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Abstract Owing to climate change, abnormal climate phenomena occur frequently, damaging aging highway facilities. Therefore, it is necessary to develop adaptation projects for highway systems. Adaptation projects focus on mitigating the impact of climate change on highway facilities and enabling safe use. The present study aims to develop quantified evaluation indicators that are necessary for planning and selecting appropriate climate change adaptation projects. By analysing previous studies, the most important factors to be considered in the evaluation process of climate change adaptation projects were defined, and 20 preliminary evaluation indicators were divided into three layers. A feasibility study for each class was performed on the preliminary evaluation indicators by an expert panel; 16 evaluation indicators were selected through a feasibility study, and the weight of each indicator was calculated using the analytical hierarchy process (AHP) method. The priority of the evaluation indicators was service improvement through adaptation projects (weight 8.76), cost reduction and job creation (weight 8.46), and climate change impact reduction and vulnerability reduction (weight 8.31). These quantified evaluation indicators can help organizations that manage highways respond to climate change and establish adaptation projects.

Heavy snowfall is a natural disaster that causes extensive damage in South Korea. Therefore, predicting heavy snowfall occurrence, identifying vulnerable areas, and establishing response plans to reduce risk are crucial. In this study, to project heavy snowfall, meteorological and geographic data from the past 30 years were collected, and four machine learning algorithms were trained and compared: multiple linear regression, support vector regression, random forest regressor (RFR), and extreme gradient boosting. We observed that the RFR model (R2 = 0.64) demonstrated the most optimal performance in projecting snowfall compared to other models. Representative concentration pathway (RCP) scenario data was input into the RFR model to generate projection data up to 2100. Projection results of more than 48.2 cm based on heavy snowfall events in the past 20 years were observed 17 times in RCP2.6, 19 times in RCP4.5, 16 times in RCP6.0, and 17 times in RCP8.5. The annual GIS-based projected snowfall images for the RCP8.5 scenario were classified into five distinct groups using K-means clustering. These groups were then further divided based on the vulnerability of regions, including Gangwon-do, Jeollabuk-do, and northern Gyeonggi-do. Our study can aid decision-making on policies related to heavy snowfall disaster prevention standards, snow removal plans, budgeting, and the establishment of mid- to long-term climate change adaptation plans for government, public institutions and private organizations.

Abstract Worldwide, catastrophic landslides are occurring as a result of abnormal climatic conditions. Since a landslide is caused by a combination of the triggers of rainfall and the vulnerability of spatial information, a study that can suggest a method to analyze the complex relationship between the two factors is required. In this study, the relationship between complex factors (rainfall period, accumulated rainfall, and spatial information characteristics) was designed as a system dynamics model as variables to check the possibility of occurrence of vulnerable areas according to the rainfall characteristics that change in real-time. In contrast to the current way of predicting the collapse time by analysing rainfall data, the developed model can set the precipitation period during rainfall. By setting the induced rainfall period, the researcher can then assess the susceptibility of the landslide-vulnerable area. Further, because the geospatial information features and rainfall data for the 672 h before the landslide's occurrence were combined, the results of the susceptibility analysis could be determined for each topographical characteristic according to the rainfall period and cumulative rainfall change. Third, by adjusting the General cumulative rainfall period (DG) and Inter-event time definition (IETD), the preceding rainfall period can be adjusted, and desired results can be obtained. An analysis method that can solve complex relationships can contribute to the prediction of landslide warning times and expected occurrence locations.

Geup Kyung Sa Ji (GGSJ) is the term used to refer to steep slopes managed by the Ministry of Interior and Safety (MOIS). The MOIS is a government agency of South Korea in charge of administration and safety, with evaluations and management carried out by local governments. There are 16,071 GGSJs as of March 2022. However, it is difficult to determine whether the evaluation results for these GGSJs are reliable and whether data are managed efficiently. Unlike the Ministry of Land, Infrastructure and Transport and the Korea Forest Service, there are personnel in charge of the relevant administration and welfare operations who are not experts in GGSJs. Therefore, a method for evaluating the currently managed steep slopes and the problems that arise in the process must be identified through evaluation by experts and non-experts in the field. Additionally, methods to increase the consistency and reliability of evaluation results must be determined. This study proposes methods designed to solve the problem of differences in evaluation results caused by the diversity of perspectives on the evaluation of GGSJs and which can be used in the assessment of complex slopes. This study aims to increase the efficiency and consistency of evaluation results in the integrated management of GGSJs to build trust among management staff.

The inner shelf and coastal region of the Yellow Sea along the Korean peninsula are frequently impacted by Typhoons. The Mokpo coastal region in South Korea has been significantly affected by typhoon Soulik in 2018, the deadliest typhoon strike to the southwestern coast, since Maemi in 2003. Typhoon Soulik overran the region, causing extensive damage to the coast, shoreline, vegetation, and coastal geomorphology. Therefore, it is important to investigate its impact on the coastal ecology, landform, erosion/accretion, suspended sediment concentration (SSC) and associated coastal changes along the Mokpo region. In this study, net shoreline movement (NSM), Normalized Difference Vegetation Index (NDVI)), coastal landform change model, Normalized Difference Suspended Sediment Index (NDSSI), and SSC-reflectance relation have been used to analyze the coastal morphodynamics over the typhoon periods. We used pre- and post-typhoon Sentinel-2B MSI images for mapping and monitoring the typhoon effect. The findings highlighted the significant impacts of typhoons on coastal dynamics, wetland vegetation and sediment resuspension along the Mokpo coast. It has been observed that typhoon-induced SSC influences shoreline and coastal morphology. The outcome of this research may provide databases to manage coastal environments and a long-term plan to restore valuable coastal habitats. In addition, the findings may be useful for post-typhoon emergency response, coastal planners, and administrators involved in the long-term development of human life. [ABSTRACT FROM AUTHOR]