Probabilistic assessment of drought states using a dynamic naive Bayesian classifier

Abstract

Drought is a slow-onset hazard affecting ecosystems and human society. Although it is difficult to assess the uncertainty associated with drought, it is very important to identify the severity of drought. Using a dynamic naive Bayesian classifier (DNBC), this study combined the strengths of three conventional drought indices, the Standardized Precipitation Index (SPI), the Evaporative Stress Index (ESI), and the Vegetation Health Index (VIII), and developed a DNBC-based drought index (DNBC-DI) to identify overall drought conditions. After comparing recent actual drought events with the drought indices, the drought severity was classified into five states using them: severe wet, moderate wet, normal, moderate drought, and severe drought. We evaluated the performance of the DNBC-DI for representing actual hydrological droughts that occurred since 2000. In this study, the actual hydrological drought was represented by the Streamflow Drought Index (SDI). Our results indicated that the accuracy of the DNBC-DI was 60%, which was higher than SPI (40%), ESI (40%), and VHI (0.41%). Even though in practice, the evaluation of drought is highly dependent on the drought index, this study tried to develop a practical drought index that can be used for comprehensive drought assessment.