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Gan (2026) Data for: Response of the Atlantic Meridional Overturning Circulation Strength to Precessional Forcing
This study investigates the response and variability of the Atlantic Meridional Overturning Circulation (AMOC) strength to orbital precessional forcing using a suite of Earth System Model simulations. The associated dataset provides model output to diagnose AMOC strength and its variability across different precessional phases.
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陈 (2026) Overland flow dynamics on a cracked soil slope under drying-wetting cycles: insights from infrared thermal imaging
This dataset provides experimental measurements of overland flow dynamics on a cracked soil slope under drying-wetting cycles, offering data for insights into these complex hydrological processes.
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Duan et al. (2026) Land Surface Temperature Shows Negligible Difference Between Inside and Outside Photovoltaic Power Plants in China
This study investigates the effects of ground-mounted photovoltaic (PV) power plants on land surface temperature (LST) across China, finding that PV plants generally induce daytime warming (0.10 °C) and nighttime cooling (−0.09 °C), with effects varying by vegetation type and season.
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Bian et al. (2026) Differential effects of thinning on soil moisture in planted and natural forests: A global meta-analysis
A global meta-analysis quantified the effects of thinning on soil moisture, finding an overall increase of 7.83%, with natural forests showing a 1.32 times greater response than planted forests. The study highlights differential responses based on forest origin, thinning intensity, soil type, stand age, and climate.
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Yang et al. (2026) Uniformity in Heavy Precipitation Microphysics During the Northward Advancement of Summer Monsoon in China Unveiled by Objective Weather Typing
This study isolates canonical East Asian summer monsoon precipitation using objective synoptic classification of satellite observations, revealing its microphysics are highly uniform and dominated by warm-rain accretion across China, in contrast to non-monsoon systems which favor ice-phase processes.
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Bidabadi et al. (2026) Historical diversion-shortfall characterization and verified operational modeling for off-farm operational risk zoning in Jarghuyeh Irrigation District, Iran
This study develops a spatially explicit framework to assess off-farm operational risk in the Jarghuyeh Irrigation District under diversion-flow shortfalls and manual canal operation. It reveals pronounced spatial clustering of vulnerability and risk, escalating from low (0-2%) under normal conditions to extreme (up to 35%) under severe stress, highlighting the limited adaptive capacity of the manual system.
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Khan et al. (2026) Flood Susceptibility Mapping of the Kosi Megafan Using Ensemble Machine Learning and SAR Data
This study developed and validated an ensemble machine learning framework for flood susceptibility mapping in the Kosi Megafan, comparing its performance against established models and a 1D-CNN. The stacked ensemble model achieved the highest performance, identifying high-risk zones with strong agreement with observed flood data and assessing the exposed population.
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Liu et al. (2026) Microphysical Characteristics of a Squall Line Modulated by the Northeast China Cold Vortex Using Polarimetric Radar and Disdrometer Observations
This study comprehensively analyzes the microphysical processes within a Northeast China Cold Vortex (NCCV)-influenced squall line using polarimetric radar and disdrometer data, revealing that convective rain exhibits a continental-type raindrop size distribution (DSD) driven by vigorous ice-phase processes, contrasting with Mei-yu events.
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Bechtold et al. (2026) Hourly ISIMIP3b bias-adjusted atmospheric climate input data
This dataset provides hourly CMIP6-based, bias-adjusted atmospheric climate input data, derived by temporally disaggregating daily ISIMIP3b data using the Teddy tool, for use in climate impact analysis.
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Sadeghzadeh et al. (2026) A Paradigm Shift to Automated Machine Learning for Local and External Reference Evapotranspiration Estimation with Uncertainty Implication
This study evaluates various automated machine learning (AutoML) algorithms coupled with base models for estimating daily reference evapotranspiration (ET0) across three diverse climatic regions. The research demonstrates that hybrid AutoML models significantly improve ET0 estimation accuracy and generalizability compared to standalone models, with optimal performance being dependent on the specific climatic conditions.
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Shukla et al. (2026) Atmospheric drivers of the 26 May 2025 heavy rainfall event over mumbai: insights from observations and reanalysis
This study investigates the atmospheric drivers of a very heavy rainfall event (0.18 m in 24 hours) over Mumbai on 26 May 2025, combining observations and reanalysis data. It reveals that the event was caused by a synergistic interaction of early monsoon onset, abundant moisture influx from the Arabian Sea, strong coastal moisture convergence, high atmospheric instability, and the dominance of low-base deep convective clouds.
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Li et al. (2026) Physics-Prior-Guided Feature Pyramid Network for Unified Multi-Angle Spectral–Polarimetric Cloud Detection
This study proposes a novel deep learning framework, the Multi-angle Polarization Feature Pyramid Structure (MP-FPS), to enhance cloud detection by leveraging joint spectral analysis and multi-angle polarization data. Evaluated on the global POLDER-3 dataset, MP-FPS achieves a mean Intersection over Union (mIoU) of 0.8662, surpassing the official baseline by 12.4%.
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Alraddawi et al. (2026) Pseudo-Monthly Raman Lidar Dataset for Reference Water Vapor Observations in the UTLS
This study evaluates 11 years of pseudo-monthly water vapor mixing ratio (WVMR) profiles from a UV Raman lidar at Réunion Island against MLS-Aura, ERA5, and GRUAN radiosondes, revealing systematic dry biases in MLS and GRUAN relative to the lidar, while ERA5 shows better agreement and is proposed for an alternative lidar calibration.
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Li et al. (2026) Spatiotemporal Variability and Dominant Driving Factors of Soil Moisture in the Yellow River Basin from 1982 to 2024
This study analyzed 43 years of data to assess soil moisture dynamics in the Yellow River Basin, revealing a statistically significant basin-wide decline, spatial variability, and the identification of key climatic drivers, highlighting the risk of ecosystems approaching tipping points.
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Ying et al. (2026) Warming-driven compound floods from extreme temperature and precipitation in global glacier covered areas
This study investigates how global warming intensifies compound flood hazards in glacier regions by enhancing the temporal synchronization of extreme temperature and precipitation, finding that flood magnitudes can increase by over 60% under 3–4 °C warming, particularly for long-duration events.
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Wang et al. (2026) Exploring the effects of antecedent rainfall characteristic on streamflow variability in a karst catchment
This study investigates the influence of antecedent rainfall characteristics on streamflow dynamics in a karst catchment using machine learning models. It found that antecedent rainfall, particularly extreme events and consecutive drought days, critically influences streamflow, with climate change being the predominant driver (73.8%) of variability.
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Chen et al. (2026) Reduced Spring Extratropical Cyclone Activity Over the East Asian Subtropical Region has Suppressed Regional Precipitation From 1979 to 2023
This study analyzes extratropical cyclone (EC) characteristics and their linkage to precipitation in the East Asian subtropical region during spring (1979–2023), revealing significant decreasing trends in both EC genesis and precipitation, primarily driven by non-uniform near-surface warming that suppresses ECs, subsequently weakening dynamic ascent and moisture transport.
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Rosen et al. (2026) Modelling forest dynamics using integral projection models and repeat lidar
This study integrates repeat airborne lidar data with an integral projection model (IPM) to analyze forest-wide demography in response to environmental drivers. It successfully modeled the survival, growth, and life expectancy of approximately 40,000 eucalypt trees over a decade, revealing distinct responses of small and large trees to competition and soil moisture, with drier conditions reducing life expectancy, especially for larger trees.
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Li et al. (2026) Subseasonal Forecasting of Snow Cover and Cold Compound Extremes: Insights From MPAS‐A Over Midlatitude East Asia
This study evaluates the subseasonal forecast skill of snow cover and cold compound extremes in midlatitude East Asia using MPAS-A, finding detectable skill up to three pentads, but highlighting that biases from underestimated snowfall and the choice of snow cover fraction scheme significantly impact forecast accuracy.
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Yang et al. (2026) Leaf thermal infrared imaging and lightweight deep learning enable early detection of water stress in watermelon for precision irrigation
This study proposes a thermal-imaging-based deep learning approach to classify watermelon water-stress status for precision irrigation. It systematically evaluates nine deep learning models, identifying EfficientNet-B0 as the most suitable for field deployment due to its optimal balance of high accuracy (0.99) and computational efficiency (0.39 GFLOPs, 8.81 ms inference latency).
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Funk et al. (2026) The Climate Hazards Center Infrared Precipitation with Stations, Version 3
This paper introduces and evaluates CHIRPS Version 3 (CHIRPS3), an enhanced quasi-global, high-resolution rainfall dataset that integrates satellite thermal infrared observations with a significantly expanded network of station data, demonstrating improved accuracy in representing observed precipitation mean and variance compared to its predecessor, CHIRPS2.
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Bai et al. (2026) The quantity of soil moisture replenishment rather than soil water content controls larch growth in semi-arid mountainous areas, Northwest China
This study investigated the control of larch growth by soil moisture replenishment (SMR) versus soil water content in semi-arid mountainous areas of Northwest China, finding that SMR is a primary determinant, especially for stem growth, and significantly influences water use efficiency. The findings suggest SMR should be a key indicator for ecological restoration in these water-limited regions.
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Mamgain et al. (2026) A satellite-based forest fire weather index for characterizing fire danger variability in the Himalaya
This study develops a satellite-based Forest Fire Weather Index (FFWI) for the data-scarce Himalayan region, integrating five satellite-derived indicators to provide spatially explicit fire danger assessments, demonstrating strong spatiotemporal variability and a robust link to large-scale climate phenomena.
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Beccaro et al. (2026) Assessing English peatland dynamics using MT-InSAR
This study applies the Enhanced Persistent Scatterers (E-PS) multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method to three English peatland sites, revealing widespread ground subsidence (up to -10 mm/year) driven by vegetation degradation, land management practices, and fluctuating water levels, indicating that peat degradation currently outpaces formation.
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Ayari et al. (2026) Comparing 1-km Sentinel-1 surface soil moisture with coarser-resolution satellite data for agricultural drought monitoring in Mediterranean regions
This study evaluates the potential of a 1-kilometer surface soil moisture (SSM) product (HRSM) derived from Sentinel-1 and Sentinel-2 data for agricultural drought monitoring in Mediterranean regions, comparing its performance with coarser-resolution satellite SSM products (SMAP, ESA CCI) and root zone soil moisture (RZSM). The HRSM product shows good coherence with coarser products, uniquely identifying drought in agricultural areas, but its lower revisit frequency can miss short-duration rainfall events compared to daily or sub-daily products.
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Wang et al. (2026) P2I-GAN Benchmark: Deep Generative Framework for Spatio-Temporal Rainfall Reconstruction from Sparse Gauges
P2I-GAN is a deep generative benchmark that reconstructs spatio-temporal rainfall by formulating interpolation from highly sparse and irregular rain-gauge observations as a video inpainting task.
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Jacobs et al. (2026) GreenScatter: Through-Canopy Soil Moisture Sensing with UAV-Mounted Radar
This paper introduces GreenScatter, a physics-based framework for retrieving soil moisture using nadir-looking wideband UAV radars, specifically addressing the challenge of vegetation-soil electromagnetic coupling. The framework demonstrates consistent soil moisture estimation through vegetation with an average volumetric water content error of 4.49% in field experiments.
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Makarieva et al. (2026) On the Methodology for Assessing Vegetation Impacts on the Atmospheric Branch of the Hydrological Cycle
This paper critically evaluates existing methodologies for assessing the impact of large-scale vegetation restoration on the hydrological cycle in China, arguing that neglecting vegetation-induced atmospheric circulation changes biases results and proposing that initial streamflow reductions may be a transient phase that could reverse over time.
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afeder17 (2026) afeder17/MPAS-to-ERA5-Adaptor: v2.0: Bilinear wind interpolation
This software provides an interpolation script to convert MPAS model outputs into ERA5-style files, specifically for Eulerian moisture tracking, making them compatible with the WAM2Layers model.
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Ma et al. (2026) A spatiotemporally differentiated hybrid hydrological modeling strategy with dynamically adaptive runoff generation modes
This study develops a spatiotemporally differentiated hybrid hydrological model (STHM) that dynamically adapts runoff generation modes using machine learning to improve flood forecasting in small-to-medium basins. The STHM demonstrates superior and more stable simulation performance compared to conventional models, effectively capturing event-dependent runoff processes.
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Ames (2026) Remote Sensing of Water: The Observation-to-Inference Arc Across Six Decades and Toward an AI-Native Future
This review traces the six-decade evolution of satellite remote sensing for water resources, demonstrating a progressive tightening of the observation-to-inference coupling, culminating in AI-driven systems, while highlighting persistent challenges.
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Mostafiz (2026) Hydrological Dataset on Flood Magnitude and Recurrence Analysis across Kansas, Iowa, Nebraska, and Missouri (1960–2020)
This dataset provides results from a long-term flood frequency and magnitude analysis for the Lower Missouri River Basin (1960–2020), supporting research on climate-driven changes in flood recurrence and magnitude using the Log-Pearson Type III distribution.
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Cherian et al. (2026) Anthropogenic aerosols induce drying in Indian monsoon dry extremes
This paper describes a dataset supporting the finding that anthropogenic aerosols induce drying in Indian monsoon dry extremes.
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Chen et al. (2026) Spatial Heterogeneity and Drivers of Vertical Error in Global DEMs: An Explainable Machine Learning Approach in Complex Subtropical Coastal Zones
This study quantitatively decomposes the vertical errors of three 30 m global DEMs (COP30, NASADEM, and AW3D30) in Southeast China using ICESat-2 ATL08 data and an XGBoost-SHAP model, finding NASADEM has the lowest RMSE and identifying TRI, Land Cover, and specific sensor-related factors as dominant error drivers.
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Tang et al. (2026) Numerical Simulation of a Heavy Rainfall Event in Sichuan Using CMONOC Data Assimilation
This study demonstrates that assimilating CMONOC GNSS tropospheric products (Zenith Total Delay/Precipitable Water Vapor) into the WRF model significantly improves the simulation of heavy rainfall events over the complex terrain of the Sichuan Basin by enhancing initial moisture and low-level convergence.
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Rafter et al. (2026) Trends in Annual Maximum Sub‐Daily to Daily Precipitation Over Australia
This study evaluates trends in annual and seasonal maxima of sub-daily precipitation accumulations (1 to 24 hours) across Australia, revealing increasing hourly rainfall trends, particularly in austral summer, but decreasing trends at longer sub-daily durations and in austral autumn.
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Xue et al. (2026) Gross Primary Production (GPP) for China from 2001–2020 Estimated by Machine Learning Methods
This study evaluated five existing Gross Primary Production (GPP) products and five machine learning methods to generate a high-fidelity GPP dataset for China from 2001–2020, identifying Categorical Boosting as the best-performing method.
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Li et al. (2026) Impacts of Flooding on Vegetation: A Case Study of the 2025 Xinglong Mountain Flood
This study investigated how terrain-driven hydrological processes control vegetation responses to mountain flood disturbances in arid and semi-arid regions, finding that areas with higher moisture accumulation potential exhibit stronger vegetation recovery compared to well-drained or steep slopes.
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Li et al. (2026) Comprehensive Evaluation of Multi-Version Global Satellite Mapping of Precipitation (GSMaP) Products over the Qinghai–Tibetan Plateau
This study systematically evaluates four GSMaP precipitation products across four versions (v05–v08) over the Qinghai–Tibetan Plateau from 2001 to 2022, finding general performance improvements in later versions, particularly v08 and gauge-corrected products, though uncertainties persist in specific conditions and regions.
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Dhinakaran et al. (2026) Forecasting Soil Moisture Dynamics from SMAP Observations via Signal Decomposition
This paper proposes a Decomposition-Guided Forecasting Framework of Soil Moisture (DGF-SM) that integrates SMAP satellite observations with Seasonal Trend Decomposition by Loess (STL) and ARIMA-based forecasting, demonstrating high predictive accuracy and improved interpretability across diverse South Asian climatic regimes.
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Abdullah et al. (2026) Applications of machine learning in enhancing evaporation estimation for small reservoirs: a case study in semi-arid South Texas
This study developed and validated a multi-reservoir machine learning (ML) framework to enhance daily open-water evaporation estimation for small reservoirs in semi-arid South Texas, demonstrating that Random Forest (RF) and Support Vector Regression (SVR) models significantly outperform traditional empirical methods.
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Alone et al. (2026) Seasonal forecasts of marine heat waves using Monsoon Mission Climate ForecaSt system
This study evaluates the seasonal prediction skill of marine heatwaves (MHWs) in the Indian Ocean and surrounding basins using the Monsoon Mission Coupled Forecast System version 1 (MMCFSv1). It finds that the system demonstrates good forecast skill in key regions and seasons, with ensemble-based thresholds effectively capturing MHW variability.
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Mohammedi et al. (2026) From Diagnosis to Rehabilitation: A Stochastic Framework for Improving Pressurized Irrigation System Performance Under Water Scarcity
This study developed an integrated stochastic-hydraulic framework to diagnose and rehabilitate large-scale pressurized irrigation systems, demonstrating its application to the Sinistra Ofanto scheme where targeted pipe upgrades costing €85,452 increased hydraulically satisfied configurations from 62% to 100% during peak demand.
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Ahmed et al. (2026) Enhanced spatial precipitation maps by integrating XGBoost machine learning, terrain indices, and optimal interpolation
This study developed a novel integrated framework combining geostatistical interpolation, terrain optimization, and XGBoost machine learning to enhance spatial precipitation estimation in topographically complex, data-scarce regions. The framework achieved superior predictive accuracy (R² = 0.87, RMSE = 70.9 mm) using a multivariate model that incorporated temperature, Topographic Ruggedness Index (TRI), and an optimized Vector Ruggedness Measure (VRM-153).
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Liu et al. (2026) Responses of forests, cultivated lands, and grasslands to climatic factors at a basin scale
This study investigated vegetation dynamics and their responses to key climatic factors (temperature, precipitation, wind, and solar radiation) across forests, cultivated lands, and grasslands in the humid East River basin from 2000 to 2018, revealing a significant basin-wide greening trend (0.006 per year) primarily driven by temperature, with differentiated sensitivities among vegetation types.
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Guan et al. (2026) A Study on the Discrimination Criteria and the Formation Mechanism of the Extreme Drought-Runoff in the Yangtze River Basin
This study addressed research gaps in extreme drought-runoff in the Yangtze River Basin by establishing quantitative discrimination criteria and exploring formation mechanisms. It found that meteorological factors (low precipitation, high temperatures) are the primary drivers, with large-scale reservoirs playing a secondary role in alleviating impacts during the non-flood season.
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Shen et al. (2026) An integrated SWAT–XGBoost–SHAP framework identifies key drivers of critical source areas during critical periods in a small watershed
This study developed an integrated SWAT–XGBoost–SHAP framework to identify critical periods (CPs) and critical source areas (CSAs) of non-point source (NPS) pollution in a small watershed, quantitatively analyzing the dominant driving factors and their nonlinear threshold effects. The framework identified February, June, and July as CPs contributing 59% of total nitrogen and 65% of total phosphorus loads, with fertilizer application amount and cultivated land proportion being the predominant drivers exhibiting critical thresholds.
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Khan et al. (2026) Advanced Bayesian spatio-temporal frameworks for predicting precipitation at ungauged sites and times
This study developed and evaluated advanced Bayesian spatio-temporal frameworks, specifically Gaussian Process (GP) and Auto-Regressive (AR) models, to predict monthly precipitation at ungauged sites and times in Pakistan's Indus Basin. The AR model, combined with a square root transformation, demonstrated superior temporal forecasting accuracy, while both models provided reliable spatial predictions.
