Enhancing the understanding of hydrological responses induced by ecological water replenishment using improved machine learning models: A case study in Yongding River

Abstract

The ecological water replenishment (EWR) of Yongding River has been an important project implemented in response to the Development of an Ecological Civilization policy in China since 2016. A reasonable amount of EWR requires a systematic understanding of the relationship among the surface water, groundwater, ecology and economy. However, studying surface water-groundwater interactions still remains an important issue. Thus, a coupled model integrating a Muskingum method-based open channel flow model and machine learning-based groundwater model is developed to describe the dynamic changes in streamflow and groundwater level in response to the EWR of Yongding River. The model is calibrated using observed streamflow data as well as groundwater level data on a daily scale for the spring EWR in 2020. The simulated results match well with the observed data and suggest that significant groundwater level increases occur only around the main channel of Yongding River. Fifteen scenarios under different EWR schemes are set to obtain reasonable streamflow during EWR, and then the responses of streamflow and groundwater level changes are simulated. Reasonable streamflow at the Guanting Reservoir need to be above 65 m³/s to ensure the streamflow can pass through Beijing and significant groundwater level recoveries of 170 million m³ through EWR. The developed models can improve the understanding of the interaction between surface water and groundwater and provide a quick assessment of the factors influencing the different EWR schemes and thus aid in effective EWR project management.

Keywords: Ecological water replenishment; Machine learning; Muskingum; Surface water-groundwater interaction; Yongding River.