The global occurrence of myriad natural disasters and incidents, catalyzed by climate change and extreme meteorological conditions, has engendered substantial human and material losses. International organizations such as the International Charter have established an enduring collaborative framework for real-time coordination to provide high-resolution satellite imagery and geospatial information. These resources are instrumental in the management of large-scale disaster scenarios and the expeditious execution of recovery operations. At the national level, the operational deployment of advanced National Earth Observation Satellites, controlled by National Geographic Information Institute, has not only catalyzed the advancement of geospatial data but has also contributed to the provisioning of damage analysis data for significant domestic and international disaster events. This special edition of the National Disaster Management Research Institute delineates the contemporary landscape of major disaster incidents in the year 2023 and elucidates the strategic blueprint of the government's national disaster safety system reform. Additionally, it encapsulates the most recent research accomplishments in the domains of artificial satellite systems, information and communication technology, and spatial information utilization, which are paramount in the institution's disaster situation management and analysis efforts. Furthermore, the publication encompasses the most recent research findings relevant to data collection, processing, and analysis pertaining to disaster cause and damage extent. These findings are especially pertinent to the institute's on-site investigation initiatives and are informed by cutting-edge technologies, including drone-based mapping and LiDAR observation, as evidenced by a case study involving the 2023 landslide damage resulting from concentrated heavy rainfall.
Choi, Hyeonjin;Lee, Songhee;Woo, Hyuna;Kim, Minyoung;Noh, Seong Jin
Journal of Korea Water Resources Association
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v.56
no.10
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pp.641-653
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2023
As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.
The conservation and restoration of wetlands are essential tasks for the sustainable development of human society and the environment, providing vital benefits such as biodiversity maintenance, natural disaster mitigation, and climate change alleviation. This study aims to analyze the strategic interactions and interests among various stakeholders using game theory and to provide significant grounds for policy decisions related to wetland restoration and development. In this study, hypothetical scenarios were set up for three types of cities: large, medium, and small. Stakeholders such as governments, development companies, environmental groups, and local residents were identified. Strategic options for each stakeholder were developed, and a payoff matrix was established through discussions among wetland ecology experts. Subsequently, non-cooperative game theory was applied to analyze Nash equilibria and Pareto efficiency. In large cities, strategies of 'Wetland Conservation' and 'Eco-Friendly Development' were found beneficial for all stakeholders. In medium cities, various strategies were identified, while in small cities, 'Eco-Friendly Development' emerged as the optimal solution for all parties involved. The Pareto efficiency analysis revealed how the optimal solutions for wetland management could vary across different city types. The study highlighted the importance of wetland conservation, eco-friendly development, and wetland restoration projects for each city type. Accordingly, policymakers should establish regulations and incentives that harmonize environmental protection and urban development and consider programs that promote community participation. Understanding the roles and strategies of stakeholders and the advantages and disadvantages of each strategy is crucial for making more effective policy decisions.
In this study, a quantitative risk impact assessment is performed using an ALOHA program to identify the risks when applying ammonia as fuel for combined cycle power plants as one of the solutions of climate change. The worst and the alternative accident scenarios are established for the Sejong combined cycle power plant and the effective ranges are calculated in terms of flammability, thermal radiation, overpressure and toxicity. The analysis results show that the toxic risk is the most critical and the effective distance is highly proportional to the mixing ratio of natural gas and ammonia by showing the Pearson's correlation coefficient over 98% as 0.991, 0.987 and 0.989 for the Level Of Concern(LOC)-1, LOC-2 and LOC-3, respectively. In addition, the coefficients of linearity for LOC-1, LOC-2 and LOC-3 are calculated to 133, 70 and 29, respectively so it can be confirmed that the effective distance increases as the criterion decreases.
