Probabilistic assessment of meteorological drought over South Korea under RCP scenarios using a hidden Markov model

Abstract

Most drought indices are evaluated based on pre-defined thresholds, which are inadequate for demonstrating the inherent uncertainty of drought. This study employed a hidden Markov model-based drought index (HMM-DI) for probabilistic assessment of meteorological drought in South Korea. The HMM-DI was developed to take into account the inherent uncertainty embedded in daily precipitation and to assess drought severity without using pre-defined thresholds. Daily rainfall data recorded during 1973-2015 at 56 stations over South Korea were aggregated with 6- and 12-month windows to develop HMM-DIs for various time scales. The HMM-DIs were extended to assess future droughts in South Korea using synthesized monthly rainfall data (2016-2100) under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The overall results indicated that the HMM-DI can classify drought conditions considering inherent uncertainty embedded in observations and can also demonstrate the probabilistic drought occurrence in the future.