Analysis of non-stationary hydrological drought in Alagou River Basin of Turpan based on GAMLSS model

Abstract

[Objective] In the context of climate change and intensified human activities, traditional hydrological drought indices based on the stationarity assumption struggle to accurately characterize the actual drought conditions. To scientifically evaluate hydrological drought under non-stationary conditions, it is necessary to develop a novel drought assessment method that can integrate both natural and human impact factors.[Methods] Based on multi-source data, the natural runoff series during the disturbance period was reconstructed, and the human activity index(HI) was quantified after comparing the performance of long short-term memory(LSTM) and random forest(RF) machine learning models. Using the generalized additive models for location, scale and shape(GAMLSS) model, stationary and non-stationary models were constructed with precipitation, temperature, and HI as covariates, and the stationary hydrological drought index(SRI) and non-stationary hydrological drought index(NSRI) were calculated. Based on the performance comparison of the two indices, the characteristics of hydrological drought under non-stationary conditions were revealed using Copula functions.[Results] The result showed that:(1) the runoff series in the study area underwent an abrupt change in 1988, exhibiting significant non-stationary characteristics, with runoff increasing by 35.5% after the change point.(2) The fitting performance of the non-stationary model was superior to that of the stationary model in all months, with the model combination including HI showing the best performance. Compared with SRI, NSRI was more accurate in identifying the process and severity of typical drought events, while the SRI showed significant underestimation.(3) From 1989 to 2020, a total of 37 hydrological drought events occurred in the river basin, with an average duration of 2.97 months and an average severity of 3.27. The overall drought trend was intensifying, with spring droughts characterized by long duration and high severity, while summer droughts exhibited short duration and high frequency.(4) Joint probability analysis based on Copula functions indicated a negative correlation between drought duration and severity. The joint return period(12.9 years) of hydrological drought was significantly shorter than the co-occurrence return period(113.4 years).[Conclusion] The constructed NSRI characterizes non-stationary drought processes more accurately than traditional method by coupling climate and human activity factors, and human activity is identified as a key driver of drought evolution. During the disturbance period, hydrological drought in the river basin has intensified. The findings can provide a scientific basis for water resource management and risk prevention in arid areas.

Keywords

Turpan Basin / hydrological drought / non-stationarity / GAMLSS model / Copula function / climate change / precipitation / water resources