Screening and Optimizing the Sensitive Parameters of BTOPMC Model Based on UQ-PyL Software: Case Study of a Flood Event in the Fuji River Basin, Japan
Lingxue Liu, Li Zhou, Xiaodong Li, Ting Chen, Tianqi Ao
Abstract
Block-wise use of TOPMODEL with the Muskingum–Cunge method (BTOPMC) is a physically based distributed hydrological model with five parameters that quantitatively reflect basin physical features, including soil type, vegetation, and land use of each grid-cell. In order to determine the sensitive model parameters and related variables more reasonably and efficiently, and to improve the model’s practical applicability and simulation accuracy, BTOPMC was integrated with the Uncertainty Quantification Python Laboratory (UQ-PyL) and used in the Fuji River Basin of Japan, by which qualitative and quantitative sensitivity analysis (SA) of variables related to the BTOPMC parameters was performed, and the sensitive ones were optimized by shuffled complex evolution (SCE-UA). The results showed that optimizing only the sensitive variables related to the three sensitive parameters of BTOPMC can ensure simulation accuracy with higher optimization efficiency, which indicates that the BTOPMC model could be applied more simply while guaranteeing the reliability of modeling.