Coupling Machine Learning with Physical Models to Improve Peak Flood Simulation under Vegetation and Rainstorm Variability

Traditional hydrological models have been widely applied to flood simulation across the globe, yet the accurate simulation of peak discharge remains a long-standing shortcoming of these models. Taking the upper reaches of the Dongjiang River and Beijiang River in South China as the study area, this study employed the Variable Infiltration Capacity (VIC) model to capture the peak discharge during flood events. The simulation period was divided into a calibration period (2011–2014) and a validation period 1 (2008–2010) before vegetation changes, as well as a validation period 2 (2015–2020) after vegetation changes. The results demonstrated that the VIC model exhibited good applicability in both the upper Dongjiang River and upper Beijiang River basins. For the upper Beijiang River basin, the Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) values were both above 0.6 during the calibration period, while these values were close to 0.6 in both validation periods 1 and 2. However, the model consistently underestimated the peak discharge in all periods. To address this limitation, a machine learning approach was introduced by coupling the VIC model with the Bidirectional Long Short-Term Memory (Bi-LSTM) network. Specifically, the soil moisture content, grid-scale runoff simulated by the VIC model, and precipitation data were used as training inputs for the Bi-LSTM model. Meanwhile, the standalone VIC model and pure Bi-LSTM model were set as control groups for comparison. The results indicated that the coupled VIC-Bi-LSTM model outperformed the control groups in capturing both the runoff process and peak discharge in the two basins. During the calibration period, the NSE values of the coupled model reached 0.9, and remained above 0.7 in both validation periods. In addition, scenarios before and after vegetation changes were designed to analyze the performance of the VIC model in simulating runoff under varying underlying surface conditions. The results revealed that the VIC model could effectively capture the impacts of vegetation changes on runoff, with the NSE value in validation period 2 (post-vegetation change scenario) being close to that in validation period 1. Moreover, the coupling with Bi-LSTM enabled more precise simulation of runoff in the upper Dongjiang and Beijiang Rivers under the scenario of altered vegetation cover.