Using the standard precipitation index (SPI), this study analyzed the drought characteristics of ten weather stations in Gyeongbuk, South Korea, that precipitation data over a period of 30 years. For the number of months that had a SPI of 1.0 or less, the drought occurrence index was calculated and a maximum shortage months, resilience and vulnerability in each weather station were analyzed. According to the analysis, in terms of vulnerability, the weather stations with acute short-term drought were Andong, Bonghwa, Moongyeong, and Gumi. The weather stations with acute medium-term drought were Daegu and Uljin. Finally the weather stations with acute long-term drought were Pohang, Youngdeok, and Youngju. In terms of severe drought frequency, the stations with relatively high frequency of mid-term droughts were Andong, Bonghwa, Daegu, Uiseong, Uljin, and Youngju. Gumi station had high frequency of short-term droughts. Pohang station had severe short-term ad long-term droughts. Youngdeok had severe droughts during all the terms. Based on the analysis results, it is inferred that the size of the drought should be evaluated depending on how serious vulnerability, resilience, and drought index are. Through proper evaluation of drought, it is possible to take systematic measures for the duration of the drought.

Jeju Island relies on subterranean water for over 98% of its water resources, and it is therefore necessary to continue to perform studies on drought due to climate changes. In this study, the representative standardized precipitation index (SPI) is classified by various criteria, and the spatial characteristics and applicability of drought in Jeju Island are evaluated from the results. As the result of calculating SPI of 4 weather stations (SPI 3, 6, 9, 12), SPI 12 was found to be relatively simple compared to SPI 6. Also, it was verified that the fluctuation of SPI was greater fot short-term data, and that long-term data was relatively more useful for judging extreme drought. Cluster analysis was performed using the K-means technique, with two variables extracted as the result of factor analysis, and the clustering was terminated with seven-time repeated calculations, and eventually two clusters were formed.