Understanding copula-based multivariate standardized drought indices for characterizing meteorological, hydrological and agricultural droughts across global land areas

Composite drought indices, which integrate multiple drought drivers, hold significant implications for agricultural water management in the context of climate change. In this study, we systemically evaluated the Copula-based Multivariate Standardized Drought Index (CMSDI) in characterizing meteorological, hydrological and agricultural droughts across global land areas. Six two-, three-, and four-dimensional CMSDIs were developed using bivariate and vine copulas by integrating precipitation, potential evapotranspiration, runoff, and soil moisture. The fitting performance, correlation, sensitivity, and time series trend of the CMSDIs were assessed. The results demonstrate the applicability and reliability of CMSDIs in monitoring and detecting diverse composite drought conditions. The vine copula models exhibit superior effectiveness in modeling the dependence structure of high-dimensional drought indices, confirming the propagation pathways from meteorological to hydrological, and then to agricultural droughts. However, their fitting performances exhibit significant spatial and seasonal heterogeneity, which are closely related to the correlations between the constructed marginal univariate drought indices. The CMSDIs that do not consider potential evapotranspiration show significant drying trends in eastern Asia and central Africa, along with significant wetting trends in central Asia, western Australia, and North America. The CMSDIs integrated with potential evapotranspiration exhibit a consistent global drying trend due to the increased atmospheric evaporative demand, particularly in eastern Asia and Africa. The findings contribute to improving composite drought monitoring and early warning systems, which are closely associated with key aspects of agricultural water management, including irrigation scheduling and water allocation planning.