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Iwakiri et al. (2026) ENSO Recharge Oscillator Theory Integrating the Southward Wind Shift
This study derives a new form of the Recharge Oscillator (RO) theory for ENSO that explicitly incorporates the seasonal migration of equatorial zonal wind anomalies. The new theory demonstrates how Bjerknes feedback depends on the central latitude of these wind anomalies and shows that a stochastic RO simulation can reproduce key ENSO characteristics like seasonal synchronization and combination tones.
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Yin et al. (2026) Future landscape pattern optimization based on runoff response across different temporal scales: A SWAT-PLUS coupled model simulation in the Three Gorges Reservoir Area
This study coupled the PLUS and SWAT models to simulate future landscape patterns and evaluate their runoff responses under natural and slope-differentiated optimized development scenarios in the Dongli River Basin, Three Gorges Reservoir Area. The research found that optimized landscape patterns, particularly on moderate slopes, can significantly regulate runoff by maintaining specific forest and grassland proportions and improving landscape connectivity, providing quantitative thresholds for sustainable eco-hydrological management.
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Zhang et al. (2026) Hidden markov models to analyze China’s total water resources states and transfer characteristics
This study introduces a novel Hidden Markov Model (HMM) to analyze the hidden states and inter-annual transfer characteristics of China's total water resources, addressing limitations of traditional methods by accounting for temporal dependency. It identifies three hidden states ("Dry," "Flat," "Abundant") and their transition patterns across China's six major geographic regions, revealing an overall increase in water resources over the past 20 years, particularly in the north.
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Cao et al. (2026) Differential impacts of soil hydrothermal properties on root and leaf phenology in cropland
This study developed a hybrid deep-learning model to extract root phenology from minirhizotron imagery and examined the differential impacts of soil hydrothermal properties on root and leaf phenology in a German cropland. It found that root growth initiates earlier and ceases later than leaf phenology, with root phenology being significantly more sensitive to soil moisture than temperature.
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Mehmood et al. (2026) Assessment of temporal and spatial shifts in climatic parameters and their impact on crop water requirement in the Lower Chenab Canal Command Area, Pakistan
This study assessed the temporal and spatial shifts in rainfall and temperature in Pakistan's Lower Chenab Canal Command Area from 1991-2020, revealing significant impacts on crop water requirements for major crops and emphasizing the need for climate-resilient agricultural planning.
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Toh et al. (2026) Enhanced IMERG SPE Using LSTM with a Novel Adaptive Regularization Method
This study develops an Adaptive Regularization framework for an LSTM model to improve satellite-gauge rainfall fusion. It dynamically adjusts learning rate and weight decay, demonstrating superior performance in refining daily IMERG-Final rainfall estimates over flood-prone Peninsular Malaysia.
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Tandon et al. (2026) Integrating IMDAA Regional Reanalysis and Machine Learning for Enhanced Detection of Extreme Precipitation Over Complex Himalayan Terrain
This study integrates high-resolution IMDAA reanalysis with machine learning to enhance extreme precipitation detection over the complex Himalayan terrain, demonstrating that Random Forest significantly outperforms Support Vector Machines in accuracy and precision for extreme events, thus establishing a reliable diagnostic framework.
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Ebrahimi et al. (2026) Editorial: Forest growth in a changing climate: insights from predictive modeling and adaptive strategies
This editorial synthesizes diverse research on forest growth in a changing climate, highlighting the context-dependent nature of forest responses to environmental stressors. It emphasizes the critical role of integrated predictive modeling and adaptive strategies for understanding and managing forest ecosystems under future climate scenarios.
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Adam et al. (2026) Application of random forest modeling to evaluate groundwater storage changes in the Breede Water Management Area, South Africa
This study integrated GRACE satellite observations, in situ groundwater levels, and GLDAS-derived hydrological variables with machine learning to downscale groundwater storage anomalies (GWSA) from 1° × 1° to 0.25° × 0.25° in the Breede Water Management Area, South Africa (2002–2022). The Random Forest model outperformed other tested models, revealing significant spatial heterogeneity in GWSA and accurately capturing major drought and recovery phases, thereby enhancing groundwater monitoring in data-scarce regions.
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Lee et al. (2026) Satellite- and Ground-Soil-Moisture Synchronization and Rainfall Index Linkage for Developing Early-Warning Thresholds for Flash Floods in Korean Dam Basins
This study developed a Random Forest model to generate continuous, daily basin-representative soil moisture by integrating various hydro-meteorological data in South Korean dam basins, demonstrating that antecedent wetness significantly influences rapid discharge events and deriving effective composite thresholds for flash-flood early warnings.
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Bahathy et al. (2026) Optimizing Irrigation Scheduling for Major Crops in the Middle of Iraq using the CROPWAT Model to Promote Sustainable Agriculture
This study optimized irrigation scheduling for wheat, barley, and rice in Iraq's Al-Qadisiyah Governorate using the FAO CROPWAT 8.0 model, revealing that rice, a summer crop, requires significantly more irrigation water than winter crops due to seasonal climatic variations. The research provides precise irrigation schedules to enhance water conservation and promote sustainable agriculture in a water-scarce region.
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Ye et al. (2026) An IoT-based predictive irrigation scheduling framework for precision soil moisture control in greenhouses
This study developed an IoT-based predictive irrigation framework that optimizes soil moisture control by establishing a proportional relationship (Δtpred = ½Δteval) between evaluation and prediction intervals, demonstrating stable moisture regulation and reduced irrigation frequency in greenhouse experiments.
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Nagaraj et al. (2026) AI-Based Energy Demand Forecasting for a Smart Farm
This paper designs and implements an AI-driven Smart Farm Energy Forecasting System that integrates machine learning and deep learning for real-time energy demand prediction with smart irrigation advisory modules. The system successfully provides environmental-aware irrigation recommendations and adaptive scheduling, establishing a scalable framework for sustainable smart farm management.
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Burić (2026) Are Changes in Seasonal and Annual Precipitation in the Balkan Peninsula Driven by Increases in Anthropogenic Greenhouse Gases or by Teleconnection Variability?
This study analyzes precipitation trends and their drivers in the Balkan Peninsula from 1950 to 2024 using ERA5-Land and GPCC datasets, revealing widespread seasonal and annual precipitation decreases primarily influenced by atmospheric oscillations, with limited impact from anthropogenic greenhouse gases or oceanic oscillations.
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Cai et al. (2026) Characterizing Temperature and Precipitation Tails via Expected Shortfall Regression
This study applies Expected Shortfall (ES) regression to analyze temperature and precipitation trends in the continental U.S. from 1950-2015, revealing distinct spatial and temporal changes in the tails of distributions, including significant decadal temperature increases in the southern and central U.S. and ENSO's influence on extreme events.
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Manfreda et al. (2026) Zero-flow and hydrotype estimation with karst-SWAT and Sentinel-2 data in the Keritis Basin, Crete
This study developed a novel methodology integrating Karst-SWAT hydrological modeling with Sentinel-2 satellite imagery to provide daily forecasts of flow conditions and estimate hydrotypes in two non-perennial reaches of the Keritis River, Greece. The approach significantly improved the understanding of flow intermittency patterns, revealing that deep aquifer recharge and evapotranspiration are key controls on zero-flow events.
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Takahashi et al. (2026) Land-Surface Influences on Precipitation Characteristics in the Wet Asian Monsoon Regions: A Review Integrating High-Resolution Satellite Observations and Convection-Permitting Climate Modeling
This review synthesizes evidence on how land-surface conditions influence precipitation intensity and frequency in wet Asian monsoon regions, integrating high-resolution satellite observations and convection-permitting climate models to re-evaluate land-atmosphere interactions within a physical hierarchy.
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Erina et al. (2026) Projected changes in streamflow seasonality and flood characteristics in the Moskva R. Basin
This study quantifies projected changes in streamflow seasonality and flood characteristics in the Moskva River Basin under future climate scenarios, revealing a significant shift from snowmelt-dominated to rainfall-influenced runoff with increased winter flow and decreased spring freshet, leading to a reorganization of the flood regime and an overall annual streamflow deficit.
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Santamarta et al. (2026) Island water stress: analyzing the Canary Islands’ hydrological response to climate change
This study projects the long-term evolution of the water balance in the Canary Islands under climate change, utilizing a high-resolution downscaling methodology. Results indicate a significant decrease in water availability across the archipelago by the end of the century, driven by increased evapotranspiration and stable or reduced precipitation, with some islands facing severe water depletion.
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Sibiya et al. (2026) Assessing trends and forecasting meteorological drought in South Africa using Savitzky–Golay enhanced hybrid deep learning
This study assessed meteorological drought trends in South Africa's uMkhanyakude District using daily rainfall data from 1980-2023 and developed a novel Savitzky–Golay enhanced hybrid deep learning model (SG–TCN–LSTM) for forecasting, demonstrating superior predictive accuracy compared to other models.
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Salini et al. (2026) Performance assessment of general circulation models for meteorological droughts: application of complex network theory
This study applies complex network theory, specifically node efficiency, to assess the performance of 53 CMIP6 General Circulation Models (GCMs) in simulating meteorological droughts in India using the Standardized Precipitation Index (SPI). The research reveals significant variability in model performance across different timescales, identifying NorESM2-MM, CESM2-FV2, and KACE-1-0-G as consistently top-performing GCMs for drought-related studies.
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Mirzaei et al. (2026) Integrated global to regional atmosphere predictors for drought modeling in Iran
This study developed a comprehensive statistical framework integrating 27 global and regional atmospheric–oceanic predictors to model and forecast meteorological drought indices (SPI and SPEI) across 18 homogeneous regions of Iran. The framework demonstrated high predictive skill, explaining 55–97% of SPI and 55–92% of SPEI variance, showing that combining global teleconnections with regional marine indicators substantially enhances drought prediction.
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Paul et al. (2026) When more rain means more drought: hydroclimatic drivers of increasing drought risk in the world’s wettest region
This study investigates the paradox of increasing drought risk in the world's wettest region, Meghalaya, by analyzing historical and projected hydroclimatic data (1981–2100). It finds that intensified rainfall events, reduced light rain, and rising temperatures lead to elevated evapotranspiration and decreased infiltration, resulting in an increased frequency of drought events and significant reductions in mean annual streamflow, particularly under high-emission scenarios.
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Giménez et al. (2026) Subsurface conditions and hydrologic accumulation drive stream connectivity and flow intermittency in urban river networks
This study investigated the controls on stream network connectivity and flow intermittency in the Little Calumet River Watershed, USA, revealing that subsurface conditions and hydrologic accumulation are primary drivers, challenging the prevailing view that impervious cover dictates these dynamics.
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Fritzsch et al. (2026) Coherent Doppler lidar for aerosol-cloud-dynamics interaction studies operating at 532.25 nm with a 3.2 GHz data acquisition
This paper describes the development and technical setup of a novel 532.25 nm coherent Doppler lidar (CDL) and demonstrates its capability to simultaneously observe diverse atmospheric particles and detect the Rayleigh-Brillouin spectrum for Mie signal calibration.
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Hair et al. (2026) Vertical Profiles of Cloud Extinction and Cloud Top Droplet Number Concentration in Warm Clouds Derived from Airborne Lidar and Polarimeter Measurements
This study combines airborne High Spectral Resolution Lidar (HSRL-2) and Scanning Polarimeter (RSP) remote sensing measurements with in situ data from a multiyear campaign over the North Atlantic Ocean to derive and compare cloud top droplet number density (Nd).
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Maynard et al. (2026) Turbulent coherent structures in the atmospheric surface layer: Detection on Doppler lidar observations by supervised machine learning
This study observed and classified turbulent coherent structures, specifically streaks, in the atmospheric surface layer using Doppler lidar scans in an industrial coastal city, developing an automated classification method that successfully discriminates between organized and disorganized streaks.
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Linets et al. (2026) An algorithm for calculating the volumetric soil moisture for a bistatic radar system controller
This paper presents an algorithm for calculating volumetric soil moisture for a UAV-based bistatic radar system, achieving an error of no more than 10% by utilizing the soil's complex dielectric constant and validating against TDR measurements.
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Stachlewska et al. (2026) EarthCARE dry-run demonstration with EMORAL lidar
This paper details the development and characterization of the ESA Mobile Raman Lidar (EMORAL), a state-of-the-art ground-based system designed for near-real-time atmospheric profiling and Cal/Val missions. The EMORAL lidar, with its new functionalities and demonstrated capability to collect quality-assured data across diverse environments, is positioned as a core ESA asset for upcoming EarthCARE Cal/Val activities.
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Uchiho et al. (2026) Development of water vapor DIAL and comparison with Raman lidar
> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...
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Nehrir et al. (2026) Enabling Technologies for Cross-Cutting Airborne and Spaceborne Water Vapor and Methane DIAL
NASA Langley Research Center developed the High-Altitude Lidar Observatory (HALO) system, an airborne water vapor and methane DIAL and HSRL, to address atmospheric observational needs and serve as a technology testbed for future space-based DIAL missions, demonstrating its architecture and measurements.
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Girolamo et al. (2026) The CALIGOLA Mission: An overview of the present status and the forthcoming steps
This paper describes CALIGOLA, a planned advanced multi-purpose space lidar mission conceived by ASI in partnership with NASA, aimed at characterizing the Ocean-Earth-Atmosphere system and its interactions by providing an unprecedented global dataset of geophysical parameters.
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Imaki (2026) Application of coherent lidar technique: Water vapor DIAL and data assimilation for three-dimensional wind flow reconstructions
This research expands coherent Doppler lidar (CDL) to coherent differential absorption lidar (DIAL) and integrates it with computational fluid dynamics (CFD) data assimilation. It demonstrates a coherent DIAL system for simultaneous water vapor and wind speed measurement, and a CFD technique for reconstructing three-dimensional wind flow using lidar data.
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Gouveia et al. (2026) Caeli Water Vapor Raman Lidar Calibration Using the 213 m Tower In-Situ Measurements at the Cabauw Experimental Site for Atmospheric Research
This study calibrated a Raman water vapor lidar using tower-based in-situ humidity measurements at the Cabauw Experimental Site, demonstrating its stability with a day-to-day calibration constant variability of less than 2% and good agreement with radiosonde data.
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Laly et al. (2026) Raman Lidar Dedicated to Water Vapor Measurements in the Lower Troposphere (WaLiNeAs)
The WaLiNeAs program aims to investigate lower tropospheric water vapor variability with high spatio-temporal resolution using Raman lidar and state-of-the-art weather models to improve resilience to extreme precipitation events in the Mediterranean basin.
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Yabuki et al. (2026) Optimization of an Optical Design for a Rotational Raman Lidar for Profiling the Accurate Atmospheric Temperature
This paper details the construction of a novel rotational and vibrational Raman lidar system, employing a 266 nm laser, designed for precise temperature and water vapor profiling in the lower troposphere. The system features an optimized optical design that enhances accuracy and minimizes the impact of laser wavelength instability.
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Lesigne et al. (2026) Using BeCOOL microlidar for satellite calibration/validation
This study evaluates the performance of the BeCOOL microlidar, designed for stratospheric balloon deployment, by comparing its high-resolution cloud profiles with CALIOP observations, demonstrating its excellent agreement and enhanced sensitivity to ultra-thin clouds.
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Speidel et al. (2026) Water vapor measurements throughout the planetary boundary layer (PBL) with the ATMONSYS DIAL: From short-term variability to the calculation of vertical fluxes
This paper introduces and characterizes the ATMONSYS DIAL system for water vapor measurements, demonstrating its capability to capture vertical latent heat fluxes when combined with Doppler wind lidars, quantifying short-term humidity variability in the Planetary Boundary Layer, and highlighting significant drift issues in radiosonde measurements.
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Saito (2026) A Pathway Toward the Retrievals of the Microphysical Properties of Mixed-Phase Clouds Using Airborne Radar-Lidar Observation
This study develops a radar-lidar remote sensing algorithm, incorporating a robust optical property model, to characterize mixed-phase clouds, demonstrating its ability to accurately determine ice/liquid fraction, total water content, and effective radii of hydrometeors.
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Prakasam et al. (2026) A Generative Model for Rainfall Prediction based on Variational Autoencoder (VAE) Using Time-Series Weather parameters
This paper proposes a novel generative probabilistic rainfall prediction framework based on Variational Auto Encoders (VAE) that improves probabilistic accuracy and uncertainty calibration compared to traditional deterministic methods.
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Hana et al. (2026) Generative 3D Gaussian Splatting for Arbitrary-ResolutionAtmospheric Downscaling and Forecasting
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Alaoui et al. (2026) A novel deep learning semi-observational analog framework for diagnosing convection over Morocco using satellite imagery and ERA5-reanalysis
This study introduces a novel semi-observational analog framework (AnOb) that leverages deep learning and integrates satellite imagery with ERA5 reanalysis data to diagnose convection parameters over Morocco. The AnOb system demonstrates enhanced performance compared to climatology and persistence, showing promising potential for detecting severe convective events.
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Fons et al. (2026) Dissipating the correlation smokescreen: Causal decomposition of the radiative effects of biomass burning aerosols over the South-East Atlantic
Information not available from provided text.
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Galatoulas et al. (2026) Development of High-Resolution Agroclimatic Zoning Method to Determine Micro-Agroclimatic Zones in Greece
This study proposes an integrated agroclimatic and micro-agroclimatic zoning approach for Greece, revealing high spatial variability with most agricultural areas classified as dry to sub-humid, which can inform efficient agrometeorological monitoring.
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Khotele et al. (2026) Disaster Detection Based on Synthetic Aperture Radar (SAR) Images
This paper proposes an AI-driven remote sensing system utilizing Synthetic Aperture Radar (SAR) images and Convolutional Neural Networks (CNN) for efficient and scalable multi-disaster detection, addressing limitations of traditional monitoring methods. The experimental analysis demonstrates the model's high accuracy and potential for real-time disaster monitoring.
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Song et al. (2026) Observationally constrained global warming hysteresis under CO2 removal
This study develops an observationally constrained data-driven emulator to quantify global warming hysteresis under carbon dioxide (CO2) removal. It projects that global warming during the CO2 removal phase will be 0.7 °C warmer than during the CO2 increasing phase at the same CO2 concentration, leading to a temporary overshoot of the 1.5 °C target even under stringent mitigation.
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Zhang et al. (2026) Cropland soil salinity retrieval using a spectral-spatial cross-attention deep learning framework with environmental interpretability
This study develops SS-SoilNet, a multimodal deep learning framework, to accurately retrieve cropland soil salinity in the Yellow River Delta by integrating multi-source remote sensing observations, topographic features, and crop growth parameters. The model achieves improved accuracy (RMSE ≈ 3.6 g kg−1, R² ≈ 0.68) and interpretability, revealing strong coupling effects among soil salinity, crop growth, and terrain.
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Buchupalle et al. (2026) Assessment of Compound Hydrological–Thermal Extremes over Indian River Systems
This study investigates the joint behavior of low river discharge (Q) and elevated river water temperatures (RWTs) in six Indian rivers, revealing significant spatial variability in compound hydrological and thermal extreme hazards and identifying suitable copula models for their assessment.
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Liu et al. (2026) Urban Land Surface Effects on Summertime Clouds and Moist Convection in Houston Under Different Synoptic Conditions
This study investigates the impact of the Houston metropolitan area on summertime cloud cover and convective cell characteristics using satellite observations, radar data, and high-resolution modeling. It reveals that urban land cover consistently enhances cloud fraction and convective activity, with these impacts being highly dependent on large-scale meteorological forcing.
