Hydrology and Climate Change Article Summaries

This dataset provides hourly precipitation data for five severe precipitation events that occurred over West Texas.

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.

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.

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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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.