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.

通过使用GAMLSS框架来评估非平稳条件下的水文干旱，提出了非平稳标准径流指数（NSRI）。NSRI的定义与SRI的定义相似，但使用非平稳Gamma分布，方法是将气象变量和先前径流作为协变量，以描述径流序列的特征。然后将新的干旱指数应用于黑河流域的上游。开发了四个模型，其中一个是固定的，另外三个是非平稳的，分别具有一个，两个和三个协变量。结果表明，对于非平稳径流序列，非平稳模型比平稳模型更健壮和可靠。在这些模型中，具有两个协变量的模型表现最佳。对于具有一个协变量的模型，在雨季，降水作为协变量显示出更好的拟合，而在旱季，径流显示出更好的拟合。NSRI比SRI识别出更多的干旱事件，在我们的案例中，干旱条件主要受降水影响。事实证明，所提出的新的干旱指数是用于非平稳条件下干旱评估的一种更有效的方法。