The rapid advance of technology has accelerated global warming. As 50.4 percent of South Korea's population is concentrated in the Seoul Metropolitan Area, which has become a considerable emitter of greenhouse gases, the city's average temperature is expected to increase more rapidly than in other areas in the country. A rise in the average temperature would affect everyday life and urban ecology; thus, appropriate measures to cope with the forthcoming disaster are in need. This study analyzed the changes in plant phenological phases from the past to the present based on temperatures (average temperature of Feb, Mar, April) observed in seven different weather stations nearthe Seoul Metropolitan Area (Ganghwa, Seoul, Suwon, Yangpyeong, Icheon, Incheon, and Paju) and the first flowering dates of Plum tree (Prunus mume), Korean forsythia (Forsythia koreana), Korean rosebay (Rhododendron mucronulatum), Cherry tree (Prunus serrulate), Peach tree (Prunus persica), and Pear tree (Pyrus serotina). Then, RCP (Representative Concentration Pathways) 2.6 and 8.5 scenarios were used to predict the future temperature in the Seoul Metropolitan Area and how it will affect plant phenological phases. Furthermore, the study examined the differences in the flowering dates depending on various strategies to mitigate greenhouse gases. The result showed that the rate of plant phenological change had been accelerated since the 1900s.If emission levels remain unchanged, plants will flower from 18 to 29 earlier than they do now in the Seoul Metropolitan Area, which would be faster than in other areas in the country. This is because the FFD (First Flowering Date), is highly related to temperature changes. The Seoul Metropolitan Area, which has been urbanized more rapidly than any other areas, is predicted to become a temperature warming, forcing the FFDs of the area to occur faster than in the rest of the country. Changes in phenology can lead to ecosystem disruption by causing mismatches in species interacting with each otherin an ecosystem. Therefore, it is necessary to establish strategies against temperature warming and FFD change due to urbanization.
In this research, a methodology was developed for constructing an appropriate rainfall image database for estimating rainfall intensity based on CCTV video. The database was constructed in the Large-Scale Climate Environment Chamber of the Korea Conformity Laboratories, which can control variables with high irregularity and variability in real environments. 1,728 scenarios were designed under five different experimental conditions. 36 scenarios and a total of 97,200 frames were selected. Rain streaks were extracted using the k-nearest neighbor algorithm by calculating the difference between each image and the background. To prevent overfitting, data with pixel values greater than set threshold, compared to the average pixel value for each image, were selected. The area with maximum pixel variability was determined by shifting with every 10 pixels and set as a representative area (180×180) for the original image. After re-transforming to 120×120 size as an input data for convolutional neural networks model, image augmentation was progressed under unified shooting conditions. 92% of the data showed within the 10% absolute range of PBIAS. It is clear that the final results in this study have the potential to enhance the accuracy and efficacy of existing real-world CCTV systems with transfer learning.
Journal of the Korean Society for Marine Environment & Energy
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v.12
no.4
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pp.307-319
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2009
Carbon dioxide capture and storage (CCS) technology has been regarded as one of the most possible and practical option to reduce the emission of carbon dioxide ($CO_2$) and consequently to mitigate the climate change. Korean government also have started a 10-year R&D project on $CO_2$ storage in sea-bed geological structure including gas field and deep saline aquifer since 2005. Various relevant researches are carried out to cover the initial survey of suitable geological structure storage site, monitoring of the stored $CO_2$ behavior, basic design of $CO_2$ transport and storage process and the risk assessment and management related to $CO_2$ leakage from engineered and geological processes. Leakage of $CO_2$ to the marine environment can change the chemistry of seawater including the pH and carbonate composition and also influence adversely on the diverse living organisms in ecosystems. Recently, IMO (International Maritime Organization) have developed the risk assessment and management framework for the $CO_2$ sequestration in sub-seabed geological structures (CS-SSGS) and considered the sequestration as a waste management option to mitigate greenhouse gas emissions. This framework for CS-SSGS aims to provide generic guidance to the Contracting Parties to the London Convention and Protocol, in order to characterize the risks to the marine environment from CS-SSGS on a site-specific basis and also to collect the necessary information to develop a management strategy to address uncertainties and any residual risks. The environmental risk assessment (ERA) plan for $CO_2$ storage work should include site selection and characterization, exposure assessment with probable leak scenario, risk assessment from direct and in-direct impact to the living organisms and risk management strategy. Domestic trial of the $CO_2$ capture and sequestration in to the marine geologic formation also should be accomplished through risk management with specified ERA approaches based on the IMO framework. The risk assessment procedure for $CO_2$ marine storage should contain the following components; 1) prediction of leakage probabilities with the reliable leakage scenarios from both engineered and geological part, 2) understanding on physio-chemical fate of $CO_2$ in marine environment especially for the candidate sites, 3) exposure assessment methods for various receptors in marine environments, 4) database production on the toxic effect of $CO_2$ to the ecologically and economically important species, and finally 5) development of surveillance procedures on the environmental changes with adequate monitoring techniques.
