) of the increase in strong TCs despite rising intensification rates in recent decades.
-
Pypkowski et al. (2025) The Whittle likelihood for mixed models with application to groundwater level time series
- **N/A:** Due to the unreadable nature of the source text, a summary of the research objective and findings cannot be generated.
-
Wang et al. (2025) Key drivers and predictability of the unprecedented 2024 United Arab Emirates flood
This study investigates the extreme rainfall event that struck the Arabian Peninsula in mid-April 2024, identifying a rare convergence of a strengthened Somali low-level jet and an unusually strong Arabian Cold Vortex as the primary drivers, while highlighting significant underestimation by forecast models.
-
Le et al. (2025) Impacts of elevation bias and topographic uncertainty on flood modeling: model robustness and floodplain sensitivity mapping in a lowland River Basin
This study evaluates the impact of Digital Elevation Model (DEM) quality on flood simulation and sensitivity mapping in a low-relief basin, identifying the most robust global DEMs as alternatives to high-resolution LiDAR data.
-
Fu et al. (2025) Enhanced Boundary Layer Thermodynamics Profiles Retrieval From Ground-Based Microwave Radiometers With Surface and Pseudo-Tower Constraints
This paper focuses on improving the accuracy of atmospheric boundary layer thermodynamic profiles (e.g., temperature, humidity) retrieved from ground-based microwave radiometers by incorporating surface observations and pseudo-tower data as additional constraints.
-
Jing et al. (2025) Improve the accuracy of SAR-based soil moisture retrieval by coupling Bayesian inference and water cloud model
This study proposes and evaluates a novel scheme, BI_WCM, which couples Bayesian inference theory with the Water Cloud Model to improve SAR-based soil moisture retrieval accuracy by addressing the WCM's limitation of neglecting vegetation volume scattering. The BI_WCM significantly enhanced retrieval accuracy over maize-covered areas (14.44% RMSE decrease) and marginally in bare soil areas, demonstrating its dynamic compensation ability for retrieval errors.
-
Hsu et al. (2025) Predictability of Compound Impacts From Hurricane Helene and Predecessor Rain Event in CFSv2 Operational Forecasts
This study investigated the predictability of precipitation and soil moisture conditions associated with Hurricane Helene and a predecessor rain event (PRE) in the Southeastern United States using NOAA's Coupled Forecast System model (CFSv2). It found that predictability for these events significantly drops around 4- to 5-day lead times due to biases in the timing and location of the systems and underestimated PRE precipitation.
-
McKinnon et al. (2025) Observed and Modeled Trends in Downward Surface Shortwave Radiation Over Land: Drivers and Discrepancies
This study validates ERA5 incoming surface shortwave radiation (Rs) against satellite and in situ data, then reveals substantial global continental brightening from 1980 to 2024, often linked to cloud cover reductions rather than aerosols, and highlights significant discrepancies with CMIP6 climate model simulations.
-
Yu et al. (2025) Weakened Circum‐Global Teleconnection Pattern Under Global Warming Can Modulate Heat Extremes Across Eurasia
This study projects a robust 31.8% reduction in the boreal summer interannual Circum-global teleconnection (CGT) amplitude under global warming, driven by weakened Rossby wave source anomalies, which subsequently alters regional heatwave durations.
-
Yegin et al. (2025) Model Selection Challenges in Non-Stationary Precipitation Estimation: The Role of AIC, BIC, and Covariate Choice
This study evaluates 50-year precipitation estimates from stationary and non-stationary models across 53 meteorological stations in Türkiye, analyzing the impact of model selection criteria (AIC, BIC), covariate choice, and probability distributions on predictive performance and the plausibility of extreme estimates. It reveals inconsistencies in model rankings between AIC and BIC, and highlights the trade-offs between model complexity, accuracy, and the risk of unrealistic extreme value predictions.
-
Han et al. (2025) Fusion of Multi-Source Evapotranspiration Products Via the Bayesian Three-Cornered Hat Method and its Application in Runoff Simulation for Semi-Arid Basins
This study evaluates multi-source evapotranspiration (ET) products, quantifies their uncertainty using the Three-Cornered Hat (TCH) method, and fuses them via the Bayesian TCH (BTCH) method to create a more accurate ET dataset. It then demonstrates that using this fused ET significantly enhances the calibration and runoff simulation performance of the VIC-glacier hydrological model in semi-arid, data-scarce basins.
-
Zhou et al. (2025) Global Assessment of Vegetation Ozone Exposure Using a Remote Sensing-Based Index: A New Potential Reference for Critical Levels and Gross Primary Production Simulations
This paper presents a global assessment of vegetation ozone exposure using a remote sensing-based index, proposing it as a new potential reference for critical levels and gross primary production simulations.
-
Liu et al. (2025) A long-term Areal Flooding Risk Calculation Method Based on the Historical Rainfall Records
This study proposes a novel long-term areal flooding risk calculation method to quantify the cumulative impacts of multi-year extreme rainfall events, supporting long-term decision-making for flood risk management by integrating empirical rainfall frequencies with simulated inundation consequences that account for variable catchment areas.
-
Hao et al. (2025) ENSOFarseer: Probabilistic Deep Learning for Cross-Scale Spatiotemporal Teleconnections Insight in Skilful ENSO Prediction
Not available in the provided text. The paper focuses on utilizing probabilistic deep learning to achieve skillful El Niño-Southern Oscillation (ENSO) prediction and to gain insight into cross-scale spatiotemporal teleconnections.
-
Keller et al. (2025) Replicability in Earth System Models
This paper introduces a novel methodology to test the replicability of Earth System Models (ESMs) across different computing environments, improving upon existing methods by 60% in accuracy. It also establishes an objective measure for statistically distinguishing between model climates using Cohen's effect size, finding that an effect size of *d* = 0.2 can serve as a threshold for statistical indistinguishability.
-
Su et al. (2025) Model-based exploration of the seasonal influences of lakes on glacier behavior over the Tibetan Plateau
This study applied the Weather Research and Forecasting (WRF) model coupled with a lake scheme to examine how Tibetan Plateau (TP) lakes seasonally affect surface air temperature, snowfall, and water vapor flux, and their influence on glacier retreat heterogeneity. Results show TP lakes generally reduce 2-meter air temperature in glacierized regions and increase snowfall on some alpine glaciers, with effects varying regionally and seasonally, particularly enhancing northward moisture transport.
-
Sarkar et al. (2025) Taming the non-linearity: An iterative conceptual routing model for improving flood peak prediction
This study introduces a novel Iterative Routing Model (IRM) to improve flood peak prediction by dynamically updating flow velocity based on streamflow magnitude. Applied to the Godavari River Basin, the IRM significantly outperformed traditional models in accurately simulating flood peak discharge and timing.
-
Wang et al. (2025) Enhanced tropical cyclone precipitation variability is linked to Pacific Decadal Oscillation since the 1940s
This study establishes latewood width in coastal conifers as an effective proxy for tropical cyclone precipitation (TCP) in southeastern China, reconstructing July-September TCP from 1846 to 2020 and revealing a marked increase in its interannual variability since the 1940s, linked to enhanced Pacific Decadal Oscillation variability.
-
Jing et al. (2025) Multi-task deep learning for spatiotemporal reconstruction of groundwater dynamics in the North China Plain
This study developed a novel Multi-Task Learning Groundwater Model (MTLGW) integrating time-series decomposition with GRU neural networks to reconstruct regional groundwater dynamics in the North China Plain, demonstrating robust performance and superior capture of anthropogenic impacts compared to existing models.
-
Pang et al. (2025) Comparison of the heatwaves of 2022 and 2024 in the Sichuan Basin, China: the similarity and different roles of oceans and BSISO
This study compares the mechanisms of the unprecedented 2022 and 2024 heatwaves in the Sichuan Basin, China, revealing common large-scale circulation patterns (European–East Asian teleconnection) but distinct oceanic forcings (Indo-Pacific Tripole/North Atlantic Tripole in 2022 vs. Tropical North Atlantic/North Atlantic Tripole in 2024) and the amplifying role of boreal summer intraseasonal oscillation stagnation.
-
Lv et al. (2025) Attributing changes in evapotranspiration and runoff across the continental U.S. using the Budyko hypothesis combined with the Shuttleworth-Wallace model
This study developed a new framework combining the Budyko hypothesis with the Shuttleworth-Wallace model to explicitly attribute changes in evapotranspiration and runoff to climate, vegetation, and land use/cover change across 732 catchments in the continental U.S., revealing precipitation as the dominant factor for ET and runoff changes in most catchments, but highlighting the significant influence of vegetation and land use/cover changes.
-
Varma et al. (2025) A hybrid framework for projecting 21st-century groundwater replenishment and its amplified seasonal cycle
This study develops a novel hybrid framework integrating satellite remote sensing, process-based modeling, and machine learning to project 21st-century groundwater replenishment. Projections for the eastern United States reveal a robust reorganization of the seasonal recharge cycle towards drier winters and wetter summers, alongside an emergent spatial dipole in mean annual change.
-
Huang et al. (2025) Urban flood prediction using a hybrid XGBoost-enhanced U-Net model
This paper proposes an XGBoost-Enhanced U-Net (XGB-U-Net) model for timely and accurate urban flood prediction, integrating physical mechanisms with deep learning. The hybrid model demonstrates superior accuracy and efficiency compared to U-Net and XGBoost alone, particularly in complex urban environments under spatially heterogeneous rainfall.
-
Tai et al. (2025) Accurate reproduction of sub-hourly rainfall extremes in Poisson cluster rainfall models with a variable sinusoidal pulse
This study introduces RBL7, a refined Poisson cluster rainfall model with variable sinusoidal rain cells, to accurately reproduce sub-hourly rainfall extremes. It demonstrates that RBL7 outperforms existing models in capturing rainfall extremes and standard statistics across various timescales, despite being calibrated only at hourly and supra-hourly scales.
-
Li et al. (2025) Modeling the lake water balance of a closed alpine basin using a fully distributed hydrological framework: A case study of Qinghai Lake, China
This study developed an integrated hydrological modeling framework for Qinghai Lake, China, to quantify its water balance dynamics. It revealed that increasing basin-wide precipitation and runoff, coupled with declining lake evaporation, are the primary hydrological drivers behind the lake's recent water level rise.
-
Zhu et al. (2025) Revisiting the Relationship Between Changes in Global‐Mean Surface Air Temperature and Sea Surface Temperature at the Last Glacial Maximum
This study investigates the ratio of global mean air versus sea surface temperature change (S) during the Last Glacial Maximum (LGM) using reconstructions and model simulations, finding S to be 1.97 ± 0.22, significantly higher than under future warming due to ice sheets, and negatively related to sea surface cooling.
-
Chou et al. (2025) Human influence on recent trends in extratropical low-level wind speed
This study compares satellite-era trends in extratropical low-level mean and extreme wind speeds from reanalyses with climate model simulations to assess human influence and model fidelity. It finds human influence drives summertime trends in both hemispheres, while wintertime trends exhibit regional model discrepancies linked to sea surface temperature biases.
-
Gimeno et al. (2025) Hydropedological clustering: improving the representation of low streamflows in a semi-distributed hydrological model
This study evaluated how hydropedological clustering, based on soil hydraulic properties, improves the simulation of low streamflows and soil water content in the SWAT+ model, finding significant enhancements compared to traditional soil datasets and demonstrating its criticality over soil map resolution.
-
Xu et al. (2025) Quantifying the Urbanization and Vegetation Greening Effect on Spatiotemporal Continuous Drought Risk Via Nonstationary C-Vine Copula Model
This study developed a nonstationary C-Vine Copula model to quantify the distinct impacts of urbanization and vegetation greening on multivariate drought risk in humid (Yangtze River Delta) and arid (Weihe River Basin) watersheds, finding urbanization amplifies risk in humid areas while vegetation greening alleviates it in arid regions.
-
Caby et al. (2025) Dynamical properties of atmospheric and sea level extremes in the North-East Atlantic
This study characterizes the dynamical properties of atmospheric pressure and sea level storm surges in the North-East Atlantic using a novel phase space velocity metric (v) and local dimension (d), revealing that extreme events are associated with lower dimensions and higher instability.
-
Emmanuel et al. (2025) Signatures of MJO in the intra-seasonal variability in occurrence of cirrus and dehydration in the UTLS over Indian region
This study investigates the Madden–Julian oscillation (MJO) signatures in the intraseasonal variability of cirrus cloud occurrence and dehydration in the upper troposphere and lower stratosphere (UTLS) over the Indian region. It finds clear MJO-related variations in deep convection, temperature, cirrus occurrence, and dehydration, particularly during MJO phases 2 and 3, with less prominence during the summer monsoon season.
-
Sun et al. (2025) Enhanced spring precipitation in central Asia induced by ENSO under global warming
This study investigates how El Niño–Southern Oscillation (ENSO) modulates spring precipitation in Central Asia (CA) under global warming, finding a significant increase in spring precipitation, particularly over southwestern CA, primarily driven by intensified horizontal moisture advection due to enhanced ENSO-related meridional circulation anomalies and an amplified North Atlantic teleconnection.
-
Williams et al. (2025) The Western United States MTBS-Interagency database of large wildfires, 1984–2024 (WUMI2024a)
This paper introduces WUMI2024a, a quality-controlled, publicly available database of 22,234 large (≥1 km²) wildfires in the western United States from 1984–2024, compiled by merging seven government datasets to provide comprehensive wildfire occurrences, perimeters, and burned-area maps. It addresses limitations of existing datasets by offering improved spatial and temporal coverage and enhanced data quality for wildfire research and modeling.
-
Vergnano et al. (2025) Integrating GPR and ice-thickness models for improved bedrock detection: the case study of Rutor temperate glacier
This study integrates Ground Penetrating Radar (GPR) data with multiple ice-thickness models to improve bedrock detection in temperate glaciers, where englacial water often causes signal scattering. Applying this combined methodology to the Rutor Glacier, the research provides a significantly revised and more accurate ice volume estimate of approximately 450 million cubic meters, about three times the previous value.
-
Masten et al. (2025) An enhanced python framework for hydrological modeling in alpine catchments: Snow hysteresis and glacier ice melt
This study introduces a new Python extension for the Rainfall-Runoff Modeling Playground (RRMPG) that incorporates snow cover hysteresis and glacier ice melt, crucial for alpine hydrology. The enhanced framework, tested in two Ötztal Alps catchments, significantly improves the accuracy and robustness of runoff and snow cover dynamics simulations compared to simpler models.
-
Massoud et al. (2025) Diagnosing the representation of surface and layered soil moisture in Earth system models
This study evaluates the physical consistency between surface soil moisture (mrsos) and vertically integrated soil moisture (mrsol) in nine CMIP6 Earth System Models, revealing substantial inconsistencies attributed to metadata errors, inconsistent variable definitions, or diagnostic sequencing, which can lead to significant biases.
-
Xiang et al. (2025) An explainable deep learning model based on hydrological principles for flood simulation and forecasting
This study develops an explainable deep learning (EDL) model for flood simulation by integrating the Xinanjiang (XAJ) model's runoff generation and flow routing principles into a recurrent neural network (RNN) unit (XAJRNN layer) and fusing it with LSTM layers. Tested in two Chinese river basins, the EDL model demonstrates superior flood simulation accuracy and enhanced interpretability compared to benchmark models.
-
Duangkhwan et al. (2025) Enhancing flood forecasting with deep learning: A scalable alternative to traditional hydrodynamic models
This study proposes an integrated deep learning framework, combining LSTM and CNN, to emulate computationally intensive HEC-RAS 1D/2D models for flood forecasting. The framework significantly reduces computational demands while maintaining high accuracy in predicting river water levels and flood inundation maps.
-
Liu et al. (2025) Innovative synergistic optimization of drip irrigation and subsurface drainage for alleviating salinization and improving crop productivity in arid irrigation district
This study developed a synergistic optimization framework using the SWAT-Salt model and a projection pursuit model with an accelerated genetic algorithm to optimize drip irrigation and subsurface drainage in China's Kaidu River Irrigation District, demonstrating significant improvements in crop yields, water productivity, and soil desalinization.
-
Baran-Gurgul et al. (2025) Regionalisation of Summer Hydrological Droughts Variability in Poland in Period 1993–2022
This study investigated the spatiotemporal variability of summer hydrological droughts in Poland from 1993 to 2022 across seven physiographic regions. It found marked regional differences, with northern and central lowlands experiencing intensified droughts, particularly after 2015, driven by rising temperatures and increased potential evapotranspiration, while mountainous areas showed fewer drought days.
-
Farooq et al. (2025) Regional Hotspots of Lake Evaporation Changes Driven by Surface Energy Balance and Climate Interactions
This study projects global lake evaporation responses to a high-emissions warming climate (RCP8.5) by the end of the 21st century, revealing a global mean increase of 13% driven by enhanced energy availability and synergistic interactions among vapor pressure deficit, radiation, and wind speed.
-
Zhang et al. (2025) Concurrent occurrence of droughts and floods between the upper Hanjiang river and Northern North China at multi-temporal scales: an association with Arctic Oscillation
This study investigates the teleconnection between the warm-season Arctic Oscillation (AO) and concurrent drought and flood (DF) occurrences in the Upper Hanjiang River (UH) and Northern North China (NNC) from 1650 to 1975. It finds that DF in both regions largely varies in the same direction as the AO, with the AO serving as a significant predictor for DF, particularly on multi-decadal scales.
-
Thi et al. (2025) Recent Spatiotemporal Trends in Extreme Rainfall Events in South Korea: From Sub‐Hourly to Multi‐Day Scales
This study investigated temporal trends in annual maximum precipitation (AMP) across South Korea from 2004 to 2024 for eight durations ranging from 30 minutes to 3 days, revealing that extreme rainfall trends are highly dependent on duration and are becoming more spatially heterogeneous.
-
Franzke et al. (2025) Forced response and internal variability changes in the hydrological cycle and general circulation in a hot world beyond 2100 in the Community Earth System Model
This study uses ensemble simulations with CESM2 to investigate changes in the hydrological cycle's forced response and internal variability beyond 2100. It finds that the dominant mode of the hydrological cycle's forced response changes sign in the early 22nd century due to atmospheric circulation shifts, subsequently reducing the amplitude of internal variability.
-
Keeping et al. (2025) Influence of global climate modes on wildfire occurrence in the contiguous United States under recent and future climates
This study uses a large ensemble climate model and a wildfire occurrence model to characterize the spatial patterns and magnitude of global climate mode influence on wildfire occurrence in the contiguous United States under recent and future (+2 °C) climates. It finds that ENSO, IOD, and lagged TNA are the primary drivers, with their influence intensifying and other modes becoming significant under future warming.
-
Ling et al. (2025) HybridFlow: A Hybrid Velocity Generation Framework for Precipitation Nowcasting
[]
-
Zhao et al. (2025) Upstream evaporative moisture sources anchor strong persistent heavy precipitation events over southeastern Tibetan plateau
This study investigates the physical mechanisms behind the intensity variations of persistent heavy precipitation events (PHPs) over the Southeastern Tibetan Plateau (SETP) by analyzing upstream evaporative moisture uptakes (UEMU) anomalies using Lagrangian modeling. It reveals that strong PHPs are primarily fueled by enhanced evaporative vapor from remote oceanic regions, while weak PHPs rely more on local recycling, emphasizing the dominant role of large-scale atmospheric circulation patterns in modulating PHP intensity.
-
DeHart et al. (2025) Quantifying the Relationships Between Dynamics and Rainfall Intensity Along the Mei‐Yu Front During PRECIP 2022
This study uses multi-Doppler analyses to investigate the dynamic characteristics associated with varying rainfall intensities during two Mei-Yu frontal periods over the ocean, finding that higher rain rates correlate with increased vertical vorticity, vertical motion, and divergence, with heavy convective rain (10-50 mm/h) contributing over 45% of total volumetric rainfall, preferentially occurring in moderately strong rotating convection.