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Dorrington et al. (2026) Dynamically‐Informed Extreme Event Attribution Using Circulation Imprints
This paper introduces a novel extreme event attribution approach that isolates dynamical contributions from other factors to changing extreme event probability, demonstrating its application to recent wildfires, floods, and storms, and finding varying climate change impacts across these event types.
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Wang et al. (2026) Reconstructing and downscaling terrestrial water storage to Reveal vegetation–water coupling across continuous areas of Western China-Central Asia-Western Asia under climate warming
This study reconstructed and downscaled terrestrial water storage anomalies (TWSA) to a continuous 0.1° resolution dataset for Western China-Central Asia-Western Asia (2001–2020) using STL and Random Forest, revealing a significant TWSA decline across over 70% of the region and elevation-dependent vegetation-water coupling.
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Bellisario et al. (2026) Meltwater Contribution and Mass Balance of the Juncal Norte Glacier During an Extreme Drought Year in the Dry Andes of Central Chile
This study provided the first glacier-specific annual melt and runoff estimate for the Juncal Norte Glacier during a megadrought, finding that glacier melt contributed approximately 30% of proglacial discharge despite covering only 2.7% of the basin, underscoring its critical and climate-sensitive role in water supply.
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Elsherbiny et al. (2026) Assessment of evapotranspiration across diverse arid settings in Saudi Arabia: A meta-learning analysis of multimodal satellite data (2003–2024)
This paper develops a novel meta-learning framework for accurate monthly actual evapotranspiration (AET) estimation using multimodal satellite data in arid Saudi Arabia, finding that a two-stage P-spline_P-spline architecture achieved superior predictive outcomes (R²=0.923, RMSE=5.337 mm).
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Lu et al. (2026) Nonlinear Convection–SST Sensitivity as a Bridge for the Asymmetric Low‐Level Wind Response to ENSO
This study investigates the cause of the observed asymmetry in atmospheric responses to El Niño and La Niña, revealing that it stems from the nonlinear sensitivity of atmospheric convection to sea surface temperature in the tropical Pacific.
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Okwang et al. (2026) Smart Sensor‐Integrated Lysimeter System for Affordable on‐Farm Crop Water Monitoring and Irrigation Management
This study developed and field-validated a low-cost, information and communication technology (ICT)-enabled weighing lysimeter system for real-time crop water use monitoring, demonstrating its reliability and affordability for precision irrigation in buckwheat fields.
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Yan et al. (2026) Film Mulching Drip Irrigation Improves the Soil Hydrothermal Environment to Enhance Photosynthetic Efficiency and Yield of Sorghum in an Agro-Pastoral Ecotone of Northern China
This study investigated the impact of film mulching drip irrigation (FMDI) on sorghum in northern China's agro-pastoral ecotone, revealing that FMDI significantly improves soil hydrothermal conditions and photosynthetic performance, leading to a substantial increase in sorghum yield.
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El-Wahed et al. (2026) Partial Root-Zone Drying and Regulated Deficit Irrigation Effects on Potato Yield, Quality, Water Productivity, and Net Profit under Surface vs. Subsurface Drip Irrigation
This study evaluated the combined effects of full and deficit irrigation levels (100%, 80%, 60% ETc), irrigation techniques (single lateral, partial root-zone drying), and drip irrigation depths (surface, 15 cm, 30 cm) on potato yield, quality, water productivity, and net profit in sandy loam soil under arid conditions in Egypt. It found that full irrigation with partial root-zone drying and subsurface drip irrigation at 15 cm depth maximized yield and net profit, while 80% ETc with the same technique saved 20% water with only a slight yield reduction, making it suitable for water-limited conditions.
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Talvinen et al. (2026) Beyond cloud cover: Low- and high-altitude clouds have distinct impacts on tree sap flow and transpiration
This study investigates how different cloud types affect tree transpiration in European boreal and temperate forests using long-term sap flow and surface-based cloud observations. It reveals that low-altitude clouds significantly suppress transpiration, while high-altitude clouds have a negligible effect, with declining low-altitude cloud cover potentially increasing annual transpiration by 0.6–1.2 mm in boreal forests.
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Shirazi et al. (2026) Estimation of onion crop evapotranspiration and crop coefficients using weighing lysimeters and machine learning models in semi-arid region
This study measured onion crop evapotranspiration (ETc) and crop coefficients using weighing lysimeters over two years in a semi-arid region of Iran, and developed highly accurate machine learning models to predict ETc for improved irrigation management.
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Liu et al. (2026) Intraseasonal Northwest–Southeast Oscillation of the Tropical Easterly Jet Core and Its Modulation by Atmospheric Internal Dynamics and Boreal Summer Intraseasonal Oscillation
This study identifies and explains a distinct northwest-southeast intraseasonal oscillation of the Tropical Easterly Jet (TEJ) core during boreal summer, revealing its primary governance by upper-tropospheric dynamic processes and modulation by the northward propagation of large-scale convective anomalies associated with the Boreal Summer Intraseasonal Oscillation (BSISO).
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Vacca et al. (2026) Subseasonal variability of the winter North Atlantic jet stream has decreased due to climate change
This study reveals a significant decrease in the subseasonal variability of the winter North Atlantic eddy-driven jet's latitude and tilt over the past 75 years due to climate change, with models projecting continued reduction throughout the 21st century.
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Vesuviano et al. (2026) Paired rainfall and high-flow event timeseries, statistics, and extraction method for 1203 stations and catchments in Great Britain recorded during 1990-2016 [UKCEH Flood Event Data Suite]
This paper introduces the UKCEH Flood Event Data Suite, a comprehensive dataset and extraction method for paired rainfall and high-flow events, along with derived statistics, for 1203 stations in Great Britain from 1990-2016. It provides a standardized procedure and a large-scale dataset to support hydrological research.
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Zhu et al. (2026) Reduced glacier mass loss rates on the southern Tibetan Plateau during a global warming hiatus
This study investigates the mechanism behind a reduced glacier mass loss rate on the southern Tibetan Plateau during 1996–2008, a period overlapping with the global warming hiatus. It finds that increased ablation-season precipitation and cloud cover, linked to specific atmospheric circulation patterns, drove this slowdown by reducing incoming shortwave radiation and increasing surface albedo.
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Dong et al. (2026) Improving vegetation photosynthesis model (VPM) by incorporating CO2 and atmospheric aerosols: A case study using urban and rural flux data from Shenzhen, China
This study enhances the Vegetation Photosynthesis Model (VPM) by integrating atmospheric CO2 and aerosol optical depth (AOD) to improve gross primary production (GPP) estimation for urban evergreen broadleaf forests. The modified VPM (VPM_urban) significantly increased GPP simulation accuracy in urban areas, offering better tools for urban ecological management.
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Shi et al. (2026) Log-Laplace Nuggets for Fully Bayesian Fitting of Spatial Extremes Models to Threshold Exceedances
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Madadikhaljan et al. (2026) Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data
[A VERY CONCISE 1-2 sentence summary of the paper's core objective and main finding.]
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Wisinski et al. (2026) What's in the latent space? Exploring coupled tropical Pacific variability within a Multi-branch $β$-Variational Autoencoder
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Bhattacharjee et al. (2026) Eliciting core spatial association from spatial time series: a random matrix approach
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joyyyxiao (2026) joyyyxiao/VPD-thresholds_paper_codes: Version 3
This study investigates how global vegetation responses to elevated atmospheric carbon dioxide are modulated by varying vapor pressure deficit thresholds.
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Yang et al. (2026) A Convective Initiation Nowcasting Algorithm Based on FY-4B Satellite AGRI and GHI Data
This study develops a convective initiation (CI) nowcasting algorithm for Sichuan Province, China, by integrating high-resolution FY-4B AGRI and GHI satellite data with optical flow compensation. The algorithm significantly improves CI detection probability and lead time compared to existing methods, offering enhanced short-term warnings for regional convective weather.
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Mahesh et al. (2026) Investigation of dielectric response of soil with varying physicochemical properties
This study investigates how the complex dielectric constant of soil varies with physicochemical properties (pH, electrical conductivity, organic carbon, and texture) across different soil types and microwave frequency bands. It reveals unique frequency-dependent dielectric signatures linked to these properties, which are vital for improving satellite-based soil monitoring and remote sensing applications.
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Preetha et al. (2026) Hydrological Stability and Sensitivity Analysis of the Cahaba River Basin: A Combined Review and Simulation Study
This paper proposes a continuous integration framework for hydrological modeling that links model sensitivity analysis with real-time sensor tasking to prioritize data collection and drive model refinement. It demonstrates that using high-resolution spatial data significantly enhances model accuracy and efficiency by focusing improvements on areas of high hydrological variability.
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Montazar et al. (2026) Field-scale evaluation of OpenET for quantifying consumptive water savings under deficit irrigation in alfalfa
This study evaluated OpenET satellite-derived actual crop evapotranspiration (ETc act) for quantifying consumptive water savings under summer deficit irrigation in alfalfa fields. It found that OpenET, particularly its ensemble product, reliably quantifies significant water savings (150–200 mm per deficit period, 40–50% reduction) in arid agricultural systems, validated by eddy covariance and soil moisture data.
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Siirila-Woodburn et al. (2026) Warming and snow loss increase reliance on old groundwater in a Colorado River headwater
Integrated hydrologic modeling in the Upper Colorado River headwaters reveals that atmospheric warming and snow loss increase reliance on older groundwater for streamflow, leading to disproportionate declines in high-elevation groundwater storage and an overall aging of streamflow contributions.
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Guan et al. (2026) A framework of coupling split-window and machine learning (SW-ML) for land surface temperature retrieval from MODIS thermal infrared data
This study proposes an SW-ML framework integrating physics-based split-window (SW) and machine learning (XGBoost) to accurately retrieve land surface temperature (LST) from MODIS thermal infrared data, demonstrating improved accuracy and interpretability, especially under challenging environmental conditions.
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Tiwari et al. (2026) Transition in Köppen Climate Zones and Its Impacts on Hydroclimatic Extremes Across India
This study investigates how spatial shifts in Köppen–Geiger climate zones across India between 1961–1990 and 1991–2020 influence long-term drought characteristics, revealing an expansion of arid zones and contraction of temperate zones, leading to more frequent and intense droughts driven by rising temperatures and increased evapotranspiration.
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Sun et al. (2026) Climate-hydrology-topography-anthropogenic factors jointly drive the evolution of vegetation coverage in semi-arid regions: A downscaling approach based on random forest and nonlinear residual correction
This study developed a synergistic downscaling approach combining random forest and nonlinear residual correction to analyze the spatiotemporal dynamics of annual mean Normalized Difference Vegetation Index (NDVI) in the Songnen Plain from 1985 to 2022, revealing a significant upward trend in NDVI and identifying key driving factors and their interactions, including the crucial role of groundwater depth.
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DAS et al. (2026) West Texas Severe Precipitation
This dataset provides hourly precipitation data for five severe precipitation events that occurred over West Texas.
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Xue (2026) Gross Primary Production (GPP) of Vegetation Calculated by Machine Learning
This study comprehensively evaluated five mainstream Gross Primary Production (GPP) products and quantified their uncertainties using flux tower observations, then generated a high-fidelity GPP dataset for mainland China by integrating multi-source data with five machine learning methods, finding that Categorical Boosting (CatBoost) performed best.
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Jia et al. (2026) Land–Water Allocation, Yield Stability, and Policy Trade-Offs Under Climate Change: A System Dynamics Analysis
This study develops an integrated ML–SD–NSGA-II framework to optimize crop areas and irrigation depths, balancing profit, water productivity, and yield stability under climate change. Applied to a rice–wheat system, it demonstrates improved irrigation water productivity and reveals a scarcity-regime threshold where economic instruments become less effective under severe drought.
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Zhu et al. (2026) DeepProfile: An inverse fusion framework for root zone soil moisture profile estimation
This study introduces DeepProfile, an inverse fusion framework that integrates multiple heterogeneous root zone soil moisture (RZSM) products to estimate continuous soil moisture profiles down to 1 meter, demonstrating strong global agreement with in-situ measurements.
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Venkatesh et al. (2026) Spatiotemporal water dynamics and micro-administrative (Hobli) scale storage variations in the coastal area of Mangaluru Taluk, India: a geospatial approach for sustainable water management
This research investigates spatiotemporal variations in terrestrial and groundwater storage in the Mangaluru coastal region, India, at the micro-administrative (hobli) level over two decades using geospatial techniques. It reveals significant seasonal variability, long-term groundwater depletion, and the exacerbating effects of urbanisation and land-use change, underscoring the need for localised adaptive water management.
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Bartfeld et al. (2026) Intrinsic predictability of heavy precipitation influenced by atmospheric rivers in the Western Iberian Peninsula
This study investigates the dynamics and intrinsic predictability of Heavy Precipitation Events (HPE) in Portugal, emphasizing the role of Atmospheric Rivers (AR). It reveals that AR-linked HPE are more intense and predictable, primarily driven by strong low-level winds, with high-predictability events associated with well-defined, deep extra-tropical cyclones.
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Maurya et al. (2026) Markov chain–based characterization of rain spell persistence and transition dynamics across the Indian Ganga Basin
This study assessed century-long rain spell dynamics in the Indian Ganga Basin using a multivariate probabilistic framework and Markov chain analysis, revealing a significant shift from historically coherent, long-duration rainfall to increasingly frequent, intense, and spatially fragmented short-duration events since the 1990s.
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Ling et al. (2026) Advancing the understanding of drought propagation in glacierized catchments by accounting for meltwater and catchment control in a newly introduced bi-stage framework
This study developed a novel bi-stage drought propagation analysis framework (BDPF) to investigate the complex process of meteorological to hydrological drought in glacierized catchments, revealing that meltwater mitigates drought severity while catchment controls tend to amplify it.
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Cui et al. (2026) A Unified Foundation Model for All-in-One Multi-Modal Remote Sensing Image Restoration and Fusion with Language Prompting
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Kelleher et al. (2026) LIVVkit/evv4esm: EVV v0.6.1
This paper announces the release of EVV 0.6.1, an updated software tool providing statistical climate reproducibility tests for Earth system models, featuring new statistical tests and bug fixes.
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Alkhawlani et al. (2026) Quantified groundwater–surface water dynamics across Tigris–Euphrates basin through integrated modelling
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Augas et al. (2026) Monolayer or Multilayer Snow Model: Implications for the HYDROTEL Hydrological Model for Flow Modeling
This study evaluates the impact of replacing the HYDROTEL hydrological model's original monolayer snow module with a multilayer formulation on streamflow simulations across ten snow-dominated watersheds in Quebec, Canada. The multilayer model significantly improved low-flow simulations, particularly during the freshet's falling limb, and reduced bias in cumulative freshet volumes, without degrading overall annual performance.
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Cai et al. (2026) Compound Hot‐Dry Extremes Amplify Disproportionate Climate Risks for Low‐Income Nations
This study provides a global and cross-national assessment of future risks from compound hot-dry extremes, revealing that under current policies leading to approximately 2.7°C warming by 2100, 28.5% ± 9.3% of the global population (approximately 2.6 ± 0.9 billion people) may face heightened exposure, with low-income and tropical island nations disproportionately affected.
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Xia et al. (2026) A first characterization of lake ice thickness on the Tibetan Plateau by leveraging satellite altimetry and ERA5 reanalysis
This study provides the first large-scale, satellite altimetry-based quantification of lake ice thickness (LIT) across 170 Tibetan Plateau lakes from 2016-2024, revealing mean annual maximum LIT values between 0.28 and 0.78 meters and an overall thinning rate of -0.8 mm per year over the past three decades, primarily driven by temperature.
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Matsuo et al. (2026) Probabilistic Assessments on Future Changes in Typhoon Characteristics Based on Fixed-SST Ensemble Experiments by Slab-Ocean Coupled MRI-AGCM
This study probabilistically assessed the variability and future changes in typhoon intensity and frequency using new large ensemble simulations with a slab-ocean coupled atmospheric global climate model (MRI-AGCM). It found a projected decrease in overall typhoon frequency but a significant increase in the annual probability of occurrence for extreme typhoons under the SSP585 future climate scenario.
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Wang et al. (2026) Canopy resistance-based modeling of maize evapotranspiration in Heilongjiang Province: a multi-site assessment integrating Sentinel-2 data and ground observations
This study developed and evaluated three canopy resistance-based evapotranspiration (λET) models within the Penman-Monteith framework, integrating Sentinel-2 data and ground observations, to improve regional farmland λET estimation for maize in Heilongjiang Province, China. The PMrc-ST model demonstrated the highest accuracy across 19 representative sites, providing valuable insights for precision agricultural water management.
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Li et al. (2026) Comprehensive assessment of six snow depth products and trends across the Qinghai-Tibet Plateau
This study comprehensively evaluated six snow depth products over the Qinghai-Tibet Plateau (QTP) to assess their ability to capture snow cover and depth. It found that the High Mountain Asia (HMA) product performed best, and revealed a general decreasing trend in snow depth and snow cover duration across the QTP from 1980 to 2023, with significant regional heterogeneity.
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Barontini et al. (2026) A parsimonius agroclimatic methodology to assess the hydrological sustainability of agriculture in the Mediterranean semiarid climate
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Falchetta et al. (2026) Street green space is relevant but not sufficient for adapting to growing urban heat in world cities
This study empirically estimates the heat stress reduction potential of street green space (SGS) across 133 cities globally using a microclimate model and a high-resolution greenness indicator, finding that while SGS expansion can offset a small percentage (2-11%) of projected urban heat increase by 2050, it is insufficient alone to adapt to growing urban heat stress.
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Wang et al. (2026) A 1-km Dataset of Water Consumption and Irrigation for Major Grain Crops in the Yellow River Basin Based on the Crop Water Production Function
This study developed a 1-kilometer resolution dataset of water consumption and irrigation for wheat, maize, and soybean in the Yellow River Basin (2000-2020) by extending reverse-logic Crop Water Production Functions (CWPFs) from field to basin scale and integrating them with the soil water balance principle. The dataset provides spatially detailed and accurate estimates crucial for water management and food security.
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Qi et al. (2026) Climate change overrides population dynamics in driving flood exposure in the Yangtze River Basin
This study quantifies future population exposure to river floods in the Yangtze River Basin under various climate change and socioeconomic scenarios, finding that climate change is the dominant driver of increased exposure, particularly in the middle and lower reaches, overriding population dynamics at higher warming levels.
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Li et al. (2026) Future Amplification of Moist Weather Extremes in the Midlatitudes
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Yousefnezhad et al. (2026) Accuracy Assessment of CMORPH and GPCP Satellite Precipitation Products Across Iran
This study evaluates and compares the accuracy of CMORPH and GPCP satellite-based precipitation products across daily, monthly, and annual scales over Iran against 128 meteorological stations. It found that CMORPH generally offers higher accuracy at finer temporal resolutions and in event detection, while GPCP shows stronger correlations at broader scales but tends to overestimate precipitation.
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Bayati et al. (2026) Enhancing large-scale basin rainfall-runoff modelling through the integration of flow routing: a case study in Iran’s Karun-4 Basin
This study developed and evaluated a daily lumped water balance model for Iran's large-scale Karun-4 Basin, demonstrating that integrating flow routing significantly enhances streamflow simulation accuracy, particularly for peak flows, in data-scarce mountainous regions.
