Korea has frequent flood damage due to localized torrential rain and typhoons as a result of climate change, which causes many casualties and property damage. In particular, much damage occurs due to urban inundation caused by stream flooding as a result of climate change. Thus, this study aims to analyze the effect of climate change on flood damage targeting the Wonjucheon basin, which is an urban stream flowing the city. For future rainfall data, RCP (Representative Concentration Pathways) 8.5 climate change scenario data was used, statistical detailed using SDQDM (Spatial Disaggregation with Quantile Delta Mapping) techniques, and daily data was downscaled using Copula model. In general, the flood damage rate is calculated by using the area ratio according to the land use in the administrative district, but in this study, the flood damage rate is calculated using the flood damage rate proposed in the multi-dimensional flood damage analysis using Building Inventory. Using the created future rainfall data and current data, the runoff in the Wonjucheon basin, Wonju-si, South Korea, by rainfall frequency was calculated through the Spatial Runoff Assessment Tool (S-RAT) model, which was a distributed rainfall-runoff model. The runoff was calculated using 100-year and 200-year frequency rainfalls for a four-hour duration and the flood damage area was calculated by applying the calculated runoff to the Flo-2D model, was developed by Federal Emergency Management Agency (FEMA) in United State of America, which was a flood inundation model. As a result of calculating the amount of discharge, it was analyzed that the average amount of discharge increased by 16% over the 100-year, 200-year frequency. The calculated result of the flood damage area was analyzed and the analysis results showed that the future flood damage area increased by around 30% at the 100-year frequency and around 15% at the 200-year frequency. The estimated flood damage by rainfall frequency was calculated using the flood damage area by frequency and multi-dimensional analysis, and the analysis result exhibited that the damage increased by around 23% at the 100-year frequency and around 45% at the 200-year frequency.

Urban flooding has become increasingly frequent due to the rising intensity of rainfall driven by urban development and climate change. Effective prevention measures are crucial to mitigate the significant human and material damages caused by such events. Rapid and accurate pre-detection techniques can help to reduce the impacts of urban flooding. With the advancement of deep learning, deep neural networks (DNNs) have been successfully applied across various domains, including computer vision and speech recognition. In particular, DNNs for computer vision demonstrate high performance with relatively low computational costs. In this paper, we propose a flooding region segmentation model for urban underpasses based on the U-Net architecture. To train and evaluate the model, we collected datasets from the Mannyeon, Oryang, and Daedong underpasses in Daejeon. The proposed method achieved Dice coefficients of 98.8%, 94.03%, and 93.85%, respectively. This model demonstrates high segmentation performance in detecting flooded regions and can be integrated into continuous flood monitoring systems.

In assessing the impact of climate change, the use of a multimodel ensemble (MME) is required to quantify uncertainties between scenarios and produce downscaled outlines for the simulation of climate under the influence of different factors including topography. This study of climate change scenarios from 13 global climate models (GCMs) assesses the impacts of future climate change. Unlike South Korea, North Korea lacks studies using climate change scenarios of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and only recently did the country start the projection of extreme precipitation episodes. As such, one of the main purposes of this study is to predict changes in the average climatic conditions of North Korea in the future. The result of comparing downscaled climate change scenarios with observation data for a reference period indicates the high applicability of the MME. Furthermore, this study classifies climatic zones by applying the Köppen–Geiger climatic zones classification to the MME, which is validated for future precipitation and temperature. The result suggests that the continental climate that covers the inland area for the reference climate is expected to shift into the temperate climate. Moreover, the coefficient of variation (CV) in the temperature ensemble is particularly low for the southern coast of the Korean Peninsula, and, accordingly, a high possibility of the shifting climatic zone of the coast is predicted.

The purpose of this study is to predict the behaviour of debris flow under climate change. The behaviour of debris flow on the slope and its mechanism is evaluated through numerical simulations using the climate change scenario Representative Concentration Pathways (RCP) 4.5 and RCP 8.5, and the developed numerical model is applied to the analysis of real areas. The results from the application of the numerical model based on climate change in this study to the Gangwon region in Korea indicated that the flow discharge and flow depth of debris flow increase drastically as the return period is longer, and the Future 2 case, a future target period, showed the largest peak value of the flow discharge of debris flow with a large value of wave amplitude on the distribution curve for the debris flow discharge. In the case of flow depth, even though the wave amplitude of flow depth slightly increased as the year increased, its distribution shared a similar tendency. The value of flow depth was high. It is expected that the results of this study will provide information necessary to predict damage due to debris flow in the climate-changing future, and to prevent damage to human life in coastal areas.

Climate change significantly affects water supply availability due to changes in the magnitude and seasonality of runoff and severe drought events. In the case of Korea, despite a high water supply ratio, more populations have continued to suffer from restricted regional water supplies. Though Korea enacted the Long-Term Comprehensive Water Resources Plan, a field survey revealed that the regional government organizations limitedly utilized their drought-related data. These limitations present a need for a system that provides a more intuitive drought review, enabling a more prompt response. Thus, this study presents a rating curve for the available number of water intake days per flow, and reviews and calibrates the Soil and Water Assessment Tool (SWAT) model mediators, and found that the coefficient of determination, Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) from 2007 to 2011 were at 0.92%, 0.84%, and 7.2%, respectively, which were “very good” levels. The flow recession curve was proposed after calculating the daily long-term flow and extracted the flow recession trends during days without precipitation. In addition, the SWAT model’s flow data enables the quantitative evaluations of the number of available water intake days without precipitation because of the high hit rate when comparing the available number of water intake days with the limited water supply period near the study watershed. Thus, this study can improve drought response and water resource management plans.