Development of multiple drought index using dynamic naive Bayesian classifier and drought risk assessment according to climate change scenarios

Abstract

Based on a variety of climate change scenarios, general circulation models (GCMs) predict that global temperature will rise from 0.64°C to 0.70°C by 2030, compared to 1980. The frequency and intensity of extreme rainfall events and extreme droughts are also expected to increase in many areas, along with heat waves. Studies on Korea’s climate predict that the frequency and intensity of droughts will increase. Korea’s precipitation characteristics are concentrated in summer and, if water is not managed efficiently, the damage due to drought will be great. The seasonal and regional imbalances in precipitation have contributed to Korea’s recent extreme drought. In addition, drought occurs due to population increase and industrialization. When drought occurs, it generally affects a broad region for seasons or years at a time. The proportion of the population affected by drought tends to be larger than that affected by other disasters. However, an effective response is possible, as droughts typically progress more slowly than other disasters, such as floods. The drought that occurred in Chungcheong, South Korea, during 2014 and 2015, resulted in a continuous drop in the levels of water stored in reservoirs by 25.5 percent, which was a historic record. After the drought of 2014-2015, the need for comprehensive risk assessment and effective response measures of extreme drought is increasing. Drought can cause significant damage to agricultural production and economies. Nevertheless, in general, the absence of a unique definition of drought makes it difficult to know when and where it occurs. There is also no recognized universal drought index, although conventional indices have been developed for use in monitoring meteorological, agricultural, and hydrological drought. More recently developed indices can determine and evaluate drought severity comprehensively. To develop a multiple-drought index, this study attempted to combine the standardized precipitation index (SPI), streamflow drought index (SDI), evaporative stress index (ESI), and water supply capacity index (WSCI). We used the dynamic naive Bayesian classifier (DNBC) to calculate a multiple-drought index. Various efforts have been made to apply the Bayesian theory to drought assessment. In this study, we developed a DNBC-based multiple-drought index (DNBC-DI-W) by combining the strengths of the SPI, SDI, ESI, and WSCI. The DNBC-MDI was applied to a bivariate drought frequency analysis to evaluate hydrologic risk of extreme droughts. In addition, future changes in the risk were investigated according to climate change scenarios