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Ratzke et al. (2026) Data Repository for Unraveling a Vicious Cycle: Extreme Weather Events, Urban Expansion, and Deforestation
This data repository provides harmonized regional panel data to examine the interrelations between extreme weather events (drought, flood), urban expansion, and tree cover loss from 2001 to 2018, facilitating reproducibility of the underlying study.
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Zhu et al. (2026) Exploring the combined effects of climate change and vegetation restoration on terrestrial water storage in China
This study quantifies the combined effects of climate change and vegetation restoration on Terrestrial Water Storage Anomaly (TWSA) in China from 1982 to 2020, revealing an overall TWSA decline and heterogeneous impacts of vegetation greening across different climate zones, mediated by precipitation and temperature.
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Huang et al. (2026) Key Contributors to Changes in Ice Phenology and Composition in Lowland Polish Lakes During 1983–2023: Climatic Variables and Lake Morphometry
This study utilized the MyLake model with 40 years of observational data to investigate lake ice changes in Polish lowland lakes, revealing a significant decline in black ice and attributing shifts in ice phenology (delayed freeze-up, advancing break-up, thinned ice) and inter-lake differences primarily to air temperature, precipitation, and lake morphometry.
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Sun et al. (2026) Sensitivity of the 2023 Asian Summer Monsoon Water Vapor Transport to Arabian Sea Surface Temperature Anomalies
This study investigates the impact of the Arabian Sea mini warm pool (ASMWP) sea surface temperature (SST) decline rate on South Asian monsoon precipitation using an SST sensitivity experiment with the WRF model. It found that a slower-than-normal SST decline significantly increases precipitation around the ASMWP while decreasing it elsewhere, driven by changes in water vapor (evaporation, advection) and wind (pressure adjustment, convergence), ultimately enhancing convection through buoyancy changes.
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Yang et al. (2026) Anthropogenic stresses on sea-level rise and land subsidence triple the future coastal flooding in Shanghai
This study quantifies the contributions of anthropogenic activities and natural processes to sea-level rise (SLR) and land subsidence (LSS) on coastal flooding in Shanghai. It reveals that anthropogenic stresses, primarily groundwater extraction and greenhouse gas emissions, will triple the future coastal flooding in Shanghai by 2100 due to amplified combined effects of SLR and LSS.
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Ortiz et al. (2026) Spatiotemporal Distribution in Rainfall and Temperature from CMIP6 Models: A Downscaling and Correction Study in a Semi-Arid Region of Mexico
This study evaluated 15 CMIP6 models over Zacatecas, Mexico, and produced a 1 km historical climate dataset for 1985–2014 by statistically downscaling bias-corrected daily fields, assessing its performance against independent observational networks.
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Kim et al. (2026) Comparison of RTTOV-SCATT simulations of brightness temperature for NOAA-21/ATMS water vapor channels with different WRF microphysics schemes: the case study of heavy rain event in the Amnok River basin on 27 July 2024
This study compared RTTOV-SCATT simulations of NOAA-21/ATMS brightness temperature using six different WRF microphysics schemes for a heavy rain event. It identified the Thompson scheme as providing the most suitable all-sky brightness temperature simulations, exhibiting the best agreement with observations and closest to a Gaussian Observation Minus Background distribution after quality control.
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Fu et al. (2026) Analysis of Driving Factors and Trend Prediction of Groundwater Levels in the West Liao River Basin Based on the STL-LSTM Model
This study characterized groundwater dynamics in the West Liao River Basin from 2000-2016, revealing a persistent decline accelerated post-2009, primarily driven by soil moisture and climatic factors, and developed an accurate STL-LSTM hybrid model for forecasting.
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mohammed (2026) Assessing the Impact of Deficit Irrigation Levels on Wheat Growth, Yield, and Water Use Efficiency in Semi-Arid Regions
This study investigated the impact of four deficit irrigation levels (100%, 80%, 60%, 40% of crop evapotranspiration) on wheat growth, yield, and water use efficiency in a semi-arid region. It found that 80% ETc irrigation maintained comparable yields to full irrigation while significantly improving water use efficiency, making it an optimal strategy for water-limited environments.
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Emami et al. (2026) Intelligent irrigation management system for arid and semi-arid regions under climate change
This study developed and validated an intelligent irrigation management system for arid and semi-arid regions by integrating actual and predicted climate data with a physical water–soil–plant model. The system demonstrated significant reductions in water use (up to 41%), increases in crop yield (up to 12.6%), and improvements in water productivity (up to 87%) for wheat, tomato, and apple compared to conventional methods.
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Al‐Nassar et al. (2026) On the Temporal Variability of Precipitation in Iraq: Arid‐Wet Years and Extreme Events
This study analyzes long-term precipitation variability in Iraq, distinguishing between arid and wet years and characterizing extreme events, revealing that the Indian Ocean Dipole (IOD) significantly influences interannual variability by modulating autumn-winter moisture arrival.
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Kalai et al. (2026) The Role of Daily and Monthly Bias Corrected Data in Preserving the Monthly Cross‐Correlation Between Precipitation and Temperature
This study evaluates the ability of two bias correction procedures, Canonical Correlation Analysis (CCA) and Quantile Regression (QR), to preserve the monthly cross-correlation between precipitation and maximum temperature from Global Climate Models (GCMs) over the Continental United States (CONUS). It finds that CCA outperforms QR in reproducing observed cross-correlations, and that bias correction applied at a daily temporal scale better preserves monthly cross-correlations compared to monthly bias correction.
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Kelebek et al. (2026) Escalating Future Climate Extremes Across the Black Sea Basin Driven by Kilometer-Scale Scenario Simulations
This study utilizes kilometer-scale convection-permitting simulations to project future climate extremes across the Black Sea Basin under the SSP3-7.0 scenario, revealing significant increases in temperature extremes, accelerated snowmelt, and intensified daily and sub-daily precipitation, particularly in urban and mountainous hotspots.
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Gharnouki et al. (2026) Assessing Uncertainty in Multi-Source Precipitation for a Semi-Arid Mediterranean Catchment Using SWAT
This study evaluates the performance of five multi-source precipitation datasets (observed, CHIRPS, PERSIANN, GPM-IMERG, ERA5) in driving the SWAT hydrological model for a semi-arid Mediterranean catchment in central Tunisia over a 17-year period. It found that CHIRPS and ERA5 provided satisfactory hydrological simulations after recalibration, with CHIRPS demonstrating the best overall performance and highlighting precipitation as the primary source of uncertainty.
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Pérez-Planells et al. (2026) Annual and diurnal temperature cycle modelling of a merged multi-annual ECOSTRESS and Landsat land surface temperature dataset
This study models annual and diurnal land surface temperature (LST) dynamics using a five-parameter Annual-Diurnal Temperature Cycle (ADTC) model fitted to a novel six-year (2018–2023) merged high-resolution (70 m) ECOSTRESS and Landsat LST dataset over four European cities. The high-resolution ADTC parameters demonstrate good agreement with MODIS-derived parameters for most variables and show clear potential for detailed urban thermal studies and land cover change monitoring.
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Xu et al. (2026) Code for evaluating WRF physics parameterization schemes over Xinjiang
This study focuses on optimizing various WRF physics parameterization schemes for multi-decadal simulation of near-surface climate specifically over arid Xinjiang, China, with the associated repository providing the code and configurations for reproducibility.
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Pednekar et al. (2026) ketan-pednekar/ccart-india: CCART v3.0 — Modular Public Release
This paper announces CCART v3.0, the first fully modular, open-source release of the Climate Catastrophe Analysis & Risk Toolkit for India, designed to assess and analyze climate-related hazards and risks.
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Trinh et al. (2026) El Nino Prediction Based on Weather Forecast and Geographical Time-series Data
## Identification - **Journal:** arXiv (Cornell University) - **Year:** 2026...
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Jawad et al. (2026) Improving Model Performance by Adapting the KGE Metric to Account for System Non-Stationarity
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Escobar et al. (2026) Mapping Water Scarcity and Aridity Trends in U.S. Drought Hotspots: Observed Patterns and CMIP6 Projections
This study investigates the spatial and temporal patterns of aridity and water scarcity across 111 drought-prone stations in the U.S. over 30 years, revealing a distinct west-east aridity gradient and projecting divergent future precipitation trends under global warming scenarios.
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Allen et al. (2026) Characterizing Improvements in Ensemble Forecast Performance Over the Last Decade: A Retrospective Analysis of the Hydrologic Ensemble Forecast Service (HEFS)
This study retrospectively analyzes the performance of operational short- and medium-range streamflow forecasts from the California Nevada River Forecast Center (2014-2025) using a novel hierarchical Bayesian model, finding improved performance for moderate and high flow events, though improvements in ensemble spread attributes are weak.
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Roulo et al. (2026) From Global to Basin‐Scale: Identifying Best‐Performing CMIP6 Models Across Indian River Basins Through Downscaled Precipitation Products
This study introduces a novel four-level framework to evaluate and rank 24 CMIP6 Global Climate Models (GCMs) across 27 Indian River Basins using a Multi-Metric Evaluation Approach. It finds significant spatial variability in model performance and highlights that GCMs performing well for mean rainfall do not necessarily capture extreme rainfall behavior accurately.
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Zarikos et al. (2026) Assessing Climate Change Impacts on Precipitation Volume and Drought Characteristics Across Basin and Sub-Basin Scales in Greece
This study quantifies the effects of climate change on precipitation and drought conditions in Greek hydrological basins using high-resolution down-scaled climate projections, providing new spatial insights into precipitation redistribution and drought frequency under multiple RCP scenarios.
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Mi et al. (2026) A UAV-Based Dual-Spectroradiometer Method for Hyperspectral Reflectance Measurement
This study developed and validated a UAV-based dual-spectroradiometer system for hyperspectral surface reflectance measurement under natural illumination. The system achieved accurate irradiance and reflectance measurements, with RMSE below 0.01 and Spectral Angle Mapper (SAM) values below 3.5 across the 400–900 nm spectral range, demonstrating a practical and ground-independent approach for quantitative hyperspectral reflectance acquisition.
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Naveen et al. (2026) Automated Leaf Damage Assessment and Crop Classification Using Convolutional Neural Networks
This paper presents an automated system utilizing Convolutional Neural Networks (CNN) for leaf damage assessment and crop classification to enhance agricultural productivity. The system integrates image capture, CNN-based disease detection and crop classification, real-time soil moisture sensing for irrigation, pest control activation, and GSM alerts to farmers, aiming to reduce manual effort and improve accuracy in smart farming practices.
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Jiang et al. (2026) Physics-constrained multimodal vision transformer for ultra-short-term solar radiation forecasting error correction
This study develops a physics-constrained multimodal vision transformer framework to correct ultra-short-term solar radiation forecasting errors, achieving significant reductions in RMSE and systematic bias by integrating satellite data with fundamental physical principles.
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Ayalew et al. (2026) Observed and projected climate extremes in northwest highlands of Ethiopia and their implications in potato-based farming systems
This study analyzed observed (1989-2018) and projected (2019-2078) extreme rainfall and temperature trends in northwest Ethiopian highland agroecosystems, revealing significant increases in both extreme precipitation and temperature events, particularly in the Upper Dega and under high emission scenarios, with critical implications for potato-based farming systems.
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Huknight (2026) Huknight/Global-groundwater-predictability-univariate-MATLAB: Global groundwater predictability univariate MATLAB v1.0
This paper investigates the primary constraints on global groundwater predictability, concluding that temporal persistence is a more significant limiting factor than model complexity.
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Rosehill et al. (2026) Why Earth Can't Hit 60°C
This paper explores the physical mechanisms that limit Earth's surface air temperature to around 54°C, detailing how convection, evaporation, and thermal radiation act as self-regulating cooling systems. It concludes that reaching 60°C would require a catastrophic convergence of extreme conditions, highlighting the critical biological limits of human survival at high wet-bulb temperatures.
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Rosehill et al. (2026) Weather Balloons: The 100-Year-Old Tech Powering Modern Forecasting
This episode explains why 100-year-old weather balloon technology, equipped with radiosondes, remains indispensable for modern weather forecasting by providing critical high-resolution "ground truth" atmospheric data that calibrates satellite measurements and enables accurate severe weather prediction.
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Birylo (2026) Non-Stationarity of Hydroclimatic Memory—Is Hydrological Memory Changing Under Climate Warming?
This study investigates the temporal stability of hydrological memory in the ten largest European basins to understand basin regime dynamics under changing climatic conditions, finding that most basins exhibit a relatively short hydrological memory with varying degrees of temporal instability.
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Liu et al. (2026) Optimized Decision Model for Soil-Moisture Control Lower Limits and Evapotranspiration-Based Irrigation Replenishment Ratios Based on AquaCrop-OSPy, PyFAO56, and NSGA-II and Its Application
This study developed a multi-objective simulation optimization framework integrating crop and irrigation models with an evolutionary algorithm to optimize winter wheat irrigation strategies for maximizing yield and minimizing water input. The framework identified Pareto-optimal strategies and an optimal two-irrigation scheme for a specific wet growing season in the North China Plain.
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Zhao et al. (2026) An Intelligent Gated Fusion Network for Waterbody Recognition in Multispectral Remote Sensing Imagery
This study introduces IGF-Net, a novel deep learning model designed for accurate water body segmentation from multispectral remote sensing imagery, which achieves superior performance and strong generalization compared to existing methods by adaptively fusing visual and spectral features.
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Shruti et al. (2026) SmartSoil: A Humanoid-Inspired Drone Framework Using Thermal Imaging and AI for Real-Time Soil Moisture Estimation
N/A (Information not extractable from provided text)
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Laouz et al. (2026) A Novel Deep Learning Framework Based on Heterogeneous Temporal Data Harmonization for Irrigation Water Amount Prediction
This paper proposes a novel deep learning framework, combining a Convolutional Neural Network (CNN) for heterogeneous temporal data harmonization and a Multilayer Perceptron (MLP) for prediction, to accurately forecast daily irrigation water amounts, achieving a Mean Absolute Error of 0.5 L/m² and an R² score of 0.82.
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Liang et al. (2026) Why the statistical relationship between east asian summer monsoon rainfall and the concurrent ENSO index is weak and nonsignificant
This study explains the weak and non-significant statistical relationship between East Asian summer monsoon (EASM) rainfall and the concurrent ENSO index by demonstrating that two opposing phase relationships between the Pacific-Japan (PJ) teleconnection pattern and ENSO statistically cancel each other out. It reveals that a positive PJ pattern can be triggered by two distinct Indo-Pacific sea surface temperature (SST) anomaly patterns associated with opposite ENSO phases, each inducing different EASM rainfall responses.
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Xiang et al. (2026) Revealing the Mechanisms of Heat Extremes Using an AI Enabled Diagnostic Framework
This study introduces a globally perturbed reforecast framework driven by the Neural general circulation model (NeuralGCM) to understand the mechanisms of the record-breaking August 2022 South China heatwave (SCH22). It identifies high impact regions (HIRs) in Europe and North America whose initial condition anomalies are crucial for accurately reproducing the heatwave's evolution and spatial pattern.
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Sink et al. (2026) MACH: A Multi-Attribute Catchment Hydrometeorological dataset
This paper introduces MACH, a comprehensive hydrometeorological dataset for 1,014 watersheds across the contiguous United States, unifying and extending existing large-sample hydrology resources with consistent daily meteorological forcings, streamflow observations, and diverse watershed attributes over a 44-year period (1980-2023), with a 75-year extension for a subset of basins.
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Huang et al. (2026) Why Do Extended‐Range Forecasts Underpredict the Extreme Negative Pacific/North American Pattern in February 2018?
This study investigates the failure of extended-range forecasts to capture the extreme negative Pacific/North American (PNA) event of February 2018, attributing the error to an underestimation of the East Asian trough, biases in sea surface temperatures, and complex, initialization-dependent stratospheric-tropospheric coupling.
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Qi et al. (2026) Spatiotemporal patterns, driving mechanisms, and threshold responses of watershed ecosystem services from a supply-demand flow perspective
This study investigates the spatiotemporal patterns, driving mechanisms, and threshold responses of carbon sequestration, water yield, and habitat quality in the Dongting Lake Basin from a supply-demand flow perspective. It reveals differentiated ecosystem service dynamics, process-dependent flow patterns, and nonlinear threshold responses to human disturbance and environmental factors, enabling threshold-driven spatial zoning for targeted watershed management.
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Stojković et al. (2026) Towards adaptive stage-flow rating curve for large lowland river streams on the lower Tisza River with backwater impacts using deep learning and copula approach
This study develops a joint machine learning (ML)–copula framework to create adaptive stage-flow rating curves for large lowland rivers with backwater impacts. It demonstrates that Kolmogorov–Arnold Networks (KAN) outperform traditional power regression and other ML models in accuracy and robustness, especially under extreme flow conditions and synthetic data extensions.
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Mechentel et al. (2026) Data-driven flood susceptibility assessment using hybrid machine learning and optimization techniques: case of the Sedrata Watershed, NE Algeria
This study developed a hybrid flood susceptibility modeling framework combining Random Forest Regressor with metaheuristic optimization algorithms (GOA, SSA, ACO) for the Sedrata Watershed, Algeria, demonstrating significantly enhanced predictive performance compared to the standalone RFR model.
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Adnan et al. (2026) Future outlook of monthly maximum daily precipitation in Pakistan’s hydroclimatic zones: high-resolution insights from CMIP6 multimodel data
This study projects future monthly maximum daily precipitation extremes (Rx1day) across Pakistan's seven hydroclimatic zones using bias-corrected CMIP6 multi-model ensembles under SSP2-4.5 and SSP5-8.5 scenarios, revealing significant spatial heterogeneity with northern highlands experiencing nearly double baseline values by late century.
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Zhou et al. (2026) Potential Causes of Anti‐Phase Summer Precipitation Variability Between the Middle and Lower Reaches of the Yangtze River
This study investigates the atmospheric circulation and land-ocean thermal anomaly mechanisms driving out-of-phase summer precipitation patterns in the Yangtze River basin. It identifies a non-canonical Silk Road Pattern (SLWT) maintained by specific land-ocean thermal anomalies as the key driver and proposes a new index to monitor this pattern.
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Hu et al. (2026) Enhancing ENSO Ensemble Forecast Skill by a Coupled Conditional Nonlinear Optimal Perturbation Method
This study compares two perturbation methods, Coupled Condition Nonlinear Optimal Perturbation (C-CNOP) and Singular Vector (SV), for El Niño–Southern Oscillation (ENSO) ensemble forecasting. It finds that the C-CNOP method, specifically its sea temperature component (CP-T), significantly improves ENSO forecast skill by better capturing nonlinear effects and extending skillful lead times, particularly during strong El Niño events.
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Bushra et al. (2026) Influence of Climate Drivers on Extreme Precipitation in Bangladesh: Spatiotemporal Patterns and Underlying Mechanisms
This study analyzed daily precipitation data from 1980-2017 across Bangladesh to investigate spatiotemporal trends in extreme precipitation indices (EPIs) and their linkages with large-scale climate drivers, revealing increasing dry spells and heavy rainfall frequencies alongside declining wet-day intensity, influenced by factors like ENSO and IOD.
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Dai et al. (2026) Impacts of surface heterogeneity on energy partitioning in paddy ecosystems: A dual eddy covariance study
This study quantifies the impacts of surface heterogeneity on energy partitioning in paddy ecosystems using dual eddy covariance and UAV-based footprint modeling. It reveals that even low heterogeneity significantly biases latent heat flux measurements due to advection and physiological responses, and proposes a two-stage correction framework to rectify these biases.