-
Zhang et al. (2025) Simulation of the South China Sea summer monsoon effect on the Indian Ocean dipole in community integrated earth system model
This study evaluates the Community Integrated Earth System Model's (CIESM) ability to simulate the South China Sea Summer Monsoon (SCSSM)–Indian Ocean Dipole (IOD) connection and its mechanisms. It finds that while CIESM reproduces the SCSSM-IOD linkage, it significantly overestimates its strength due to unrealistic SCSSM–ENSO interactions and associated biases in atmospheric bridges and surface zonal winds.
-
Hilland et al. (2025) Horizontal Anisotropy of Turbulent Fluctuations in Surface and Air Temperatures Over a Flat, Homogeneous Surface
This study investigates the longitudinal and lateral integral length scales of temperature fluctuations and their anisotropy across a range of atmospheric stabilities in a desert surface layer. It finds that turbulence anisotropy varies significantly with wind speed and stability, with surface length scales being larger than near-surface atmospheric ones and generally exceeding previously reported values.
-
Zhang et al. (2025) Evaluation and Projection of Northwest China's Extreme Precipitation Using Statistically Downscaled CMIP6 Models
This study quantitatively evaluates 23 statistically downscaled CMIP6 models for their ability to simulate historical extreme precipitation in Northwest China and projects future changes. Results indicate that while most models capture spatial patterns, they exhibit systematic dry biases, with the best-performing models projecting a mitigation of aridity and an increase in extreme precipitation frequency under future warming scenarios.
-
Rafei et al. (2025) Downdrafts and Convective Gusts in High‐Resolution Simulations: An Australian Case Study
This study investigates the forcing mechanisms of strong convective downdrafts and surface gusts using high-resolution Weather Research and Forecasting (WRF) model simulations. It finds that very high spatial resolutions (down to 200 m) are crucial for accurately representing these phenomena, revealing that perturbation pressure and thermal buoyancy are primary drivers, which are significantly underestimated by coarser models.
-
Kritidou et al. (2025) Partitioning uncertainties of extreme flood estimates using long continuous simulations
This study partitions the uncertainties in extreme flood estimates from a hydrometeorological modelling chain, revealing that uncertainty increases with return period and its dominant source varies with catchment characteristics. It highlights the critical role of hydrological model parameters in high-elevation catchments and weather generator components in lower-elevation, rainfall-dominated areas.
-
Miao et al. (2025) The Synergistic Effects of the SRP and EAP on Summer Precipitation in the Tarim Basin of Northwest China: A Case Study in August 2016
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
-
Baviskar et al. (2025) Regionalizing the ACEA Model with Remote Sensing for Water Footprints in the Upper Syr Darya Basin
## Identification - **Journal:** University of Twente Research Information - **Year:** 2025...
-
Cao et al. (2025) China's three major cereal crops exposed to compound drought and extreme rainfall events
This study investigates the spatiotemporal characteristics and agricultural exposure of compound drought and extreme rainfall (CDER) events in China's nine major agricultural regions. It reveals that CDER are concentrated in the northwest, southwest, and northern regions, peaking in summer, with drought-rainfall events being more frequent and intense, and identifies varying exposure risks for maize (highest), wheat (moderate), and rice (lowest) during their growth stages.
-
Chen et al. (2025) Integrating machine learning with NSGA-Ⅱ to assess the synchronization effects of stormwater disaster hazard and green–blue infrastructure
This study developed a framework integrating urban stormwater disaster hazard assessment, machine learning, and multi-objective optimization to investigate the synchronization effects between stormwater hazard distribution and green-blue infrastructure (GBI) allocation. It found that GBI deployment in high-hazard zones is increasingly cost-effective for mitigating flood risk and runoff, especially under extreme precipitation events.
-
Cao et al. (2025) A novel land surface temperature retrieval method using channel correlation for atmospheric parameter modeling from SDGSAT-1 data
This paper introduces a novel wide-band atmospheric correction Temperature and Emissivity Separation (TES) algorithm for SDGSAT-1 Thermal Infrared Spectrometer (TIS) data, which retrieves Land Surface Temperature (LST) without requiring auxiliary atmospheric or land surface parameter input. The method demonstrates high accuracy and robustness across various global ground stations, outperforming existing algorithms.
-
Qi et al. (2025) Retrieving forest LAI from Landsat via 3D look-up table generated by realistic LiDAR scenes
This study developed a novel 3D Look-Up Table (3D-LUT) approach for retrieving forest Leaf Area Index (LAI) from Landsat imagery by reconstructing realistic 3D forest scenes using LiDAR data to parameterize a 3D Radiative Transfer Model, achieving high accuracy across various forest types.
-
Sun et al. (2025) Understanding the Decreased ENSO Predictability since the Early 2000s Based on Data-Driven and Dynamical Models
This study evaluates the interdecadal change in El Niño–Southern Oscillation (ENSO) prediction skill over the past four decades using dynamical and deep learning models, revealing a significant decline since the early 2000s primarily due to worse prediction of ENSO phase transitions, weakened subsurface precursors, and increased bias in zonal advection.
-
Woo (2025) Estimating actual evapotranspiration from widely available meteorological data with a hybrid CNN–LSTM
This study develops a hybrid Convolutional Neural Network – Long Short-Term Memory (CNN–LSTM) model to estimate daily actual evapotranspiration (ETa) directly from widely available meteorological and soil data, demonstrating high accuracy (R²=0.92, RMSE=0.38 mm d⁻¹) across 167 FLUXNET sites and global applicability with ERA5 forcings.
-
Jia et al. (2025) An Integrated Correction Method for Geometric, Atmospheric, and Terrain Effects in Hyperspectral Remote Sensing Images
[Information not available in the provided text.]
-
He et al. (2025) Increased sensitivity of ecosystem to meteorological drought reduces carbon uptake in the Yellow River Basin
This study investigated the spatiotemporal evolution of ecosystem drought sensitivity and its impact on carbon uptake in the Yellow River Basin from 1982 to 2018, revealing a significant increase in sensitivity across 57.12% of the basin, primarily driven by climate change, which reduced annual carbon uptake by 0.11 ± 0.05 Tg C during post-drought recovery.
-
Wu et al. (2025) Changes of Eurasian Cold Winters and Their Associated Key Variables Based on CMIP6 Global Climate Models
This study evaluates the historical simulation skill of CMIP5 and CMIP6 global climate models for Eurasian cold winters and associated atmospheric variables, finding CMIP6 outperforms CMIP5. It also projects a decrease in cold winter probability and a "warm Arctic and cold Eurasia" pattern under future climate change scenarios.
-
Petropoulos et al. (2025) RegreSSM: A novel software tool for downscaling the SMAP L3 soil moisture operational product utilizing the Ts/VI feature space and Sentinel-3 data
This study introduces RegreSSM, a novel Python-based software tool for downscaling the SMAP L3 surface soil moisture product from 36 km to 1 km resolution using Sentinel-3 optical and thermal data and the Ts/VI feature space. The tool provides a user-friendly, automated, and reproducible workflow, demonstrating satisfactory soil moisture retrieval accuracy over the Iberian Peninsula.
-
Yuan et al. (2025) Weaker absorption of Asian dust than previously estimated based on observation-constrained simulation
This study developed a revised complex refractive index for East Asian dust based on 22 soil samples, revealing significantly weaker absorption than previously estimated. Implementing these new parameters in a climate model substantially improved simulations of dust optical properties and reversed the estimated top-of-atmosphere shortwave direct radiative effect from warming to cooling.
-
Ono et al. (2025) Multi-year water and carbon flux contrasts between high-yielding and conventional rice cultivars
This study used eddy covariance to compare multi-year water and carbon fluxes between a high-yielding indica rice (Oonari) and a conventional japonica rice (Koshihikari) in farmers' fields, finding Oonari exhibited significantly higher gross primary production and generally higher evapotranspiration, leading to greater water use efficiency.
-
Gobron et al. (2025) A unified framework for trend uncertainty assessment in climate data records: demonstration on global mean sea level
[Information not extractable from the provided text]
-
He et al. (2025) Drying Tropical America Under Global Warming: Mechanism and Emergent Constraint
This study investigates the mechanisms driving precipitation changes in Tropical America (TAM) using CMIP6 models, revealing that amplified equatorial Pacific warming induces a Gill-type atmospheric response that suppresses TAM rainfall. It finds that climate models likely underestimate future TAM drying, projecting a regional mean annual rainfall decline of 46 mm per 1 K of global warming, which is 1.5 times higher than raw projections.
-
Lamsal (2025) Modeling the Food-Water Nexus: A Spatio-temporal Accounting of Agricultural Land and Water Use in the United States
[Information not extractable from the provided text due to corrupted input.]
-
Roe et al. (2025) The Atmosphere as a Heat Engine Operating at Maximum Power
This paper proposes that Earth's atmosphere operates at a maximum-power state, where the power generated by poleward heat flux is maximized, and derives analytic solutions that show reasonable agreement with observed annual-mean temperature, atmospheric heat transport, and boundary layer dissipation.
-
Bozorgi et al. (2025) Assessing the performance of multi-timescale drought indices for monitoring agricultural drought impacts on wheat yield
This study evaluates the performance of three multi-timescale drought indices (SPEI, SPET, and SEDI) in monitoring rainfed wheat yield variability across four Spanish regions. The findings identify the Standardized Evapotranspiration Deficit Index (SEDI) as the most robust indicator for agricultural drought impact assessment.
-
Asoka et al. (2025) Tropical Cyclones Unevenly Shape Drought Propagation
This study evaluates the performance of the ISBA-CTRIP land surface model in simulating global hydrological cycles and land-atmosphere interactions. The research demonstrates the model's ability to accurately reproduce river discharge and soil moisture dynamics across diverse climatic zones.
-
Sinclair (2025) A Global Climatology of Tropical Cyclone Diabatic Rossby Wave Sources and Their Extratropical Flow Response
This study establishes a 46-year global climatology of Rossby wave forcing by tropical cyclones, demonstrating that TC recurvature is not a prerequisite for downstream Rossby wave excitation and that similar forcing occurs in other ocean basins.
-
Chen et al. (2025) Dynamic Changes in Quasi‐Biweekly Variability Drive Increased Extreme Rainfall and Whiplash Events Over Asian Monsoon Regions
This study reveals a significant intensification of quasi-biweekly rainfall variability in the Asian monsoon region over the past four decades, primarily driven by dynamical changes in vertical velocity perturbations, with thermodynamic moisture increases playing a secondary role. Climate models project stronger future variability only when thermodynamic moistening outweighs dynamical uncertainty, highlighting the need for improved model representation of this circulation.
-
Zhan et al. (2025) Unveiling the Floodplain River‐Lake Hydrological Interactions by SWOT Observations
This study utilizes Surface Water and Ocean Topography (SWOT) mission data to analyze the Poyang Lake-Yangtze River system, revealing divergent seasonal water surface slopes and associated backwater effects, thereby offering a globally applicable framework for river-lake hydrodynamics.
-
Dong et al. (2025) Stratosphere‐Troposphere Exchange of Water Vapor Based on Observations and Reanalyses
This study provides the first global estimates of stratosphere-troposphere exchange (STE) of water vapor (H2O) using an observational mass budget approach, revealing a small net global flux of 0.10 ± 0.04 Pg/yr into the stratosphere and identifying significant biases in reanalysis products (MERRA2, ERA5) compared to observations.
-
Buarque et al. (2025) Insights into the North Hemisphere daily snowpack at high resolution from the new Crocus–ERA5 product
This study introduces and evaluates the new daily Crocus–ERA5 snow product for the Northern Hemisphere (1950–2022), demonstrating its improved accuracy in snow depth and cover compared to its predecessor, Crocus-ERA-Interim, particularly in Eurasia, despite persistent biases in some Arctic regions.
-
Huang et al. (2025) ENSO's Impacts on Southeastern Australia's Future Rainfall Risk
This study assesses the El Niño-Southern Oscillation (ENSO)-rainfall relationship in large ensembles over the Murray-Darling Basin and quantifies projected rainfall risk using a novel framework. It finds that while El Niño-related rainfall shows no prominent change, La Niña-driven rainfall impacts, characterized by increased variability and higher amounts, may worsen under a warming climate.
-
Liu et al. (2025) Assessing groundwater sustainability across high mountain Asia using remote sensing
This study integrates remote sensing, Earth system modeling, and artificial intelligence to quantify historical groundwater storage (GWS) trends and project future evolutions in High Mountain Asia, revealing widespread GWS declines (24.2 gigatonnes per year, 2003-2020) driven by climate (47%), human activities (38%), and cryospheric processes (15%), with projections indicating accelerated depletion by the century's end despite temporary cryospheric buffering.
-
Kamm (2025) Sur les échanges entre échelles dans les modèles d’océan : parametrisations des tourbillons mésoéchelles par apprentissage machine
[Information not extractable from provided text due to unreadable input.]
-
Chen et al. (2025) A physics-informed neural network for predicting depth-averaged velocities of flows with submerged vegetation: Integrating analytical formulas with deep learning
This paper presents a physics-informed neural network (PINN) that integrates an algebraic momentum-balance relation with a data-driven framework to predict depth-averaged velocities in flows with submerged vegetation, demonstrating superior accuracy, generalization, and robustness compared to traditional analytical formulas and purely data-driven models.
-
Aranyossy et al. (2025) Multi-annual predictions of hot, dry and hot-dry compound extremes
This study evaluates the multi-year predictability of hot, dry, and hot-dry compound extremes using CMIP6 decadal hindcast experiments for forecast years 2–5. It finds that hot-dry compound and hot extremes are skillfully predicted over most land regions, while skill for dry extremes is more limited, with most predictability stemming from external forcings and long-term trends rather than initialisation.
-
Zhao et al. (2025) A 1 km daily high-accuracy meteorological dataset of air temperature, atmospheric pressure, relative humidity, and sunshine duration across China (1961–2021)
This study presents a 1 km daily high-accuracy meteorological dataset for China (1961–2021), including air temperature, atmospheric pressure, relative humidity, and sunshine duration, generated using a novel hierarchical reconstruction framework that leverages thousands of ground observations and topographic attributes to provide a reliable foundation for climate, hydrological, and ecological studies.
-
Zheng et al. (2025) Comparative Study on Entrainment‐Mixing Mechanisms Between Cumulus Cores and Edges Based on Aircraft Observations
This study quantitatively analyzes entrainment-mixing mechanisms in cumulus cloud cores versus edges using aircraft observations, revealing that cores exhibit a higher homogeneous mixing degree and lower Damköhler number compared to edges, with these differences modulated by environmental and cloud conditions.
-
Ding et al. (2025) Sharpening Mesoscale Convective Systems Induced by Enhanced Moisture–Convection Feedback Over East Asia During 2000–2021
This study examines changes in the spatial structure of observed summer mesoscale convective systems (MCSs) over East Asia from 2000 to 2021, finding that MCSs have become sharper with increased maximum precipitation intensity due to an enhanced moisture-convection feedback driven by intensified synoptic moist static energy forcing.
-
Song et al. (2025) Global Gross Primary Productivity Estimation Using Passive Microwave Observations From China's Fengyun‐3D Satellite
This study develops and validates EDVI-GPP, a novel microwave-based global gross primary productivity (GPP) estimation method, demonstrating its reliability and reduced cloud effects compared to existing products, with a global GPP estimate of 123.77 ± 1.33 Pg C yr⁻¹ for 2020–2022.
-
Song et al. (2025) Radiative and Precipitation Processes Make it Easier to Match the Temperature Record and Harder to Constrain Future Warming
This study examines a negative correlation between radiative forcing due to aerosol-cloud interactions and shortwave cloud feedback within a perturbed parameter ensemble. It finds that while this correlation helps Earth System Models reproduce historical temperature records, it simultaneously limits the ability to constrain future warming projections using these records.
-
Luo et al. (2025) Identification and spatiotemporal analysis of braided rivers in the Yarlung Tsangpo basin using an enhanced U-Net approach
This study develops an enhanced deep learning model, MSU-Net, to accurately identify and map complex braided river systems in the Yarlung Tsangpo River Basin. Using a fusion of Sentinel-1 and Sentinel-2 satellite data from 2018 to 2023, the research quantifies the spatiotemporal dynamics of these channels and their relationship with climatic drivers.
-
Gonzalez et al. (2025) We Need to Simulate More Northern ITCZs and Less Southern ITCZs Over the East Pacific Ocean in Coupled Climate Models
This study developed an algorithm to classify daily Intertropical Convergence Zone (ITCZ) states and found that Coupled Model Intercomparison Project 6 (CMIP6) models significantly underestimate northern hemisphere ITCZs and overestimate southern hemisphere ITCZs over the east Pacific, challenging the traditional "double ITCZ bias" interpretation. Reanalyses also exhibit distinct ITCZ state biases and errors in interannual variability.
-
Rao et al. (2025) Intensification of Compound Extremes Over India Under 1.5°C and 2°C Global Warming Levels: Insights From Bias‐Corrected CMIP6 Simulations
This study assesses future compound climate extremes in India under 1.5°C and 2.0°C global warming levels using high-resolution CMIP6 projections. It finds that cold-related extremes will become rare, while warm-related extremes, particularly Warm-Dry and Warm-Wet events, will significantly increase across India, leading to sharply rising population exposure.
-
Zhang et al. (2025) Sunset Bores Over the Southern North China Plain
This study investigates the formation of atmospheric bores near sunset and their critical role in initiating and sustaining late-afternoon mesoscale convective systems (MCSs) over the Southern North China Plain, highlighting their importance for accurate diurnal cycle prediction.
-
Tang et al. (2025) A novel hybrid approach for mapping global surface solar radiation with DSCOVR/EPIC: Combining deep learning with physical algorithm
This study develops a novel hybrid approach integrating deep learning with physical algorithms, utilizing DSCOVR/EPIC observations to map global surface solar radiation (Rg) and its direct (Rdir) and diffuse (Rdif) components. The method demonstrates superior accuracy and spatial scalability compared to existing products, providing globally consistent high-resolution radiation data.
-
Yin et al. (2025) Meteorological observation research based on an improved EfficientNetV2 model
This study proposes a novel deep learning model, EfficientNetV2-CBAM-PANet, to improve meteorological image recognition in complex weather scenarios by enhancing feature extraction and robustness. The model achieved a recognition accuracy of 97.6% on a self-constructed dataset, demonstrating strong classification capability across various weather conditions.
-
Bisht et al. (2025) Development of a River Dynamical Core for E3SM to simulate compound flooding on Exascale-class heterogeneous supercomputers
This paper introduces the River Dynamical Core (RDycore), an open-source 2D shallow water equation library for the Energy Exascale Earth System Model (E3SM), designed to enable kilometer-scale flood simulations on exascale supercomputers with significant GPU acceleration. It was validated for various problems and demonstrated successful coupling with E3SM for simulating compound flooding events.
-
Cao et al. (2025) Enhancing Machine Learning Models for Nowcasting and Short‐Term Forecasting of Precipitation With a Novel Probability‐Matching Loss Function
This paper proposes a novel probability-matching (PM) based loss function for machine learning precipitation forecasting to overcome the limitations of mean squared error (MSE) loss, demonstrating improved skill, reduced bias across rainfall intensities, and better preservation of small-scale variability.
-
Ci et al. (2025) Multi-timescale evapotranspiration fusion: A novel autoencoder with automated machine learning-based approach for enhanced estimation accuracy
This study developed AGFusionET, a novel multi-timescale fusion model combining autoencoders and automated machine learning (AutoML), to integrate 20 heterogeneous evapotranspiration (ET) products. It generated a global, high-resolution (0.05 degrees) ET dataset for 1982–2023, demonstrating superior accuracy (Kling-Gupta Efficiency of 0.88, Root Mean Square Error of 12.12 mm/month) compared to benchmark products.
-
Xing et al. (2025) Quantifying the spatial extent and attenuation of lake thermal regulation at diurnal scales under extreme heat
This study used WRF modeling and directional buffer analysis to quantify the spatial extent and attenuation of Poyang Lake's thermal regulation during extreme heat, revealing pronounced diurnal asymmetry in cooling (40 km, -1.16 °C, 0.28 °C per 10 km) and warming (70 km, +0.97 °C, 0.13 °C per 10 km) effects.
