Droughts are prolonged abnormalities of moisture deficits that vary widely across temporal and spatial scales. Many hydrometeorologic variables are used to monitor the status of a drought. However, because of the dependence structure between all affecting variables under various temporal windows, an integrated spatio-temporal analysis of droughts cannot be easily achieved. In this study, a copula-based
drought analysis was performed by using long-term monthly precipitation dataset for the upper Midwest United States. The spatio-temporal dependence relationships between various drought variables were investigated, and their joint probability distribution was constructed by combining drought marginals and the dependence structure. A copula-based joint deficit index (JDI) was adopted for an objective (probability-based) description of the overall drought status and compared to the Palmer drought severity index results. Results from the copula-based JDI provide
information for drought identification, and further allow a month-by-month
assessment for future drought recovery.
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