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Wan et al. (2026) Integrated relative humidity as a layer-resolved indicator for moisture-precipitation interactions
This study introduces Integrated Relative Humidity (IRH) as a layer-resolved diagnostic to quantitatively investigate moisture-precipitation interactions in subtropical coastal environments, revealing distinct vertical saturation structures across precipitation lifecycle stages and identifying specific IRH thresholds for precipitation events.
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Li et al. (2026) Improving CWRF Prediction of Summer Monthly Precipitation Over the Yangtze River Basin With Spatio‐Temporal Graph Neural Network
This study proposes a novel deep learning framework utilizing a spatio-temporal graph neural network (GNN) to enhance the accuracy of summer precipitation forecasts from the Climate-Weather Research and Forecasting (CWRF) model over the Yangtze River Basin (YRB). The GNN model significantly improves prediction skill across multiple metrics, outperforming existing neural network-based models.
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Wang et al. (2026) Modeling saline soil complex permittivity from bound-water microphysics: Laboratory validation at L-/C-bands
This study develops a microphysical-electromagnetic coupling model for soil complex permittivity (SCP) in saline soils, validated in the laboratory across L- and C-bands. The proposed model achieves high accuracy (R² > 0.95) and outperforms existing models, demonstrating its potential for improving microwave remote sensing of soil salinity.
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Sozzi et al. (2026) Assessing vineyard irrigation uniformity and drip system malfunction by remote and ground sensing: Insights from Sentinel-1, Sentinel-2 and Planet monitoring
This study evaluates the effectiveness of Sentinel-1, Sentinel-2, and Planet satellite data for monitoring drip irrigation uniformity and detecting malfunctions in a vineyard, demonstrating their potential to optimize water management in viticulture.
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Mercier et al. (2026) Toward an Operational GNN-Based Multimesh Surrogate for Fast Flood Forecasting
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Asagha et al. (2026) Utilizing Artificial Neural Networks to Predict El Niño Southern Oscillation Events Using Nigerian Rainfall Data: A Teleconnection Analysis
This study investigates the teleconnection between Nigerian rainfall variability and El Niño–Southern Oscillation (ENSO) events using Artificial Neural Networks (ANNs), demonstrating that ANNs can accurately model these climate signals for enhanced adaptation and early warning systems.
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arosenaos (2026) arosenaos/jupyter_notebooks: v1.0 - Initial release for Rosen et al. (2025)
[Information not available from provided text]
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Zhang (2026) Aya-ya/SOM_trans: SOM_trans_v1.0.0
This study investigates the regional atmospheric circulation patterns and their shifts that are associated with the occurrence of weather whiplash events within the Upper Yangtze River Basin.
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wangchong96 (2026) wangchong96/Physics-Guided-Sudden-Tropical-Cyclone-Track-Turns-Forecasting: Physics-Guided-Sudden-Tropical-Cyclone-Track-Turns-Forecasting
This paper proposes a Mixture-of-Experts model with physics-guided gating to enhance the short-range forecasting of sudden tropical cyclone track turns, demonstrating improved predictive accuracy for these critical events.
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Han et al. (2026) Refining Public DEMs for Urban Waterlogging Simulation via Vector–Raster Integration
This study develops a novel semi-automated technique to refine public 30 m resolution Digital Elevation Models (DEMs) to 1 m resolution for urban areas. The method significantly improves the accuracy of urban inundation simulations by correcting road and waterway elevations, leading to better topological representation and more reliable flood depth predictions.
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Kumhálová et al. (2026) Monitoring of Agricultural Crops by Remote Sensing in Central Europe: A Comprehensive Review
This review article provides an overview of crop monitoring in Central Europe over the past fifteen years, highlighting technological and procedural developments, the integration of multi-sensor remote sensing data (Sentinel-1 and Sentinel-2), and future trends in digital agriculture.
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Таранова et al. (2026) Статистичний Аналіз Та Індикатори Кліматичної Нестабільності Атмосферних Опадів У Тернопільській Області (1969-2024 Рр.)
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Zhao et al. (2026) Utilizing Multi-Source Remote Sensing Data and the CGAN to Identify Key Drought Factors Influencing Maize Across Distinct Phenological Stages
This study integrates an improved Conditional Generative Adversarial Network (CGAN) with SHapley Additive exPlanations (SHAP) and multi-source remote sensing data to accurately identify the dominant environmental factors driving maize drought stress at different growth stages in rain-fed Northwest China, revealing a dynamic evolution of these factors across phenological stages.
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Jang (2026) Laboratory calibration dataset for tipping-bucket rain gauges: manual burette vs automated device
This dataset provides laboratory calibration results for tipping-bucket rain gauges using both automated and manual burette methods across five rainfall intensities, enabling comparison of calibration performance and serving as a benchmark.
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Nordling et al. (2026) First crossings of global warming levels in CMIP6 in light of observed 1.5 °C exceedance
This study quantifies the timing and likelihood of first crossing global warming levels in CMIP6 models, revealing that the observed 2024 exceedance of 1.5 °C occurred 3–7 years earlier than projected. It highlights the difficulty of avoiding a pre-mid-century 2 °C crossing even with stringent mitigation, while emphasizing the critical role of immediate mitigation in avoiding higher long-term warming.
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Chevuru et al. (2026) Global Gridded Climate-Responsive Crop Selection: Sowing Dates and Crop Varieties in a Warming World
This paper presents a global gridded dataset of simulated crop yields and consumptive water use for multiple crop varieties and sowing dates, developed to support assessments of future crop production and adaptation strategies under climate change. The dataset covers five major crops from 1961 to 2100, enabling the identification of climate-responsive crop selections.
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Tian et al. (2026) Differential response of soil moisture in surface and root zones to climate and land use changes
This study investigates the global dynamics and drivers of surface (0–10 cm) and root-zone (10–250 cm) soil moisture from 1982 to 2021, revealing significant regional differences and a stronger dominance of climate factors over land use/cover change (LUCC), particularly for root-zone soil moisture. It highlights that surface soil moisture is more sensitive to the coupling effects of LUCC and climate, while root-zone soil moisture is primarily controlled by long-term climatic conditions.
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Zhu et al. (2026) Enhanced leaf area index estimation in the Drip-Irrigated kiwifruit orchard based on optimized multi-source UAV-based indices
This study developed optimized multi-source UAV-based spectral, spectral-texture, and thermal infrared-texture indices combined with machine learning models to enhance Leaf Area Index (LAI) estimation in drip-irrigated kiwifruit orchards across different growth stages. The optimized spectral-texture index (VI_MT) coupled with Random Forest Regression (RFR) significantly improved LAI prediction accuracy, especially for stage-specific estimations.
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Takami et al. (2026) Hill‐Terrain Modulation of Inland Snow‐Cloud Microphysics: Polarimetric Radar and Balloon‐Borne Particle Imaging Radiosonde Observations
This study investigated snow-cloud microphysics over inland hills in Japan, revealing that even modest terrain significantly modulates snow-particle growth and graupel formation through various atmospheric processes.
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Berner et al. (2026) Quantifying Sources of Subseasonal Prediction Skill in CESM2 Within a Perfect Modeling Framework
This study uses a perfect modeling framework with the Community Earth System Model to estimate the theoretical limit of subseasonal-to-seasonal predictability from initialization, revealing that land initialization is the dominant source of predictability over land beyond week four.
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Zha et al. (2026) Machine learning-based precipitation dataset for the Yarlung Zangbo River Basin: Generation, evaluation, and environmental factor analysis
This study developed a machine learning framework to merge multiple precipitation products and environmental variables, generating a high-precision precipitation dataset (MMPD) for the Yarlung Zangbo River Basin. The MMPD significantly improved precipitation accuracy across multiple timescales, and an interpretable analysis identified key environmental factors and their nonlinear thresholds influencing precipitation intensity.
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Zhang et al. (2026) Three-dimensional cloud radar reflectivity reconstruction from geostationary multispectral imagery using a context-aware Transformer
This study develops a novel Transformer-based framework to retrieve continuous three-dimensional (3D) radar reflectivity fields from geostationary satellite imagery, demonstrating robust performance against CloudSat observations (R=0.80, RMSE=6.75 dBZ for composite reflectivity) and utility in monitoring severe weather like hurricanes.
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Chen et al. (2026) Accuracy and uncertainty quantification of using a large climate ensemble dataset for process-based flood quantile estimation
This study evaluates two event-based simplification approaches, the Aggregating Grid Event (AGE) method and the Precipitation Stretching (PrecStre) method, for process-based regional flood quantile estimation using a large climate ensemble. It finds that the AGE method achieves superior accuracy and lower uncertainty compared to the PrecStre method, providing a robust benchmark for regional flood risk management.
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Zhang et al. (2026) Height‐Dependent Sensitivity of Cloud Scales to Surface Temperature Anomaly Observed by Active Satellites
This study investigates how cloud horizontal scales vary with surface temperature anomalies and their impact on cloud radiative effect and precipitation, finding that cloud-scale temperature sensitivity is strongly dependent on cloud height, with significant changes in small-scale clouds driving radiative and precipitation responses.
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Ou et al. (2026) Foundation‐Scale Satellite Embeddings Reframe Hydrological Generalization as a Representation Problem
This study addresses the challenge of transferring hydrological predictive skill across time and distinct basins by introducing 64-dimensional satellite embeddings (SE) from Google's AlphaEarth Foundations model, demonstrating consistent and significant performance improvements over static-attribute baselines in Australian catchments.
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Tian et al. (2026) Development of a coupled hydro-economic model to support groundwater irrigation decisions
This study develops a farm-level coupled hydro-economic model incorporating Conditional Value-at-Risk to evaluate groundwater irrigation strategies under uncertainty. It demonstrates that optimal strategies balance short-term profitability with long-term sustainability, showing that increased pumping does not always lead to greater profitability due to diminishing returns and aquifer depletion.
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Mouneesh et al. (2026) Automated Fruit Disease Detection and Smart Irrigation System Using YOLOv8
This paper presents an automated system for precision agriculture that integrates YOLOv8-based fruit disease detection and growth monitoring with a smart irrigation system, utilizing real-time sensor data for soil moisture and pH to optimize water usage and crop management.
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Felipe et al. (2026) Evaluation of cropping calendar adherence on an irrigated lowland rice production area using remote sensing-derived aboveground biomass production
[Information not extractable due to unreadable paper text.]
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Tefera et al. (2026) Determination of Crop Water Requirements and Crop Coefficients for Onion Crops Using Lysimeter in the Semi‐Arid Regions of Ethiopia
This study established locally calibrated crop coefficient (Kc) values and determined the crop water requirement (CWR) for onion in the semi-arid Central Rift Valley of Ethiopia, finding a seasonal CWR of 472 mm and site-specific Kc values that deviated from standard guidelines.
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Currier et al. (2026) Scale and seasonal dependent sensitivity of hydrologic projections in the Colorado River Basin to different downscaling methods
This study compared future streamflow projections derived from dynamic (ICAR) and statistical (LOCA) downscaling methods to assess how downscaling choices impact water supply estimates. It found that while annual regional streamflow changes were not significantly different between methods, local-scale streamflow projections were more sensitive to downscaling choices due to differing seasonal and spatial precipitation and temperature patterns.
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Guo et al. (2026) Evaluating the correction methods for NDVI angular effect based on decametric resolution BRDF observations over cropland
This study comprehensively evaluated seven methods for correcting the Normalized Difference Vegetation Index (NDVI) angular effect using over 1,800 UAV-based BRDF observations and Sentinel-2 time series across seven crop types. It found that NDVI-Coupled methods, particularly the newly proposed mSVAC, significantly outperformed NDVI-Independent methods in reducing angular effects at decametric resolution.
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Liu et al. (2026) Crop height retrieval across different scenes with dual-pol backscattering and one-time in-situ data
This paper introduces a novel SAR Height Index (SHI)-based algorithm for crop height estimation that relies solely on dual-polarization SAR backscattering and a one-time in-situ calibration, demonstrating enhanced flexibility and cost-effectiveness by avoiding interferometric baseline constraints and complex parameterization. The method, validated across various crops and regions, integrates a logistic growth model to improve temporal consistency and fill data gaps, achieving high accuracy in daily crop height retrieval.
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Chen et al. (2026) Downscaling of satellite passive microwave brightness temperature through super-resolution reconstruction
This study develops an integrated land and atmospheric model approach (LEM-RTTOV) to simulate high-resolution passive microwave brightness temperature (PMW BT) and proposes a novel decoupled residual diffusion model (DRDiff) for super-resolution reconstruction to downscale satellite PMW BT, achieving accurate results for AMSR2 data.
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Alenezi et al. (2026) Thermodynamic Refugia in the Arabian peninsula: the diurnal moisture pulse as a physical Indicator of desert habitability
This study identifies 109 thermodynamic refugia in the hyper-arid Arabian Peninsula, characterized by significantly lower diurnal temperature ranges, which are maintained by a persistent nocturnal moisture pulse enabling morning evaporative buffering. These refugia non-linearly suppress lethal heat exposure, demonstrating that habitability in extreme deserts is governed by the truncation of thermal extremes rather than mean temperature moderation.
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Jiang et al. (2026) Disentangling the contributions of hydrothermal factors to fractional vegetation cover dynamics on the Qinghai-Tibet plateau
This study quantifies the independent contributions of vapor pressure deficit (VPD) and soil moisture (SM), alongside temperature (Ta) and precipitation (PRE), to fractional vegetation cover (FVC) dynamics on the Qinghai-Tibet Plateau (QTP) from 1982 to 2020, revealing VPD as the dominant driver with spatially heterogeneous effects modulated by soil moisture.
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Wang et al. (2026) Revisiting the application of variable infiltration capacity (VIC) model in the Colorado River Basin using SMAP and GRACE
This study implemented a multi-source calibration and evaluation framework for the Variable Infiltration Capacity (VIC) model in the Colorado River Basin, demonstrating its robust capacity to reproduce spatially distributed hydrologic processes, including soil moisture and terrestrial water storage dynamics, beyond traditional streamflow-only calibration. The enhanced model performance provides increased confidence for water management in the drought-stricken basin.
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Lyons (2026) speclib: Federated Spectral Signature Library
This paper introduces `speclib`, a federated spectral signature library platform designed to aggregate reflectance spectra from various sources, including USGS, ECOSTRESS, ASTER/JPL, and EMIT L2B, alongside specialized Kentucky-focused collections like invasive species data.
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SEGUY et al. (2026) seguyr/pic_equ_hist_response_CNRMCM61_SEGUY_paper: Code for : Impact of the equilibrium level of the CNRM-CM6-1 pre-industrial simulation on the historical climate response
This paper investigates how the initial equilibrium state of the CNRM-CM6-1 pre-industrial climate simulation influences the model's representation of historical climate changes.
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Ross (2026) TIDRoss/cm1v19.6-newif: 2026 JGR Atmospheres submission release
This paper presents the archived release of a modified CM1 v19.6 source code, which incorporates NSSL microphysical scheme modifications, specifically used for experiments investigating the regulation of severe hail growth by ice-nucleating particle-mediated raindrop freezing.
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Razeghi-Jahromi et al. (2026) Climate Variables Dataset for Iran’s Climate Zones (CMIP6, SSP5-8.5, 2020–2050)
This paper presents a dataset of projected monthly climate variables, including temperature, precipitation, wind speed, evaporation, and solar radiation, for Iran's main climate zones from 2020 to 2050, derived from CMIP6 under the SSP5-8.5 climate change scenario. The dataset aims to facilitate climate change impact analysis and energy modeling in the region.
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Guo et al. (2026) Interval-Based Tropical Cyclone Intensity Forecasting with Spatiotemporal Transformers
This paper proposes TC-QFormer, an interval-based probabilistic framework for 24 h tropical cyclone intensity forecasting, which combines transformer-based spatiotemporal modeling with scalar conditioning to achieve improved deterministic accuracy and well-calibrated prediction intervals.
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Zhang et al. (2026) Remote sensing-based failure risk assessment of Himalayan glacial lakes due to seismic-induced water waves
## Identification - **Journal:** Geomatics Natural Hazards and Risk - **Year:** 2026...
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汪 (2026) Derived annual pixel tables, landscape-position proxy outputs, boundary-risk outputs, and analysis scripts for feasible-frontier analysis of forest restoration outcomes in Tibet (2002–2024)
This study evaluates forest restoration outcomes across Tibet, providing a harmonized dataset and analytical framework to understand landscape-position controls and hydroclimatic constraints on the feasible frontier of restoration from 2002 to 2024.
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Wu et al. (2026) Hail Event Detection Using Power Spectrum Characteristics of Coherent Doppler Lidar: A Case Study in Hefei
This study employed a compact all-fiber coherent Doppler lidar (CDL) at 1.5 µm wavelength, combined with reanalysis and satellite data, to detect and characterize a hail event, identifying distinctive power spectrum characteristics of hail and verifying CDL's potential for high-spatiotemporal-resolution short-term hail forecasting.
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Yin et al. (2026) A Two‐Dimensional Deep Learning Scheme With One Predicator Only to Parametrize Global Lightning
This study introduces a novel two-dimensional artificial intelligence-based global lightning scheme utilizing convective available potential energy (CAPE) as a single predictor, achieving significant improvements in global lightning simulation performance and effectively mitigating the underestimation of extreme lightning density.
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Davies et al. (2026) Mass Addition to Timpanogos Rock Glacier: Debris‐Covered Snow and the Importance of Interannual Variability in Headwall Erosion and Climate
This study develops a probabilistic numerical model to quantify how debris deposition on snow contributes to ice accumulation in rock glaciers, regardless of permafrost presence. The model, which incorporates stochastic mountain processes, successfully reproduces observed internal stratigraphy and demonstrates that rock glaciers can add ice even in arid, warming climates.
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Tian et al. (2026) Advances in Subsurface Drip Irrigation System Design, Water–Fertilizer Synergy, and Sustainable Wheat Production in Xinjiang
This review synthesizes current knowledge on Subsurface Drip Irrigation (SDI) for wheat production in arid regions, focusing on system design, water-fertilizer management, and soil-crop responses, highlighting its potential to improve water use efficiency and yield while identifying challenges and future research directions for region-specific optimization in Xinjiang.
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Donkor et al. (2026) Modeling Spatiotemporal Streamflow Patterns in the Missouri River Basin Under Future Climate Scenarios
This study utilized the Soil and Water Assessment Tool (SWAT) driven by five downscaled and bias-corrected CMIP6 global climate models to assess historical (2008–2024) and future (2025–2049) streamflow patterns in the Missouri River Basin, revealing spatially variable hydrological responses with reduced extreme events in the upper basin and increased extreme events in the lower basin.
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Taranova et al. (2026) Statistical Analysis and Indicators of Climatic Instability of Atmospheric Precipitation in the Ternopil Region (1969-2024)
This study statistically analyzes the spatio-temporal variability of atmospheric precipitation in Ukraine's Ternopil region from 1969-2024, revealing increasing climatic instability and hydroclimatic risks, particularly in the western part of the region, by introducing a novel "climatic swings" indicator.
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Xu et al. (2026) Characteristics of meteorological drought propagation to agricultural drought and trigger thresholds in Xinjiang, China
This study quantifies the propagation characteristics and trigger thresholds from meteorological to agricultural drought in Xinjiang, China, revealing significant spatial heterogeneity and providing a quantitative basis for improved drought early warning systems and adaptation strategies.