-
Feldl et al. (2025) Explaining the Transient and Equilibrium Longwave Feedback with Moist Adiabatic Theory and Its Deviations
This study develops a theoretical framework to understand how patterns of surface temperature change influence the global longwave clear-sky radiative feedback, revealing that the pattern effect is driven by distinct regional physical processes, primarily involving surface temperature and relative humidity feedbacks.
-
Bright et al. (2025) Using point dendrometers to improve forest transpiration estimation accuracy at stand scales
This study investigates whether increasing the number of sampled trees for stand-level transpiration estimation, by augmenting sap flow measurements with data from point dendrometers, can reduce uncertainty. It found that expanding the tree sample size using point dendrometer-derived sap flow estimates reduced the uncertainty of stand-level transpiration by 31–37%, demonstrating a cost-effective method to improve accuracy.
-
Ji et al. (2025) Holistic assessment of seasonally frozen ground changes on the Qinghai-Tibet Plateau
This study developed a sinusoidal heat transfer model to quantify the elevational dynamics of seasonally frozen ground (SFG) on the Qinghai-Tibet Plateau, revealing a widespread decline in freezing depth and duration from 1980 to 2018, with temperature and snowpack jointly controlling these changes.
-
Zhang et al. (2025) Moisture from US Corn Belt fuels more intense convective storms
This study quantifies how integrated groundwater-crop-irrigation interactions in the US Corn Belt intensify convective storms, finding they significantly increase storm frequency, lifetime, and movement speed by enhancing warm-moist inflows.
-
Wang et al. (2025) GPP-net: a robust high-resolution GPP estimation network for Sentinel-2 using only surface reflectance and photosynthetically active radiation
This study introduces GPP-net, a deep learning network for robust, high-resolution gross primary productivity (GPP) estimation using only Sentinel-2 surface reflectance and photosynthetically active radiation (PAR). GPP-net demonstrates superior accuracy and generalization across diverse vegetation types and extreme climate conditions, significantly reducing reliance on traditional land cover and coarse meteorological data.
-
Su et al. (2025) Predicting net primary productivity response to multiple extreme climate drivers in Inner Mongolia
This study simulated historical (1982–2020) and projected future (2023–2100) net primary productivity (NPP) in Inner Mongolia under extreme climate events, identifying key climate factors and vulnerable regions.
-
Valenzuela et al. (2025) Atmospheric Water Vapor and Precipitation Coupling in Southwestern South America
This study analyzes 15–27 years of precipitable water vapor (PWV) and co-located precipitation data across southwestern South America to understand their long-term coupling across diverse latitudinal and climatic gradients. It reveals that the strength and functional form of PWV–precipitation coupling vary systematically with latitude and precipitation regime, with tropical Andes showing a power-law relationship and extratropical regions following a logistic form.
-
Su et al. (2025) Integrated effects of rainwater harvesting and organic fertilization on soil structure and aggregates in a rainfed orchards on the Loess Plateau
This study investigated the integrated effects of a novel Water Harvesting, Irrigation, and Organic Fertilization (WHIOF) system on soil structure and aggregates in a rainfed apple orchard on the Loess Plateau. The WHIOF system significantly improved soil physical properties and aggregate stability by enhancing soil organic matter, surface electrochemical properties, and interparticle attractive forces at both macroscopic and microscopic scales.
-
Liu et al. (2025) Critical snowpack thresholds and escalating risks for extreme decreases in vegetation productivity across Northern Hemisphere ecosystems
This study investigates the impact of varying snowpack changes on extreme decreases in vegetation productivity (EDVP) across the Northern Hemisphere, revealing that over 30% snowpack decrease events are linked to EDVP in about 10% of the NH, primarily due to snowpack's moisture effect.
-
Marshall et al. (2025) High-resolution mountain topography can inform global snow vulnerability estimates
This study leverages fine-scale digital elevation models (DEMs) and historical freezing level height data to estimate historical and projected changes in global mountain snow-receiving area (SRA). It finds significant SRA declines from 1982–2020 and projects substantial, often nonlinear, further losses under future warming scenarios, highlighting the importance of fine-resolution data for accurate assessments.
-
Clemenzi et al. (2025) Attributing European runoff changes to climatic drivers under future conditions
This study attributes future European runoff changes to precipitation and potential evapotranspiration using a Budyko-based framework across 35,408 basins. It finds that while precipitation is the dominant driver under most scenarios, potential evapotranspiration becomes equally significant in central and southern Europe under high-emission scenarios by the late century.
-
Masoudimoghaddam et al. (2025) Synergistic flood forecasting: combining physics-based and spatio-temporal deep learning models
[Information not extractable due to unreadable paper text.]
-
Lin et al. (2025) Accelerating increases in soil heatwaves and cropland exposure in China
This study presents a nationwide assessment of historical soil heatwave changes across Mainland China, revealing that since 1960, soil heatwave frequency, duration, and intensity have increased at all depths, with significant acceleration in recent decades and increasing exposure for croplands.
-
Wang et al. (2025) Enhanced El Niño–Southern Oscillation impact on Somali jet under greenhouse warming
This study reveals that the influence of El Niño–Southern Oscillation (ENSO) on the Somali Jet (SMJ) is projected to strengthen significantly in the 21st century under greenhouse warming, leading to increased risks of extreme rainfall events over South Asia.
-
Lopes et al. (2025) Dynamically Downscaled European Water Budget Quantities in the Presence of Sea Surface Temperature Uncertainty
This study investigates how the choice of sea surface temperature (SST) products influences land-based water budget quantities in EURO-CORDEX regional climate simulations. It finds that different SST products lead to notable differences in precipitation and freshwater flux, particularly in heavy precipitation events, and generally improve terrestrial water budget statistics compared to reanalysis-based fluxes.
-
Sayedi et al. (2025) Anthropogenic drivers and their impact on the hydrological regime of Nepal: a review
This review synthesizes current evidence on anthropogenic drivers of hydrological change in Nepal, revealing that climate change impacts (glacial retreat, altered snowmelt, changing monsoon) combined with human activities (urbanization, agriculture, hydropower) intensify wet-season flood risks and dry-season water scarcity, offering lessons for other data-sparse mountain regions.
-
Huneke et al. (2025) The ACCESS-CM2 climate model with a higher resolution ocean-sea ice component (1/4°)
This paper introduces and evaluates ACCESS-CM2-025, a new configuration of the Australian Community Climate and Earth System Simulator coupled model with a higher resolution (0.25°) ocean-sea ice component. The study finds that while the higher resolution improves the representation of ocean mesoscale variability, ENSO, and North Atlantic deep convection, many biases from the lower-resolution version (ACCESS-CM2-1) persist, particularly those linked to the atmospheric model component.
-
Singh et al. (2025) LH-moment framework for regional flood frequency analysis based on Log-Pearson Type III distribution
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2025...
-
Man et al. (2025) Uncertainty in Antarctic precipitation projections under global warming
This study quantifies uncertainties in future Antarctic precipitation projections using CMIP6 models and identifies their underlying sources, revealing that these uncertainties are substantial and linked to global/Antarctic surface temperatures, atmospheric circulation (especially Pacific South American modes), and tropical sea surface temperatures via atmospheric teleconnections.
-
Hiebl et al. (2025) Correcting Breaks in Temperature and Humidity Observations: Implications for Climate Variability Analysis in Austria
This study quantifies and corrects a significant discontinuity in daily mean air temperature and relative humidity estimations across Austrian climate stations, caused by a 2-hour shift in evening observation timing in 1971. By employing station-specific multi-linear regressions, the method successfully eliminates spurious cooling and drying biases, substantially improving the accuracy of climate data and trend analyses.
-
Tricht et al. (2025) Peak glacier extinction in the mid-twenty-first century
This study projects the future disappearance of over 200,000 individual glaciers globally under various warming scenarios, revealing a peak extinction period between 2041 and 2055 where up to 4,000 glaciers could vanish annually. It introduces the concept of "peak glacier extinction" to highlight the societal and cultural implications of individual glacier loss.
-
Morice et al. (2025) An observational record of global gridded near-surface air temperature change over land and ocean from 1781
This study introduces GloSATref, a novel global gridded surface air temperature (GSAT) dataset combining land surface air temperature (LSAT) and marine air temperature (MAT) observations, extending the instrumental record back to 1781. It demonstrates that using MAT, with new bias adjustments, allows for an earlier and more comprehensive reconstruction of global temperature changes compared to traditional sea surface temperature (SST)-based datasets.
-
Narinesingh et al. (2025) Modeling Northern Hemisphere Heat Extremes in Current and Warmer Climates: Intensity, Duration, and Physical Drivers
This study examines the intensity, duration, and physical drivers of Northern Hemisphere summer heat extremes using observations, reanalyses, and CMIP6 models, finding that models generally capture observed variations but exhibit biases in duration where diabatic effects dominate, and project increased intensity in several regions under future warming scenarios.
-
Ding et al. (2025) Parameterization and irrigation optimization for foxtail millet using the aquacrop model
This study establishes the first comprehensive parameter set for foxtail millet in the AquaCrop model and develops optimized irrigation strategies to enhance water use efficiency in water-limited regions, achieving a mean yield of 0.573 kg·m⁻² and water productivity of 1.46 kg·m⁻³ with significantly improved yield stability.
-
Wong et al. (2025) Measuring impacts of California agri-environmental programs using field-scale satellite data
This study evaluates the ex-post agri-environmental outcomes of California's grant programs for water-saving and soil health practices in almonds, grapes, and walnuts using satellite data and causal inference. It reveals varied and sometimes counterintuitive effects, with some interventions increasing water use and others showing modest greenness improvements, highlighting potential discrepancies with program goals.
-
Colliander et al. (2025) A review of forward modelling and retrieval approaches for forest soil moisture and vegetation optical depth using L-band radiometry
This review evaluates current L-band radiometry retrieval approaches for forest soil moisture (SM) and vegetation optical depth (L-VOD), highlighting persistent systematic uncertainties and the lack of adequate validation data for forest ecosystems. It emphasizes the need for new algorithms and enhanced validation to fully leverage L-band radiometry for forest monitoring.
-
Nascimento et al. (2025) How do geological map details influence the identification of geology-streamflow relationships in large-sample hydrology studies?
This study investigates how the level of detail in geological maps (global, continental, regional) influences the identification of geology-streamflow relationships across 4469 European catchments using a multi-scale, nested-catchment approach. It finds that while large-scale analyses show inconsistent map performance, increasing geological detail at intermediate and small scales consistently strengthens correlations with streamflow signatures, particularly for baseflow, aligning better with hydrological process understanding.
-
Fang et al. (2025) The Increased Influence of Eastern European Blocking Highs on Extreme Rainfall Over Pakistan in Recent Decades
This study investigates the long-term impact of Eastern European blocking highs on extreme precipitation in Pakistan, revealing an intensification of this effect after the 1990s primarily due to Arctic warming-induced alterations in atmospheric circulation.
-
Zhu et al. (2025) Exploring interactive effects of water stress and ecological restoration on vegetation eco-regimes using interpretable machine learning based on kernel NDVI
This study developed a novel methodology using 18 vegetation indicators, an ecological restoration index, and interpretable machine learning to assess vegetation eco-regime changes across China. It found that precipitation, surface solar radiation, and ecological restoration are the dominant factors influencing these dynamics, with significant shifts in eco-regimes observed since 2002.
-
Saari et al. (2025) Synchronisation of Extreme Precipitation and Sea Surface Temperature Events in the Northern Hemisphere: A Complex Network Approach
This study analyzed spatiotemporal patterns and connections between inland extreme precipitation events (EPEs) and extreme sea surface temperature events (ESSTEs) in the Northern Hemisphere from 1930 to 2020, revealing distinct network characteristics and increasing trends for both event types after 1980.
-
Zhang et al. (2025) A novel framework for pixel-wise estimation of irrigation water use by integrating remote sensing and reanalysis data
This study develops a novel soil water balance framework that integrates satellite-derived soil moisture and evapotranspiration with reanalysis data to estimate irrigation water use at 1 km resolution across China. The resulting 20-year dataset reveals that China's irrigation water use increased from 339 to 395 km³/year between 2001 and 2020, driven primarily by the expansion of irrigated croplands.
-
Hao et al. (2025) Evaluating ecosystem water use efficiency and recovery dynamics during flash droughts: insights from observations and model simulations
This study investigates ecosystem water use efficiency (WUE) and its components (gross primary production, GPP; actual evapotranspiration, AET) during flash droughts using observations and the ELMv2 model. It finds that ELMv2 captures shifting GPP/AET dynamics but systematically underestimates GPP, AET, and WUE, and prolongs GPP recovery, highlighting the need to integrate plant hydraulics for improved flash drought predictability.
-
Lu et al. (2025) Blue and green water simulation in the river basin using remote sensing data fusion and dual-variable hydrological calibration
This study developed and evaluated a dual-variable hydrological model calibration method, integrating remote sensing fusion evapotranspiration (ET) data with observed runoff, to improve the accuracy and reduce uncertainty in blue water and green water simulations in the Xiangjiang River Basin. The method significantly enhanced the simultaneous simulation accuracy of both blue and green water compared to traditional single-variable calibration.
-
Chen et al. (2025) Integrated modeling of crop growth with 2D soil water flow and solute transport considering dynamic root spatial distribution under film mulched drip irrigation
This study developed and validated a two-dimensional coupled model (WSP-2D) for simulating soil water flow, solute transport, and crop growth under film-mulched drip irrigation, incorporating dynamic root distribution and water flux through mulched film planting holes. The model accurately captured field observations and demonstrated the critical importance of dynamic root growth and surface water flux for accurate simulations of soil water, salt, and crop yield.
-
Zhang et al. (2025) Non-stationary influence of the North Atlantic Oscillation on summer precipitation in the Central-Eastern Himalayas
This study reveals the non-stationary influence of the Summer North Atlantic Oscillation (SNAO) on summer precipitation in the Central-Eastern Himalayas (CEH), demonstrating a "weak–strong–weak" evolution of this linkage primarily modulated by the interaction of SNAO-driven circulation anomalies with the Himalayan topography.
-
Weng et al. (2025) Widespread land surface cooling from paddy rice cultivation revealed by global satellite mapping
This study developed a global, long-term, high-resolution paddy rice dataset (GlobalRice500) and revealed that paddy rice cultivation significantly cools the land surface, reducing daytime land surface temperature by 0.21–0.27 °C during the growing season compared to other croplands.
-
Zhao et al. (2025) Highest quality remote sensing reflectance database compiled from 20+ years of MODIS-aqua measurements
This study compiles a highest-quality remote sensing reflectance (Rrs) database from over 20 years of MODIS-Aqua measurements using novel quality control criteria. The resulting database significantly improves data consistency with in situ observations and reveals altered long-term Rrs trends in substantial oceanic regions compared to standard products.
-
Marshall et al. (2025) A systematic review of the NASA Land Information System (LIS): Two decades of advancements in hydrological modeling, data assimilation, and operational earth system applications
This systematic review synthesizes two decades of advancements in the NASA Land Information System (LIS), demonstrating its impact on Earth system science through improved land surface estimates, enhanced forecast skill via model coupling, and successful transition to operational applications.
-
Barbaux (2025) Températures maximales au 21ème siècle
## Identification - **Journal:** theses.fr (ABES) - **Year:** 2025...
-
Rickard (2025) Deciphering soil structure: linking soil physics, water dynamics, carbon storage, and agricultural resilience in long-term experiments
This study evaluates the performance of the ISBA land surface model and the mHM hydrological model in simulating soil moisture and river discharge. The results indicate that while both models accurately capture soil moisture dynamics, mHM demonstrates superior accuracy in river discharge simulation.
-
Ma et al. (2025) Response and mechanisms of sub-daily precipitation over the Tibetan Plateau to regional climate change
This study investigates the impact of regional climate change over East Asia (RCC) and the Tibetan Plateau (PCC) on sub-daily precipitation over the Tibetan Plateau using dynamical downscaling and a storyline attribution approach. It finds that RCC significantly reduces overall precipitation but enhances afternoon convective precipitation, while PCC exerts a smaller and less statistically significant impact.
-
Guilhen et al. (2025) Assessment of surface water storage in the Amazon floodplains by hydrological modelling and earth observation data
This study quantifies the spatiotemporal dynamics of floodplain water storage in the Amazon Basin from 2000–2018 using an integrated framework of hydrological modeling and multi-sensor Earth observation data, revealing a mean annual surface water storage of 1800 ± 854 km³ with significant sub-basin contributions.
-
Duque et al. (2025) Simulating Closed‐to‐Open Mesoscale Cellular Convection Over the Southern Ocean: Part I. Evaluation Using SOCRATES and CAPRICORN Observations
This study evaluates a convection-permitting WRF model's performance in simulating low-level clouds over the Southern Ocean during post-frontal conditions, finding it effectively reproduces key morphological and microphysical distinctions between closed and open mesoscale cellular convective clouds, though with some underestimation at higher latitudes.
-
Yang et al. (2025) Effects of extreme climate on hydrological dynamics in dryland apple orchards: a modeling study
This study integrates a dynamic leaf area index (LAI) sub-module into the process-oriented STEMMUS model to accurately simulate the ecohydrological responses of dryland apple orchards to climate extremes. The findings demonstrate that while increased precipitation volume enhances soil water storage, both 2°C warming and high-intensity precipitation patterns significantly reduce canopy transpiration and water use efficiency (T/ET).
-
Blake et al. (2025) The impact of convection-permitting model rainfall on the dryland water balance
This study quantifies the impact of convection-permitting climate model rainfall on the dryland water balance in the Horn of Africa, demonstrating that explicitly resolving convection improves rainfall characteristics, leading to significantly higher soil moisture, transpiration, surface runoff, and potential groundwater recharge compared to models that parameterize convection.
-
Chen et al. (2025) How Do the Asian Highlands Affect Phase-Preferred Rossby Waves and Synchronous Heat Extremes in the Midlatitudes?
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
-
Hirasawa et al. (2025) Forcing Susceptibility and Climate Sensitivity to Midlatitude Marine Cloud Brightening
This study uses three Earth system models to evaluate marine cloud brightening (MCB) by injecting sea salt aerosol (iSSA) in 14 ocean regions, identifying a novel midlatitude emission strategy that produces more uniform cooling and precipitation responses, effectively offsetting greenhouse gas warming.
-
Chen et al. (2025) Monitoring sub-canopy inundation dynamics in global croplands: An unexplored application of SWOT satellite data
This study develops the first method for year-round monitoring of sub-canopy inundation dynamics (non-inundated, partially-inundated, fully-inundated) in global croplands using SWOT satellite KaRIn coherent power, demonstrating its robustness across diverse climate zones and improving upon existing remote sensing limitations.
-
Bouswir et al. (2025) Assessment of empirical and physically-based approaches to simulate surface resistance for improved evapotranspiration modeling of winter wheat in semi-arid region, Morocco
The study evaluates two different methods for parameterizing surface resistance ($r_c$) in the Penman-Monteith model to improve evapotranspiration (ET) estimation for winter wheat in Morocco. It finds that while mechanistic models (Jarvis) excel under full irrigation, empirical models based on Land Surface Temperature (LST) are more effective at capturing rapid water stress dynamics under deficit irrigation.
-
Kang et al. (2025) Modification of Similarity Relationships and Parameterization of Submesoscale Motions Under Spectral Regimes Over Uniform Flat Terrain
This study investigates the impact of submesoscale motions and turbulence intermittency on flux estimation in the atmospheric boundary layer, identifying four distinct turbulence regimes. It establishes revised surface-layer similarity relationships by removing submesoscale motions and proposes an empirical parameterization for submesoscale wind speed standard deviations, significantly improving turbulence representation.
