Analysis and prediction of the changes in groundwater resources under heavy precipitation and ecological water replenishment

Abstract

Abstract Identifying the influence of heavy precipitation and ecological water replenishment (EWR) on groundwater resources is essential for groundwater resources management and risk prevention. This study innovatively developed a groundwater resource analysis and prediction model integrated with the water level fluctuation method, correlation analysis, and machine learning method under the influence of heavy precipitation and EWR. Water level fluctuation method results showed that compared with January 1, 2021, the groundwater resources of the study area increased 4.46 × 108 m3 on August 28. Compared with small flow of EWR, heavy precipitation was the main contributor to the rise in the groundwater level. Correlation analysis found that elevation, specific yield, and permeability coefficient show positive correlations with groundwater resource recharge. Machine learning results showed that among the water level prediction models of 35 monitoring wells, extreme gradient boosting (XGB) and random forest (RF) performed best in 30 wells and five wells, respectively. The increase in groundwater storage predicted deviated from the actual value by only 0.6 × 107 m3 (prediction bias of 1.3%), indicating that the model prediction performance was good under the heavy precipitation condition. This study can help to better understand the change trend of groundwater resources under the conditions of heavy precipitation and EWR.