Hydrology and Climate Change Article Summaries

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

This paper describes a dataset supporting the finding that anthropogenic aerosols induce drying in Indian monsoon dry extremes.

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.

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.

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.

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.

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.

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.

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.

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.

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.