This study developed an integrative model combining sediment trapping of check dam networks with the Revised Universal Soil Loss Equation, index of connectivity, and sediment delivery ratio to reconstruct grid-scale sediment yield across the middle Yellow River Basin (1970–2020). The proposed model achieved about 20 % increase of simulation accuracy compared to ignoring check dam trapping. The sediment reduction contribution by check dams was quantified and controlling factors were detected.

We evaluated the open-source hydrodynamic model SFINCS using satellite data from 499 historical flood events across 96 countries. Our study shows that larger upstream river basins are modelled more accurately, while using observed river gauges and high-resolution elevation data can improve results. Our findings highlight the importance of large-scale validation with satellite data and sensitivity analyses to enhance future global flood hazard assessments and prediction accuracy.

This study integrates Xinanjiang (XAJ) and Temporal Convolutional Network - Gated Recurrent Unit (TCN-GRU) via Random Forest (RF) for streamflow simulation. It combines XAJ’s physical modeling with TCN-GRU’s temporal analysis. Validated in four hydrologically diverse basins, the model achieves Nash-Sutcliffe Efficiency (NSE) 0.971–0.991, outperforming traditional models. Robust in flood/interval simulations, analysis identifies dew point temperature and evaporation as key factors through three interpretable methods.

Vegetation affects surface runoff in several ways. Surface roughness is increased by stems and leaves, roots and their remnants enhance infiltration into the soil, and through evaporation, the soil water content changes at the beginning of a rainfall event. Our study investigates, how a simulation model is able to react to these effects. While roughness and infiltration are well covered, initial soil moisture is still an unsolved problem.

HydroModPy was developed to make it easier and faster to deploy groundwater models across multiple catchments. Although such models are widely used, setting them up often requires significant time and expertise. HydroModPy automates key steps, from preparing maps and input data to building models and running simulations. It is specifically designed for shallow aquifers and supports transparent, reproducible studies of groundwater. This tool can improve water management, research, and education.

We present the design of an actual evapotranspiration product which joined the Copernicus Land Monitoring Service portfolio in December 2025. The product relies on free and open data and advanced modelling methods and has global coverage with a 300 m pixel resolution. A prototype dataset was compared against measurements from 206 geographically distributed stations, achieving good results. Such product will find multiple uses, including in water resources management and food security fields.

Nitrate from farms and villages often seeps into groundwater, threatening water safety. We studied a farming area in northern China across dry, wet, and normal seasons to track and predict nitrate. Nitrate peaked in the dry season due to evaporation, and manure plus household wastewater supplied three quarters of it. Combining artificial data samples with advanced learning methods, we predicted nitrate accurately. This helps officials find pollution hotspots cheaply and protect rural water.