Assessment of hydrological drought based on nonstationary runoff data
Xueli Sun, Zhanling Li, Qingyun Tian
Abstract
Abstract A nonstationary standardized runoff index (NSRI) is proposed by using the GAMLSS framework to assess the hydrological drought under nonstationary conditions. The definition of the NSRI is similar to that of SRI, but using a nonstationary Gamma distribution by incorporating meteorological variables and antecedent runoff as covariates to describe the characteristics of runoff series. The new drought index is then applied to the upper reach of the Heihe River basin. Four models are developed, in which one is stationary, and the other three are nonstationary with one, two and three covariates, respectively. Results show that, for the nonstationary runoff series, the nonstationary models are more robust and reliable than the stationary one. Among these models, the model with two covariates performs the best. For the model with one covariate, the precipitation shows better in the fitting as a covariate in rainy seasons, and the antecedent runoff shows better in dry seasons. The NSRI identifies more drought events than SRI does, and the drought conditions in our case are mainly affected by precipitation. It is proved that the proposed new drought index is a more effective method for drought assessments under nonstationary conditions.