资源环境科技发展态势分析平台

In this paper, a process-based analytical derivation approach is proposed to perform a nonstationary analysis for annual runoff distribution by taking into account the information of nonstationarities in both hydrological inputs and runoff generation processes. Under the Budyko hypothesis, annual runoff is simulated as a formulation of hydrological inputs (annual precipitation and potential evaporation) using an annual runoff model based on the Fu equation with a parameter w accounting for the runoff generation processes. The nonstationarity of the runoff generation process is captured by the dynamic Fu-equation parameter w. Then the multivariate joint probability distribution among the hydrological inputs, the Fu-equation parameter w, and the runoff model error k is constructed based on the nonstationary analysis for both the hydrological inputs and w. Finally, the annual runoff distribution is derived by integrating the multivariate joint probability density function. The derived distribution by the process-based analytical derivation approach performs well in fitting distributions of the annual runoffs from both the Yangtze River and Yellow River, China. For most study watersheds in these two basins, the derived annual runoff distributions are found to be nonstationary, due to the nonstationarities in hydrological inputs (mainly potential evaporation) or the Fu-equation parameter w.

Plain Language Summary In this paper, a nonstationary process-based analytical derivation approach is proposed to estimate the annual runoff distribution by taking into account information of nonstationarities in both hydrological inputs and runoff generation processes. The annual runoff generation processes are modeled by Fu equation expressing annual runoff as a formulation of the hydrological inputs (annual precipitation and potential evaporation). Based on the nonstationarity identifications of hydrological inputs and the Fu-equation parameter, the annual runoff distribution is derived by integrating the multivariate joint probability density function among the hydrological inputs, the Fu-equation parameter, and the runoff model error. The nonstationary process-based analytical derivation approach is applied to the annual runoff series from the Yangtze River and Yellow River, China, and performs well in fitting annual runoff distributions. This approach is able to provide a process-based insight into the nonstationarity in annual runoff.