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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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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.
