Enhancing hydrological model performance through multi-source open-data utilization in the highly managed, data-scarce basin

Abstract

Developing reliable hydrological models in highly managed basins is challenging due to multiple sources of uncertainty. The advent of open-source platforms providing publicly available datasets has the potential to mitigate these uncertainties. However, a comprehensive understanding of how these datasets impact model performance is lacking. This study takes the lower part of the YongDing River Basin (LYDRB) in northern China as a case to develop a hydrological model leveraging various open-source datasets, including water withdrawal activities, satellite-based streamflow, and remotely sensed evaporation. We design four comparative experiments to assess the impact of utilizing different data combinations on model performance. We find that the satellite-based streamflow data has the most significant impact, greatly enhancing streamflow simulation performance, with the NSE improving from the range of −1.5 to −0.39 to the range of 0.48 to 0.54 and the PBIAS improving from the range of −28 % to −63 % to the range of −3 % to −10 %. Water withdrawal data and remotely sensed evaporation data contribute to smaller performance improvements. The use of these two datasets may lead to poorer performance during the calibration period but better performance during the validation period. Specifically, remotely sensed evaporation data enhances model performance in streamflow simulation during the validation period, with NSE increasing by up to 0.1, although it results in a decrease of up to 0.04 in NSE during the calibration period. Overall, this study provides valuable insights for developing reliable and low-uncertainty hydrological models in highly managed and data-scarce basins by effectively utilizing various information sources.