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In Korea, it is difficult to efficiently manage water resources, due to the variability of rainfall in addition to the increasing outflow due to climate change in recent years. Therefore, if the trends and characteristics of rainfall at each station can be identified in advance, the problems caused by the variability of rainfall can be effectively dealt with. In this study, the data on rainfall characteristics of 64 rainfall stations in the Nakdong river basin were collected for the period 2000 to 2019. The data were analyzed according to the elevation of each station using K-means cluster analysis, and the rainfall trends at each station were identified by homogeneity test and modified Mann-Kendall test. The analysis showed an increasing trend in March, April, November, December, spring and autumn, and a decreasing trend in January, May, September, summer and year. Also, based on the cluster analysis, it was confirmed that when the number of clusters was set to three, the rainfall characteristics were different depending on the elevation of each station. It is believed that linking the characteristics of rainfall by cluster and the results of trend analysis by station derived from the study can be used to come up with a water resource management plan that takes into account the variability of rainfall.

In Korea, flood damage occurs every year due to typhoons and heavy rains, resulting in increasing damage to human lives and properties in urban areas. To reduce the scale of flood damage, economic analyses of flood-control work are conducted as part of efficient disaster management in the context of a limited budget. In this study, a quantitative evaluation of flood damage in Ulsan due to Typhoon Chaba was conducted using multi-dimensional flood damage analysis (MD-FDA). However, the land cover map applied to MD-FDA has limited data resolution and update intervals. Examination of domestic and foreign research cases to complement these spatial analysis data showed that grid data were being used in disaster-related fields. This study evaluated whether grid data are suitable for quantitative assessment through economic analyses conducted using new spatial analysis data such as road name address digital maps and 100 × 100 m grid-based spatial analysis data. The results of this study confirm that center-point-method grid data constitute spatial analysis data suitable for economic analysis.

Natural disasters caused by climate change frequently occur around the world. In Korea, flood warnings and storm warnings are used to prepare for flood damage. However, flood warnings are specifically issued for rivers while storm warnings use the same criteria throughout the country. Accordingly, there is a limit to effective flood preparation which reflects the regional characteristics of each administrative district. In this study, the flood risk matrix for each administrative district is developed in order to provide flood risk forecast information that reflects upon local characteristics. Moreover, the applicability is evaluated by using dichotomous forecasting. As a result of the forecast's evaluation, the probability of detection was determined to be over 80%. Therefore, it is possible to prepare for a flood through the forecast information of the administrative district using the flood risk matrix. In the future, it will be necessary to study how to improve the utilization of flood risk forecasting information through the continuous investigation of past damage phenomenon along with a review of flood-influencing factors.