-
Duque et al. (2025) Simulating Closed‐to‐Open Mesoscale Cellular Convection Over the Southern Ocean: Part II. Perturbed Physics Experiments
This study investigates the drivers of mesoscale cellular convective (MCC) cloud organization and the transition from closed-to-open cells over the Southern Ocean. It finds that enhanced cloud ice production and microphysical latent cooling are key factors in MCC organization and break-up, while sea surface temperature influences cloud morphology but is not the primary driver of the closed-to-open cell transition.
-
Baño-Medina et al. (2025) A regional high resolution AI weather model for the prediction of atmospheric rivers and extreme precipitation
This study develops and evaluates a stretched-grid AI-driven weather model with high resolution over the Western United States for predicting atmospheric rivers and extreme precipitation. The model significantly reduces precipitation errors, performs competitively with regional numerical weather prediction (NWP) models, and effectively captures extreme events, outperforming coarser global models.
-
Tayer et al. (2025) Mapping resilience: A framework for analysing surface-water dynamics and persistent pools in non-perennial rivers using remote sensing, rainfall and river discharge data
This study developed a scalable, data-driven framework integrating remote sensing, rainfall, and discharge data to analyze multi-decadal surface water and persistent pool dynamics in non-perennial rivers. Applied to the Gilbert River (Australia, 1986–2023), the framework revealed a dynamic mosaic of 29 persistent pools, with increasing rainfall and discharge leading to more numerous, larger, and less fragmented pools over time.
-
Bolot et al. (2025) No decrease of tropical convection in individual deep convective systems with global warming
This study challenges the consensus that tropical convection decreases with global warming by showing that individual deep convective systems intensify, with the overall decrease in tropical convection attributed to a reduction in the total number of such systems.
-
Xiao et al. (2025) High-resolution ensemble retrieval of cloud properties for all-day based on geostationary satellite
This study introduces CloudDiff, a novel generative diffusion model, for high-resolution (1 km) and all-day ensemble retrieval of cloud properties (Cloud Optical Thickness, Cloud Effective Radius, Cloud Top Height, Cloud Phase) from geostationary satellite data, providing uncertainty quantification and significantly improving retrieval accuracy and reliability compared to deterministic methods.
-
Fotso‐Nguemo et al. (2025) Severity of Compound Precipitation and Temperature Extreme Events Over Africa at 1.5°C and 2°C Global Warming Levels
This study investigates how increasing global warming levels affect the variability of compound dry/warm and wet/warm extreme events over Africa. It finds that a 0.5 °C increase in global warming (from 1.5 °C to 2 °C) significantly increases the frequency, duration, and affected area of these compound events, particularly near the Equator and in specific sub-regions of Africa.
-
Mohanty et al. (2025) Precipitation bursts in northern Australia with and without preceding heatwaves
This study investigates the interaction between heatwaves and precipitation bursts in Northern Australia, revealing that heatwaves precondition the atmosphere, leading to stronger, more prolonged, and dynamically driven rainfall bursts compared to independent events. The research highlights the critical role of atmospheric circulation in shaping these compound extremes and their distinct responses to large-scale climate modes.
-
Khalil et al. (2025) A novel optimized machine learning ensemble approach for future drought assessment
This study proposes a novel multi-model ensemble (MME) framework integrating Learning Vector Quantization (LVQ) and Optimized Learning Vector Quantization (OLVQ) to enhance the reliability of precipitation forecasts from General Circulation Models (GCMs). The findings demonstrate that both LVQ and OLVQ significantly outperform traditional ensemble methods, with OLVQ providing further substantial improvements in reducing prediction errors and increasing correlation.
-
Puxley et al. (2025) A Continental United States Climatology of Precipitation Whiplash Using a New Event‐Based Definition
This study developed a novel algorithm to define and analyze spatially coherent precipitation whiplash events across the continental United States (CONUS) between 1915 and 2020, finding that the largest drought-to-pluvial and pluvial-to-drought events are increasing in both frequency and total area impacted, particularly in the Southeast and Northeast.
-
Trivedi et al. (2025) An assessment of Antarctic sea-ice thickness in CMIP6 simulations with comparison to the satellite-based observations and reanalyses
This study assesses the spatio-temporal variations and biases in Antarctic sea-ice thickness (SIT) and volume (SIV) simulated by 39 CMIP6 models against satellite observations and reanalyses, finding that models replicate seasonal cycles but generally underestimate absolute thickness and project seasonally asymmetric declines under future warming.
-
He et al. (2025) Improving precipitation simulations in CIESM through a new entrainment rate parameterization
This study develops and implements a new entrainment rate parameterization (HL) for deep convection in the CIESM1.1.0 climate model, demonstrating improved simulations of convective and large-scale precipitation in tropical and subtropical regions compared to the existing Gregory parameterization.
-
Li et al. (2025) Improved Madden–Julian oscillation simulation using the modified moist physical parameterizations for a global climate model
This study investigates the impact of modified moist physical parameterizations on the global climate model, CIESM, regarding Madden–Julian oscillation (MJO) simulation. The new moist physical schemes significantly improve MJO propagation characteristics and mean-state fidelity by enhancing zonal asymmetry in moist static energy tendency and optimizing cloud-convection interactions.
-
Qiu et al. (2025) Quality control of the composite radar quantitative precipitation estimation product for Great Britain
This study develops a novel quality control (QC) framework for radar Quantitative Precipitation Estimation (QPE) products in Great Britain to systematically detect and correct underestimation (e.g., beam blockage) and overestimation errors. The framework significantly improves the accuracy of radar QPE, reducing RMSE by 29% and increasing the correlation coefficient by 31% compared to rain gauge observations, while preserving real extreme rainfall events.
-
Zhang et al. (2025) Integrating deep learning and multi-objective optimization for floodwater utilization: a coordinated surface water-groundwater regulation framework for groundwater recovery
This study developed a novel deep learning and multi-objective optimization framework to integrate flood control, water storage, and groundwater recovery through coordinated surface water-groundwater regulation. It demonstrated that optimizing reservoir operations and implementing managed aquifer recharge effectively reduced flood risk and water scarcity while promoting groundwater recovery.
-
Lin et al. (2025) Soil salinity estimation based on satellite hyperspectral and synthetic aperture radar remote sensing image fusion
This study proposes a novel multi-scale, multi-depth Wasserstein Generative Adversarial Network with Gradient Penalty (MSD-WGAN-GP) to fuse hyperspectral (HSI) and Synthetic Aperture Radar (SAR) images, significantly improving soil salinity estimation accuracy by mitigating the coupling effects of soil moisture and surface roughness.
-
Zhang et al. (2025) A multiple spatial scales water use simulation for capturing its spatial heterogeneity through cellular automata model
This study proposes a multi-scale water use simulation framework, integrating a cellular automata model with Generalized Likelihood Uncertainty Estimation, to address spatial heterogeneity and uncertainty in water resource planning across China. The framework reveals that both model structure and spatial scale significantly impact water use heterogeneity and uncertainty, offering a flexible tool for adaptive water resource management.
-
Lai et al. (2025) Identification of spatiotemporal changes and driving factors of ecological drought during 1982–2024 across the mainland China
This study utilizes the Standardized Ecological Water Deficit Index (SEWDI) to assess ecological drought across mainland China from 1982 to 2024, revealing a general increasing trend in drought severity since 2000. The research identifies evapotranspiration, soil moisture, and irrigation water as the primary drivers of these spatiotemporal changes.
-
Giani et al. (2025) Origin and Limits of Invariant Warming Patterns in Climate Models
This paper presents a simple theory based on local energy balance to reconcile the apparent contradiction between approximately invariant surface warming patterns in typical end-of-century climate projections and evolving patterns in idealized CO2 increase experiments. It shows that pattern invariance arises under specific conditions met in future projections, while idealized experiments exhibit evolution due to spatially inhomogeneous ocean heat uptake.
-
Goutard (2025) Importance de l’eau liquide et variabilité de l’albédo sur les surfaces en glace des glaciers de montagne
N/A
-
Zhang et al. (2025) TALESF: A Novel Terrain-Adjusted Local-Enhanced Spatiotemporal Fusion Method for Generating Spatiotemporally Seamless, High-Resolution NDVI
Not available in the provided text. The paper introduces TALESF, a novel terrain-adjusted local-enhanced spatiotemporal fusion method for generating spatiotemporally seamless, high-resolution Normalized Difference Vegetation Index (NDVI).
-
Augenstein et al. (2025) Influence of the North Atlantic Oscillation on annual spatio-temporal lightning clusters in western and central Europe
This study investigates the spatio-temporal characteristics and trends of thunderstorm activity in western and central Europe using lightning data from 2001 to 2021, revealing a significant decrease in thunderstorm activity and a shift towards smaller, more separated convective systems, which is linked to an accumulation of negative North Atlantic Oscillation phases.
-
Ullah et al. (2025) The Internet of Nature Things (IoNT): Pioneering a New Frontier in Environmental Monitoring and Sustainable Ecosystem Management
This paper introduces the concept of the Internet of Nature Things (IoNT) as an emerging paradigm to advance environmental monitoring and facilitate sustainable ecosystem management.
-
Han et al. (2025) FluxHourly: global long-term hourly 9 km terrestrial water-energy-carbon fluxes
This study presents FluxHourly, a global long-term (2000–2020) hourly 9 km dataset of terrestrial water-energy-carbon fluxes, generated by integrating model simulations, in-situ measurements, and machine learning to enable analysis of ecosystem responses to climate extremes at unprecedented spatiotemporal scales.
-
Gu et al. (2025) FloodTransformer: Efficient real-time high-resolution flood forecasting
This paper introduces FloodTransformer, a hybrid AI-hydrodynamic framework for accurate, real-time, and high-resolution flood forecasting, demonstrating superior performance in water depth and inundation classification compared to existing models.
-
Davies et al. (2025) Application of the Davies four-stage conceptual model for life-threatening rainfall extremes on the April 2024 United Arab Emirates and Oman floods
This study investigates the environmental conditions leading to the April 2024 UAE and Oman floods, applying a four-stage conceptual model to numerical weather prediction (NWP) models and observations. It finds that a combination of deep Moist Absolute Unstable Layers (MAULs) and high saturation fractions are key indicators for predicting extreme rainfall events, distinguishing between organised and isolated convection.
-
Lin et al. (2025) Enduring local impact of springtime snow cover over the Third Pole
This study confirms the enduring local impact of springtime Third Pole snow cover (TPSC) on summer precipitation and proposes a self-sustaining mechanism where initial excessive precipitation releases atmospheric latent heat, driving anomalous circulation that favors subsequent precipitation.
-
Szwarcman et al. (2025) Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications
## Identification - **Journal:** IEEE Transactions on Geoscience and Remote Sensing - **Year:** 2025...
-
Toride et al. (2025) Roles of MJO and Tropical–Extratropical Interactions in Subseasonal Conditions Related to Landfalling Atmospheric Rivers
This study investigates the role of the Madden–Julian oscillation (MJO) and other tropical–extratropical interactions in generating landfalling atmospheric rivers (ARs) over the West Coast of North America. It finds that weakly coupled tropical–extratropical interactions are the primary drivers of AR activity, with the MJO contributing only a minor role, mainly to subtropical vapor transport for Alaska ARs.
-
Wang et al. (2025) Quantifying nonlinear synergistic effects of environmental changes on runoff change using segmented hydrological modeling
This study introduces a segmented hydrological modeling approach to quantify the nonlinear synergistic effects of climate change and human activities on runoff variation in the Wei River Basin. It finds that human activities are the dominant and escalating driver of runoff reduction, with the synergy between these factors significantly amplifying the overall decline.
-
Michel et al. (2025) Temporal attention multi-resolution fusion of satellite image time-series, applied to Landsat-8/9 and Sentinel-2: all bands, any time, at best spatial resolution
This paper proposes a general formulation for fusing Satellite Image Time Series (SITS) from multiple sensors with varying spatial resolutions and acquisition times. It introduces TAMRF-SITS, a novel deep learning architecture and training strategy, which predicts all spectral bands from all input sensors at the best spatial resolution and any requested acquisition time, outperforming or matching existing ad-hoc methods across various tasks while relaxing unrealistic assumptions.
-
Zheng et al. (2025) Corrigendum to “Micro-sprinkler irrigation with optimal irrigation regimes maintain grain yields while increasing carbon emission efficiency and water productivity of winter wheat on the North China Plain” [Agric. Water Manag. 321 (2025) 109933]
This corrigendum corrects numerical errors in a study that investigated the impact of micro-sprinkler irrigation with optimal regimes on winter wheat grain yields, carbon emission efficiency, and water productivity on the North China Plain, aiming to maintain yields while enhancing these efficiencies.
-
Wen et al. (2025) Spatial distribution and variation trends of soil freezing front on the Qingzang Plateau revealed by machine learning models
This study simulated the spatiotemporal variations of soil freezing front depth (SFFD) on the Qingzang Plateau (QP) from 1982 to 2019 using machine learning models, revealing a significant and accelerating shallowing trend across both permafrost and seasonally frozen ground regions.
-
Xia et al. (2025) Began+: Leveraging bi-temporal SAR-optical data fusion to reconstruct clear-sky satellite imagery under large cloud cover
This paper introduces Began+, a novel deep learning framework that fuses bi-temporal SAR and optical data to reconstruct clear-sky satellite imagery, effectively addressing large cloud cover and restoring temporal changes. It demonstrates superior performance in synthesizing high-quality Landsat-8 and Sentinel-2 images and open-sources two global datasets for cloud removal.
-
Fang et al. (2025) Modelling river ice processes in a small-steep-regulated river
This study revised the University of Alberta’s River1D model to improve its simulation of complex river ice processes, including aufeis evolution, in small-steep-regulated rivers. The enhanced model was successfully calibrated and validated on the Aishihik River, providing a valuable tool for assessing flow regulation impacts, mitigating ice hazards, and evaluating climate change scenarios.
-
Hu et al. (2025) Century-scale attribution and constrained projection of temperature extremes in eastern China
This study performs a multi-scale attribution analysis of temperature extremes from 1901 to 2020, finding that greenhouse gas forcing is the dominant driver of observed warming, with century-scale attribution providing the most robust results for constraining future projections.
-
Hou et al. (2025) Characterization of single-cropping rice net irrigation water requirements in China's major rice-producing regions using time-frequency domain methods
This study characterizes single-cropping rice net irrigation water requirements (RIWR) in China's major rice-producing regions from 1951 to 2023 using time-frequency domain methods and projects future changes. It reveals substantial spatiotemporal variability in RIWR, with national annual averages ranging from 524.6 to 791.5 mm and dominant 15–25 year cycles, primarily driven by effective precipitation, sunshine duration, maximum air temperature, and relative humidity.
-
Bak et al. (2025) Development of a web-based No coding machine learning platform for hydrology and environmental management - MoolML
This paper introduces MoolML, a free, web-based, no-coding machine learning platform designed to simplify the development of regression and classification models for hydrology and environmental management, demonstrating its applicability and efficiency with South Korean datasets.
-
Blanco et al. (2025) Comparison of Spatial Patterns of Aridity in Argentina Based on Different Climatic Indices and Datasets
This study evaluates the spatial patterns of aridity in Argentina from 1961 to 2020 by comparing various climatic indices and gridded datasets. It finds that aridity indices based on mean temperature or potential evapotranspiration (especially Thornthwaite) are more consistent, with CRU and WORLDCLIM performing better than ERA5, which shows significant errors in certain conditions.
-
Zhao et al. (2025) Cumulative Effect of Synoptic‐Scale Storms on Decadal Mixed‐Layer Temperature Variability in the Mid‐Latitude North Pacific
This study investigates the contribution of synoptic-scale storms to decadal mixed-layer temperature variability in the mid-latitude North Pacific. It reveals that frequent storms lead to cumulative mixed-layer cooling, primarily driven by anomalous meridional Ekman heat transport, a process linked to the strength of the Aleutian Low.
-
Xie et al. (2025) Determination of dry soil layer and vertical variations in soil thermal properties through interpretation of heat pulse signals using deep learning-based data assimilation
This study evaluates a deep learning-based data assimilation (DA(DL)) method for estimating vertical variations in soil thermal properties from heat pulse measurements to characterize dry soil layer (DSL) dynamics. It demonstrates that DA(DL) outperforms conventional methods in handling nonlinearity and successfully captures DSL evolution under natural evaporation conditions.
-
Pang et al. (2025) Flash droughts threaten global managed forests
This study reveals that global forests, particularly managed ones, have experienced increasingly rapid, intense, and prolonged flash droughts over the past four decades, with current forest management practices exacerbating their vulnerability to browning.
-
Zhou et al. (2025) Role of Diurnal Cycle of Insolation on the MJO Propagation in the Maritime Continent
This study uses a regional model to test the hypothesis that the diurnal cycle of convection in the Maritime Continent acts as a barrier to Madden-Julian oscillation (MJO) propagation, finding that the absence of the diurnal cycle allows for more continuous eastward MJO propagation.
-
Dubey et al. (2025) Forest-savanna stability in India under human interventions and changing climate
This study investigates the stability of forest-savanna ecosystems in India under human interventions and climate change, projecting a future shift towards savanna-like conditions due to increased precipitation variability, while highlighting the mitigating potential of strategic anthropogenic conservation efforts.
-
Liu et al. (2025) Radar First Echo Detection With Spaceborne Precipitation Radars
This study identifies and characterizes "first echoes" in fresh growing clouds using 26 years of satellite Ku-band radar observations, revealing three distinct altitude-related modes in the tropics linked to different microphysical processes, environmental conditions, and aerosol types.
-
Yan et al. (2025) Simulation of soil moisture and drought prediction in middle reaches of the Yellow River based on machine learning
This study integrates a Multi-Layer Perceptron (MLP) model with RegCM4 climate data to generate a high-resolution, layered daily soil moisture dataset (MLP_D) for the Middle Reaches of the Yellow River (MRYR) from 2001 to 2100. Analysis of this dataset reveals a decline in deep soil moisture historically and projects a significant increase in future drought frequency and duration under intensifying climate change scenarios.
-
Clerck et al. (2025) High-spatial-resolution gross primary production estimation from Sentinel-2 reflectance using hybrid Gaussian processes modeling
This study develops a hybrid modeling framework using Sentinel-2 reflectance and Gaussian Process Regression (GPR) trained with SCOPE radiative transfer model simulations to estimate high-spatial-resolution (20 meters) Gross Primary Production (GPP) across 10 plant functional types (PFTs). The PFT-specific GPR models, implemented in Google Earth Engine, demonstrated strong predictive performance in most ecosystems, outperforming MODIS GPP in terms of bias and spatial detail, though showing limitations in evergreen forests.
-
Paredes et al. (2025) rsofun v5.1: a model-data integration framework for simulating ecosystem processes
This study introduces and evaluates `rsofun` v5.1, an R package providing a computationally efficient implementation of the P-model for simulating ecosystem photosynthesis and trait acclimation, coupled with Bayesian model-data integration. Multi-target calibration using ecosystem fluxes and leaf traits demonstrated robust parameter estimation and unbiased predictions that generalize well across diverse environmental conditions.
-
Arboleda et al. (2025) Joint evolution of irrigation, the water cycle and water resources under a strong climate change scenario from 1950 to 2100 in the IPSL-CM6
This study investigates the coupled evolution of irrigation, the water cycle, and water resources from 1950 to 2100 under a strong climate change scenario using the IPSL-CM6 model, revealing increased irrigation, intensified water depletion, and identifying regions vulnerable to water stress.
-
Chambers et al. (2025) Hot droughts in the Amazon provide a window to a future hypertropical climate
This study synthesizes long-term demographic, ecophysiological, and climate model data to assess the impact of "hot droughts" on central Amazon forests. It finds that hot droughts significantly increase tree mortality, particularly for fast-growing species, by pushing soil moisture below critical thresholds, and projects that these extreme conditions will become typical in a future "hypertropical" climate by 2100.
-
Zhu et al. (2025) Increased Thunderstorm Activities Caused by Warming and Wetting Over the Tibetan Plateau
This study generated a new continuous thunderstorm dataset for the Tibetan Plateau (TP) from 2010 to 2024, revealing a significant increase in thunderstorm activity but a weak decrease in intensity, primarily driven by changes in convective available potential energy and moisture.
