Hydrology and Climate Change Article Summaries

This study applies the SWAT+ paddy rice module to simulate the water balance of a Mediterranean rice irrigation district in Albufera de Val`encia, Spain, under climate change scenarios. Projections indicate significant precipitation reductions (9–31%) and potential evapotranspiration increases (8–18%) by mid- and late-century, leading to higher irrigation requirements (4–10%) and modest rice yield declines (up to 8%).

This review assesses the current status and future frontiers of irrigated agriculture in the United States, analyzing regional trends, water sources, crop diversity, and management practices from 2003-2023, and identifies key challenges like groundwater depletion and an eastward shift in irrigation.

This study evaluated the association of absolute aridity and standardized drought indices with tree mortality for four major tree species in Germany (1990–2022). It found that standardized drought indices, particularly the Standardized Precipitation Evapotranspiration Index (SPEI), better explained mortality for Norway spruce, European beech, and Scots pine, with effects increasing over longer aggregation periods of up to five years.

> ⚠️ **Warning:** This summary was generated from the **abstract only**, as the full text was not available. ...

This study quantified the multi-source drivers of forest fire occurrence in Heilongjiang Province and developed a long-term fire risk forecast using a Deep Neural Network with Residual Connections (ResDNN), which achieved 85.6% accuracy and was applied with CMIP6 projections to map future fire probability from 2030 to 2070.

This study proposes a novel framework for selecting optimal Global Climate Model (GCM) subsets and developing weighted Multi-Model Ensembles (MMEs) to improve drought projection accuracy. It introduces the Multi-Location Multimodel Standardized Drought Index (MLMSDI), demonstrating its effectiveness in assessing future drought across various Shared Socioeconomic Pathways (SSPs) and timescales in Punjab Province, Pakistan.

## Identification - **Journal:** Geomatics Natural Hazards and Risk - **Year:** 2026...

This study developed a novel multi-model integrated error correction framework for CMIP6 extreme precipitation projections, significantly improving simulation accuracy in the Hanjiang River Basin (HRB). The corrected data reveal pronounced upward trends in extreme precipitation in the HRB, particularly in its southwestern and downstream areas, under future moderate to high radiative forcing scenarios.

This study develops a remote sensing-based early warning system for agricultural flood damage mitigation in recurrent flood-prone areas, using Sentinel-1 SAR data and a localized change detection approach in Demak Regency, Indonesia, to identify high-risk zones and estimate potential crop losses.

This study develops MAMnet, a deep learning model, to predict the aerosol size distribution (ASD) and mixing state for seven lognormal modes based on bulk aerosol mass and meteorological conditions. MAMnet accurately reproduces the output of a two-moment modal aerosol scheme and shows good agreement with field measurements when driven by reanalysis data, offering an efficient way to improve aerosol representation in atmospheric models.

This study investigates the influence of El Niño Southern Oscillation (ENSO) events on precipitation and flood dynamics in Nepal's transboundary Karnali River Basin (KRB) from 1964 to 2020, revealing a strong positive correlation between basin mean precipitation and discharge, and significant river channel shifts during strong ENSO events.

This study adapted and validated the VegSyst model for precise soilless crop fertigation in greenhouses, integrating climate forecasts to predict water and macronutrient needs for cucumber and tomato, demonstrating reduced nutrient leaching and increased agronomic efficiency without compromising yield.

This study quantifies the cumulative and lagged effects of temperature, solar radiation, and precipitation on the end of the growing season (EOS) in the Yangtze River Basin from 2001-2023. It reveals that incorporating these temporal effects significantly improves the explanation of EOS variability and prediction accuracy, highlighting their critical role in phenology modeling.

This study introduces START, a hybrid deep learning framework for multivariable meteorological bias correction over the contiguous United States, integrating heterogeneous data streams to achieve substantial improvements in forecast accuracy and provide explainable, calibrated uncertainty estimates.

This study presents and analyzes a comprehensive, long-term (2009-2025) dataset of ground-based atmospheric measurements from the Onsala Space Observatory, Sweden, revealing a statistically significant warming trend of approximately 0.15 kelvin per year, most pronounced in winter, and a significant decrease in rain rate intensity.

This study investigates spring monthly drought variations in central-eastern China (CEC) and the synergistic effects of multi-timescale atmospheric teleconnections. It finds that in-phase alignments of high-frequency and low-frequency teleconnections (SCA, WP, NAO) amplify specific atmospheric circulation anomalies, leading to decreased precipitation and increased potential evapotranspiration, thus causing pronounced droughts in the CEC.

This study developed a novel framework integrating a deformable UNet (DUNet) deep learning model and a discrete, shape-preserving skeleton evolution algorithm to accurately monitor glacier-fed river width dynamics in High Mountain Asia using Sentinel-2 time series. The proposed method demonstrated superior performance over conventional deep learning models and existing global datasets, revealing significant seasonal variations in river width.

This study assesses flood-prone areas in the Lacramarca River basin, Peru, under historical and 2050 climate change scenarios, revealing a significant increase in flood extent due to projected climate variability and highlighting the inadequacy of current protection infrastructure.

This study systematically investigates the spatiotemporal evolution and drivers of urban wet-bulb temperature across 56 global cities from 2005-2024, revealing significant increases since 2020 with responses regulated by local climate types.

This paper introduces DANRA, a novel 2.5-kilometer resolution regional atmospheric reanalysis dataset covering Denmark and its surrounding regions from 1990 to 2023. DANRA demonstrates superior performance compared to global reanalyses like ERA5 in representing essential climate variables and extreme weather events, providing unprecedented detail for climate adaptation and impact modeling.