## 1 Introduction
[23] developed a new drought index called the Soil Moisture Drought Index (SODI) based on the quantity of water required to attain soil moisture at field capacity. To assess the ecological impact of droughts, the Normalized Difference Vegetation Index (NDVI) [24; 25] is often combined with a drought index to assess environmental droughts. Conventional drought indices assume stationarity. However, under the combined effects of climate change and anthropogenic influences, such as land-use change [26] and reservoir operations [27], and hydro-meteorological factors, including precipitation, temperature, and runoff not necessarily being stationary, the traditional drought index that is based on the stationary assumption of fixed distribution parameters, ends up being inapplicable in the future [28]. [PERSON] et al. [29] famously asserted that \"stationarity was dead\"; since then, an increasing number of studies have considered non-stationary factors in the hydrological calculation process. The Generalized Additive Models for Location, Scale, and Shape (GAMLSS) proposed by [PERSON] [30] have been widely applied in nonstationary drought assessments. Nonstationarity has been characterized by the construction of linear or non-linear parameters between response variables (drought factors) and explanatory variables (time, climate, and anthropogenic factors) to obtain changes in the distribution parameters [6; 31; 32]. Including time as a covariate is the most common approach in nonstationary drought models. Considering that a longer time series may contain significant climate change signals, it is not feasible to model time series longer than approximately 30 years using the SPI [33]. Fitting a time-varying gamma distribution to a trending rainfall series makes it possible to attenuate the effect of 100-year precipitation time-series trends on the SPI and reflect more severe and frequent droughts [34]. [PERSON] et al. [35] studied the evaluation of the summer drought in Northern China using the SPI with time as a covariate; the resultsshowed that the non-stationarity of the hydrological time series could not be ignored in drought analyses and forecasts. The nonstationary SPI was superior to the traditional SPI in disclosing the trend of temporal and spatial changes in Southwest China [36]. Similar time-varying nonstationary drought evaluations were conducted using the Standardized Streamflow Index (NSSI) [37, 38] and the Non-Stationary Reconnaissance Drought Index (NRDI) [39], and significant differences between the stationary hydrological indices and non-stationary hydrological indices were captured. With the further developments in research, meteorological and anthropogenic factors have also been introduced into the non-stationary drought assessments. Using climate as a covariate, [PERSON] et al. [40] estimated that in Northern China, the Nonstationary Standardized Precipitation Index (NSPI) could capture drought characteristics better than the SPI could; the same results could also be recognized in the middle and lower reaches of the Yangtze River [31]. [PERSON] et al. [41] explained the non-stationarity of hydro-meteorological variables (precipitation and evapotranspiration) affecting droughs by constructing a GAMLSS-based non-stationary model with a SPEI and a Standardized Deficit Distance Index (SDDI). The climate-driven and human-induced NSSI could provide more reasonable and satisfactory results than the stationary index (SSI) [42]. The NSSI, when used downstream of the Yangtze River, implied the weakening of the reservoirs used to reduce hydrological droughs [38]. As the number of covariates increased, the performance of the nonstationary model improved [32]. Combined with the time-varying copula model, the Nonstationary Meteorological and Hydrological Drought Index (NMHDI) identified more frequent extreme droughs in western China, which could be attributed to its ability to respond to a continuously changing environment [28].