WRF/WRF-Hydro coupled streamflow forecasting based on real-time updating using LSTM

In order to improve the runoff prediction performance of the WRF/WRF -Hydro coupled atmospheric -hydrologic systems and reduce the errors in peak time and flood peak flow prediction, this study uses variational data assimilation technology to reduce the rainfall prediction error, at the same time, a real-time correction study on the runoff prediction process of the WRF/WRF - Hydro system is conducted using the long short-term memory (LSTM), and compare the real -time correction results with the autoregressive moving average model (ARM A). The research results indicate that data assimilation technology can effectively improve the accuracy of WRF model rainfall prediction and reduce the input error of WRF -Hydro model, but the accuracy of runoff prediction still needs to be improved. Comparing the real -time correction results of LSTM and ARMA models for runoff forecasting, it was found that during the first three hours of the foresight period, the performance of the two models is basically similar of small watersheds in semi humid and semi -arid mountainous areas in northern China. Except for Event 4, the attenuation rates of LSTM and ARMA models in the three hours of the foresight period are 2.04-23.08 and 9.18-36.47, respectively. As the foresight period extends, the decay rate of LSTM runoff prediction accuracy is generally slower than the ARMA model, and the prediction effect is better than the ARMA model. © 2023 China Water Power Press. All rights reserved.