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Rao et al. (2026) An Integrated Gpr and Machine Learning Approach for Precision Soil Moisture Monitoring
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A et al. (2026) Sensor Enabled - Soil and Plant Health Portable Device
This project develops a portable, sensor-enabled device leveraging IoT and Machine Learning for real-time soil and plant health monitoring, demonstrating reliable performance in detecting plant diseases and monitoring soil conditions to enhance precision agriculture.
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Veysi et al. (2026) Spatiotemporal dynamics of single and compound drought–heatwave extremes impacts on water productivity
This study investigated the spatiotemporal impacts of single and compound drought-heatwave extremes on agricultural water productivity (WP) in Iran over 30 years, revealing that Compound Drought–Heatwave Extremes (CDHEs) pose the most significant threat, causing substantial WP declines (13.5% in irrigated and 27.4% in rainfed systems).
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Yang et al. (2026) Evaluating the Performance of AlphaEarth Foundation Embeddings for Irrigated Cropland Mapping Across Regions and Years
This study systematically assessed the utility of AlphaEarth Foundation (AEF) model embeddings for irrigated cropland mapping, demonstrating their superior performance in class separability and classification accuracy compared to traditional Sentinel features, with strong temporal transferability.
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Zhong et al. (2026) FADiff: A Frequency-Aware Diffusion Model Based on Hybrid CNN–Transformer Network for Radar-Based Precipitation Nowcasting
This paper proposes FADiff, a novel frequency-aware diffusion model based on a hybrid CNN–Transformer network, to address challenges in deep learning-based precipitation nowcasting such as blurry predictions and signal–noise confusion. FADiff significantly outperforms state-of-the-art methods, particularly in generating high-fidelity meteorological structures under high-intensity precipitation thresholds.
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Martineau et al. (2026) Projected and historical amplification of moisture fluxes towards Antarctica by synoptic eddies
This study investigates projected and historical changes in moisture fluxes towards Antarctica driven by synoptic eddies using CMIP6 models and reanalysis data. It finds a significant amplification of synoptic moisture fluxes across the Antarctic Circle, primarily due to enhanced eddy moisture anomalies, with implications for Antarctic ice mass balance.
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Goyal et al. (2026) Estimation of Evaporation Losses in Arid Areas: A Case Study of Kailana and Takhatsagar, Jodhpur, Rajasthan, India
This study quantifies evaporation losses from the Kailana and Takhatsagar reservoirs in Jodhpur, India, developing an evaporation estimation model that incorporates depth-area relationships. It reveals significant seasonal variability in evaporation, ranging from 2.73 mm day⁻¹ in winter to 13.76 mm day⁻¹ in summer, and estimates a combined average loss of 9,733.6 m³ day⁻¹ at full supply level, providing a practical tool for water management in arid environments.
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Saadi et al. (2026) Evaluation of Tropical Cyclone Genesis Potential in the Alfred Wegener Institute Climate Model Version 3
This study evaluates the Alfred Wegener Institute Climate Model version 3 (AWI-CM3)'s performance in reproducing tropical cyclone genesis potential using two indices, finding it to be a high-fidelity model despite specific biases related to sea surface conditions and regional monsoon representation.
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Luo et al. (2026) Cropland biophysical impacts on land surface temperature show diurnal differences across tropical Africa
This study quantifies the diurnal biophysical impacts of cropland expansion on land surface temperature across tropical Africa, revealing consistent nighttime cooling and hydroclimatically-dependent daytime effects (cooling in arid, warming in less arid regions) driven by turbulent heat flux changes linked to leaf area index.
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Soleimanipour et al. (2026) Correction to: An analysis of the relation between drought occurrence and changes in the production capacity of mountain forests: a prerequisite for the development of climate change adaptation programs
This document is a correction notice for a previously published article titled "An analysis of the relation between drought occurrence and changes in the production capacity of mountain forests: a prerequisite for the development of climate change adaptation programs." The correction specifically addresses a typographical error in an author's name.
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Lin et al. (2026) Data-driven attribution of evapotranspiration dynamics in the Heihe River Basin: Controlling factors from site measurements to regional satellite observations
This study quantifies scale-dependent evapotranspiration (ET) dynamics and their controlling factors in the Heihe River Basin by integrating decade-long in-situ flux measurements with multi-source satellite products using an interpretable ensemble machine learning framework. It reveals that while air temperature and leaf area index are primary drivers at the site scale, regional ET patterns are dominated by climatic factors with divergent sensitivities across satellite products, emphasizing the need for scale-aware water management strategies.
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Chakraborty et al. (2026) Trends and drivers of ecosystem water use efficiency and carbon uptake modeled across South Asia
This study quantifies the spatiotemporal variability, long-term trends, and climatic drivers of gross primary productivity (GPP), evapotranspiration (ET), and ecosystem water-use efficiency (WUE) across South Asia from 1985–2023 using the Indian Land Data Assimilation System (ILDAS) with a dynamic vegetation scheme, revealing significant increases in GPP and WUE attributed to vegetation greening.
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Cabral et al. (2026) Interpretable machine-learning diagnosis of forest gross primary productivity patterns in China’s protected areas
This study developed an interpretable machine-learning framework to diagnose spatial patterns and dominant drivers of forest gross primary productivity (GPP) in China's national-level protected areas, finding that precipitation, temperature, and solar radiation are the primary drivers, with precipitation being the most dominant factor across the study area.
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Pellicciotti et al. (2026) DCG-MIP: the Debris-Covered Glacier melt Model Intercomparison exPeriment
This study intercompared 15 debris-covered glacier melt models across nine global sites to assess their performance in simulating ice melt under debris. It found that models with higher complexity at the atmosphere-debris interface perform best, but identified critical data gaps, particularly regarding debris thermal properties, which hinder accurate global modeling and necessitate further model development and standardized data collection.
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Cui et al. (2026) Unraveling the long-term persistence of streamflow in China and its controlling factors
This study conducted the first nationwide, spatially and seasonally resolved assessment of long-term persistence (LTP) in Chinese river streamflow using 45 years of monthly runoff data from 60 stations. It found a national annual mean Hurst coefficient of 0.710 with significant spatial and seasonal variability, primarily controlled by land cover (forest, soil texture), catchment area, and climatic factors, with their relative importance shifting seasonally.
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Li et al. (2026) A two-layer intelligent decision-making framework for optimizing irrigation and fertilization scheduling in irrigated farmland systems
This study developed a two-layer AI-based framework to optimize irrigation and nitrogen fertilization timing in irrigated farmlands, demonstrating significant improvements in agricultural productivity, economic benefits, and sustainability indicators while reducing pollution and global warming potential compared to traditional practices.
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Olsen et al. (2026) A first approach towards dual-hemisphere sea ice reference measurements from multiple data sources repurposed for evaluation and product intercomparison of satellite altimetry
This paper introduces the Climate Change Initiative (CCI) Sea Ice Thickness (SIT) Round Robin Data Package (RRDP), a comprehensive collection of dual-hemisphere sea ice reference measurements (freeboard, thickness, draft, snow depth) from various non-satellite sources (1960-2024), repurposed and quality-controlled for evaluating and intercomparing satellite altimetry products (1993-2024).
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Liu et al. (2026) Root zone adaptive irrigation technology: A novel subsurface irrigation method using for drought-resistant afforestation in water shortage regions
This study developed and evaluated a novel root zone adaptive irrigation (RZAI) technology, demonstrating its superior efficacy in improving soil water-heat conditions and promoting seedling growth for drought-resistant afforestation across diverse arid, semi-arid, and semi-humid regions.
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Luo et al. (2026) Global net increase in surface water connectivity in river–floodplain systems
This study analyzed nearly four decades (1984–2019) of satellite observations to assess global changes in surface water connectivity across 1.6 million kilometers of river–floodplain systems, revealing a net global increase of 3% driven primarily by climatic factors and modulated by human activities.
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Nuñez-Ibarra et al. (2026) From grid to ground: how well do gridded products represent soil moisture dynamics in natural ecosystems during precipitation events?
This study evaluates four gridded soil moisture (SM) products against in situ observations from ten natural ecosystems in central and southern Chile to assess their ability to represent SM dynamics, especially during precipitation events. It finds that ERA5 and ERA5-Land generally outperform other products, particularly in humid regions, while highlighting challenges in arid areas and the diagnostic value of event-based SM signatures.
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Li et al. (2026) Physical process-based attention encoder-decoder LSTM model to improve global soil moisture prediction
This study introduces the AEDLSTM-HBV model, which integrates physical features from the Hydrologiska Byråns Vattenbalansavdelning (HBV) model into an Attention-Enhanced Encoder-Decoder Long Short-Term Memory (AEDLSTM) network to improve global soil moisture prediction. The model significantly outperforms state-of-the-art methods, particularly in permafrost and desert regions, by effectively leveraging the fusion of physical and deep learning features.
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Dzwonkowski et al. (2026) Evaluation of radar-based precipitation estimates during a flood event using rain gauge validation
This study evaluates the accuracy of radar-based precipitation estimates using classical empirical and novel polarimetric Z-R relationships during an extreme flood event in Poland. It found that a locally calibrated polarimetric relationship (ZDR3) significantly improved rainfall estimation accuracy, particularly by reducing bias, compared to standard methods.
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Dalbianco et al. (2026) Water infiltration and saturated hydraulic conductivity in an agricultural watershed with pedogenetic discontinuity
This study investigates soil hydraulic properties, including infiltration and saturated hydraulic conductivity, across different hillslopes and soil layers in a tobacco-cultivated watershed characterized by pedogenetic discontinuities. It reveals significant spatial heterogeneity in these properties, with surface layers showing the highest conductivity due to tillage, and highlights the critical influence of pedogenetic discontinuities on hydrological response.
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Elfaki et al. (2026) An auto-validation method for a complete IoT pivot irrigation model based on the Penman–Monteith equation
This paper develops and validates an Internet of Things (IoT) pivot irrigation model based on the Penman–Monteith equation, incorporating an auto-validation method to mitigate sensor errors. The proposed system demonstrates significant improvements in optimizing water usage and enhancing agricultural productivity compared to traditional irrigation methods.
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Batool et al. (2026) Assessing seven-decade precipitation patterns using CRU, ERA5, and WorldClim with space–time cube trend detection
This study assessed seven-decade spatiotemporal precipitation patterns across Pakistan using CRU, ERA5, and WorldClim datasets with a space–time cube framework. It revealed a distinct north-south precipitation polarity, with increasing trends and intensifying hotspots in the northern and northeastern regions, contrasting with decreasing trends and persistent coldspots in the arid southwest, and identified a transitional diminishing coldspot zone in central Pakistan.
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Crowley et al. (2026) Uncertainty Analysis of Internal Hydrological Processes in Two Agricultural Catchments in Ireland via Stochastic Calibration of SWAT +
This study propagates parameter uncertainty from streamflow calibration to quantify and analyze the uncertainty of hydrological fluxes via different pathways in two Irish agricultural catchments. It identified lateral flow as a key pathway in the Bandon Catchment and agricultural tile drainage in the Owenabue Catchment, providing a more comprehensive understanding of streamflow generation and associated process uncertainty.
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Li et al. (2026) Shifts in precipitation and associated large-scale climate patterns in Northeast China
This study investigates precipitation changes in Northeast China from 1985–2020, identifying a robust regime shift from significant drying (1985–2000) to wetting (2001–2020) characterized by increased frequency and intensity of extreme events, particularly in mountainous regions, linked to anomalous monsoon dynamics and strengthened large-scale climate drivers.
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Dommenget et al. (2026) Basic atmospheric dynamics control on ENSO and tropical basin interactions
This study uses a simplified atmospheric model to investigate how basic atmospheric dynamics, influenced by the shape and interaction of tropical ocean basins, control the growth rate (Bjerknes feedback) and period of the El Niño Southern Oscillation (ENSO). It finds that atmospheric processes, including an optimal zonal length for heat sources and the influence of remote basins, strongly regulate ENSO dynamics, challenging existing theories that often neglect these factors.
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Zhang et al. (2026) Characterising Rainfall‐Induced Soil Water Dynamics in Soil Profiles and Quantifying the Influencing Factors Over Mainland China
This study characterizes rainfall-induced soil water dynamics (SWD) and preferential flow (PF) patterns across soil profiles and spatial scales using in situ hourly data. It reveals novel profile patterns of SWD and PF, identifies scale-dependent environmental impacts, and elucidates complex controlling factors, providing insights for improving Earth System Models.
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Ljutic et al. (2026) Comparing Seasonal Soil Water Storage and Flow Processes Under Different Soil Conditions
This study investigated soil moisture storage and flow dynamics over two years under three agricultural management treatments (control, cover crop, compacted soil) in Southern Ontario, Canada, revealing that vertical soil moisture profiles provide critical insights into flow pathways and storage capacity often missed by total water storage analyses.
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Adigarla et al. (2026) Corrigendum to “Contributions of meteorological and vegetation factors to surface and root zone soil moisture variability across India’s Agro-ecological regions”[Remote Sensing Applications: Society and Environment 40 (2025) 101803]
This corrigendum addresses an error in the author name metadata for the first author of a previously published article, correcting the family name from "Naidu" to "Adigarla".
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Chu et al. (2026) An interpretable AutoML–SHAP approach for rapid urban pluvial flooding prediction
This study introduces an interpretable AutoML-SHAP framework for rapid urban pluvial flooding prediction, achieving a 2000-fold speedup over traditional hydrodynamic models while providing transparent insights into key flood drivers and their nonlinear responses.
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Rivera‐Vidal et al. (2026) Estimating the Seasonal Variations of Multiple Recharge Sources’ Contribution in Mountainous Mediterranean Basins
This study quantifies the seasonal variability of groundwater recharge sources in the Ñuble–Perquilauquén basins of Central Chile, revealing distinct seasonal dominance of diffuse, focused, and mountain-front recharge mechanisms.
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Srivastava et al. (2026) Evaluating the Impacts of Agriculture Conservation on Water Quantity and Quality Through Trend, Predictability, and Causality Analysis
This study investigated the impact of agriculture conservation practices (nature-based solutions) on watershed hydrology and water quality in Shell Creek, Nebraska, finding increased winter cover cropping correlated with reduced flood frequency and some improvements in water quality.
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Wang et al. (2026) A multi-source precipitation blending method combining hydrological model-guided precipitation adjustment and double transfer learning-based data merging
This paper introduces a novel multi-source precipitation blending method that integrates hydrological model-guided precipitation adjustment with a double transfer learning-based data merging approach to enhance precipitation data accuracy.
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Qi et al. (2026) Assessing the impact of ice thickness uncertainty on future glacier evolution in the Himalayas using a higher-order glacier flow model
This study quantifies the impact of initial ice thickness uncertainty on future glacier evolution in the Himalayas using a higher-order flowline model and seven global ice thickness datasets. It finds that by 2100, this uncertainty leads to a standard deviation of 1.5%–6.2% in projected volume loss, comparable to climate model resolution uncertainty, with greater divergence in the Central Himalayas and for smaller, lower-elevation glaciers.
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Nguyen et al. (2026) Characterizing Patterns of Drought Synchronicity in the Contiguous United States
This study investigates the regional and temporal dynamics of drought synchronicity across the contiguous United States from 1980 to 2021, revealing an increasing influence of low-frequency oscillations and higher synchronicity in the Great Plains and Midwest, particularly for short-term droughts, driven by factors including potential evapotranspiration and large-scale climate variability.
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Zhu et al. (2026) A hybrid VMD–BiLSTM–XGBoost approach for multi-scale drought forecasting in urbanizing monsoon transition zones
This study proposes a hybrid VMD–BiLSTM–XGBoost model for multi-scale meteorological drought forecasting in urbanizing monsoon transition zones. The model effectively disentangles non-stationary drought signals and adaptively captures both long-term trends and short-term extremes, demonstrating superior accuracy and reliability compared to benchmark models.
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Shi et al. (2026) Increasing carbon emissions despite declining burned area in the Northern Hemisphere wildfires
This study reveals a critical trend in Northern Hemisphere wildfires, demonstrating an increase in carbon emissions despite a concurrent decline in the total burned area.
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Yu et al. (2026) Coupled Bayesian inversion of time-lapse dispersive ground penetrating radar data to estimate soil hydraulic parameters
This study introduces a coupled Bayesian inversion framework to estimate soil hydraulic parameters by analyzing time-lapse dispersive ground penetrating radar data.
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Antoniadou et al. (2026) A Bayesian spatial framework for modeling sub-hourly to daily extreme precipitation in Denmark using SPDE with INLA
This study introduces a new Bayesian spatial framework for modeling sub-hourly to daily extreme precipitation in Denmark, generating spatially continuous return level maps with associated uncertainties. The two-stage model, utilizing Negative Binomial and Generalized Pareto distributions with latent spatial random effects, captures spatial variation in extreme event frequency and magnitude, showing improved performance over the existing national model for shorter durations.
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Khemiri et al. (2026) The Superposition of Climate Change and Anthropogenic Pressure Threatens Blue Water Resources in Tunisia
This study assesses the combined impacts of climate change, land use/land cover change, and irrigation on surface and groundwater in a Tunisian agro-hydrological basin. It identifies critical hydrological thresholds for irrigation-induced groundwater recharge and projects future reservoir decline, threatening water sustainability.
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Huang et al. (2026) A cascading risk and resilience assessment framework for urban lifeline systems under extreme flooding
This study develops an integrated assessment framework combining cross-dimensional cascading failure models, social vulnerability assessment, and emergency recovery capacity assessment to understand and mitigate cascading risks in urban lifeline systems under extreme flooding. Applied to Xiamen, China, the framework quantifies flood impacts, identifies high-risk zones and critical infrastructure, and proposes targeted restoration priorities to enhance urban resilience.
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Yang et al. (2026) Combined effects of arctic thermal conditions and ENSO on Eurasian winter temperature extremes
This study investigates the nonlinear combined effects of Arctic (Barents-Kara Seas) thermal conditions and ENSO on Eurasian winter mean and extreme temperatures, identifying tropospheric wave trains as the primary underlying mechanism. The findings highlight that specific Arctic-ENSO combinations lead to distinct Eurasian temperature responses, with significant implications for predicting cold extremes.
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Al-Shaqsi et al. (2026) Probable maximum flood of the Usk dam spillway in Wales
This study aims to estimate the probable maximum flood (PMF) for the Usk Reservoir in southern Wales, UK, using the HEC-HMS model, providing crucial hydrological data for water resource management in an area previously lacking such investigations.
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Azadgar et al. (2026) Flood-sensitive land take (FSL) analysis: A new way to read how urban sealing shapes flood risk
This study investigates how land take and land-use transitions influence urban flood risk by analyzing water accumulation in four European cities (Gda´nsk, Milan, Oslo, Ghent) between 2012 and 2018. It introduces the Water Accumulation Sensitivity Index (WASI) and Normalised Water Accumulation Sensitivity Index (NWASI) to quantify and compare hydrological impacts, revealing that land take typology significantly shapes flood sensitivity, with green infrastructure mitigating impacts while industrial and transport conversions amplify them.
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Patricola et al. (2026) Correction: Mid-twenty-first century climate change in the Central United States. Part II: Climate change processes
This document is a correction notice for a previously published article titled "Mid-twenty-first century climate change in the Central United States. Part II: Climate change processes," primarily correcting an author's name and email address. The core objective and main findings of the original paper are not detailed in this correction.
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Tan et al. (2026) Observational characteristics of cloud-radiation-precipitation during 2019 drought period in Yunnan of Southwest China
This study analyzes cloud-radiation-precipitation changes during the extreme drought period from April to June 2019 in Yunnan, Southwest China, revealing that anomalous subsidence and reduced water vapor transport led to decreased cloud cover and a weakened cloud radiative cooling effect, intensifying and sustaining the drought through a "hot-dry" feedback cycle.