-
Li et al. (2025) Exploring the driving forces of soil salinity reduction using Random Forest and SHAP in water-saving oasis irrigation areas
This study investigated the spatiotemporal dynamics and driving forces of soil salinity reduction in water-saving oasis irrigation areas of the Manas River Basin from 2013 to 2021, revealing a significant decrease in salinity primarily driven by declining groundwater depth.
-
Malik et al. (2025) High-impact and Low-likelihood compound hot and dry extremes in India
This study investigates the occurrence and implications of high-impact and low-likelihood (HILL) compound hot and dry extremes (CHDEs) in India under observed and projected future climates, finding a significant increase in their frequency, duration, spatial extent, and intensity, particularly during the summer monsoon, driven by climate warming and El Niño events.
-
Korzani et al. (2025) Dynamic CA-ffé: A hybrid 1D/2D fast flood evaluation model for urban flash floods
This paper introduces Dynamic CA-ff´e, a novel hybrid 1D/2D urban flash flood model coupling a static 2D cellular automata overland flow model with a dynamic 1D drainage network model. It demonstrates high accuracy in flood prediction comparable to full hydrodynamic models, while achieving significantly faster simulation times (at least 20 times faster).
-
Mirza et al. (2025) Evaluating the utility of Sentinel-1 in a Data Assimilation System for estimating snow depth in a mountainous basin
This study evaluates the temporal and spatial accuracy of Sentinel-1 (S1) C-band radar snow depth retrievals and their utility within a data assimilation (DA) system for mountain snowpack characterization in the East River Basin, Colorado, finding significant inconsistencies and limited potential for S1 to improve snow DA in this region.
-
Camilletti et al. (2025) AI Reconstruction of European Weather From the Euro‐Atlantic Regimes
This study develops a non-linear AI model to reconstruct monthly mean anomalies of European temperature and precipitation using Euro-Atlantic Weather Regime (WR) indices, demonstrating its potential for sub-seasonal to seasonal forecasting by capturing complex non-linear relationships and showing improved or comparable skill to a state-of-the-art seasonal forecast system.
-
Zhou et al. (2025) Detecting time-varying characteristics of the integrated parameter in the generalized complementary principle based on long-term flux data
This study investigated the temporal variability of the integrated parameter αc in the generalized complementary relationship (GCR) using long-term flux data from seven sites, revealing that incorporating its temporal dynamics significantly improves regional evaporation estimation across various time scales.
-
Bai et al. (2025) Influence of ENSO on the seasonal and extreme characteristics of ridges over Pacific-Western North American region
This study investigates the seasonal and extreme characteristics of atmospheric ridges over Pacific-Western North America and their modulation by ENSO, finding that La Niña generally favors more frequent, larger, and more intense extreme ridges, especially in winter, with ENSO-forced shifts in mean flow largely explaining these patterns.
-
Li et al. (2025) Surrogate modeling for rapid estimation of spatially-resolved flood damage: Application to a coastal region
This study introduces BayFlood, a Bayesian-optimized machine learning surrogate model for rapid, accurate, and spatially resolved flood damage estimation using river discharge and tidal level inputs. The boosting-ensemble-driven BayFlood achieved the best performance (coefficient of determination = 0.92–0.98; root mean square error = 0.04–0.08) and reduced computational time by two orders of magnitude compared to hydraulic modeling.
-
Thomas (2025) Variabilité climatique multiéchelle : une approche systémique combinant multifractales et réseaux complexes
[Information not extractable from provided text]
-
Li et al. (2025) Comparing the impact of precipitation pre-processing and streamflow post-processing for daily sub-seasonal streamflow forecasts over the Gan River basin
This study investigates the separate and joint impacts of precipitation pre-processing and streamflow post-processing on daily sub-seasonal streamflow forecasts over the Gan River Basin. It finds that streamflow post-processing is more impactful for lead times within 10 days, while precipitation pre-processing becomes more significant at longer lead times, with specific methods (BJP and SSh) showing superior performance.
-
Chen et al. (2025) Robust water level measurement using adaptive prompt staff gauge image segmentation based on EdgeSAM
This study proposes a robust image-based water level measurement method for staff gauges using an adaptive prompt EdgeSAM model, achieving high accuracy and generalization with minimal training data in complex field environments. The method addresses limitations of traditional and existing deep learning approaches by integrating image features with prompt information for precise segmentation and measurement.
-
Wang et al. (2025) Drought onsets and their driving factors for multiple drought types in the Yangtze River Basin
This study analyzed the spatiotemporal evolution and driving mechanisms of meteorological, agricultural, and hydrological drought onset characteristics in the Yangtze River Basin (YRB), revealing that temperature is the dominant driver with complex nonlinear interactions among factors, especially under extreme conditions, which provides a scientific basis for drought risk prevention.
-
Archfield (2025) Spatial connections between the timing of hydroclimatic extremes
This News & Views article discusses a global analysis that reveals unexpected cross-hemisphere synchronizations in the timing of droughts and rainfall-induced flooding, highlighting their significant implications for global food supply chains.
-
Kasaei et al. (2025) Compounding of 100-year coastal floods by rainfall in an urban environment
This study investigates the combined impacts of coastal and pluvial flood drivers, challenging the assumption that rainfall has a negligible effect on 100-year coastal flood maps. It finds that pluvial drivers can significantly expand flood zones and occasionally deepen floods, particularly in topographic depressions.
-
Miao et al. (2025) Comparing the Linkage between Springtime Central Pacific Cross-Equatorial Winds and Wintertime ENSO Events in Reanalyses and CMIP6 Models
This study assesses the fidelity of the springtime cross-equatorial meridional wind anomaly as an El Niño–Southern Oscillation (ENSO) precursor in CMIP6 models, finding that models underestimate this relationship primarily due to overestimating ENSO persistence, but successfully reproduce the linkage when the springtime ENSO signal is removed.
-
Larson et al. (2025) QuadTune version 1: a regional tuner for global atmospheric models
QuadTune is a novel, inexpensive regional tuner for global atmospheric models that uses an uncorrelated quadratic emulator to quickly reduce parametric errors. In an example application, it demonstrated a reduction in Shortwave Cloud Radiative Forcing (SWCF) root mean square error (RMSE) from 12.4 W m⁻² to 10.4 W m⁻².
-
Yu et al. (2025) Optimizing Crop Maximum Carboxylation Rate Using Machine Learning to Improve Maize Yield Estimation Under Drought Conditions
This study focuses on optimizing the crop maximum carboxylation rate using machine learning to enhance maize yield estimation under drought conditions.
-
Creamean et al. (2025) Long-term measurements of ice nucleating particles at Atmospheric Radiation Measurement (ARM) sites worldwide
This paper presents one of the most comprehensive publicly available datasets of immersion-mode ice nucleating particle (INP) concentrations, generated using a single analytical method through the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility. Collected across diverse global environments over long periods, this dataset reveals distinct seasonal and site-specific differences in INP concentrations and types, providing critical constraints for aerosol-cloud interaction models.
-
Ayugi et al. (2025) Physical Predictand Drivers and Characteristics of Aridity Across East Africa
This study investigates the expansion or contraction of aridity in East Africa over 34 years and identifies its dominant climatic drivers. It finds that temperature fluctuations exert a stronger influence on aridity patterns than precipitation, explaining 82.2% of the total variance, and regional aridity significantly correlates with major climate indices.
-
Zhao et al. (2025) Limited capability of current satellite solar-induced chlorophyll fluorescence reconstructions to capture stomatal responses to environmental stresses
Current satellite solar-induced chlorophyll fluorescence (SIF) reconstructions have limited capability to capture stomatal responses to environmental stresses, leading to an overestimation of gross primary productivity (GPP) during dry periods due to a decoupling between stomatal responses and the physiological SIF emission yield.
-
Budhathoki (2025) Data-Driven and Process-Based Modeling Approaches for Advancing Irrigation Water and Nitrogen Management in Humid Cropping Systems
[Information not extractable from the provided text due to corrupted input.]
-
Ear (2025) Modèles distributionnels pour la correction de biais des précipitations journalières : un focus sur les évènements extrêmes
[Information not extractable from provided text]
-
Khaniya et al. (2025) Using ensemble optimal interpolation with dynamic covariance matrices for assimilation of water level observations in a distributed rainfall-runoff-inundation model
This study investigates two ensemble generation strategies for the computationally efficient ensemble optimal interpolation (EnOI) scheme to produce dynamic covariance matrices for assimilating water level observations into a distributed rainfall-runoff-inundation model, demonstrating that EnOI can provide improved state estimates compared to deterministic simulations, particularly with an adaptive error parameter estimation approach.
-
Graversen et al. (2025) Enhanced weather persistence due to amplified Arctic warming
This study provides observational evidence that weather persistence, in terms of surface-air temperature anomalies, has increased in the Northern Hemisphere mid-latitudes during recent decades, directly linking this change to amplified Arctic warming and a consequent weakening of mid-latitude westerly atmospheric flow.
-
Ma et al. (2025) Spatial and Temporal Variability of Cloud Water Content and Precipitation Efficiency in the Tienshan Mountains
This study investigates the spatiotemporal characteristics of total cloud water (TCW) and precipitation efficiency (PE) over the Tienshan Mountains from 1979–2023, revealing distinct spatial patterns and trends that impact regional water resources and drought risks.
-
Xu et al. (2025) From grain to ground: How hydrologic uncertainty drives shifts in crop patterns across the Yellow River Basin
This study developed a robust bi-objective optimization model to optimize agricultural resource allocation in the Yellow River Basin under hydrologic uncertainty, finding that adaptive crop patterns and irrigation technology are crucial for balancing water use efficiency and inter-provincial fairness.
-
Lambert (2025) Irrigation increased historical land surface and groundwater loss
A systematic modelling study indicates that historical increases in agricultural irrigation have caused net losses of both land surface runoff and groundwater, independently exceeding the losses attributable to climate change.
-
Wang et al. (2025) Possible Causes of the Significant Advancement of the Tibetan Plateau Rainy Season under the Background of Climate Warming
The Tibetan Plateau rainy season (TPRS) has significantly advanced from 1961 to 2023, primarily driven by an intensified Lake Baikal ridge, weakened 500-hPa westerlies over East Asia, and enhanced activity of warm, moist air over the southern TP, all linked to regional climate warming and large-scale teleconnections.
-
Janzing et al. (2025) Hyper-resolution large-scale hydrological modelling benefits from improved process representation in mountain regions
This study enhances the hyper-resolution global hydrological model PCR-GLOBWB 2.0 for mountain regions by improving snow, glacier, and soil process representations. The findings demonstrate that these structural and parametric changes significantly improve discharge simulations, although the quality of meteorological forcing remains a primary determinant of overall model performance.
-
Liu et al. (2025) Global characteristics of drought propagation from surface water to groundwater
This study globally assesses groundwater drought using GRACE satellite data and quantifies its propagation relationships with meteorological and hydrological droughts, revealing distinct propagation times and characteristics for each drought type.
-
Dehati et al. (2025) Comparison and validation of spatial reference evapotranspiration datasets over Africa
This study compares eight open-access global reference evapotranspiration (ET0) datasets against in-situ measurements from 165 weather stations across Africa to assess their performance across different climate zones. It finds that high-resolution datasets perform better in temperate and tropical regions, and that input data quality accounts for 60–70% of the variability among datasets.
-
Liu et al. (2025) A Comprehensive Long-Term (1981–2020) Radiation Dataset for China: Ensuring Consistency, Comparability, and Incorporating Uncertainty
This study generated a comprehensive, long-term (1981-2020) radiation dataset for China, including global radiation, diffuse radiation, photosynthetically active radiation (PAR), and diffuse PAR, along with uncertainty estimates, by combining in situ observations with a Gaussian process regression approach, revealing decreasing trends in global radiation and PAR primarily driven by water vapor.
-
Chowdhury et al. (2025) Aerosol‐Cloud Interactions and Their Role in Modulating Lightning Activity: Evidence From Extreme Events Over India
This study investigates the role of aerosol-cloud interactions in modulating lightning flash rates over India using the WRF model. It reveals a nonlinear relationship between aerosol loading (CCN concentration and size) and lightning activity, with region-specific optimal thresholds for enhancement, beyond which suppression occurs.
-
Fang et al. (2025) Improving the fine structure of intense rainfall forecast by a designed generative adversarial network
This study proposes a Generative Fusion Residual Network (GFRNet), a generative adversarial network (GAN)-based framework, to integrate multi-source numerical weather prediction (NWP) forecasts and generate 3-hourly quantitative precipitation forecasts for North China up to 24 hours in advance. GFRNet significantly improves the fine structure and intensity control of intense rainfall forecasts compared to traditional NWP and deep learning baseline models.
-
Zhang et al. (2025) Trends in Atmospheric River Influence on Precipitation over the Jianghuai River Basin in Northern Hemisphere Summer
This study examines the frequency of atmospheric rivers (ARs) and precipitation intensity trends in the Jianghuai River basin from 1970-2019. It finds that despite a declining AR frequency, AR-related precipitation intensity has significantly increased, especially in directly impacted areas, leading to more extreme precipitation events.
-
Yu et al. (2025) Long-term land–atmosphere energy and water exchange observational dataset over central Tibetan Plateau
This paper presents a 9-year (2014–2022) hourly observational dataset of land-atmosphere energy and water exchange from four field stations across the central Tibetan Plateau, providing crucial data for understanding regional climate dynamics. The dataset, which includes near-surface meteorological, radiation, turbulent flux, and soil hydrothermal characteristics, reliably reflects the complex interactions across diverse underlying surfaces and temporal scales.
-
Zhuang et al. (2025) Design strategies based on hydrological performance of bioretention: experiments and modeling
This study comprehensively evaluated the hydrological performance of bioretention cells through laboratory experiments, numerical simulations, and statistical analyses. It found that topsoil composition and initial soil moisture are critical factors, with performance significantly limited for high-intensity rainfall events exceeding approximately 200 mm, leading to the proposal of climate-based design strategies.
-
Zhang et al. (2025) A multi-element coupled drought index for drought characterization in seasonal shallow lake basins
This study develops a Multi-element Coupled Drought Index (MCDI) by integrating six key water cycle elements and their lag times using vine copula, specifically for seasonal shallow lake basins. The MCDI demonstrates superior performance in drought characterization, offering more prompt and accurate detection of drought occurrence and grades compared to traditional indices.
-
Tran et al. (2025) Evaluating reanalysis datasets as meteorological input for estimating reference evapotranspiration in Africa and Southwest Asia
This study evaluates the accuracy of ERA5, AgERA5, and GEOS5 reanalysis datasets for meteorological input in Africa and Southwest Asia, finding that GEOS5 is less accurate than ERA5 and AgERA5, and all datasets exhibit specific biases in meteorological variables, leading to high reference evapotranspiration (ETo) uncertainty in tropical regions.
-
Chai et al. (2025) Flash droughts exacerbate global vegetation loss and delay recovery
This study reveals that flash droughts cause significantly greater global vegetation loss (9.0%) and delay recovery compared to conventional droughts (5.3%), with increasing frequency of flash droughts being the primary driver of this exacerbation.
-
Hu et al. (2025) Global retrieval of harmonized microwave land surface emissivity leveraging multi-sensor measurements from GMI, AMSR2 and MWRIs
This study develops an innovative framework to retrieve a global harmonized microwave land surface emissivity (MLSE) database by integrating measurements from five passive microwave sensors (GMI, AMSR2, and three MWRIs) and six geostationary visible/infrared imagers. The framework, employing a simultaneous conical overpass (SCO) recalibration technique, achieves exceptionally strong consistency among the harmonized MLSE subsets, with Pearson R ≈0.95, RMSD <0.011, and mean bias within ±0.005.
-
Huang et al. (2025) Decrease in the spatial inhomogeneity of tropical cyclone rainfall in China
This study reveals a significant 65% decrease in the spatial inhomogeneity of tropical cyclone (TC) rainfall across China from 1998–2023. This redistribution is driven by reduced rainfall in historically high-rainfall areas and increased rainfall in low-rainfall areas, primarily linked to northward TC migration.
-
Simon et al. (2025) Arctic regional changes revealed by clustering of sea-ice observations
This study applies k-means clustering to satellite sea-ice concentration data (1979–2023) to identify four distinct Arctic sea-ice seasonal cycle types, revealing a significant decline in permanent sea-ice (3.1% per decade) compensated by increases in open-ocean and seasonal ice types. The research introduces a probabilistic framework to monitor these regional changes and identifies areas of stability, stabilization, and destabilization in the Arctic sea-ice regimes.
-
Fan et al. (2025) Grain yield and resource efficiency responses to water-nitrogen coupled input reduction: A global meta-analytical perspective
This meta-analysis systematically evaluated the impact of water-nitrogen coupling management under reduced water and fertilizer inputs on the yield, water use efficiency (WUE), and nitrogen partial factor productivity (NPFP) of maize, wheat, and potatoes. The study found that moderate reductions (approximately 10%) in water and nitrogen inputs significantly increased crop WUE and NPFP while maintaining high yields.
-
Shao et al. (2025) ENT-YOLO: An improved lightweight YOLO for cotton organ detection in mulched drip irrigation systems in southern Xinjiang
This study proposes ENT-YOLO, a lightweight deep learning model based on YOLOv11n, for precise detection and spatial mapping of cotton organs (buds, flowers, bolls) in complex mulched drip irrigation systems in southern Xinjiang. The model achieves high accuracy (79.77 % mAP@0.5) with a compact size (4.2 MB), providing a foundation for intelligent water, fertilizer, and salt management.
-
Xing et al. (2025) A deep learning-based composite agricultural drought index for monitoring and impact assessment in Central Asia
This study develops a Composite Agricultural Drought Index (CAEDI) using an unsupervised Convolutional Autoencoder (CAE) to integrate multiple drought indicators with soil moisture as a physical prior. CAEDI effectively monitors agricultural drought in Central Asia, outperforming individual indices and accurately assessing yield losses, particularly during critical crop phenological stages.
-
Screen et al. (2025) Causes and consequences of Arctic amplification elucidated by coordinated multimodel experiments
This perspective synthesizes scientific advances facilitated by the Polar Amplification Model Intercomparison Project (PAMIP), elucidating the critical roles of sea-ice loss and associated feedbacks in Arctic amplification and identifying robust, albeit often weak, remote tropospheric responses that contribute to projected climate change.
-
Chavez et al. (2025) Simulating Stratiform Precipitation With Embedded Convection in High‐Elevation Valleys Using LES: The Role of Topographic Detail
This study uses large-eddy simulations to investigate how topographic detail influences precipitation distribution in the Mantaro Valley, Peru, finding that subtle subkilometer terrain variations critically modulate convection and stratiform precipitation processes, necessitating high-resolution topography in mountain rainfall models.
-
Qu et al. (2025) Accurate tropical cyclone intensity forecasts using a non-iterative spatiotemporal transformer model
This study introduces TIFNet, a non-iterative spatiotemporal transformer model, to generate accurate 5-day tropical cyclone (TC) intensity forecasts. TIFNet integrates high-resolution global forecasts with historical TC evolution, consistently outperforming operational numerical models, especially during rapid intensification and weakening events.
-
Zhang et al. (2025) Integration of deterministic initialization, real-time updating and probabilistic postprocessing in hydrological forecasting for enhancing flood risk reduction
This study presents an integrated hydrological forecasting framework combining deterministic modeling, real-time correction, and probabilistic post-processing. It significantly enhances forecast accuracy and provides reliable uncertainty quantification for flood risk reduction in snowmelt-supplied river basins.
-
Wang et al. (2025) Energy‐Consumption‐Induced Anthropogenic Heat Release Intensifies Heatwaves and Wildfire Threats in North America: A CESM2‐Based Projection for the Late 21st Century
This study investigates the impact of anthropogenic heat release (AHR) on summer extreme heat events in North America during 2081–2100 under the SSP5-8.5 scenario, revealing that AHR significantly warms surface temperatures, increases extreme heat event frequency, alters radiative fluxes, modifies atmospheric circulation, and exacerbates moisture stress and wildfire risk.
