Articles | Volume 379
Proc. IAHS, 379, 335–341, 2018
Proc. IAHS, 379, 335–341, 2018

Pre-conference publication 05 Jun 2018

Pre-conference publication | 05 Jun 2018

Multi-model ensemble hydrological simulation using a BP Neural Network for the upper Yalongjiang River Basin, China

Zhanjie Li1,2, Jingshan Yu1,2, Xinyi Xu1, Wenchao Sun1,2, Bo Pang1,2, and Jiajia Yue3 Zhanjie Li et al.
  • 1College of Water Sciences, Beijing Normal University, Beijing 100875, China
  • 2Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China
  • 3School of Geographical Science, Qinghai Normal University, Xining 810016, China

Abstract. Hydrological models are important and effective tools for detecting complex hydrological processes. Different models have different strengths when capturing the various aspects of hydrological processes. Relying on a single model usually leads to simulation uncertainties. Ensemble approaches, based on multi-model hydrological simulations, can improve application performance over single models. In this study, the upper Yalongjiang River Basin was selected for a case study. Three commonly used hydrological models (SWAT, VIC, and BTOPMC) were selected and used for independent simulations with the same input and initial values. Then, the BP neural network method was employed to combine the results from the three models. The results show that the accuracy of BP ensemble simulation is better than that of the single models.

Short summary
Multi-model ensemble hydrological simulation has been an effective method for improving simulation accuracy. This study explored the feasibility of applying a multi-model ensemble simulation to the upper Yalongjiang River Basin. The results of the BPNN multi-model ensemble simulation are better than that of a single model. Multi-model ensemble simulation should become an important direction in hydrological simulation research.