Hydrology and Climate Change Article Summaries

This study investigates the response and variability of the Atlantic Meridional Overturning Circulation (AMOC) strength to orbital precessional forcing using a suite of Earth System Model simulations. The associated dataset provides model output to diagnose AMOC strength and its variability across different precessional phases.

This dataset provides experimental measurements of overland flow dynamics on a cracked soil slope under drying-wetting cycles, offering data for insights into these complex hydrological processes.

This study investigates the effects of ground-mounted photovoltaic (PV) power plants on land surface temperature (LST) across China, finding that PV plants generally induce daytime warming (0.10 °C) and nighttime cooling (−0.09 °C), with effects varying by vegetation type and season.

A global meta-analysis quantified the effects of thinning on soil moisture, finding an overall increase of 7.83%, with natural forests showing a 1.32 times greater response than planted forests. The study highlights differential responses based on forest origin, thinning intensity, soil type, stand age, and climate.

This study isolates canonical East Asian summer monsoon precipitation using objective synoptic classification of satellite observations, revealing its microphysics are highly uniform and dominated by warm-rain accretion across China, in contrast to non-monsoon systems which favor ice-phase processes.

This study develops a spatially explicit framework to assess off-farm operational risk in the Jarghuyeh Irrigation District under diversion-flow shortfalls and manual canal operation. It reveals pronounced spatial clustering of vulnerability and risk, escalating from low (0-2%) under normal conditions to extreme (up to 35%) under severe stress, highlighting the limited adaptive capacity of the manual system.

This study developed and validated an ensemble machine learning framework for flood susceptibility mapping in the Kosi Megafan, comparing its performance against established models and a 1D-CNN. The stacked ensemble model achieved the highest performance, identifying high-risk zones with strong agreement with observed flood data and assessing the exposed population.

This study comprehensively analyzes the microphysical processes within a Northeast China Cold Vortex (NCCV)-influenced squall line using polarimetric radar and disdrometer data, revealing that convective rain exhibits a continental-type raindrop size distribution (DSD) driven by vigorous ice-phase processes, contrasting with Mei-yu events.

This dataset provides hourly CMIP6-based, bias-adjusted atmospheric climate input data, derived by temporally disaggregating daily ISIMIP3b data using the Teddy tool, for use in climate impact analysis.

This study evaluates various automated machine learning (AutoML) algorithms coupled with base models for estimating daily reference evapotranspiration (ET0) across three diverse climatic regions. The research demonstrates that hybrid AutoML models significantly improve ET0 estimation accuracy and generalizability compared to standalone models, with optimal performance being dependent on the specific climatic conditions.

This study investigates the atmospheric drivers of a very heavy rainfall event (0.18 m in 24 hours) over Mumbai on 26 May 2025, combining observations and reanalysis data. It reveals that the event was caused by a synergistic interaction of early monsoon onset, abundant moisture influx from the Arabian Sea, strong coastal moisture convergence, high atmospheric instability, and the dominance of low-base deep convective clouds.

This study proposes a novel deep learning framework, the Multi-angle Polarization Feature Pyramid Structure (MP-FPS), to enhance cloud detection by leveraging joint spectral analysis and multi-angle polarization data. Evaluated on the global POLDER-3 dataset, MP-FPS achieves a mean Intersection over Union (mIoU) of 0.8662, surpassing the official baseline by 12.4%.

This study evaluates 11 years of pseudo-monthly water vapor mixing ratio (WVMR) profiles from a UV Raman lidar at Réunion Island against MLS-Aura, ERA5, and GRUAN radiosondes, revealing systematic dry biases in MLS and GRUAN relative to the lidar, while ERA5 shows better agreement and is proposed for an alternative lidar calibration.

This study analyzed 43 years of data to assess soil moisture dynamics in the Yellow River Basin, revealing a statistically significant basin-wide decline, spatial variability, and the identification of key climatic drivers, highlighting the risk of ecosystems approaching tipping points.

This study investigates how global warming intensifies compound flood hazards in glacier regions by enhancing the temporal synchronization of extreme temperature and precipitation, finding that flood magnitudes can increase by over 60% under 3–4 °C warming, particularly for long-duration events.

This study investigates the influence of antecedent rainfall characteristics on streamflow dynamics in a karst catchment using machine learning models. It found that antecedent rainfall, particularly extreme events and consecutive drought days, critically influences streamflow, with climate change being the predominant driver (73.8%) of variability.

This study analyzes extratropical cyclone (EC) characteristics and their linkage to precipitation in the East Asian subtropical region during spring (1979–2023), revealing significant decreasing trends in both EC genesis and precipitation, primarily driven by non-uniform near-surface warming that suppresses ECs, subsequently weakening dynamic ascent and moisture transport.

This study integrates repeat airborne lidar data with an integral projection model (IPM) to analyze forest-wide demography in response to environmental drivers. It successfully modeled the survival, growth, and life expectancy of approximately 40,000 eucalypt trees over a decade, revealing distinct responses of small and large trees to competition and soil moisture, with drier conditions reducing life expectancy, especially for larger trees.

This study evaluates the subseasonal forecast skill of snow cover and cold compound extremes in midlatitude East Asia using MPAS-A, finding detectable skill up to three pentads, but highlighting that biases from underestimated snowfall and the choice of snow cover fraction scheme significantly impact forecast accuracy.

This study proposes a thermal-imaging-based deep learning approach to classify watermelon water-stress status for precision irrigation. It systematically evaluates nine deep learning models, identifying EfficientNet-B0 as the most suitable for field deployment due to its optimal balance of high accuracy (0.99) and computational efficiency (0.39 GFLOPs, 8.81 ms inference latency).