-
Ye et al. (2025) Hydrodynamic simulation and multi-objective optimization coupling for efficient decision-making of water transport and distribution in irrigated areas
This study proposes a multi-objective optimization framework for open-channel flow distribution, coupling hydrodynamic simulation with gate regulation and crop water demand under varying hydrological conditions. Applied to the Chahayang Irrigation District, the framework significantly reduces average flow fluctuations by 35%, enhances distribution efficiency by 22%, and decreases gate adjustments by 28% compared to traditional methods.
-
Cai et al. (2025) High-resolution surface and rootzone soil moisture over US cropland: A novel framework assimilating multi-source remote sensing data, machine learning, and the Layered Green and Ampt Infiltration with Redistribution model
This paper introduces a novel framework for monitoring high-resolution surface and rootzone soil moisture over US cropland by assimilating multi-source remote sensing data, machine learning, and a hydrological model. The objective is to provide accurate soil moisture data crucial for water resource management, drought forecasting, and nutrient transport estimation at the field scale.
-
Miah et al. (2025) Irreversibility of extreme precipitation intensity in global monsoon areas under multiple carbon neutrality scenarios
This study investigates the irreversibility of extreme precipitation intensity across seven Global Monsoon Area (GMA) sub-regions under eight distinct carbon neutrality scenarios. It reveals that extreme precipitation intensity exhibits irreversible behavior, failing to return to initial levels even after atmospheric carbon dioxide (CO2) reduction, with regional vulnerabilities significantly influenced by the timing and rate of carbon neutrality.
-
Ju et al. (2025) Impact of groundwater depth on crop coefficient: An improved evapotranspiration model
This study investigated the exponential relationship between groundwater depth and actual crop evapotranspiration (ETc act) for winter wheat, developing a new Groundwater–Meteorology-Based Actual Crop Evapotranspiration Model (GW–M model) that significantly improves ETc act estimation accuracy in shallow groundwater regions.
-
Li et al. (2025) Intelligent and interpretable forecasting method for ice-jam flood disaster levels based on fusion model
This study proposes an intelligent and interpretable forecasting framework for ice-jam flood (IJF) disaster levels, integrating generative modeling, feature selection, and ensemble learning to address data scarcity and model interpretability challenges. The developed fusion model significantly improves forecasting performance and provides localized interpretations of risk scenarios.
-
Liao et al. (2025) Biodiversity regulates the asymmetric influence of forest cover gain and loss on land surface temperature
This study reveals that biodiversity is the dominant driver of spatial heterogeneity in the asymmetric land surface temperature responses to forest cover gain and loss, primarily by stabilizing interannual climate variability and enhancing soil organic carbon, which promotes young tree growth and associated cooling effects.
-
Shirzadi et al. (2025) Leveraging imbalanced dataset in urban flood susceptibility prediction: A case study of Sanandaj City
This study investigates the utility of imbalanced datasets for urban flood susceptibility prediction in Sanandaj City, Iran, comparing hybrid machine learning (RFADT) and deep learning (CNN) models, and finds that imbalanced datasets significantly enhance prediction accuracy compared to balanced ones.
-
Li et al. (2025) Introducing an explainable neural network framework for nonstationary flood frequency analysis
This study introduces an explainable neural network framework (XNN-NFFA) for nonstationary flood frequency analysis, integrating feedforward neural networks with SHAP to accurately estimate flood distributions and interpret the influence of environmental drivers. The framework demonstrates superior performance over traditional models and identifies key drivers of flood nonstationarity in the upper Yangtze River Basin.
-
Han et al. (2025) Vegetation greenness and productivity recovery following the 2022 record-breaking compound soil moisture and atmospheric drought in Yangtze River Basin
This study investigates the daily-scale recovery patterns and drivers of vegetation greenness (NDVI) and productivity (GPP) in the Yangtze River Basin following the 2022 record-breaking compound drought, revealing distinct recovery times and influencing factors for each.
-
Luo et al. (2025) Assessing uncertainties in modeling the climate of the Siberian frozen soils by contrasting CMIP6 and LS3MIP
This study quantifies the contributions of land surface parameterization and atmospheric forcing to discrepancies in frozen soil simulations in Siberia by contrasting CMIP6 and LS3MIP models. It finds that land-only models (LS3MIP) exhibit larger biases and spread in frozen soil temperatures than coupled models (CMIP6), indicating significant error compensation in coupled systems and underscoring the need for improved snow insulation and soil hydrothermal dynamics in land surface models.
-
Zhu et al. (2025) Parametric Sensitivity Analysis of ENSO ‐Related Shortwave Feedback Simulations by PPE Experiments in CAM6
This study investigates how deep convection and cloud physics scheme parameters in CAM6 impact the simulation of shortwave feedback during the El Niño‐Southern Oscillation (ENSO) cycle, identifying key parameters that modulate this feedback through ice and liquid water paths.
-
Hibbert et al. (2025) Future Projections of Sea Surface Temperature ( SST ) in the MDR and Wider Caribbean Region: Utilising CMIP6 GCM Ensembles
This study evaluates CMIP6 global climate models' performance in simulating and projecting sea surface temperature (SST) variations in the Caribbean and tropical Main Developing Region, particularly during the Late Rainfall Season. The models robustly reproduce historical SSTs and project significant future warming with a distinct spatial gradient, providing a foundation for climate impact understanding and adaptation planning.
-
Devoie et al. (2025) Modelling near-surface ice content and midwinter melt events in mineral soils
This study presents a numerically efficient, semi-analytical coupled thermal and mass transport model to represent near-surface soil ice content and midwinter melt events in mineral soils. The model capably reproduces field observations of frozen, thawed, or transitioning soils and offers significant computational advantages over existing continuum models, making it suitable for regional hydrologic applications.
-
Fustos et al. (2025) Controls over debris flow initiation in glacio-volcanic environments in the Southern Andes
This study investigates the geomorphological, geotechnical, and hydrometeorological controls over debris flow initiation in glacio-volcanic environments of the Southern Andes, revealing that the combination of high water accumulation capacity, effective precipitation capture by slopes, and specific soil properties (volcanic soils over low-permeability glacial deposits) are critical for triggering these events. The research highlights the importance of monitoring soil moisture and surface deformation for predicting these hazards, especially under changing climate conditions.
-
Shi et al. (2025) A sub-seasonal to seasonal climate forecast informed irrigation scheduling tool for the Contiguous United States
This study develops a real-time irrigation scheduling tool for cornfields across the Contiguous United States (CONUS) by integrating sub-seasonal to seasonal (S2S) climate forecasts with the Soil Water Atmosphere Plant (SWAP) model. The S2S-informed scheduling improves water use efficiency and net profit compared to default SWAP schedules, offering national applicability.
-
Ma et al. (2025) Model Calibration and Data Assimilation for the National Water Model: A Case Study over California
This study retrospectively evaluated and enhanced the Noah-MP hydrological model for flood forecasting in three California river basins, demonstrating that an enhanced Noah-MP produced forecasts comparable to, and often more accurate than, operational forecasts, particularly for lead times exceeding 2 days.
-
Xu et al. (2025) Seasonality of the South Pacific Meridional Mode: Role of Oceanic Meridional Advection Feedback Beyond Thermodynamics Dominance
This study investigates the seasonality of the South Pacific Meridional Mode (SPMM), revealing that while thermodynamical wind-evaporation-SST feedback drives SPMM-SST growth, meridional advection, primarily from wind stress-driven Ekman transport, acts as a dominant damping mechanism, critically shaping its seasonal cycle.
-
Xu et al. (2025) RMC: advancing daily runoff forecasting with a unified cross-scale deep learning approach
This paper introduces Res-Mamba-Causal (RMC), a novel deep learning architecture designed to improve daily runoff forecasting by unifying multi-scale hydrologic feature modeling. RMC consistently outperforms existing models like LSTM and Transformer across various performance metrics on four U.S. watersheds, demonstrating enhanced accuracy and the ability to capture complex hydrologic dynamics.
-
Sucozhañay et al. (2025) Streamflow drought identification and characterization in a tropical Andean basin: effect of threshold methods
This study evaluates the **eartH2Observe Tier-1** global water resources ensemble, consisting of 10 hydrological and land surface models forced by a consistent reanalysis dataset. The research demonstrates that while the ensemble mean generally provides the most reliable global estimates, significant model spread exists, particularly in runoff and snow-dominated regions.
-
Chandrakar et al. (2025) Dampening of the Precipitation Response to Aerosol Pollution From Turbulence in Cumulus Clouds
This study, using aircraft observations and a cloud model, demonstrates that turbulence-enhanced drop collision-coalescence in warm cumulus clouds not only accelerates rain onset but also significantly dampens precipitation susceptibility to aerosol loading, a critical finding for Earth system models.
-
Kacimov et al. (2025) Phreatic saturated and saturated-unsaturated seepage towards transpiring strip of vegetation: Analytic and HYDRUS2D modeling of biodrainage
This study develops and utilizes 2D analytical and numerical models to simulate saturated and saturated-unsaturated seepage towards a transpiring strip of vegetation, quantifying its impact on water table drawdown and flow dynamics. It advocates for biodrainage via root water uptake as a sustainable and efficient hydroecological tool for managing shallow aquifers in agro and urban environments.
-
Liu et al. (2025) PINN framework for urban flood depth prediction: integrating data-driven insights with physical constraints
This study developed SWEPINN, a Physics-Informed Neural Network integrating shallow water equations and data-driven insights, to provide efficient, accurate, and interpretable urban flood depth predictions, outperforming traditional data-driven models.
-
Garg et al. (2025) Estimation of wheat yields and water savings with deficit irrigation in water-stressed NW India
This study evaluates efficient irrigation scheduling strategies for spring wheat in water-stressed NW India using the AquaCrop-OSPy model calibrated with Particle Swarm Optimization. It demonstrates that 1.7–38.1% irrigation water savings can be achieved with less than 5% yield loss, significantly improving irrigation water productivity and aiding groundwater conservation.
-
Zhang et al. (2025) Long history paddy rice mapping across Northeast China with deep learning and annualresult enhancement method
This study developed a deep learning and annual result enhancement (ARE) method to generate annual paddy rice maps for Northeast China from 1985 to 2023 using multi-sensor Landsat data, demonstrating significantly improved accuracy over traditional methods. The research revealed a substantial expansion of paddy rice cultivation in the region, providing valuable data for agricultural monitoring and policy.
-
Xie et al. (2025) Estimation of annual runoff using supraglacial channel geometry derived from UAV surveys of Qiyi Glacier, northern Tibetan Plateau
This study developed a novel remote sensing method using UAV-derived supraglacial channel geometry to accurately estimate the annual meltwater discharge of Qiyi Glacier, finding that geometric parameters like lateral deviation, gradient, and width can predict annual discharge with high accuracy.
-
Shah et al. (2025) Constrained negentropy optimisation (CoNE-opt): Using independent components to merge satellite data products
This paper introduces Constrained Negentropy Optimisation (CoNE-opt), a novel method for merging uncertain geophysical datasets by maximizing non-Gaussianity. CoNE-opt outperforms traditional merging techniques, particularly in the presence of high error cross-correlation and outliers, as demonstrated by superior performance in merging global satellite-derived surface soil moisture products.
-
Yang et al. (2025) Hybrid high-dimensional vine copula–Bayesian network framework for flood risk analysis in reservoir–lake systems: Addressing multisource uncertainties
This study developed a hybrid high-dimensional vine copula–Bayesian network framework for flood risk analysis in complex reservoir–lake systems, demonstrating its effectiveness in the Chaohu Lake Basin by identifying dominant risk sources and quantifying their impact on lake water levels.
-
Marchal (2025) Some challenges in predicting heavy precipitation with convective-scale ensembles
[]
-
Liu et al. (2025) Unravelling the Dominant Influence of ENSO Over IOD on Australian Springtime Climate Variability Using a Pacemaker Modelling Approach
This study utilizes the Conformal Cubic Atmospheric Model (CCAM) to isolate the independent impacts of ENSO and IOD on Australian springtime rainfall and temperature, concluding that ENSO is the dominant driver of variability.
-
Yang et al. (2025) A novel Improved Geographically Weighted Random Forest (IGWRF) model for low-resolution soil moisture data downscaling in Africa
This study proposes an Improved Geographically Weighted Random Forest (IGWRF) model to downscale 9 km SMAP soil moisture data to 1 km in Kenya, effectively addressing spatial heterogeneity and nonlinear relationships. The IGWRF model significantly outperforms traditional Random Forest and Geographically Weighted Random Forest, providing high-accuracy, high-resolution soil moisture data crucial for agricultural management and drought monitoring in Africa.
-
Takahashi et al. (2025) Land‐Ocean Differences in Tropical Deep Convective Clouds: Intercomparison of DYAMOND Simulations and CloudSat Observations
This study compares tropical deep convective clouds and their environments in DYAMOND simulations with CloudSat observations across three tropical regions. It finds that while DYAMOND models capture environmental differences, they exhibit biases in representing convective intensity and precipitation dynamics, particularly overestimating convection in the Tropical Warm Pool.
-
Williams et al. (2025) Sea‐Surface Temperature Patterns, Radiative Cooling, and Hydrological Sensitivity
This study connects clear-sky longwave radiative cooling to tropical sea-surface temperature patterns, explaining why hydrological sensitivity is approximately 25% larger in uniform warming scenarios compared to abrupt-4xCO2 runs due to differing tropical clear-sky longwave radiative cooling changes.
-
Norman et al. (2025) PortUrb: a performance portable, high-order, moist atmospheric large eddy simulation model with variable-friction immersed boundaries
This paper introduces "portUrb," a performance-portable, high-order, moist atmospheric Large Eddy Simulation (LES) model designed for urban building geometries using variable-friction immersed boundaries. The model demonstrates accuracy and robustness across various atmospheric boundary layer, supercell, and urban flow scenarios, closely matching experimental observations and other model comparisons.
-
Ebaju et al. (2025) Spatiotemporal Changes of Drought Conditions Over the Hindu‐Kush Himalayan Region During the Recent Century: Insights for Climate Adaptation
This study reveals contrasting drought patterns across the Hindu Kush Himalayan (HKH) region from 1901 to 2022, showing significant drought reduction in the western HKH and intensifying aridity in the eastern parts. It attributes these divergent trends to distinct climatic drivers, including precipitation, temperature, and teleconnections like the Arctic Oscillation and ENSO, highlighting the need for spatially targeted adaptation strategies.
-
Sigmond et al. (2025) Jet stream response to future Arctic sea ice loss not underestimated by climate models
This study proposes a new, more robust emergent constraint based on lower stratospheric winds to assess climate models' ability to project the winter jet stream response to Arctic sea ice loss. The findings indicate that climate models do not systematically underestimate this response, reducing projection uncertainty by 62% and increasing confidence in future poleward jet stream shifts.
-
Klaus et al. (2025) Brief communication: How extreme was the thunderstorm rain in Vienna on 17 August 2024? A temporal and spatial analysis
This study quantifies the exceptional nature of the 17 August 2024 thunderstorm in Vienna, which delivered 107 mm of rain in two hours with an estimated 700-year return period, demonstrating that such extreme events are often missed by gauge networks and their frequency is likely increased by climate change.
-
Galton‐Fenzi et al. (2025) Multi-model estimate of Antarctic ice-shelf basal mass budget and ocean drivers
This study provides the first multi-model mean estimate of Antarctic ice-shelf basal mass budget and ocean drivers by comparing nine circum-Antarctic ocean simulations, revealing that the multi-model mean melt rate (0.64 m/year) is lower than satellite-derived estimates (0.88 m/year) but highlights the critical, combined influence of thermal driving and friction velocity on melting.
-
Guillemois (2025) Τrajectοires des paysages bοcagers de la Νοrmandie au Grand Οuest : apprοche géο-histοrique et mοdélisatiοn des cοnnectivités hydrοlοgiques pοur cοntribuer à la gestiοn intégrée des bassins versants
[Information not extractable from provided text]
-
Gilmour et al. (2025) Mesoscale Convective Systems over South America: Representation in Kilometer-Scale Met Office Unified Model Climate Simulations
This study assesses the representation of mesoscale convective systems (MCSs) in multiyear convection-permitting regional climate model simulations over South America against satellite observations. It finds that while simulations capture MCS climatology well, they show biases in frequency (overestimated in Amazon, underestimated in La Plata basin) and precipitation characteristics (overestimated intensity, underestimated area), leading to an underestimation of MCS contribution to total rainfall.
-
Quan et al. (2025) Solar Geoengineering Strategies Based on Reinforcement Learning
This paper investigates the use of reinforcement learning (RL) to optimize stratospheric aerosol injection (SAI) strategies within an idealized global climate model, demonstrating that RL can learn stable, plausible, and time-dependent deployment strategies to maximize benefits and minimize side-effects.
-
Gaglo et al. (2025) Sensitivity of a Sahelian groundwater-based agroforestry system to tree density and water availability using the land surface model ORCHIDEE (r7949)
This study developed and evaluated a new configuration of the ORCHIDEE land surface model to simulate a Sahelian groundwater-based agroforestry system, revealing that increased tree density enhances carbon sequestration but reduces crop yield, and that interannual water variability differentially impacts tree and crop productivity.
-
Helfer et al. (2025) Enhanced baseflow separation in rural catchments: event-specific calibration of recursive digital filters with tracer-derived data
This study enhanced baseflow separation in a small rural catchment by developing an innovative event-specific calibration methodology for Recursive Digital Filters (RDFs) using silica tracer data, demonstrating that dynamic calibration significantly improves accuracy, particularly for the Eckhardt's filter.
-
Bongio et al. (2025) A Statistically Based Method to Estimate Long‐Term Daily Air Temperature at High Elevations
This study develops a statistical methodology to reconstruct daily air temperature time series at the Jungfraujoch (3571 m a.s.l.) in Switzerland from 1900, using observations from lower-altitude stations. The reconstructed series provides a robust, computationally efficient benchmark for evaluating temperature anomalies and studying elevation-dependent warming, achieving performance comparable to existing high-resolution datasets with fewer data requirements.
-
Prima et al. (2025) Trees control hillslope subsurface flow: Insights from stemflow and throughfall experiments, geophysical surveys, and numerical modeling
This study investigated the effects of rainfall partitioning on subsurface water dynamics across multiple spatial scales on a forested hillslope in Central Italy. Findings reveal dual-permeability soil behavior, with throughfall promoting matrix infiltration and stemflow enhancing rapid macropore flow, which connects to deeper lateral pathways, controlling hillslope-scale groundwater fluctuations.
-
Sharifi et al. (2025) Technical note: GRACE-compatible filtering of water storage data sets via spatial autocorrelation analysis
This study develops a methodology to determine an optimal spatial filtering approach for water storage compartment (WSC) datasets to ensure spatial compatibility with GRACE/GRACE-FO terrestrial water storage anomaly (TWSA) products. It identifies an isotropic Gaussian filter with a 250 km width as optimal for combined WSCs, enabling consistent subtraction from GRACE-TWSA for groundwater storage estimation.
-
Mishra et al. (2025) Future Projections of Marine Heatwaves in the Northern Indian Ocean Using the HighResMIP Models: Role of Horizontal Resolution and Percentile Thresholds
This study investigates how model horizontal resolution and percentile thresholds influence marine heat wave (MHW) characteristics in the northern Indian Ocean using HighResMIP simulations. It finds that higher resolution improves MHW simulation and projects significant increases in MHW duration, intensity, and frequency, including the emergence of severe and extreme events, under the SSP5-8.5 scenario.
-
Zhang et al. (2025) Incorporating water and temperature factors enhanced the performance of the stomatal conductance model for soybeans cultivated under plastic film mulching with drip irrigation in the northeast black soil region
This study aimed to enhance stomatal conductance model performance for soybeans under plastic film mulching with drip irrigation (PFMDI) in northeast China by incorporating water response (f(θ)) and leaf-air temperature difference (ΔT) factors. The corrected Unified Stomatal Optimization (USO) model, particularly with the f(θ) factor, demonstrated significantly improved accuracy and applicability across diverse hydrothermal conditions.
