Evaluation of the impact of reservoirs on flood simulations using a hydrological model

Abstract

The frequency of extreme flood and rainfall disasters worldwide has seriously impacted socioeconomic development in recent years, highlighting the critical nature of accurate flood simulations and quantitative forecasts. With rapid advances in hydraulic engineering, regional hydrological conditions, particularly underlying surface characteristics and runoff generation mechanisms, have been significantly altered, thereby influencing flood simulations and forecasts. In this study, the Liuxihe model, a next-generation distributed physical hydrological model, was employed to quantitatively simulate flood events in the Liuxihe Reservoir Basin and Huanglongdai Reservoir Basin. The model incorporates a reservoir module to assess the effect of water engineering on flood forecasting. By comparing simulations with and without the reservoir module for 14 flood events, 8 for parameter calibration and 6 for validation, the role of reservoirs in flood prediction was evaluated. The results revealed that simulations integrating the reservoir module were highly consistent with the observed data. Key performance metrics, including the Nash coefficient, peak flow error, peak time error, process relative error and correlation coefficient, were significantly improved. Specifically, the inclusion of the reservoir module increased the average Nash coefficient by 70.63% and the correlation coefficient by 6.29% and reduced the peak flow error, process relative error, and peak time error by 79.45%, 53.03%, and 23.53%, respectively. These satisfactory flood simulation findings indicate that water engineering, particularly reservoirs, substantially influences the simulation accuracy of the Liuxihe hydrological model. Therefore, when hydrological forecasting is conducted in river basins with reservoirs, it is necessary to account for reservoir regulation in hydrological forecasting to minimize uncertainties. Moreover, this study provides critical technical support for increasing flood prediction accuracy, offering theoretical guidance for flood mitigation and water resource management.

Graphical abstract

Keywords

flood forecast / hydraulic engineering / Liuxihe model / flood disaster reduction

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