Impact of rainfall spatiotemporal variability and model structures on flood simulation in semi-arid regions

Abstract

The spatiotemporal heterogeneity in precipitation and underlying surfaces and hybrid runoff generation mechanism make hydrological modelling and forecasting in semi-arid regions becoming a challenging work. Therefore, to provide a reference for the development of hydrological models in such regions, two nested hydrological experimental watersheds in semi-arid regions were selected for attribution analysis. Based on the concept of large-sample hydrology, large-scale numerical simulation experiments were performed by constructing different spatial and temporal scale rainfall schemes and combining three hydrological models with different runoff generation mechanisms. Finally, the influences of the time step, station density, and model structure on the flood simulations in semi-arid regions were evaluated. The spatial interpolation technique was used simultaneously to describe the high-dimensional complicated nonlinear relationships between the influencing factors and simulation results. The results show the following: (1) the flood simulation accuracy was more sensitive to the time step than the spatial station density of the rainfall schemes and was highly dependent on the time step of the original observation data, and (2) compared with the accuracy of the rainfall schemes, the model structure plays a dominant role in flood simulation accuracy. Thus, the hybrid model has significant potential for flood forecasting in semi-arid regions by combining different runoff generation mechanisms. (3) The spatial interpolation technique based on the k-nearest neighbour algorithm can construct a high-dimensional distribution between the influencing factors and model simulation accuracy, and describe the complicated relationships among the time step, station density, model structure, and flood simulations.