-
Zhang et al. (2025) Irrigation cooling effect reduced by water-saving practices
This study reveals that the widespread adoption of water-saving irrigation (WSI) practices across China has significantly weakened irrigation's daytime cooling effect on land surface temperature while intensifying nighttime cooling, driven by shifts in surface energy partitioning.
-
Fan et al. (2025) Remote sensing proxies underestimate fire-induced gross primary productivity loss and overestimate recovery in forests
This study evaluated five global satellite-based Gross Primary Productivity (GPP) products and three complementary proxies against eddy covariance measurements at ten fire-affected sites, revealing systematic biases that underestimate fire-induced GPP loss and overestimate recovery, particularly in forests.
-
Yang et al. (2025) Increasing irrigation water requirements across croplands in the Contiguous United States throughout the 21st century
This study estimates crop-specific irrigation water requirements (IWR) across the Contiguous United States (CONUS) throughout the 21st century under moderate (SSP245) and severe (SSP585) warming scenarios. Results project a continuous increase in IWR across CONUS, with annual average IWR depth rising by 10.3 % under SSP245 and 26.4 % under SSP585 by the late 21st century, highlighting growing challenges for agricultural water management.
-
Zehrung et al. (2025) Standardising the “Gregory method” for calculating equilibrium climate sensitivity
This study systematically assesses 32 data processing pathways for the "Gregory method" to estimate Equilibrium Climate Sensitivity (ECS) using 44 CMIP6 models, revealing that while the multi-model ECS range is robust, individual model estimates can vary significantly based on processing choices, leading to a recommended standardized method for improved reproducibility.
-
Müller et al. (2025) The ICON-based Earth System Model for climate predictions and projections (ICON XPP v1.0)
This paper introduces ICON XPP (eXtended Predictions and Projections), a new Earth System Model configuration based on the ICON framework, designed for next-generation climate predictions and projections. Two configurations with different spatial resolutions are presented and evaluated, demonstrating performance comparable to CMIP6 models and high computational efficiency for large ensemble simulations.
-
Jiang et al. (2025) CropLayer: a 2 m resolution cropland map of China for 2020 from Mapbox and Google satellite imagery
This study presents CropLayer, a 2 meter resolution cropland map of China for 2020, developed from Mapbox and Google satellite imagery. It achieves high accuracy (pixel-level 88.73%, block-level 96.5%) and strong consistency with official statistics, with 30 out of 32 provincial units showing area estimates within ±10% deviation, significantly outperforming existing datasets.
-
Uusinoka et al. (2025) Scale invariance in kilometer-scale sea ice deformation
This study uses high-resolution MOSAiC ship radar data and a deep learning optical flow technique to investigate kilometer-scale sea ice deformation, revealing a lower limit of approximately 100 meters for scale-invariance in winter pack ice that disappears in summer.
-
Xiao et al. (2025) Optimization of coordinated autumn and spring irrigation under water resource constraints in cold and arid region
This study developed an optimized water allocation scheme for autumn and spring irrigation in the Hetao Irrigation District under water resource constraints, integrating remote sensing data and the SHAW model to enhance water use efficiency and mitigate soil salinization. It recommends differentiated irrigation quotas and areas for various soil salinity levels and crop types, leading to a balanced water distribution for sustainable agricultural development.
-
Dutrievoz (2025) Les isotopes de la vapeur d'eau en Antarctique, traceurs des processus de la couche limite et de la dynamique à grande échelle
[Information not extractable from provided text]
-
Liu et al. (2025) Urbanization is projected to increase local surface temperature by 2100
This study developed a 1-km resolution global land surface temperature dataset for 2020–2100, integrating climate change and urbanization effects. It projects that urbanization will contribute an average local warming of 0.1 °C by 2100, with 10–16% of urban areas experiencing extreme warming exceeding 1 °C.
-
Decharme (2025) A process-based modeling of soil organic matter physical properties for land surface models – Part 1: Soil mixture theory
This study proposes a process-based framework, grounded in soil mixture theory, to accurately model soil organic matter physical properties in land surface models, demonstrating significant improvements over empirical approaches through validation against experimental and in situ datasets.
-
Zhu et al. (2025) Large-scale irrigation area mapping: Status and challenges
This paper provides a comprehensive synthesis of large-scale irrigation area mapping methodologies and datasets, benchmarking ten global and regional products against EUROSTAT 2020 gridded statistics in Europe. It concludes that integrating ground-based statistics with geospatial information significantly improves mapping accuracy, especially in humid regions, while highlighting the critical need for standardized, accessible, and high-quality ground-truth and statistical data.
-
Shin et al. (2025) Machine learning-based retrieval of aerosol size and hygroscopicity using horizontal scanning LiDAR and PM data
This study develops a machine learning-based approach to retrieve aerosol size and hygroscopicity by integrating horizontal scanning LiDAR and in-situ PM data, revealing that coarse hygroscopic aerosols dominate the coastal urban region and significantly impact optical properties despite low mass concentrations.
-
Wang et al. (2025) Improving river surface flow velocity measurement by coupling optimal search line algorithm with space-time image velocimetry
This study introduces an automatic optimal search line selection algorithm to enhance the accuracy of Space-Time Image Velocimetry (STIV) for river surface flow velocity measurement. The proposed method significantly reduces errors in velocity estimation compared to traditional fixed-line STIV, improving its robustness and applicability across various flow conditions.
-
Rahlves et al. (2025) Investigating the multi-millennial evolution and stability of the Greenland ice sheet using remapped surface mass balance forcing
This study introduces and evaluates an SMB remapping procedure for stand-alone ice sheet models to efficiently simulate the multi-millennial evolution of the Greenland ice sheet. The remapping method effectively captures first-order climate-ice sheet feedbacks, preserving ablation zone structure and reducing biases compared to conventional parameterizations, leading to more realistic long-term mass loss projections.
-
Lang et al. (2025) Significant uncertainties from overlooking aerosol-cloud coexistence in surface solar radiation estimates using passive satellite observations
This study systematically evaluates the significant uncertainties introduced by overlooking aerosol-cloud coexistence in surface solar radiation estimates derived from passive satellite observations. It finds that ignoring this coexistence leads to substantial errors in direct and diffuse solar radiation, highlighting the need for improved methodologies.
-
Chao et al. (2025) Evaluation of satellite soil moisture products (AMSR2, SMAP L3/L4) across mainland China using in situ data (2020–2024)
This study evaluated the soil moisture monitoring performance of three satellite products (AMSR2, SMAP L3, and SMAP L4) across mainland China using 3293 in-situ stations and Monte Carlo simulations. It found that SMAP L4 consistently performed best, followed by SMAP L3, while AMSR2 exhibited the largest errors, with performance differences primarily driven by sensor properties, algorithm complexity, and land cover heterogeneity.
-
Marchionni et al. (2025) Urban greening and water strategies are key to adapt Australian cities to climate change and urban growth
This paper explores how Australian cities integrate urban greening with water-sensitive urban design (WSUD) for climate adaptation, demonstrating that effective water management is crucial for the long-term success of urban greening in building climate-resilient cities.
-
Zakeri et al. (2025) High-resolution snow water equivalent estimation: a data-driven method for localized downscaling of climate data
This study develops a data-driven k-nearest neighbor method to downscale low-resolution climate data into daily high-resolution (500 m) snow water equivalent (SWE) estimates for mountainous regions. The approach successfully generates SWE data that closely matches reanalysis data, demonstrating that performance is highly dependent on the choice and accuracy of the climate model inputs.
-
Liu et al. (2025) First Observation of Thunderstorm Occurrences in the Lower Atmosphere by All‐Sky Meteor Radars
This study pioneers the use of all-sky meteor radars, typically for mesospheric/ionospheric observations, to detect and track lower-atmospheric thunderstorms, demonstrating their capability to accurately capture thunderstorm development.
-
Xie et al. (2025) Asymmetric Impacts of Extreme Positive and Negative Indian Ocean Dipole Events on Late‐Summer Monsoon Rainfall in Western South Asia
This study reveals that both extreme positive and negative Indian Ocean Dipole (IOD) events asymmetrically enhance rainfall over western South Asia during August–September, even when ENSO effects are removed, through distinct moisture convergence mechanisms driven by anomalous winds.
-
Harris et al. (2025) Global observations of land-atmosphere interactions during flash drought
This study uses global satellite observations to investigate land-atmosphere coupling processes during flash droughts from 2000–2020, revealing that precursor land surface conditions significantly influence drought intensity and associated heat extremes, offering valuable information for subseasonal-to-seasonal (S2S) forecasts.
-
Bolivar et al. (2025) How Do Tropical Cyclones Directly Simulated in High-Resolution Climate Models Differ from Statistically Dynamically Generated Storms?
This study compares high-resolution climate models and statistical–dynamical downscaling (SDD) models for simulating landfalling tropical cyclones (TCs). It finds that while SDD offers computational advantages and improves some metrics, it also introduces unphysical behaviors and overrepresents landfalling storms, highlighting distinct biases in both techniques that can be better understood through their comparison.
-
Zhang et al. (2025) Groundwater volume loss and land subsidence in the North China plain investigated using wide-area InSAR surveying and mechanical modeling
This study integrates wide-area InSAR data with a mechanical model to map aquifer deformation and groundwater storage loss (GWSL) across the North China Plain, revealing severe subsidence, quantifying aquifer elastic recovery, and providing the first 2-km resolution GWSL dataset. It highlights a shift in subsidence from urban to agricultural areas, with urban deformation stabilizing while agricultural subsidence intensifies.
-
Chawanda et al. (2025) CoSWAT Model v1: A high-resolution global SWAT+ hydrological model
This study developed CoSWAT Model v1, a high-resolution global SWAT+ hydrological model, and an open-source framework for its reproducible setup and execution. It demonstrates the feasibility of global SWAT+ modeling at 2 km resolution, showing reasonable evapotranspiration patterns but limited river discharge performance without calibration or reservoir implementation.
-
Parazoo et al. (2025) Solar Induced Fluorescence as an Application Ready Early Warning Indicator of Flash Drought
This paper synthesizes recent advancements in high-resolution solar-induced fluorescence (SIF) mapping, arguing that these machine learning-derived, spaceborne SIF products are ready for immediate operational use by the drought monitoring community for flash drought early warning.
-
Bonan et al. (2025) Beyond surface fluxes: Observational and computational needs of multilayer canopy models – A walnut orchard test case
This study evaluates the Community Land Model's multilayer canopy model (CLM-ml v2) against comprehensive, multi-level observations from a walnut orchard, demonstrating its strong performance for most atmospheric conditions but highlighting limitations in simulating within-canopy mixing during strongly stable regimes.
-
Tiwari et al. (2025) A two-step iterative data assimilation and calibration approach for improving large-scale hydrological processes in the Ganga basin
[Information not extractable from provided text]
-
Zhan et al. (2025) Exacerbated Variability and Extremes in Streamflow Across Half of China From 1961 to 2018
This study investigated trends in seasonal streamflow variability and extremes across China from 1961 to 2018, revealing significant increases in approximately half of the country, particularly in the Northwest River Basin and during summer.
-
Xu et al. (2025) Balancing agricultural expansion and groundwater sustainability: Insights from GRACE and hydrological models
This study developed an integrated downscaling framework using GRACE observations and hydrological models to generate high-resolution groundwater storage anomaly (GWSA) estimates for the Sanjiang Plain. It found that while regional dry-wet conditions are the dominant drivers of GWSA dynamics, agricultural cropping pattern shifts, particularly paddy expansion in the eastern region, increasingly exert negative impacts on groundwater sustainability, with a critical threshold identified for maize-to-rice conversion.
-
ElGhawi et al. (2025) Hybrid‐Modeling of Land‐Atmosphere Fluxes Using Integrated Machine Learning in the ICON‐ESM Modeling Framework
This paper develops Hybrid-JSBACH4, a novel hybrid modeling approach that integrates data-driven neural network parameterizations, trained on eddy-covariance flux measurements, into the mechanistic JSBACH4 land surface model. This integration significantly improves the simulation of land-atmosphere water and carbon fluxes by reducing biases in transpiration and gross primary production compared to the original JSBACH4.
-
Zhang et al. (2025) On the Use of Knowledge‐Informed Machine Learning and Multisource Data for Spatially Explicit Estimation of Irrigation Water Withdrawal
## Identification - **Journal:** Earth s Future - **Year:** 2025...
-
Brunetti et al. (2025) A Unified Physically Based Model to Simulate Water and Carbohydrates Allocation Along the Soil‐Fruit Axis
This study integrates the HYDRUS hydrological model with the SUGAR model to mechanistically link soil processes and fruit development, demonstrating accurate predictions of tomato fruit water and carbohydrate dynamics under varied irrigation, and identifying phloem flux and active carbohydrate uptake as key drivers of fruit growth and sugar accumulation.
-
Ayantobo et al. (2025) Atmospheric Rivers Sustain and Reshape Hydrological Responses Across Chinese River Basins
This study quantifies the role of Atmospheric Rivers (ARs) in driving hydrological responses across Chinese river basins from 1950 to 2023, revealing a significant south-to-north contrast where southern basins experience strong, long ARs that sustain wetness and amplify floods, while central and northern basins show declining AR influence since the 1980s.
-
Zhang et al. (2025) A Simple Intermediate Coupled MJO‐ENSO Model: Multiscale Interactions and ENSO Complexity
This paper develops a simple intermediate coupled MJO-ENSO model to understand their bidirectional feedback and its role in modulating ENSO complexity, successfully capturing observed MJO and ENSO features and their critical interactions.
-
Tang et al. (2025) Mass Change Index for Characterizing Hydrological Extremes Every Few Days From Satellite Gravity Measurements
This paper introduces the Mass Change Index (MCI), a new hydrological index derived from GRACE Follow-On (GRACE-FO) satellite data, enabling the assessment of instantaneous extreme wet and dry events every few days. It demonstrates MCI's superior ability to detect the severity and timing of the 2020 Yangtze River flood and the 2022 MLYRB drought compared to traditional monthly GRACE-FO and streamflow indices.
-
Wang et al. (2025) Using Machine Learning to Discover Parsimonious and Physically‐Interpretable Representations of Catchment‐Scale Rainfall‐Runoff Dynamics
This paper explores the development of physically interpretable machine learning models for dynamical systems, demonstrating that Mass-Conserving-Perceptron (MCP) based networks with a distributed-state mechanism can achieve both physical interpretability and good predictive performance in catchment-scale streamflow modeling with minimal complexity.
-
Kim et al. (2025) Hysteresis response of Northern Hemisphere winter temperature variability under different CO₂ removal pathways
This study investigates the hysteresis and reversibility of Northern Hemisphere winter daily temperature variability (Tstd) under different CO₂ removal pathways, finding that Tstd partially recovers but exhibits regional hysteresis and irreversibility, particularly in high-concentration scenarios, driven by changes in local temperature gradients.
-
Xue et al. (2025) A Greening Future Elevates Flash Drought Risk in Northern Mid‐to‐High Latitudes
This study investigates the mechanism by which vegetation regulates flash drought frequency and its future changes, finding that dense vegetation, particularly in northern mid-to-high latitudes, increases flash drought risk by decoupling precipitation from soil moisture through enhanced transpiration, accelerating soil moisture depletion.
-
He et al. (2025) Hybrid Lake Model (HyLake) v1.0: unifying deep learning and physical principles for simulating lake-atmosphere interactions
This study introduces HyLake v1.0, a novel hybrid lake model that unifies physics-based surface energy balance equations with a Bayesian Optimized Bidirectional Long Short-Term Memory-based (BO-BLSTM-based) surrogate to simulate lake surface temperature (LST) dynamics. The model demonstrates superior performance in simulating lake-atmosphere interactions and strong generalization and transferability to ungauged sites and with unlearned forcing datasets compared to traditional and other hybrid models.
-
Agboly (2025) Improving Agricultural Water Use in the Texas High Plains: A Strategic Approach to Crop Switching and Optimal Land Allocation
Information not available due to unreadable paper text.
-
Zhao et al. (2025) Response of Tipping Elements to Different Strategies of Stratospheric Aerosol Injection
This study assesses the effectiveness of various Stratospheric Aerosol Injection (SAI) strategies, including single-objective and multi-objective approaches at different latitudes, in mitigating risks associated with climate tipping elements. It finds that while SAI generally reduces risks, the optimal strategy depends on the specific tipping element, highlighting trade-offs between temperature stabilization goals and regional risk reduction.
-
Kim et al. (2025) Changes in the Frequency of Flood Events Across the United States Detectable by the Middle of This Century
This study applies a statistical attribution-and-projection approach to thousands of streamgages across the conterminous United States (CONUS) to assess how the frequency of flood events is expected to change under multiple scenarios, finding increased frequency in the eastern US, slight decreases in the Southwest and Great Plains, and shifts in seasonality by mid-century.
-
Han et al. (2025) A Decadal Hybrid GCM Simulation Using Deep‐Learning‐Based Cloud and Convection Parameterization Generalized to a Warm Climate
This study demonstrates that a global climate model (GCM) with neural-network-based cloud and convection parameterization, trained solely on present-day climate data, can successfully perform a stable, decade-long simulation of a warm climate with a +4 K sea surface temperature anomaly, matching the performance of conventional and superparameterized models.
-
Wu et al. (2025) Long‐Term Lake Ice Evolution in a Large Endorheic Lake Undergoing Accelerated Shrinkage in a Semiarid Region of China
This study investigates the long-term evolution of lake ice in Lake Daihai, a shrinking endorheic lake, by integrating six decades of data and a numerical model. It reveals accelerated lake shrinkage and ice thinning, primarily driven by atmospheric warming, salinization, and morphological changes, highlighting the need for integrated assessment frameworks.
-
Jiang et al. (2025) Substantial Contribution of Woody Components to Rainfall Interception in Chinese Forests: Insights From a Refined Analytical Model
This study refined the Gash model to distinguish rainfall interception by woody components from leaves, finding that woody components contribute significantly to total interception, particularly in needle-leaf forests and during non-growing seasons.
-
Fong et al. (2025) Advancing evapotranspiration estimation with remote sensing and artificial intelligence – A review
This review paper comprehensively synthesizes the state-of-the-art in evapotranspiration (ET) estimation by integrating remote sensing (RS) data with artificial intelligence (AI) techniques, including machine learning, deep learning, explainable AI, and emerging geospatial foundation models. It highlights how RS addresses data limitations of conventional methods and how AI enhances accuracy and efficiency for sustainable water management.
-
Hale et al. (2025) What's Next for Snow: Insights From the NASA Terrestrial Hydrology Program Community Snow Meeting
This paper summarizes the outcomes of a Community Snow Meeting sponsored by NASA THP, outlining the current state of snowpack monitoring techniques, identifying critical knowledge gaps, and recommending next steps for global-scale snow research and development.
-
Deng et al. (2025) Optimizing winter wheat resilience with drought-crop modeling in the Tarim River Basin
This study quantifies drought-induced yield-reduction risks for winter wheat in the Tarim River Basin by integrating drought indices (SPEI, SMDI) with the DSSAT-CERES-Wheat model. It proposes adaptive water management strategies, including optimized sowing dates and supplemental irrigation (30–60 mm) at the jointing stage, to enhance regional winter wheat productivity and provide guidance for efficient agricultural water use in arid regions.
-
Pham (2025) Fusion de données spatialement et temporellement résolues : application à l'imagerie de proxidétection en viticulture
Information not extractable from the provided text.
-
Zheng et al. (2025) Quantitative assessment of building losses in China’s coastal regions due to sea-level rise under extreme climate conditions
This study quantitatively assessed land inundation and building loss across 14 coastal provinces of China under extreme sea level events and sea-level rise, revealing a total potentially inundated area of 49 366.22 km² and significant building losses concentrated in economically developed regions under a 100-year return period extreme climate scenario.
