Hydrology and Climate Change Article Summaries

This study evaluates vegetation drought in the Yellow River Water Conservation Area (YRWC) from 2001 to 2022 using the TVPDI index, identifying precipitation as the primary driver and analyzing the spatial heterogeneity of cascading environmental effects.

This review evaluates the theoretical foundations and recent advancements in water balance methods for estimating evapotranspiration (ET), emphasizing the shift toward hybrid physics-based and machine learning approaches.

This work provides a comprehensive IoT dataset from a precision-irrigation farm in Italy, integrating soil sensor readings, irrigation actuator telemetry, and weather data across five different crop sectors.

The study proposes a cross-scale collaborative extraction framework using CycleGAN and transfer learning to map summer harvest crops in the Baojixia Irrigation District, effectively bridging the resolution gap between UAV and satellite imagery.

This systematic review evaluates the state of soil moisture science in Kenya, finding a predominance of remote sensing and agricultural applications while highlighting a critical lack of research in arid and semi-arid lands (ASALs).

The study develops a hybrid CPO-VMD-LNN model for one-month-ahead meteorological drought forecasting, which significantly outperforms SARIMA, LSTM, and standalone LNN models in the Urmia Lake Basin.

The study develops a predictive framework using Classification and Regression Tree (CART) and Symbolic Regression to estimate the minimum amount and optimal timing of calibration data required for hydrological models based on physical catchment characteristics.

This study evaluates the new Meteo-HySEA model against the AdriSC-ADCIRC system for simulating meteotsunamis in the Adriatic Sea, finding that Meteo-HySEA effectively captures sea-level oscillation dynamics and is suitable for operational forecasting.

This study analyzes hydroclimatic trends in the Arno River basin from 1994 to 2023, identifying significant warming in summer and autumn but no significant monotonic trends in precipitation or streamflow.

The study proposes a leakage-safe, horizon-specific forecasting framework for solar irradiance, demonstrating that boosted trees are most effective for short-term predictions while seasonal combinations prevail at longer lead times.

The study finds that droughts are significantly more damaging when moisture deficits occur simultaneously across multiple soil layers, as this eliminates vertical hydrological buffering and threatens global carbon uptake.

This study evaluates the Canadian regional climate model (CRCM) v6 in simulating extratropical cyclones (ETCs) over northeastern North America, demonstrating that a 2.5-km convection-permitting configuration improves the representation of extreme precipitation and wind speed biases compared to a 12-km configuration.

The study develops an Ecological Drought Index (EDI) using a three-dimensional Copula function to analyze the spatiotemporal patterns and risk uncertainties of ecological droughts in the Yellow River Basin.

This study analyzed vegetation dynamics in the Jabel Marra region of Sudan from 1994 to 2024, finding a significant increase in greenness primarily driven by precipitation and temperature variability.

This study analyzes the temporal symmetry and persistence of global meteorological droughts using CMIP6 projections, finding a transition from seasonal droughts toward more persistent, multi-year drought regimes under future climate scenarios.

This study examines the asymmetric impact of decaying El Niño and La Niña events on September precipitation in East China, finding that La Niña leads to significant precipitation deficits while El Niño results in weak positive anomalies.

The study proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Vision Transformers (ViT) to monitor crop and drought stress using multi-temporal Sentinel-2 satellite imagery.

This study develops a machine learning framework to reconstruct missing hourly meteorological data in West Africa by integrating in situ AWS observations with ERA5-Land reanalysis and GPM satellite products.

The study presents a wavelet-based method to detect irrigation by analyzing spectral differences between Noah-MP model simulations and SMOS satellite soil-moisture observations across the contiguous United States.

This study quantifies the individual and combined impacts of climate variability and land-use change on runoff in the Dongwan Watershed, China. It finds that climate change is the primary driver of runoff dynamics, while land-use change acts as a secondary, moderating factor.