A river network-based hierarchical model for deriving flood frequency distributions and its application to the Upper Yangtze basin.

Abstract

In flood frequency analysis, expanding the additional information with hydrological reasoning beyond local at-site flood samples can be very useful for improving the accuracy of flood frequency distribution (FFD) estimation as well as reflecting a better understanding of flood characteristics. In this study, a river network-based hierarchical model is developed to estimate the FFDs in the Upper Yangtze basin by making full use of the hydrological reasoning information of both the flood dependence within the river network and reservoir regulation. Under this hierarchical model, a covariate analysis based on the generalized additive model for location, scale and shape is performed to obtain the conditional distribution of the interested flood variable given both its upstream flood variables and the reservoir index quantifying reservoir regulation; and then, the FFD of the interested flood variable is derived by combining its conditional distribution with the probability distribution of the upstream flood variables. The application to the Upper Yangtze basin indicates that the proposed hierarchical model suggests a satisfactory performance in FFD estimation. It is also found that the reservoir regulation, especially that of the Three Gorges Reservoir, is of great significance in reducing the flood magnitude in the basin. Compared to the conventional FFD estimation method that directly fits the assumed theoretical probability distributions to the at-site flood samples, the hierarchical model incorporating the flood dependence within the river network exhibits an advantage in capturing the effect of reservoir regulation on the floods as well as in reducing the uncertainty in flood quantile estimation.