Evaluation of Drought Severity with a Bayesian Network Analysis of Multiple Drought Indices

Abstract

Drought indices assimilate meteorological and/or hydrological information to come up with a comprehensible index. Over the last few decades, hundreds of drought indices have been developed in order to improve monitoring and impact assessment. For a particular drought event, these multiple indices sometimes indicate different levels of drought severity, creating confusion among stakeholders and posing challenges for decision making. To overcome the problem, this study suggests a novel methodology using a Bayesian network. There are several advantages of this proposed method: (1) it pools information from multiple drought indices and comes up with a better estimate for drought severity; (2) instead of a deterministic drought-severity outcome from the individual indices, it offers probabilistic estimates for drought severity; and (3) it reduces the uncertainty of the individual drought indices. The robustness of the methodology is further checked with a case study of an actual drought event in South Korea.