Hydrology and Climate Change Article Summaries

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.

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 dataset provides high-resolution soil moisture, weather, and irrigation actuator telemetry from a multi-crop precision-irrigation farm in Italy to facilitate the development of actionable irrigation management models.

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.

This study presents the first annually and seasonally resolved documentary rainfall reconstructions for Mozambique from 1817 to 1900, revealing significant spatial-temporal variability and non-stationary relationships with ENSO.

This study quantifies the impact of urbanization on compound heat and drought events (CHDEs) in the Beijing–Tianjin–Hebei region, finding that urban expansion significantly increases the frequency, duration, and severity of these events.

This study examines soil moisture persistence and coupled drought-state variability in the southern Peruvian Andes to improve drought monitoring. It introduces an Integrated Drought State (IDS) index to synthesize the delayed responses of land-surface conditions relative to precipitation anomalies.

This study evaluates the impact of various mulching materials on soil properties and broccoli yield, finding that silver plastic mulch optimizes productivity while organic mulch enhances soil health.

The study introduces SMFS-RF, a knowledge-guided machine learning framework that combines shape model fitting with random forest regression to accurately extract eight key rice phenological stages from fine-resolution NDVI data.