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Deng et al. (2026) A generalized enhanced snow/ice index based on Landsat imagery for accurate snow/ice mapping in mountainous areas
This paper proposes a generalized enhanced snow/ice index derived from Landsat imagery to improve the accuracy of snow and ice mapping, specifically targeting mountainous regions.
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Li et al. (2026) The combined impacts of NAO and ENSO on the surface air temperature anomalies in boreal winter
This study investigates the combined impacts of the North Atlantic Oscillation (NAO) and the El Niño–Southern Oscillation (ENSO) on boreal winter surface air temperature (SAT) anomalies using reanalysis and numerical simulations. It reveals that the combined effects exhibit nonlinear characteristics, with ENSO modulating NAO-induced circulation anomalies through distinct Rossby wave trains, thereby amplifying or weakening regional SAT anomalies.
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Jadhav et al. (2026) Multi-Temporal Flood Assessment of Mahad, Maharashtra Hybrid Method of Using Sentinel-1 SAR and NDWI Technique on Google Earth Engine
This study presents a multi-temporal hybrid approach combining Sentinel-1 SAR and Sentinel-2 NDWI data on Google Earth Engine to assess flood inundation in Mahad, India, from 2020 to 2024, demonstrating its effectiveness in localizing true flood zones, especially during monsoon cloud cover.
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Nieto et al. (2026) dms-bias-correction: Enhancing the dynamic LST range of sharpened LST scenes, by fusing them with Landsat LST imagery
This software, `dms-bias-correction`, enhances the dynamic range of sharpened Land Surface Temperature (LST) scenes by fusing them with Landsat LST imagery.
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Yamaguchi et al. (2026) Dynamical and Radiative Influence on the Hadley Circulation by Aerosol‐Cloud Interactions
This study investigates how aerosols influence large-scale atmospheric circulation and the cloud radiative effect (CRE) using a 2D Hadley circulation model, revealing that aerosols intensify circulation and brighten clouds, but this effect is significantly weaker when coupled with a slab ocean model, indicating a moderating role of the Hadley circulation in a dynamically coupled atmosphere-ocean system.
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Shang et al. (2026) Spatiotemporal patterns of drought-driven mechanism transition in the mu us Sandy land: A multi-scale observational perspective based on vegetation dynamics
This study investigates a potential transition in vegetation drought-driving mechanisms in China's Mu Us Sandy Land from water-supply dominance to atmospheric-demand dominance. It found significant vegetation greening occurred concurrently with stable water supply and intensifying atmospheric aridity, with a critical shift towards atmospheric-demand dominance around 2012.
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Naeini et al. (2026) Projecting Hurricane Risk in Atlantic Canada under Climate Change
This study projects future tropical cyclone (TC) risk in Atlantic Canada under climate change, quantifying the evolution of wind and coastal flood hazards and associated economic losses. Findings indicate an intensification of wind extremes and substantial coastal inundation amplification, leading to higher wind-proxy risk for many coastal communities.
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Capozzi et al. (2026) Identification of atmospheric circulation schemes that promote summer hail events in the Northern Italy
This study identifies three distinct atmospheric circulation schemes that promote summer hail events in Northern Italy from 2014-2023, revealing a recent increase in hail frequency and a shift towards circulation types favoring Alpine hail.
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Long et al. (2026) Quantifying the uncertainty influence of meteorological forcing and land surface parameterizations on energy flux simulations over southeastern Tibet
This study aims to quantify the uncertainty contributions of meteorological forcing and land surface parameterizations to energy flux simulations over southeastern Tibet.
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Li et al. (2026) Divergent Subtropical Forest Functional and Structural Responses to the 2022 Yangtze River Extreme Drought
This study investigated the functional (greenness, photosynthesis) and structural (leaf area) responses of humid ecosystems in the Yangtze River Basin to the record-breaking 2022 drought. It revealed a striking decoupling where functional indicators declined while the structural indicator, leaf area index, unexpectedly increased, particularly in subtropical forests at higher elevations.
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Pallavi et al. (2026) Spatial analysis of maize crop distribution in Telangana’s deccan plateau using multi-temporal Landsat 8 data
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Tien et al. (2026) Evaluation of Seasonal Precipitation Forecasts over the Upper Tigris–Euphrates Basin
This study assesses the performance of seasonal precipitation forecasts for the upper Tigris–Euphrates basin, revealing that statistical models based on atmospheric–oceanic indices, particularly a hybrid model, outperform complex dynamic models from the North American Multi-Model Ensemble.
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Liu et al. (2026) A hybrid machine learning and optimal stomatal behavior model to reveal the role of vegetation dynamics in potential evapotranspiration and drought
This study develops a hybrid machine learning and optimal stomatal behavior model to investigate the influence of vegetation dynamics on potential evapotranspiration and drought conditions.
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Jeong et al. (2026) Atmospheric circulation and moisture budget drivers of dominant modes of winter extreme precipitation variability over North America
This study identifies three dominant modes of winter extreme precipitation variability (monthly maximum consecutive 5-day precipitation, Rx5day) across North America, explaining 30.7% of total variance, and attributes them to large-scale atmospheric circulation and lower-tropospheric wind-driven moisture transport.
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Wang et al. (2026) Westerly reorganization driven by orbital forcing: Late Miocene aridification in the Tarim Basin, Central Asia
This study utilizes high-resolution multi-proxy records from the western Tarim Basin to demonstrate that Late Miocene aridification (~8.1 Ma) was primarily driven by eccentricity-paced Antarctic ice sheet expansion, which reorganized Northern Hemisphere westerlies and amplified Central Asian aridification.
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Song et al. (2026) The Construction and Validation of a Distributed Xin’anjiang Model for Hilly Areas Considering Non-Steady-State Evaporation
This paper develops a new distributed Xin’anjiang model for hilly areas, incorporating non-steady-state evaporation and a semi-analytical solution of the Richards equation for soil water deficit, demonstrating superior performance in simulating soil moisture content compared to existing models.
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Shen et al. (2026) Regional Calibration of a Statistical Rainfall Retrieval Method for Microwave Links Using Local Probability Distributions
This study calibrates a statistical rainfall retrieval method for Commercial Microwave Links (CMLs) in China by incorporating localized Gamma rainfall distribution parameters, demonstrating significantly improved accuracy, especially for high-intensity rainfall events, compared to traditional models.
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Zhang et al. (2026) Spatiotemporal characteristics of surface longwave radiation over China from the situ measured data
This study investigates the spatiotemporal characteristics of surface longwave radiation over China using in-situ measured data.
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Lung et al. (2026) The Influence of Open Boundary Conditions and Model Resolution on Shallow Cloud Organization in Atmospheric Large Eddy Simulations
This study presents and compares an open boundary, one-way nested high-resolution Large Eddy Simulation (LES) with a periodic LES, both forced by the HARMONIE-AROME regional weather model, to evaluate their representation of cloud structures. It finds that the open boundary LES, by inheriting the full atmospheric state, maintains more constant, larger, and organized clouds compared to the periodic LES, which exhibits stronger daily cycles and intermittent cloud behavior.
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Audette et al. (2026) Numerical Water Tracers in the Atmospheric Component of the Energy Exascale Earth System Model: Implementation and Changes in Moisture Origin
This study implements numerical water tracers in the E3SMv2 model to investigate changes in the global hydrologic cycle under future climate scenarios, revealing increased moisture export from mid-latitude and southern subtropical regions and enhanced local evaporation in polar areas. A novel statistical reconstruction method for water vapor origin is also proposed to reduce computational cost.
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Ventura et al. (2026) Optimizing canopy cover evaluation: A machine learning approach using LiDAR data
This study develops AI-CanopyMapper, a machine learning framework leveraging LiDAR data for efficient and accurate prediction of canopy cover, achieving a mean absolute error of 6.47% and an R² of 0.88 for the full model in Catalonia. The framework demonstrates strong generalization capabilities and computational efficiency, even with limited data, offering a fast and scalable alternative to traditional methods.
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Sun et al. (2026) Linkage between the Tibetan Plateau and Global Extreme Heat in 2023
This paper investigates the teleconnection and influence of the Tibetan Plateau on the occurrence and characteristics of global extreme heat events observed during 2023.
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Zhang et al. (2026) Anthropogenic warming of China and constrained future projection: updated investigation based on urbanization-bias adjusted observations and CMIP6 models
This study quantifies anthropogenic contributions to China's regional warming since the 1960s using urbanization-bias adjusted observations and CMIP6 models, finding that greenhouse gases are the dominant driver and that previous projections likely overestimated future warming due to unadjusted observational biases.
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Bhattarai et al. (2026) Do the Wettest Days Occur Together? A Global Analysis on Disentangling Precipitation Intensity From Seasonal Timing
This study introduces a novel framework to analyze precipitation patterns by distinguishing between the number of wettest individual days and the minimum number of consecutive days contributing to annual totals. It reveals that these two dimensions evolve independently across global land surfaces, with significant regional variations impacting flood risk, drought duration, and water storage.
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Rashid et al. (2026) Integrated Data-Driven Multi-Criteria Analysis and Machine Learning Approaches for Assessment of Flood Susceptibility Mapping
This study identifies key factors contributing to flood occurrence and maps flood susceptibility in the Mohmand Dam catchment, Pakistan, finding that rainfall, LULC, and soil texture are the most influential factors, with approximately 31.67% (4320.40 km²) of the area at high flood risk.
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Shi et al. (2026) Mechanisms of MCS Initiation and Maintenance During Extreme Rainstorm Events in Semi‐Arid Regions: A Case Study of Qingyang
This study investigates the dynamical and thermal mechanisms of an extreme rainstorm in a semi-arid valley using the WRF model, revealing that terrain-induced dynamics, low-level convergence, and diabatic heating, influenced by large-scale systems, are crucial for its development and maintenance.
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Alpysbay et al. (2026) Beyond Vegetation Indices: Winter Solar Radiation and Soil Properties Drive Wheat Yield Prediction in the Arid Steppes of Kazakhstan Using Gradient Boosting
This study developed a robust XGBoost-based framework for spatio-temporal spring wheat yield forecasting in rainfed agricultural zones, achieving R² values of 0.69 (interpolation) and 0.65 (extrapolation). It revealed that pre-seasonal agroclimatic drivers, particularly winter insolation and April soil moisture recharge, are more influential on yield than mid-season vegetation indices in arid rainfed systems.
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Guo et al. (2026) Research on soil moisture inversion in maize root zone of fenhe irrigation area based on UAV multispectral remote sensing
## Identification - **Journal:** Geocarto International - **Year:** 2026...
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Li et al. (2026) A physically based neural network for flood routing: The Muskingum-Recurrent neural network
This study develops a Muskingum-Recurrent Neural Network (MRNN) that integrates the Muskingum flood routing equations directly into the RNN architecture, enforcing mass conservation as a hard constraint. The MRNN demonstrates superior data efficiency, robustness, and physical consistency in flood routing compared to conventional neural networks and traditional process-based methods across artificial, benchmark, and real-world flood events.
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Li et al. (2026) Anthropogenic Forcing Amplifies Concurrent Risk of Pluvial Pakistan–Hot Yangtze
This study quantifies the role of anthropogenic forcing in driving trans-regional concurrent extreme events, specifically the 2022 Pakistan floods and Yangtze River Basin heatwaves, finding that anthropogenic forcing accounts for nearly 100% of the likelihood of such events, with their probability projected to increase significantly by the end of the century.
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Wang et al. (2026) A novel temporal distribution smoothing alignment network for water level interval forecasting in hydropower systems
This paper introduces a novel temporal distribution smoothing alignment network designed to improve water level interval forecasting in hydropower systems.
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Agosta et al. (2026) From dynamic control to thermodynamic amplification: The structural redistribution of Iberian precipitation (1950–2024)
This study evaluates the hydroclimatic transition of the Iberian Peninsula (1950–2024), revealing a structural redistribution of precipitation where total volume remains stable but extreme precipitation intensifies, particularly along coasts, driven by thermodynamic amplification in a moisture-enriched environment.
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Gümüş et al. (2026) Identifying priority zones for rainwater harvesting to support sustainable water management in arid and semi-arid regions
This study developed a hybrid multi-criteria decision analysis (MCDA) framework, combining fuzzy analytic hierarchy process (F-AHP) and technique for order preference by similarity to an ideal solution (TOPSIS), to identify optimal spatial zones for rainwater harvesting in arid and semi-arid regions. The framework successfully identified 57 candidate sites, with the top five (A28, A14, A17, A21, A52) characterized by favorable hydrological and topographical conditions.
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Tang et al. (2026) Unprecedented 2024 East Antarctic winter heatwave driven by polar vortex weakening and amplified by anthropogenic warming
This study investigates the unprecedented July-August 2024 East Antarctic winter heatwave, identifying polar vortex weakening as the primary driver and quantifying a significant amplification by anthropogenic warming, which increased its likelihood by more than twofold.
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Men et al. (2026) The global response patterns of diurnal temperature range to soil moisture under different climatic backgrounds
This study investigates the global response patterns of diurnal temperature range (DTR) to soil moisture (SM) variations across different climatic backgrounds from 1980 to 2022, revealing a significant, often nonlinear, negative correlation where DTR is more sensitive to SM changes under low SM conditions.
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Panchal et al. (2026) Analysis of ensemble and control forecasts from GEFS and NEPS for reservoir inflow prediction
This study comprehensively analyzes and compares the performance of ensemble and control forecasts from GEFS and NEPS for reservoir inflow prediction at the Ukai Reservoir, India. It demonstrates that bias-corrected ensemble forecasts significantly outperform deterministic control forecasts, particularly at longer lead times (1-5 days), thereby enhancing flood risk management and operational decision-making.
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Sathiyamoorthy et al. (2026) STORM-Net for urban flood risk prediction: an AI-based spatiotemporal tracking and mapping approach
This paper proposes STORM-Net, a novel hybrid AI-based spatiotemporal deep learning model, for high-precision urban flood risk prediction. It integrates SAFER for intelligent feature elimination and BRAVE for adaptive attention scaling, achieving superior accuracy and computational efficiency compared to existing models across diverse datasets.
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Todaro et al. (2026) Skill of CMIP6 decadal climate predictions at the subregional scale
This study assesses the skill of a CMIP6 decadal climate prediction model (HadGEM3-GC31-MM) in simulating subregional climate conditions for precipitation and temperature in the Emilia-Romagna region, Italy. It finds that while drift correction improves performance, particularly for temperature, substantial uncertainties and challenges remain, especially for precipitation in areas with complex topography.
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Fan et al. (2026) When the Past Matters: How Model Initialization Can Lead to Surprises in Long‐Term Simulations in Glaciated Environments
This paper highlights that improper initialization of surface and subsurface water storage and the inability of hydrological models to account for landscape evolution lead to significant errors in long-term distributed streamflow simulations, recommending long exploratory runs to steady state.
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Kobayashi et al. (2026) Separating Water-Level Variations and Phenological Changes in Rice Paddies: Integrating SAR with Ground-Based GNSS-IR Observations
This study combined satellite synthetic aperture radar (SAR) and ground-based Global Navigation Satellite System (GNSS) interferometric reflectometry (GNSS-IR) to assess their sensitivities to water-level variations and rice phenology in paddy fields. It found that L-band SAR and GNSS-IR spectral peaks are sensitive to water level, while a GNSS Phenology Indicator (GPI) and SAR polarization ratio effectively track phenological stages, suggesting a consistent electromagnetic interpretation framework.
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李 (2026) Research Data
This study provides SSMI data and results for identifying agricultural drought events in the Turpan-Hami Basin, along with the associated source code.
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Verjans et al. (2026) Large potential of performance-based model weighting to improve decadal climate forecast skill
This study implements a performance-based model weighting scheme for decadal climate predictions, focusing on sea-surface temperature, demonstrating its potential to improve forecast skill, particularly when predicting pseudo-observations, but revealing challenges in validating these gains against real-world observations.
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Biswas et al. (2026) Comprehensive evaluation of state order variants of Markov chain for stochastic rainfall simulation across diverse climatic regimes of India
This study systematically evaluates various Markov chain state-order variants, including a novel Hybrid Three-state model, for stochastic daily rainfall simulation across 58 diverse climatic stations in India. It identifies the most suitable model for each station and proposes the Hybrid model as a robust, parsimonious option for nationwide application, while also assessing model suitability across Köppen climate classifications.
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Cao et al. (2026) Evaluation of FY-3E, CRA, and ERA5 Temperature and Humidity Profiles over North China in Summer
This study systematically evaluates the accuracy of temperature and humidity profiles from the FY-3E/VASS satellite over North China using ground-based microwave radiometer observations, revealing significant height, station, and weather-dependent errors, particularly underestimation in the boundary layer and under cloudy conditions, in contrast to stable reanalysis datasets.
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白 (2026) Research Data_Jintao Bai_2026.04
This paper investigates the application of cosmic-ray neutron sensing for area-wide soil moisture monitoring in complex terrain, specifically within China's loess hilly-gully region.
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Bartolo et al. (2026) Spatial persistence in the Brahmaputra river: rescaled range and multiscaling analyses
This study proposes a novel methodological framework integrating Rescaled Range (R/S) analysis with Multifractal Detrended Fluctuation Analysis (MF-DFA) to analyze the spatial scaling behavior and persistence of the braiding index (\(N_{wc}\)) in the Brahmaputra River. The findings consistently reveal significant long-range spatial persistence and a stable multifractal signature, indicating that intrinsic self-organizing processes govern the river's morphology.
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Huang et al. (2026) Global hotspots of compound extreme heat-pollution linked to local surface and atmospheric conditions
This study provides a global assessment of compound extreme heat and particulate matter (PM2.5) pollution events from 2003 to 2020, identifying Sub-Saharan Africa and the Indus River Valley as hotspots and linking these events to specific local surface and atmospheric conditions.
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Li et al. (2026) High Spatio-Temporal Resolution CYGNSS Reflectivity Reconstruction via TCN for Enhanced Freeze/Thaw Retrieval
This paper proposes a Partial Convolution–Time Convolutional Network (PTCN) to reconstruct high-resolution Cyclone Global Navigation Satellite System (CYGNSS) data, significantly improving spatial and temporal coverage for freeze/thaw (F/T) state retrieval while maintaining accuracy.
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Mashori et al. (2026) Remote Sensing through UAVs for Precision Agriculture: Applications, Technical Foundations, Current Barriers, and Future Opportunities
This paper systematically reviews the evolving applications of Unmanned Aerial Vehicles (UAVs) in precision agriculture, detailing their technical foundations, current barriers, and future opportunities in enhancing operational efficiency and sustainability. It concludes that UAVs, integrated with advanced remote sensing and AI/ML, are pivotal for data-driven farming, despite challenges like limited endurance and regulatory hurdles.
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Zhao et al. (2026) Analysis of urban design rainstorm patterns based on parameter estimation and model approaches
This study develops an integrated multi-scale framework for urban design storm characterization in Nanjing, China, by evaluating sampling methods and parameter estimation techniques for Pearson Type III distribution and proposing a hybridized approach for constructing design rainstorm patterns across various durations. The findings recommend the annual multiple sampling method and the double weight function for optimal parameter estimation, and an integrated framework for hyetograph construction, enhancing urban flood risk assessment and climate-adaptive engineering.
