Copula-Based Stochastic Simulation for Regional Drought Risk Assessment in South Korea

• Published in: Water (Basel) Vol. 10; no. 4; p. 359
• Main Authors: Azam, Muhammad, Maeng, Seung, Kim, Hyung, Murtazaev, Ardasher
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2018
• Subjects: Bivariate analysis; Climate change; Coastal zone; Computer simulation; Distribution (Probability theory); Drought; Droughts; Environmental risk; Frequency analysis; Goodness of fit; Mathematical models; Netherlands; Precipitation; Probability; Random variables; Regional analysis; Regions; Risk assessment; Simulation; South Korea; Standardized precipitation index; Statistical analysis; Statistical tests; Stochasticity; Trends; Water demand; Water management; Water shortages; Weather; Netherlands; South Korea
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w10040359

In South Korea, meteorological droughts are becoming frequently-occurring phenomena in different parts of the country, because precipitation varies significantly in both space and time. In this study, the quantiles of four identified homogeneous regions were estimated by incorporating major drought variables (e.g., duration and severity) based on the Standardized Precipitation Index (SPI). The regional frequency analysis of drought was performed by evaluating a variety of probability distributions and copulas, using graphical comparisons and goodness-of-fit test statistics. Results indicate that the Pearson type III (PE3) and Kappa marginal distributions, as well as Gaussian and Frank copulas, are better able to simulate the drought variables across the region. Bivariate stochastic simulation of selected copulas showed that the behavior of simulated data may change when the degree of association (e.g., Kendall’s τ) between the drought variables was considered. Results showed that the south-west coast and east coastal areas are under high drought risk, and inland mid-latitude areas (surrounding areas of Yeongju station) and northwest parts are under low drought risk. The joint distributions were used to compute conditional probabilities, as well as primary, secondary, and conditional return periods, which can be useful for designing and managing water demand and the supply system on a regional scale.