Bayesian-based time-varying multivariate drought risk and its dynamics in a changing environment

Abstract

Drought is one of the most damaging but least understood environmental disasters. The time-varying multivariate drought risk and its dynamics have remained unresolved in a changing environment. To this end, a Bayesian framework with time in its location parameter as a covariate was introduced in this study to conduct time-varying distributions of duration and severity. Besides, the joint distribution of precipitation and runoff was developed by bivariate non-parameter density kernel estimation for multivariate drought index NKMSDI (Nonparametric Kernel Multivariate Standardized Drought Index) and Expected Waiting Time (EWT)-based return period was used to estimate drought risk. Finally, the time-varying risk trends were explored and verified via correlations between drought risk and Normalized Difference Vegetation Index series. Results indicate that: (1) bivariate return period is more accurate than univariate return period for drought risk assessment and return periods under non-stationary assumption are more reasonable than those under stationary assumption; (2) the multivariate drought risks present obviously increasing trends and the western basin shows the highest increasing rate; and (3) the increasing drought risks exhibit strong association with sunspot activities and local vegetation dynamics. In general, this study provides new insights into drought risk and its dynamics under the time-varying drought properties condition, which is highly important for robust and effective management practices.