Nonstationary analysis of hydrological drought index in a coupled human-water system: Application of the GAMLSS with meteorological and anthropogenic covariates in the Wuding River basin, China.

• Optimal GAMLSS models were selected by meteorological and anthropogenic covariates. • A nonstationary SRI was proposed for a coupled human-water system. • NSRI excels in accuracy and recall, showing an advantage in drought recognition. Significant nonstationarity of hydrological sequences, driven by the coupled influence of climate change and anthropogenic activities, has challenged traditional hydrological drought analysis under the stationarity assumption in some regions, and has questioned regional hydrological drought analysis under the changing environment. To evaluate hydrological drought characteristics, the novelty of this paper is the proposition of a GAMLSS (Generalized Additive Models for Location, Scale, and Shape)-based nonstationary standardized runoff index (NSRI), considering climate change and anthropogenic influence, for investigating the hydrological drought regime in the Wuding River basin. To that end, nonstationary test methods were performed on hydrological sequences, and data was segmented into three parts according to the results of the Pettitt test. A nonstationary probability distribution was fitted to runoff data with meteorological covariates (precipitation, temperature) and anthropogenic covariates (water consumption for social development demand, and water consumption triggered by water impounding by check dams). After selecting an optimal combination of covariates, the NSRI was calculated utilizing the GAMLSS model. A newly constructed evaluation threshold was proposed, based on the traditional SRI threshold. The performance of SRI and NSRI were compared at the Dingjiagou station in the Wuding River basin. Results of comparison between index identification and recorded drought events demonstrated that the NSRI better performed in drought event identification. Moreover, the NSRI identified more frequent severe droughts and extreme droughts. Therefore, the proposed NSRI provided a more accurate basis for the identification of drought events, which can offer valuable information for drought planning, preparedness, and mitigation. [ABSTRACT FROM AUTHOR]