Urban pluvial flooding process: semi-distributed tank model and river flood simulation

To analyze two sub-processes (rainfall-runoff in upstream urban watersheds and river flood evolution), semi-distributed hydrological model of FLOWS-Tank combining both mechanism-driven and data-driven approaches was applied to the small watersheds of Bayi and Douding reservoirs in Fuzhou, and the main stem of Jin’an River． Sensitivity of FLOWS-Tank model parameters and effectiveness of flood simulation in the river channel were studied． Most parameters of the FLOWS-Tank model were found to exhibit low sensitivity． For the Nash efficiency coefficient and root mean square error, the model parameters of Side Orifice Height 7 and Confluence Parameters (Nonlinear Reservoir 2) showed strong sensitivity in both first-order and total sensitivity analysis． Water level simulation at the Wusi Station achieved an MSE of 0.001, MAE of 0.012, MSLE of 0.0007, and RMSE of 0.033． The FLOWS-Tank model demonstrated good simulation performance for the Bayi and Douding reservoir catchments, with total runoff increasing gradually as return period increased． In addition, coupling of Long Short-Term Memory (LSTM) neural networks and Generative Adversarial Networks (GANs) proved to be well-suited for river flood simulation．