Development of a Multiple-Drought Index for Comprehensive Drought Risk Assessment Using a Dynamic Naive Bayesian Classifier

Abstract

Korea has made various efforts to reduce drought damage; however, socio-economic damage has increased in recent years due to climate change, which has led to increasing frequency and intensity of drought. In South Korea, because precipitation is concentrated in the summer, drought damage will be significant in the event of failure of water resources management. Seasonal and regional imbalances in precipitation have contributed to recent extreme droughts in South Korea. In addition, population growth and urbanization have led to increased water use and contributed to water shortage. Drought risk analysis must address multiple contributing factors and comprehensively assess the effects or occurrence of future droughts, which are essential for planning drought mitigation to cope with actual droughts. Drought mitigation depends on the water supply capacity during dry spells. In this study, a dynamic naive Bayesian classifier-based multiple drought index (DNBC-MDI) was developed by combining the strengths of conventional drought indices and water supply capacity. The DNBC-MDI was applied to a bivariate drought frequency analysis to evaluate hydrologic risk of extreme droughts. In addition, future changes of the risk were investigated according to climate change scenarios. As a result, the drought risk had a decreasing trend from the historic period of 1974–2016 to the future period of 2017–2070, then had an increasing trend in the period of 2071–2099.

Keywords

• dynamic naive Bayesian classifier
• multiple drought index
• hydrologic risk
• climate change