Hydrology and Climate Change Article Summaries

The study proposes a framework combining machine learning and Monte Carlo simulations to estimate the probability of flood flowrates within hydrographic basins.

This study evaluates how different envelope materials influence the drainage performance of subsurface pipe systems and their effectiveness in regulating water and salt levels in agricultural land.

The study develops a soil moisture threshold-based irrigation strategy (SMTIS) for cotton in Xinjiang, China, using the AquaCrop model and a nonlinear optimization framework. The results demonstrate that SMTIS significantly reduces irrigation water use and increases water productivity and economic benefits under both historical and future climate change scenarios.

The paper proposes the Mask-informed Spectral-temporal Transformer (MISTR), a framework designed to reconstruct missing values in long-term satellite time series (STS) by utilizing mask-informed embeddings and adaptive spectral-temporal blocks.

This study develops an interpretable machine learning framework to assess urban flood susceptibility in Yancheng, China, demonstrating that XGBoost provides the highest predictive accuracy and that flood drivers vary significantly across different geomorphic zones.

This study analyzes the spatiotemporal patterns and drivers of meteorological flash droughts (MFDs) in China from 1980 to 2024, revealing that humid regions are experiencing increasing frequency and accelerated onset despite an overall national decline in MFD frequency.

The study developed a physically guided deep learning framework integrating Earth observation data and water balance modeling to estimate high-resolution (500 m) irrigation water use (IWU) across China from 2004 to 2019.

This study evaluates the impact of climate change on streamflow and paddy yields in the Bharathapuzha Basin, Kerala, using SWAT and CMIP6 models, predicting significant declines in crop production and altered hydrological regimes by 2100.

The study develops a harmonized global daily solar-induced chlorophyll fluorescence (SIF) product (HSIF) for the period 2003–2023 by integrating data from three satellite platforms using CDF matching and machine learning.

This study evaluates the application of remote sensing and AI in irrigation management for fruit and vegetable crops. The findings indicate that these technologies can enhance water use efficiency by 15–22%.

The study proposes an integrated precision irrigation system combining hyperspectral remote sensing and IoT technology to optimize water management for small-scale farmers in India.

The study develops and applies a Change-Based Drought Severity Index (WCDI) using fused Sentinel-1 and Sentinel-2 satellite data within Google Earth Engine to quantify drought impacts on two wetlands in Central Iran.

The study develops a quantitative identification method for drought–flood abrupt alternation (DFAA) events in the Yellow River Basin (YRB) using SPEI data from 1982 to 2021, revealing an increasing trend in event frequency, particularly in the middle and lower reaches.

This dataset provides continuous meteorological and sap flow measurements recorded between 2022 and 2023 in the Taihang Mountains to support transpiration research.

This dataset provides processed model output and Python plotting scripts designed to track the Atlantic Meridional Overturning Circulation (AMOC) under climate change scenarios.

This study evaluates relative water availability in Coyhaique Province, Chile, revealing that water stress increases toward the east due to the combined effects of climatic gradients and anthropogenic pressure.

This study evaluates the application of the peaks-over-threshold (POT) approach to Lithuanian rivers, finding that it provides a more comprehensive characterization of flood magnitude, frequency, and seasonality than traditional annual maximum series.

The paper introduces the Thin-Cloud-Sensitive Network (TCSNet), a dual-branch deep learning architecture designed to improve the detection of thin clouds in hyperspectral imagery by balancing spatial and spectral feature extraction.

This paper corrects a previous error regarding the comparison of precipitation variability between coupled climate models and models run with prescribed sea surface temperatures (AMIP models).

This study evaluates a subsurface water management system using a sheet pipe (SPC) enveloped in calcite-reinforced sand, demonstrating improved water flow rates and productivity in rice cultivation compared to traditional methods.