A Coupled Dynamic System Inversion Model for Higher Accuracy in Flood Forecasting

Abstract

AbstractFlood routing is a critical component of real‐time flood forecasting and plays a pivotal role in reducing economic and life losses due to flooding. However, flood routing always possesses errors because it is affected by various sources of uncertainty, which are transmitted and accumulated in the routing process from upstream to downstream, particularly for long or complex river systems. Thus, a real‐time error correction is rapidly becoming a key measure for reducing errors and improving flood forecasting accuracy. In this study, a dynamic system inversion (DSI) model was developed as a new correction method to update forecasting errors for long river systems. The new method utilizes all observed, forecasted, and corrected information at upstream stations and couples the matrix‐based Muskingum routing method with an error‐inversed updating technique to form the DSI equation. Multisource spatiotemporal errors of the river system are simultaneously combined into the DSI equation, and the discharges at the downstream station are reforecasted, thereby providing error correction in flood routing. Based on a case study on the Le'an River (China), the DSI model performed better than the traditional autoregressive (AR) model based on five metrics for both small and large flood events. Moreover, with an increase in lead time, the metrics of AR deteriorated rapidly, whereas that of DSI deteriorated slightly. In conclusion, the proposed DSI model is a sophisticated and robust method for flood routing and provides a new alternative for error correction in flood routing simulations of complex river systems.