Seamless Integration of Rainfall Spatial Variability and a Conceptual Hydrological Model

Abstract

Rainfall is an important input to conceptual hydrological models, and its accuracy would have a considerable effect on that of the model simulations. However, traditional conceptual rainfall-runoff models commonly use catchment-average rainfall as inputs without recognizing its spatial variability. To solve this, a seamless integration framework that couples rainfall spatial variability with a conceptual rainfall-runoff model, named the statistical rainfall-runoff (SRR) model, is built in this study. In the SRR model, the exponential difference distribution (EDD) is proposed to describe the spatial variability of rainfall for traditional rain gauging stations. The EDD is then incorporated into the vertically mixed runoff (VMR) model to estimate the statistical runoff component. Then, the stochastic differential equation is adopted to deal with the flow routing under stochastic inflow. To test the performance, the SRR model is then calibrated and validated in a Chinese catchment. The results indicate that the EDD performs well in describing rainfall spatial variability, and that the SRR model is superior to the Xinanjiang model because it provides more accurate mean simulations. The seamless integration framework considering rainfall spatial variability can help build a more reasonable statistical rainfall-runoff model.

A novel coupled rainfall prediction model based on stepwise decomposition technique

“…In recent years, the frequent occurrence of extreme rainfall events has posed a significant threat to socioeconomic well-being and human safety. Accurate simulation of rainfall processes holds crucial significance for water resources management 1 , 2 . The formation of precipitation is influenced by multiple factors, exhibiting a high degree of complexity and uncertainty 3 , 4 .…”