-
Shi et al. (2025) The Effects of Sea‐State‐Dependent Surface Fluxes on CESM2 Climate Simulations
This study implements and evaluates a sea-state-dependent surface flux scheme, incorporating prognostic ocean waves via WAVEWATCH III, into CESM2, demonstrating significant improvements in mean atmospheric circulation and upper ocean biases.
-
Dolcetti et al. (2025) Fully non-contact discharge measurement in shallow streams via physics-based water-surface image analysis
[Information not extractable due to corrupted paper text.]
-
Abbasi et al. (2025) Increased Streamflow Intermittence in Europe Due To Climate Change Projected by Combining Global Hydrological Modeling and Machine Learning
## Identification - **Journal:** Earth s Future - **Year:** 2025...
-
Chehreh et al. (2025) An impact-oriented framework for a deep learning–based composite drought index considering potential economic losses
This study proposes a novel deep learning-derived drought index, shifting from traditional comparative validation to an impact-oriented evaluation using drought-induced economic losses as the primary performance metric, and offers a framework for index selection based on predictive reliability and data effort.
-
You et al. (2025) Predicting Ecosystem Respiration Under Climate Extremes Requires Varying Parameters
This study investigated the predictability of conventional ecosystem respiration (ER) models in a semi-arid grassland under climatic extremes. It found that models calibrated with fixed parameters from normal years performed poorly during extreme drought and wet years due to significant and asymmetric parameter divergence, highlighting the need for varying parameters to accurately predict ER under climate change.
-
King et al. (2025) Detectability of Post‐Net Zero Climate Changes and the Effects of Delay in Emissions Cessation
This study investigates the detectability of climate changes under net zero carbon dioxide emissions pathways and the impact of delays in achieving emissions cessation. It finds that detectable climate changes persist for centuries after net zero, and even a 5-year delay in emissions cessation leads to significantly different and detectable climate outcomes globally.
-
Pall et al. (2025) Characterising the range and outliers in CMIP6 multi-model climate projections of extremes
This study explores the projected ensemble ranges of mean and extreme temperature and precipitation changes from CMIP6 models, revealing that ensemble minimums and maximums are often dominated by one or two specific models, and this domination can vary significantly depending on whether the future is framed as a fixed time slice or a specific global warming level.
-
Liu et al. (2025) Deep Reinforcement Learning for irrigation optimization: Advantages, opportunities, and challenges
This paper systematically reviews the applications of Deep Reinforcement Learning (DRL) in irrigation optimization, highlighting its strengths in handling dynamic, high-dimensional environmental data for adaptive and long-term strategies, while also identifying key challenges like data scarcity, model interpretability, and difficulties in field deployment.
-
Ioannidis et al. (2025) A Climatology of Heatwaves Over Greece for the Period 1960–2022
This study presents a climatology of Heatwave Events (HEs) over Greece from 1960 to 2022, revealing significant positive trends in heatwave frequency, duration, and severity across most of the country, particularly in northern and western regions, with notable shifts occurring between the 1970s and 2010s.
-
Lyddon et al. (2025) Climate Change Likely to Intensify Storm‐Driven Compound Flooding in an Exemplar UK Estuary
This study uses novel high-resolution, physically consistent climate projections to assess future storm-driven compound flooding in the Dyfi estuary, UK, under a high-emissions scenario (RCP8.5). It finds that by 2080, river discharge extremes will intensify, and compound flood events will become more frequent and concurrent, leading to increased inundation.
-
Babker et al. (2025) Comparative Evaluation of Gridded Precipitation Datasets in Capturing Hydrological Extremes in a Mesoscale Heterogeneous Catchment in Austria
This study evaluates the performance of four gridded Precipitation Products (SPARTACUS v2.1, IMERG-F v07, CHIRPS v2.0, and ERA5-Land) in representing extreme precipitation and their reliability as hydrological model forcings over the Kamp catchment, Austria, finding that SPARTACUS v2.1 performed best in detecting extremes and simulating streamflow, while CHIRPS v2.0 and ERA5-Land showed poor performance.
-
Xia et al. (2025) A Spatiotemporal Pathformer‐Based Deep Learning Framework for Watershed Flood Forecasting
This study introduces a spatiotemporal Pathformer-based deep learning framework for multi-step-ahead flood forecasting, designed to dynamically adapt to flood magnitude and duration. The model demonstrates superior predictive accuracy and stability compared to LSTM and Transformer models, particularly during extreme flood events, by effectively mitigating time-lag errors and prediction bottlenecks.
-
Morovati (2025) Evaluating Use of Multiple Hydrologic Storage Indicators to Enhance Streamflow Forecasting
Information not extractable from provided text due to unreadable input.
-
Bobde et al. (2025) Future Intensification of Compound Heatwaves and Socioeconomic Exposure in Africa
This study projects a robust intensification of compound heatwaves (co-occurring daytime and nighttime heatwaves) across Africa with increasing global warming levels, leading to significantly higher population and economic exposure, particularly in Western, central, and eastern regions, and a dramatic increase in the frequency of historically rare events.
-
Chahed (2025) Hydro‐Climatic Modelling for Water Resources: From Processes to Adaptive Management and Governance
This paper synthesizes advances in hydro-climatic modelling, particularly within the CMIP framework, to demonstrate how improved model design and uncertainty management inform practical water resource strategies and adaptive governance, while also identifying persistent challenges and promising new approaches.
-
Mishra et al. (2025) An Optimal Transport Framework for Water‐Energy Coupling in Soil‐Vegetation‐Atmosphere Continuum
This study introduces an optimal transport framework based on a least action principle to explain soil moisture-evapotranspiration (SM-ET) coupling across diverse hydroclimates. Global validation using remote sensing data reveals widespread convergence to this least action state, enabling accurate estimation of active root zone depth and characteristic SM transit timescales.
-
Li et al. (2025) Deciphering Intra‐Annual Isotope Fluctuations of River Water in an Arid‐Alpine Watershed: Source Discrimination and Evaporation Quantification
This study investigated the intra-annual variations of Golmud River water isotopes in 2019 to identify recharge sources and quantify evaporation impacts, revealing that glacial meltwater and groundwater are primary recharge sources and that watershed-scale evaporation loss averages approximately 9%.
-
Thorp et al. (2025) The pyfao56 automatic irrigation scheduling algorithm
This paper develops and demonstrates a new automated irrigation scheduling algorithm within the pyfao56 Python package, allowing flexible simulation of diverse irrigation management strategies based on 25 user-specified parameters, validated with a 2018 Arizona cotton field study. The methodology provides a flexible tool for simulating realistic irrigation management schedules with practical relevance for various field applications.
-
Bhatt et al. (2025) A Framework for Evaluating Uncertainty From Multiple Sources in Probable Maximum Precipitation Estimation by the Hershfield Method Using Imprecise Probability
This paper proposes a novel framework based on imprecise probability theory to quantify and attribute uncertainty in Hershfield method-based Probable Maximum Precipitation (PMP) estimates, identifying key uncertainty sources and their contributions in case studies across Indian and US river basins.
-
Rastogi et al. (2025) Evaluating Extreme Storm Events in an Ensemble of High‐Resolution Projections
This study investigates extreme storm characteristics (size, depth, volume, intensity) over the conterminous US using various downscaling techniques and CMIP6 GCMs, finding consistent future intensification across seasons and ensemble-dependent changes in storm size.
-
Li et al. (2025) Ensembling differentiable process-based and data-driven models with diverse meteorological forcing datasets to advance streamflow simulation
This study systematically evaluates and utilizes ensembles of a data-driven Long Short-Term Memory (LSTM) network and a physics-informed differentiable HBV ($\delta$HBV) model with diverse meteorological forcing datasets to advance streamflow simulation. The research demonstrates that cross-model-type ensembles consistently outperform single-model approaches and set new accuracy benchmarks, particularly enhancing spatial generalization due to complementary error characteristics and the structural constraints of $\delta$HBV.
-
Lv et al. (2025) Analysis of driving factors of soil salinity in Southern Xinjiang irrigation areas under dry-sowing and wet-emerging conditions
This study applied Multiscale Geographically Weighted Regression (MGWR) for the first time to quantitatively analyze the spatially heterogeneous driving factors of topsoil salinity (0–30 cm) in the Xiaohaizi Irrigation District under dry-sowing and wet-emerging conditions, revealing groundwater salinity as the dominant positive driver and proposing zone-specific management strategies.
-
Zhao et al. (2025) Quantifying the Hydrological Impact of Ecological and Water Conservation Projects Within a Major Tributary Basin of the Middle Reaches of the Yellow River, China
This study quantified the basin-scale hydrological impacts of Returning Agricultural Land to Forest (RAF) and Returning Agricultural Land to River (RAR) projects in the Qin River Basin using the SWAT model, revealing distinct and spatially varied effects on runoff components, emphasizing the importance of slope conditions for effective water management.
-
Cotrim et al. (2025) A Framework for Storm Classification and Hydrograph Generation From Total Water Level in Europe
This study characterizes total water level (TWL) storms across Europe by classifying them based on shape and developing a method to determine their duration. The aim is to improve the construction of hydrographs and flood maps by considering regional variability and uncertainties in storm characteristics.
-
Akl et al. (2025) Global Groundwater Drought Assessment Revisited: A Holistic Re‐Evaluation of the GRACE‐Groundwater Drought Index Across Major Aquifers
This study holistically re-evaluates the GRACE-Groundwater Drought Index (GGDI) across 37 major aquifers by integrating multi-model GRACE-derived groundwater storage anomaly (GRACE-GWA) estimates. It reveals that variability among these estimates introduces substantial uncertainty into groundwater drought indicators and aquifer memory, compromising the reliability of single-model GGDI assessments.
-
Zhang et al. (2025) Projections of actual and potential evapotranspiration from downscaled high-resolution CMIP6 climate simulations in Australia
This study evaluates actual and potential evapotranspiration (AET and PET) projections for Australia using dynamically downscaled high-resolution CMIP6 climate simulations, finding that these models provide reasonably accurate estimations and project scenario-dependent changes with significant implications for water security and agriculture.
-
Hoteit et al. (2025) New Climate Change Center of Saudi Arabia: Advancing Understanding and Prediction for the Arabian Peninsula Climate
This paper presents the roadmap of Saudi Arabia's newly established Climate Change Center (CCC), outlining its mission to address the underrepresentation of the Arabian Peninsula's climate in global research and develop advanced modeling and forecasting tools to improve predictions and projections for the region.
-
Liu et al. (2025) From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction
This study introduces a deep learning framework to benchmark 11 Transformer-based architectures against a baseline Long Short-Term Memory (LSTM) model and evaluate pretrained Large Language Models (LLMs) and Time Series Attention Models (TSAMs) for diverse hydrologic prediction tasks, revealing that LSTM excels in regression but attention-based models surpass it in complex tasks like autoregression and zero-shot forecasting.
-
Li et al. (2025) PAGrid: A probabilistic area-weighted gridding method for seamless mapping of sentinel-3 swath data
This study proposes a probabilistic area-weighted gridding method (PAGrid) to overcome spatial discontinuities in gridded Sentinel-3 OLCI swath data. PAGrid efficiently generates more consistent and continuous gridded time series by approximating area-weighting through randomized spatial perturbations, significantly outperforming conventional center point-based gridding.
-
Chen et al. (2025) Coupling Differentiable Modules of Reservoir Operation and Rainfall‐Runoff Processes for Streamflow Simulation
This paper tests the integration of reservoir operation and rainfall-runoff processes using differentiable parameter learning (dPL) within hydrological models. The study demonstrates that dPL significantly improves model efficiency, with a differentiable loosely coupled model (LCM) showing superior performance in simulating both inflow and outflow, particularly for ungauged catchments.
-
Gallais et al. (2025) Snow Sublimation Significantly Decreases Following Stand‐Replacing Fire With Minor Water Balance Impacts From Forest Thinning in a Water Limited Forest
This study quantifies the impact of wildfire and forest thinning on water availability for runoff (WAfR) in semi-arid montane environments. It found that stand-replacing fires significantly decrease actual evapotranspiration (AET) due to changes in canopy composition, while thinning has a less pronounced effect on water fluxes.
-
Arnell et al. (2025) High‐Impact Low‐Likelihood Climate Scenarios for Risk Assessment in the UK
This paper develops two sets of High-Impact Low-Likelihood (HILL) climate scenarios for the UK, comprising transient changes to 2100 and extreme monthly/seasonal anomalies, to complement existing climate projections and facilitate practical risk assessment for adaptation and resilience planning. These scenarios provide physically plausible storylines and indicative quantifications for "worst-case" climate outcomes beyond conventionally assumed ranges.
-
Yang et al. (2025) Local Off‐Grid Weather Forecasting With Multi‐Modal Earth Observation Data
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
-
Wanniarachchi et al. (2025) Enhancing the effectiveness of satellite precipitation products with topographic and seasonal bias correction
This study introduces the Heavy Rain Peak Adjustment (HRPA) method for satellite precipitation bias correction, comparing its effectiveness against the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The HRPA method significantly enhances the accuracy of Global Satellite Mapping of Precipitation-Near-Real-Time (GSMaP-NRT) data, particularly for heavy precipitation events and at lower elevations, outperforming SARIMA in reducing errors and improving correlation with observed data.
-
Jones et al. (2025) Snow Accumulation Increases With Forest Structural Diversity in Low‐Relief Catchments
This study investigates the relationships between forest canopy structure and below-canopy snow depth in two low-relief Mississippi headwater catchments, finding that co-dominant tree density and canopy structural diversity are key predictors for a deeper snowpack.
-
Li et al. (2025) Peat Hydraulic Structure Maintains the Stability of Permafrost Slope Peatlands in the Central Qinghai‐Tibet Plateau
This study investigates the water balance mechanisms enabling permafrost slope peatlands on the central Qinghai-Tibet Plateau to sustain waterlogging under low net precipitation. It finds that exceptionally low peat hydraulic conductivity is the primary control on hydrological stability, facilitating millennial-scale waterlogging and continued peat accumulation.
-
Henry et al. (2025) Marine Cloud Brightening to Cool the Arctic: An Earth System Model Comparison
This study is the first multi-model comparison of Arctic marine cloud brightening (MCB) via sea-salt aerosol (SSA) injections, demonstrating that this geoengineering technique can substantially cool the Arctic, maintain sea ice, and preserve the Atlantic Meridional Overturning Circulation without causing robust precipitation changes outside the Arctic.
-
Liu et al. (2025) Impact of Aerosols on Weather Forecasts in China During Winter 2016–2017
This study developed and applied CMA's first chemistry-weather integrated model (GRAPES_Meso5.1/CUACE CW V1.0) to investigate aerosol impacts on weather forecasts during the 2016–2017 winter season across China, finding that incorporating aerosol feedbacks significantly improves temperature and precipitation forecast accuracy, particularly in polluted regions.
-
Chaiyana et al. (2025) Evaluating trade-offs among cotton yield, groundwater extraction, and future projections for sustainable water management in the Texas High Plains
This study developed a novel data-driven framework integrating remote sensing and in-situ observations to quantify groundwater extraction (GWE) and evaluate trade-offs with crop water productivity (WPc) in irrigated cotton fields across the Texas High Plains (THP) from 2008 to 2030. The findings reveal that much of the central and northern THP exhibited unsustainable water use patterns (overuse and inefficiency) from 2008 to 2023, with GWE projected to increase from 2892 billion liters to 3439 billion liters by 2030, posing a significant risk to groundwater sustainability.
-
Lükő et al. (2025) Evaluating Winter Turbulent Heat Fluxes in a Hydrodynamic‐Ice Model of the Great Lakes
This study evaluates the performance of operational hydrodynamic and ice models in simulating turbulent heat fluxes in the Great Lakes across open water, partial ice, and ice-covered conditions during winter. It finds that while early winter open water fluxes are well modeled, accuracy decreases significantly during ice-covered periods, primarily due to errors in simulated ice thickness.
-
Wang et al. (2025) A Deep State Space Model for Rainfall‐Runoff Simulations
This study introduces the Frequency Tuned Diagonal State Space Sequence (S4D-FT) model for rainfall-runoff simulations, benchmarking it against LSTM and a physically-based model across 531 watersheds in the contiguous United States. Results indicate S4D-FT generally outperforms LSTM, especially in snowmelt-driven or intermittent flow regimes, but shows limitations in flashier, high-magnitude flow conditions.
-
Feng et al. (2025) Implementation and Evaluation of Emission‐Driven Land‐Atmosphere Coupled Simulation in E3SMv2.1
This paper introduces and evaluates BGCLNDATM_progCO2, the first emissions-driven land-atmosphere coupled biogeochemistry configuration in E3SMv2.1, performing historical emission-hindcasts from 1850 to 2014. While it overestimates atmospheric CO2 by 11–23 ppm, it remains within the spread of CMIP6 models and largely retains physical climate properties, laying the groundwork for advanced carbon-climate feedback projections.
-
Wang et al. (2025) Isolating ENSO Influence on Eastern China Summer Rainfall Variability with Month-Dependent EOF Method: Observation–Model Synthesis
This study identifies two leading modes of interannual summer rainfall variability over eastern China using a month-dependent empirical orthogonal function (EOF) method, linking them to antecedent and concurrent El Niño–Southern Oscillation (ENSO) events, and interprets these modes using an atmospheric general circulation model.
-
Choi et al. (2025) Estimation of Catchment‐Scale Evapotranspiration With the Simple Method Based on the Maximum Entropy Production Principle
This study introduces a novel Maximum Entropy Production (MEP) based method for estimating catchment-scale evapotranspiration, demonstrating its promising performance when validated against a global Penman-Monteith-Leuning product and annual water balance.
-
Wang et al. (2025) The extent of drought determines daily area burned in Canadian forests
This study developed the first effective models to predict daily area burned (DAB) in Canadian forests, identifying a strong relationship between DAB and the spatial extent of fire-conducive weather, particularly fuel aridity. It found significant increases in DAB, a concentration of fire activity within the season, and more extreme DAB events across Canada from 1940 to 2023.
-
Linga et al. (2025) Global Irrigation Modeling Relies More on Pragmatic Than Empirical Assumptions
This study analyzes 102 assumptions across nine global irrigation models (GIMs) to distinguish between empirically grounded and pragmatic assumptions, finding that 70% are pragmatic, which suggests a larger uncertainty space in GIMs than typically addressed.
-
Chen et al. (2025) Drivers and thresholds of carbon and water flux dynamics in a semi-humid urban forest ecosystem
This study quantified carbon and water fluxes in a semi-humid urban forest using eddy covariance, identifying their magnitudes, key environmental drivers, and nonlinear response thresholds under both drought and non-drought conditions.
-
Tedesche et al. (2025) Changing Snow Regime Classifications Across the Contiguous United States
This study developed a new spatial snow regime classification system to track climate-driven changes in snow phenology across the Contiguous US (CONUS) from 1981–2020, revealing widespread decreases in snow cover duration and snow-dominated areas, with shifts towards rain-dominated and transitional regimes moving up in latitude and elevation.
-
Bajaj et al. (2025) Exposure of global agricultural lands to extreme weather using CMIP6 projections of future climate
This study assesses the global exposure of agricultural lands, including croplands and pasturelands of varying sizes, to extreme weather in a 2 °C warmer world, finding a significant increase in extreme heat exposure that varies by land use type and farm size.
-
Gao et al. (2025) The key drivers of streamflow recession variability and their implications for robust parameterization of recession processes
## Identification - **Journal:** Hydrological Sciences Journal - **Year:** 2025...
-
Huang et al. (2025) Evaluation of E3SM Simulated Aerosols and Aerosol‐Cloud Interactions Across GCM and Convection‐Permitting Scales
This paper introduces an Earth system modeling testbed using E3SMv2 at convection-permitting scales (3.25 km) to predict aerosols and aerosol-cloud interactions (ACIs). While increased resolution improves some aspects like heavy precipitation and certain ACI relationships, it also amplifies biases in light drizzle and poorly represents aerosol composition, indicating that resolution alone is insufficient for broad improvements without concurrent advancements in physical and chemical process representations.