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Türk et al. (2026) Tracking Event‐Scale Precipitation Partitioning Reveals Comparable Roles of Event Characteristics and Seasonality in Shaping Precipitation Fate in a Forested Landscape
This study investigated how precipitation event characteristics and seasonality influence the partitioning of precipitation into streamflow and evapotranspiration at the event scale over a 1-year tracking period. It found that event characteristics play an equally important role as seasonality in determining the fate of precipitation, with summer/spring precipitation returning to the atmosphere faster and in greater proportion than autumn/winter precipitation.
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Collado et al. (2026) Upper-tail correction of multivariate synthetic environmental series using annual maxima
This paper presents an annual maxima (AM)-centric, marginal post-processing method to correct upper-tail misrepresentation in multivariate synthetic environmental time series, ensuring consistency with historical AM distributions while preserving rank-based dependence. The method is shown to effectively mitigate the overstatement of extreme event hazards in synthetic wave simulations, which would otherwise bias risk assessments.
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Patricola et al. (2026) Correction: Sub-Saharan Northern African climate at the end of the twenty-first century: forcing factors and climate change processes
This document is a correction notice for a previously published article, primarily updating an author's name from Christina M. Patricola to Christina M. Patricola-DiRosario and her email address.
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Perera et al. (2026) Hybrid methods in flood inundation modeling: a systematic review
This systematic review defines and classifies hybrid flood inundation models, evaluates their advantages and limitations over standalone models, and proposes a standardized benchmarking framework to guide their development and application, highlighting Physics-Informed Neural Networks (PINNs) as a promising future direction.
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Hu et al. (2026) Monte-Carlo-assisted endo-exo temporal transformer for high-confidence interval forecasting of daily runoff
This study introduces the Endo-Exo Temporal Transformer (ETT) model, which fuses endogenous and exogenous hydrological features with a Monte Carlo-assisted interval forecasting framework, significantly improving daily runoff prediction accuracy and uncertainty quantification across diverse watersheds.
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Zhou et al. (2026) Predicting the unprecedented: assessing contributions from large-scale modes of variability and climate change to Southeast Australia’s record spring rainfall in 2022
This study quantifies the contributions of large-scale climate drivers and anthropogenic global warming to Southeast Australia's record spring 2022 rainfall. It reveals that while these factors explained a substantial portion, local atmospheric conditions and an increased frequency of intense weather systems played a critical role in amplifying the event's unprecedented extremity, with anthropogenic climate change contributing approximately 12% to the total rainfall.
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Syarifuddin et al. (2026) Integrating rainfall return periods in MCDA-based flood risk mapping: a fuzzy-AHP case study in an ungauged watershed
This study developed a Fuzzy Analytic Hierarchical Process (Fuzzy AHP) framework integrated with GIS and Multi-Criteria Decision Analysis (MCDA) to map flood risk in an ungauged watershed, explicitly incorporating rainfall return periods. The framework significantly improved flood risk assessment accuracy, correctly classifying over 90% of observed flooded areas into high-risk categories, demonstrating the critical value of probabilistic rainfall data.
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S. et al. (2026) A four dimensional vine copula-based probabilistic framework for intra-seasonal design flood hydrograph generation
This study develops a four-dimensional vine copula-based probabilistic framework to generate intra-seasonal design flood hydrographs, capturing both flood magnitude and shape variability. Applied to the Nacimiento Dam, the framework provides robust, sub-seasonal design hydrographs for improved flood mitigation strategies.
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Zhang et al. (2026) Influence pathways of hydrological processes: Perspectives from the pathway probability in different types of watersheds
This study investigated the influence pathways and associated probabilities of daily average evapotranspiration and soil moisture content in two distinct watersheds (grassland-dominated and forestland-dominated) in arid and semiarid northern China, revealing different soil–vegetation–hydrology coupling mechanisms.
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Patricola et al. (2026) Correction: Northern African climate at the end of the twenty-first century: an integrated application of regional and global climate models
The provided text is a correction notice for an original article, primarily addressing an author's name misspelling and an email address update. It does not contain a summary of the original paper's core objective or main finding.
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Yaqoob et al. (2026) Variable Rate Irrigation Through Digital Agriculture for Sustainable Water Management: A Meta Review on Current Challenges and Future Directions
This meta-review synthesizes advancements, challenges, and future directions in Variable Rate Irrigation (VRI) systems, integrating digital agriculture technologies like AI, ML, and smart sensing for sustainable water management. It highlights VRI's potential to optimize water use, increase crop yield, and reduce greenhouse gas emissions by addressing spatial variability in agricultural fields.
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Neto et al. (2026) Hydroclimatic Variability Shapes Long‐Term Water Balance
This study demonstrates that sub-annual hydroclimatic variability, including seasonal covariance, monthly variance, and event-scale storm structure, significantly influences long-term water balance, proposing an expanded aridity framework to explicitly integrate these factors into water-balance theory.
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Dykman et al. (2026) Annual Streamflow and Flood Event Simulation for Future Water Supply—A Multiple Lines of Evidence Approach
This study investigates a multiple-lines-of-evidence approach to reduce uncertainty in streamflow projections, particularly for extreme flood events, by comparing regional climate model (RCM) downscaling with a continuous precipitation generation approach. It finds that continuous simulation can offer more reliable and computationally efficient inputs for water resource planning, especially in wetter regions, by producing lower biases in modeled streamflow compared to RCM downscaling.
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Gampe et al. (2026) The emergence of snow droughts as drivers of negative extremes in plant productivity over the past decades.
This study quantifies the significant and increasing impact of snow droughts as drivers of negative gross primary production (GPP) anomalies across the Northern Hemisphere, revealing their prominent role in the global carbon cycle.
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Xu et al. (2026) Multiscale processes maintaining a baroclinic Mongolian Plateau high and anchoring extreme rainfall over North China during Typhoon Doksuri (2023)
This study investigates the multiscale atmospheric processes responsible for maintaining a baroclinic Mongolian Plateau high and its role in anchoring extreme rainfall over North China during Typhoon Doksuri in 2023.
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Zhang et al. (2026) A global dataset of reservoir in-situ water levels for hydrological and remote sensing applications
This paper introduces the Global Reservoir Observed Water Levels (GROWL) dataset, a harmonized compilation of 4,134 global reservoir water level time series, to address the critical absence of a unified in-situ dataset for validating and inter-comparing remote sensing algorithms and hydrological models.
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Jahangir et al. (2026) A novel hybrid fine-tuning method for supercharging deep learning model development for hydrological prediction
This study introduces a novel hybrid Long Short-Term Memory (LSTM) and Random Forest (RF) fine-tuning method that significantly accelerates and enhances deep learning model development for streamflow prediction, demonstrating superior efficiency and accuracy compared to conventional methods.
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Zhou et al. (2026) Comprehensive UAV and ground data for typical semiarid sites in the midstream of the Heihe River Basin
This data descriptor presents a comprehensive multi-scale dataset from the MUlti-Scale Observation Experiment on land Surface temperature using UAV remote sensing (MUSOES-UAV). It comprises high-resolution UAV thermal infrared and multispectral imagery, complemented by ground-based observations, collected from June to October 2020 in the Heihe River Basin to advance understanding of semiarid land surface processes and validate remote sensing algorithms.
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Nasseri et al. (2026) Exploring Accuracy and Uncertainty in Watershed-Scale Estimation of Actual Evapotranspiration: Comparing Conceptual Budyko Framework and Machine Learning Methods
This study compared Budyko-like conceptual frameworks with Random Forest and XGBoost machine learning models for actual evapotranspiration (Eₐ) estimation across 598 sub-basins in Iran. Machine learning models significantly outperformed conceptual approaches in accuracy and robustness, with dryness index and basin slope identified as dominant controls, while also providing more comprehensive uncertainty quantification.
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Din et al. (2026) Assessing Nonstationary Hydroclimatic Impacts on Streamflow in the Soan River Basin, Pakistan, Using Mann–Kendall Test and Artificial Neural Network Technique
This study assessed long-term nonstationary hydroclimatic impacts on streamflow in the Soan River Basin, Pakistan, revealing a warming trend, decreasing precipitation, and a significant decline in streamflow, with streamflow patterns being highly responsive to these changes.
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Guo et al. (2026) Quantitative Research on the Interaction Relationship between Water and Land Resources Based on the Binary Water Cycle
This study quantitatively analyzes the dynamic feedback between water and land resources in Luoyang City, China, using a "natural-social" binary water cycle framework, finding that cultivated land expansion negatively impacts available water long-term, while precipitation is the primary positive determinant.
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Neelam (2026) Global analysis of watershed characteristics modulating the sensitivity of evapotranspiration to heat and dry extremes
This study investigates how Climate Extreme Indices (CEIs) affect fractional evapotranspiration (fET) across global watersheds, specifically quantifying the modulating role of watershed characteristics. It reveals that soil texture and topography distinctly influence fET responses to dry events, while different heatwave metrics provide nuanced insights into vegetation stress.
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Haixia et al. (2026) Multimodel ensemble heavy precipitation forecast with U-Net deep learning model integrating the spatial FSS loss function
This study develops a U-Net deep learning model, incorporating a novel differentiable Spatial Fractional Skill Score (FSS) loss function, for multi-model ensemble post-processing to improve heavy precipitation forecasts, demonstrating enhanced skill in capturing spatial patterns and extreme intensities over the Middle and Lower Reaches of the Yangtze River.
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Zhao et al. (2026) Exploration on Coupling Machine Learning with Hydrological Model to Enhance Runoff Simulation
This study investigates how coupling process-driven hydrological models with varying physical mechanisms with a Long Short-Term Memory (LSTM) model, and introducing a Stacking structure, impacts runoff simulation accuracy and robustness in the Yalong River Basin. It demonstrates that models with stronger physical mechanisms enhance coupling performance, and the Stacking structure significantly improves simulation stability and consistency.
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Moradi et al. (2026) Evaluating Design Peak Flow Estimation Methods
This study evaluates four hydrological approaches for design peak flow estimation across five sub-basins of the Gidra River in Slovakia for return periods of 2–1000 years. It finds that the STORAGE + COSMO4SUB (S + C) model, particularly with the Pearson Type III distribution, provides more consistent and balanced results compared to empirical methods, especially in ungauged basins, though uncertainties remain for extreme events.
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Largeau et al. (2026) Investigating the robustness of extreme precipitation super-resolution across climates
This study introduces a novel framework for super-resolving the Generalized Extreme Value (GEV) distribution parameters of hourly precipitation extremes using Vector Generalized Additive Models (VGAMs) and Vector Generalized Linear Models (VGLMs). It quantifies model robustness to climate change via a "robustness gap" and identifies limits to super-resolution factors based on spatial correlations.
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Wu et al. (2026) Efficient large-scale land cover change detection using Google Earth Engine: Climate-driven vegetation dynamics in Asian drylands (2001–2022)
This study analyzed land cover dynamics and climate-driven vegetation changes in Asian drylands from 2001 to 2022 using MODIS, TerraClimate, and Google Earth Engine. It found significant land cover changes, including grassland and cropland expansion, primarily influenced by increasing temperatures, soil moisture, and vapor pressure, coupled with decreasing precipitation and drought indices.
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Geng et al. (2026) Daily-scale propagation dynamics between meteorological and soil drought events in the Wei River Basin, China: a three-dimensional perspective
This study investigated the daily-scale spatiotemporal propagation dynamics between meteorological and soil droughts in the Wei River Basin using high-resolution data and a three-dimensional clustering and matching approach, revealing distinct characteristics and migration patterns for different drought types and propagation categories.
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Kharraz et al. (2026) Intelligent Division of Agricultural Farms into Homogeneous Management Zones for Precision Irrigation Using Remote Sensing and Artificial Intelligence
This study developed a hierarchical framework integrating multi-source remote sensing data, topographic information, and soil properties with machine learning (LightGBM) to delineate homogeneous management zones for precision irrigation, achieving 94.1% accuracy in agricultural land discrimination and providing a physically interpretable basis for water management.
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Eini et al. (2026) Rising heavy precipitation extremes in Central European river basins under a high emission scenario
This study assesses historical trends and future projections of extreme precipitation events in Central Europe’s Vistula and Oder transboundary river basins using ETCCDI climate indices. Findings demonstrate a consistent and statistically significant increase in extreme precipitation events under the high-emission RCP8.5 scenario, notably in heavy rainfall days and their contribution to total precipitation, particularly in southern mountainous areas.
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Bo et al. (2026) Multidimensional evaluation of four high-resolution precipitation products based on REOF zones in the upper and middle Hanjiang River Basin
This study developed a novel multi-scale framework using Rotated Empirical Orthogonal Function (REOF) to delineate precipitation zones in the Hanjiang River Basin and evaluated four high-resolution precipitation products, finding CHM_PRE to be the most reliable overall, though all products showed significant performance deterioration in detecting extreme precipitation at the zonal scale.
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Lipfert et al. (2026) Comparing 600 years of extremely hot Central European summers to future projections
This study compares 600 years of Central European summer heat extremes (1421-2008) using paleo-reanalysis and model simulations with future CMIP6 projections, revealing that historical events like 1540 and 1590 were more extreme relative to their contemporary climate than 2003, and similar anomalies in the future will be significantly hotter in absolute terms.
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Dong et al. (2026) Complex-terrain correction of land surface temperature using a temporal–spatial coupling data-driven model
This paper introduces a novel temporal–spatial coupling data-driven model designed to improve the accuracy of land surface temperature (LST) measurements in complex terrain by correcting for associated errors.
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Butler et al. (2026) Shifts in rain-snow partitioning drive faster water transit times in the US Pacific Northwest
This study estimated historical and future water transit times in five headwater catchments in the U.S. Pacific Northwest. It found that water transit times are projected to be 18% (35–64 days) faster on average under the RCP 8.5 climate scenario due to shifts in rain-snow partitioning.
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Laluet et al. (2026) Assessing the suitability of global evapotranspiration products over irrigated areas
This study evaluates the suitability of six global evapotranspiration (ET) products over irrigated croplands by comparing their spatial patterns, seasonal dynamics, and magnitudes against irrigation maps, an independent ET ensemble, and eddy covariance measurements across diverse agro-climatic regions. The assessment reveals significant differences, with PMLv2, SSEBop v6.1, and FLUXCOM RS generally showing the strongest and most consistent agreement with reference datasets, while ERA5-Land exhibits the weakest correspondence.
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Liu et al. (2026) Predicting Lake Surface Water Temperature With Transfer‐Based Physics‐Informed Deep Learning
This study introduces Transfer-PIDL, a transfer learning framework, to enhance the generalizability of physics-informed deep learning (PIDL) for lake surface temperature prediction, demonstrating superior accuracy and reduced data requirements across diverse lakes.
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Pious et al. (2026) Spatial-temporal variability and risk assessment of surface and groundwater resources under climate change and urbanization: A physics-informed analysis
This study developed a Physics-Informed Neural Network (PINN) framework to simulate groundwater dynamics and assess groundwater stress risk in the Chennai metropolitan region, India, revealing that 34% of the area faces high-to-critical stress, largely driven by climate and land-use changes.
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Li et al. (2026) Dryland dominance in the slowdown of global vegetation carbon uptake
This study reveals an asymmetric slowdown in global vegetation carbon uptake, dominated by drylands since 2001 due to water constraints from rising vapor pressure deficit, while humid regions maintain increased uptake. Current global vegetation and Earth system models fail to capture this divergence, indicating a potential limitation to the future land carbon sink.
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García-Gamero et al. (2026) Predicting hydrological drought at global scale: an analysis of the CEMS seasonal forecasts
This study evaluates the performance of the Copernicus Emergency Management System (CEMS) seasonal forecasts in detecting global hydrological drought, demonstrating high skill for 1- to 3-month horizons and identifying key drivers of predictability and the utility of the signal-to-noise ratio (SNR) for forecast reliability.
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Ghaneei et al. (2026) The Role of Baseflow Data Assimilation in Hydrologic Modeling and Peak Flow Prediction
This study applies a Hydrologic Generative Ensemble Data Assimilation method to merge observed baseflow data with hydrologic model outputs, updating lower-zone water storage states across the eastern U.S. The assimilation significantly shifts runoff partitioning towards higher baseflow contributions, leading to improved characterization of the full hydrograph and more accurate peak flow detection without altering the model structure.
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Thakur et al. (2026) How well does the Evaporative Stress Index from ECOSTRESS capture site-based stresses?
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Li et al. (2026) Soil moisture determines the maximum drought loss to vegetation in Central Asia
This study quantifies the spatiotemporal patterns and driving mechanisms of maximum drought-induced vegetation loss (MDVL) in Central Asia from 1982 to 2022, revealing a significant intensification of vegetation loss since 1992, primarily driven by soil moisture during the resistance period.
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Şen et al. (2026) Prediction of present and future flood discharges in catchments with sparse data coverage
This study developed a novel empirical approach for curve number (CN) estimation in data-scarce mountainous catchments to predict present and future flood discharges under climate change scenarios. The findings indicate a general decreasing trend in flood discharges until 2069, followed by an increase by the end of the century, though remaining below present levels, with significant basin sensitivity to precipitation changes.
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Valencia et al. (2026) Improvements and limitations of the new Climate Hazards Center Infrared Precipitation with Stations (CHIRPSv3) dataset: Insights from multiple spatio-temporal scales in Colombia
This study evaluates the improvements and limitations of the new Climate Hazards Center Infrared Precipitation with Stations (CHIRPSv3) dataset across multiple spatio-temporal scales within Colombia.
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Haruna et al. (2026) Regional hotspots and contrasts in the trends of mean and extreme daily precipitation in France
This study analyzes the spatio-temporal trends of mean and extreme daily precipitation in metropolitan France from 1950 to 2022 using a non-stationary statistical framework, revealing complex, non-uniform changes including widespread summer drying, increased autumn wet-day frequency, and localized hotspots of increasing extreme precipitation.
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Zhao et al. (2026) Ecological drought patterns and drivers in Inner Mongolia using a modified temperature vegetation drought index
This study proposes a novel ecological drought index (kTVDI) for Inner Mongolia, analyzing its spatial-temporal dynamics and drivers from 2000 to 2022. It reveals a general amelioration of ecological drought during the growing season, with complex regional and temporal variations influenced by climate and human activities.
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Parisoto et al. (2026) Interannual and Intraseasonal Effects of Drought and Heatwaves on Expanding Soybean Production Regions in Brazil
This study analyzed the spatiotemporal impact of droughts and heatwaves on soybean yields in Brazil from 1989 to 2020, revealing an increasing frequency and severity of compound drought-heat events that are driving significant yield losses, particularly due to short-term dry events in vulnerable regions.
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Monerie et al. (2026) Global wind stilling and the role of sub-monthly variability in explaining deficiencies in atmospheric reanalyses
This study confirms the robustness of global wind stilling (1980-2010) in Northern Hemisphere land observations and reveals that most atmospheric reanalyses fail to reproduce this trend, primarily due to their inability to capture changes in sub-monthly wind speed variability. It also highlights the significant implications for wind power density assessments and reconciles contradictory findings in previous literature based on data processing methods.
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Wu et al. (2026) Structural Responses of Vegetation Resilience to Background-State and Temperature Asymmetry Across China: An Annual-Scale Causal Analysis
This study quantified vegetation resilience in mainland China from 2000 to 2024 using kNDVI data, revealing its spatiotemporal patterns, dominant environmental drivers, and dynamic shifts in underlying mechanisms across breakpoints. It found that resilience varies spatially, primarily shaped by persistent climate conditions, with temperature being a key control, and that driver networks undergo significant reorganization over time.
