A framework for drought monitoring and assessment from a drought propagation perspective under non-stationary environments.

According to the coupled influence of climate variation and anthropogenic activities, hydro-meteorological variables are hard to keep stationary in a changing environment. Consequently, the efficacy of traditional standardized drought indices, predicated upon the assumption of stationarity, has been called into question. In China, the challenge of drought monitoring and declaration is exacerbated by the need for multiple drought indices covering meteorological, agricultural, hydrological, and groundwater aspects, often lacking real-time availability. To address these challenges, we developed a framework for drought monitoring and assessment from a drought propagation perspective. Central to this is the Nonstationary Integrated Drought Index (NIDI), which integrates responses from meteorological, agricultural, hydrological, and groundwater droughts, accounting for climate change and anthropogenic influences. First, we analyse the process of drought propagation to select the suitable time scale standardized drought index. Subsequently, significant large-scale climatic indices are selected through linear and nonlinear correlation analyses to identify climate anomalies. Anthropogenic influences are assessed using indicators such as the Normalized Difference Vegetation Index (NDVI), Impervious Surface Ratio (ISR), and population density (POP). Nonstationary probability models are then developed for precipitation, soil moisture, runoff, and groundwater series, incorporating climatic and human-induced factors. Finally, the NIDI is calculated using a D-vine copula model, with parameter estimation and updating facilitated by a genetic algorithm, representing the temporal dependence structure among the variables. A case study in the Hulu River Basin of western China validated the NIDI. Results showed that the NIDI effectively accounts for nonstationary hydro-meteorological variables due to climate change and human activities, accurately reproducing their time-dependent structure. Compared to conventional indices like SPI, SSI, SRI, and SGI, the NIDI identifies more extreme drought events. In conclusion, the presented NIDI offers a more comprehensive approach to drought identification, providing valuable insights for accurate drought detection and effective drought-related policy-making.