Recently, urban areas have experienced frequent, large-scale flooding, a situation that has been aggravated by climate change. This study aims to improve the urban drainage system to facilitate climate change adaptation. A methodology and a series of mitigation strategies are presented to efficiently improve the urban drainage system in light of climate change. In addition, we assess the impact of climate change and predict the scale of potential future flood damage by applying the methodology and mitigation strategies to urban areas. Based on the methodology presented, urban flood prevention measures for Gyeyang-gu (Province), Incheon, Korea, was established. The validity of the proposed alternatives is verified by assessing the economic feasibility of the projects to reduce flood damage. We expect that the methodology presented will aid the decision-making process and assist in the development of reasonable strategies to improve the urban drainage system for adaptation to climate change.

Since its recurrence in 1986, scrub typhus has been occurring annually and it is considered as one of the most prevalent diseases in Korea. Scrub typhus is a 3rd grade nationally notifiable disease that has greatly increased in Korea since 2000. The objective of this study is to construct a disease incidence model for prediction and quantification of the incidences of scrub typhus. Using data from 2001 to 2010, the incidence Artificial Neural Network (ANN) model, which considers the time-lag between scrub typhus and minimum temperature, precipitation and average wind speed based on the Granger causality and spectral analysis, is constructed and tested for 2011 to 2012. Results show reliable simulation of scrub typhus incidences with selected predictors, and indicate that the seasonality in meteorological data should be considered.

Since its reappearance at the Military Demarcation Line in 1993, malaria has been occurring annually in Korea. Malaria is regarded as a third grade nationally notifiable disease susceptible to climate change. The objective of this study is to quantify the effect of climatic factors on the occurrence of malaria in Korea and construct a malaria occurrence model for predicting the future trend of malaria under the influence of climate change. Using data from 2001–2011, the effect of time lag between malaria occurrence and mean temperature, relative humidity and total precipitation was investigated using spectral analysis. Also, a principal component regression model was constructed, considering multicollinearity. Future climate data, generated from RCP 4.5 climate change scenario and CNCM3 climate model, was applied to the constructed regression model to simulate future malaria occurrence and analyze the trend of occurrence. Results show an increase in the occurrence of malaria and the shortening of annual time of occurrence in the future.

The 2007 World Economic Forum (WEF) referred to climate change as the overriding problem we face. Concerns have been raised about how global warming would accelerate future climate change and its consequences. Many climate change studies expect the possible occurrence of extreme high temperature and increase in heavy rains and strong typhoons in the near future. Currently, climate change scenarios are used to prepare an appropriate plan for these phenomena under climate change. The main purpose of this paper is to suggest and evaluate an operational method of assessing the potential impact of climate change on hydrologic components and water resources at the regional scale. Future runoff was simulated using high resolution RCM (12.5 km X 12.5 km) RCP scenario operated by the Korea Meteorological Administration (KMA) and a semi-distribution model or SLURP. The study was carried out on the Han River including its nine dams. The study found that runoff characteristics, especially annual distribution, could change. The discharge in July tends to decrease while runoff can increase in August and September. The flow duration curve was estimated and compared with observed data and simulated daily runoff data for Paldang-dam to evaluate the effect of climate change. The analysis of the flow duration curve shows that the mean average low flow increased while the average wet and normal flow decreased under the climate change scenario.

In recent years, extreme weather events due to climate change have frequently occurred over the world. Meanwhile, the Korean peninsula has suffered from natural disasters such as floods and droughts. Especially in Korea, the flood damage tends to be increasing rapidly. Therefore, it is necessary to estimate the flood discharge and flood stage under climate change for the purpose of reducing flood damage. This study is to assess the impact of climate change on flood discharge and flood stage in Sumjin river basin, Korea. Sumjin, Yochun, and Bosung rivers are selected for this study, and these are the areas in which the model is constructed and applied. SRES A1B climate change scenarios, provided by the IPCC for the simulation of future climate data, were applied for the four different targets periods (Target I : 1971~2010, Target II : 2011~2040, Target III : 2041~2070, Target IV : 2071~2100), and HEC-GeoHMS and HEC-HMS models are used to estimate flood discharge for each target period. Then HEC-RAS model is served for investigating the variation of flood stage due to the climate change at Songjung station, which is located in the basin outlet. The results are compared with the previous studies in which climate change was not considered. Those results could be used as the basic information for the future disaster prevention planning under climate change.