The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the HEC’s River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.

Damage caused by natural disasters produces the difference of damage size not only according to damage volume or size, but a national economic level. In addition, budgets and aids should be constantly acquired for disaster management since natural disasters sporadically or irregularly occur. This study proposed disaster management methods by countries considering natural disaster damage documents and economic indicators from 1900 to 2017 among 187 countries in the world. It developed a damage prediction formula considering damage documents of previous natural disasters, economic indicators by countries, and basic indicators as disaster management methods by countries. Independent variables of the damage prediction formula include GDP, population, and area. It applied multiple regression analysis and calculated average human losses due to death, human losses affected, and damage costs by countries. Regarding the adjusted R² of the natural disaster damage prediction formula, the human losses from deaths mean was 0.893, the human losses affected mean was 0.915, and the damage costs mean was 0.946, which had higher explanatory powers. Therefore, results from this study are considered to calculate quantitative damage sizes considering uncertain damage sizes of natural disasters, economic indicators by countries, and are used as indicators for disaster management.

Drought is one of the disasters that causes the most extensive and severe damage. Therefore, drought prevention must be performed for administrative districts at the national level rather than the individual level. This study proposes a drought index estimation method for Gyeongsangnamdo, South Korea that evaluates its appropriateness through a comparison with damage data over several years. The standardized precipitation index (SPI) by duration was used as the drought index that was estimated for 13 rainfall stations located inside and outside Gyeonsangnam-do using the Thiessen method and cluster analysis. The SPI of Gyeongsangnamdo by duration based on the Thiessen method and cluster analysis for the years when drought damage occurred was compared with an SPI value of −2.0, which is the extreme drought condition, to determine its appropriateness. For the evaluation of the appropriateness, the performance indicators of the mean absolute deviation (MAD), mean squared error (MSE), and root mean square error (RMSE) were used. The analysis results showed that SPI by duration based on the cluster analysis method was more appropriate for damage data over many years than that based on the Thiessen method.

Inundation damage occurs in urban regions due to short flood reach time and increased surface runoff caused by urbanized impervious areas. Furthermore, heavy rainfall frequency has increased because of climate change, thus exceeding the design frequency and resulting in sewer pipes’ lack of flood control capacity, with damage expanding from low-lying areas. Despite many urban disaster-mitigation policies, complex causes and uncertainties make reducing urban inundation damage difficult. This study established a rainfall-related disaster-prevention standard by time duration considering rainfall characteristics targeting urban disaster-induced inundation damage. Based on the South Korean urbanization rate and population, seven target regions were selected. Rainfall by time duration was analyzed with respect to disaster length (number of days) from 2010–2017. The average rainfall for durations from 1–24 h were analyzed according to disaster length (1–13 days). Using the results, an equation was proposed for rainfall estimation by length of disaster resulting in urban inundation damage, through multiple regression analysis. An equation was developed for rainfall-related disaster-prevention in urban areas considering the length of disaster and rainfall duration. This was accomplished by selecting a duration whereby the average rainfall of duration corresponded to 50% of total rainfall during the disaster period.

In this study, we conduct an estimation study of the damage costs, recovery costs, and human losses in the case of natural disasters in the Republic of Korea. This research method analyzed human losses, damage costs, and recovery costs caused by natural disasters that swept across the Republic of Korea over the past 16 years, from 2000 to 2015, including extreme wind, heavy snowfall, typhoon, wind wave, and heavy rainfall. Damage status and trend of occurrence were reviewed for each year’s human losses, damage costs, and recovery costs. We propose a calculating equation of the linear regression equation that estimates damage costs and recovery costs considering human losses. The correlation coefficient was 0.898 for the estimation of human losses and damage costs, and 0.889 for the estimation of human losses and recovery costs. In addition, the correlation of both equations was found to be 166% of damage costs when calculating recovery costs. The results of this study can be used as descriptive statistical data to estimate damage costs and recovery costs according to human losses in case of natural disasters.