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.