Regionalization of Drought across South Korea Using Multivariate Methods

Abstract

Topographic and hydro-climatic features of South Korea are highly heterogeneous and able to influence the drought phenomena in the region. The complex topographical and hydro-climatic features of South Korea need a statistically accurate method to find homogeneous regions. Regionalization of drought in a bivariate framework has scarcely been applied in South Korea before. Hierarchical Classification on Principal Components (HCPC) algorithm together with Principal Component Analysis (PCA) method and cluster validation indices were investigated and used for the regionalization of drought across the South Korean region. Statistical homogeneity and discordancy of the region was tested on univariate and bivariate frameworks. HCPC indicate that South Korea should be divided into four regions which are closer to being homogeneous. Univariate and bivariate homogeneity and discordancy tests showed the significant difference in their results due to the inability of univariate homogeneity and discordancy measures to consider the joint behavior of duration and severity. Regionalization of drought for SPI time scale of 1, 3, 6, 12, and 24 months showed significant variation in discordancy and homogeneity of the region with the change in SPI time scale. The results of this study can be used as basic data required to establish a drought mitigation plan on regional scales.

Spatial and Temporal Trend Analysis of Precipitation and Drought in South Korea

“…Trends in drought frequency, duration or severity can be expressed through the changes in precipitation [2]. Since precipitation is a highly important climatic variable and has a direct impact on the occurrence of drought [3][4][5] or flood [6,7], joint trend analysis of drought and precipitation has been gaining more importance in recent studies [2,[8][9][10]. Identification of trends in precipitation and drought helps to understand the long-term variation of hydrometeorological processes and explore their periodicities [11].…”

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

“…Clusters formed by HCPC algorithm were validated using distance-based validating indices (e.g., connectivity, silhouette width, Dunne index, and Calinski and Harabasz index). A detailed explanation about the bivariate discordancy and homogeneity tests applied for regionalization of drought across South Korea is provided in [32]. Spatial distribution of (a) mean duration (months); (b) mean severity, using the inverse distance weighted (IDW) method.…”

“…A more robust and statistic based clustering algorithm, the hierarchical classification on principal components (HCPC), was applied for the delineation of homogeneous regions. The regionalization of drought variables was performed, considering the topographical variables such as latitude, longitude, and elevation above sea level (m), as well as climatological variables, such as mean annual precipitation (mm), mean daily maximum temperature ( • C), mean daily minimum temperature ( • C), annual evaporation (mm/year), and mean relative humidity (%) (refer to [32]). The HCPC clustering algorithm proposed in this study dealt with the above-stated eight topographical and climatological variables, which are likely to affect the drought mechanism in the region.…”

“…K C , denoting Kendall's distribution, can be computed following K C (t) = P(C(F (d, s) ≤ t)). The related secondary return period can be derived as follows [32]:…”

“…Table 2 showed that all the regions fulfill the criteria of homogeneity. The detailed results of the bivariate homogeneity tests are provided in [32]. Since regional drought frequency analysis is based on the extreme values of drought variables (duration and severity), basic statistics such as mean, maximum, minimum, standard deviation, skewness, and kurtosis were computed for four homogeneous regions ( Table 3).…”