This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.
Kang, Tae Soon;Cho, Kwangwoo;Lee, Jong Sup;Park, Won Kyung
Journal of Korean Society of Coastal and Ocean Engineers
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v.26
no.4
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pp.197-206
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2014
The purpose of this study is to evaluate coastal erosion due to a sea-level rise. The shoreline retreat rate was calculated due to future sea-level rise. Shoreline retreat rates were quantified with the cross-sectional data of 23 sandy coasts (12 sites from east coast, 5 sites from south coast, and 6 sites of west coast) and 3 cross-sectional profiles from each side of the coasts in Korea. The theory of equilibrium beach profile was employed in this study to evaluate the applicability of the theory into the coast of Korea and was tested with 15 cross-sectional beach profiles. Four scenarios of future sea level rise such as 38 cm, 59 cm, 75 cm, and 100 cm were adopted to estimate the shoreline retreat rates. Overall shoreline retreat rates for the coasts in Korea were predicted as 43.7% for 38 cm, 60.3% for 59 cm, 69.2% for 75 cm, and 80.1% for 100 cm sea level rises, respectively. Retreat rates in the east coast (29.6% for 38 cm, 45.1% for 59 cm, 56.0% for 75 cm, and 69.9% for 100 cm) showed relatively low compared to the south coast (51.9%, 67.6%, 77.2%, 87.3%) and the west coast (53.8%, 71.0%, 78.5%, 86.4%). However, all sandy coasts in Korea were assessed to be vulnerable with increasing sea-level rise. There are uncertainties in the assessment of this study, which include the limitation of the assessment model and the lack of the spatio-temporal data of the beach profiles. Therefore, this study shows that it is very important to spend integrated efforts to respond coastal erosion including comprehensive observations(monitoring) and the development of scientific understanding on the field.
Cho, Wonhee;Lim, Wontaek;Kim, Eun-Sook;Lim, Jong-Hwan;Ko, Dongwook W.
Korean Journal of Agricultural and Forest Meteorology
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v.22
no.3
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pp.215-231
/
2020
Forest landscape models (FLMs) can be used to investigate the complex interactions of various ecological processes and patterns, which makes them useful tools to evaluate how environmental and anthropogenic variables can influence forest ecosystems. However, due to the large spatio-temporal scales in FLMs studies, parameterization and validation can be extremely challenging when applying to new study areas. To address this issue, we focused on the parameterization and application of a spatially explicit forest landscape model, LANDIS-II, to Mt. Gyebang, South Korea, with the use of the National Forest Inventory (NFI) and long-term ecological research (LTER) site data. In this study, we present the followings for the biomass succession extension of LANDIS-II: 1) species-specific and spatial parameters estimation for the biomass succession extension of LANDIS-II, 2) calibration, and 3) application and validation for Mt. Gyebang. For the biomass succession extension, we selected 14 tree species, and parameterized ecoregion map, initial community map, species growth characteristics. We produced ecoregion map using elevation, aspect, and topographic wetness index based on digital elevation model. Initial community map was produced based on NFI and sub-alpine survey data. Tree species growth parameters, such as aboveground net primary production and maximum aboveground biomass, were estimated from PnET-II model based on species physiological factors and environmental variables. Literature data were used to estimate species physiological factors, such as FolN, SLWmax, HalfSat, growing temperature, and shade tolerance. For calibration and validation purposes, we compared species-specific aboveground biomass of model outputs and NFI and sub-alpine survey data and calculated coefficient of determination (R2) and root mean square error (RMSE). The final model performed very well, with 0. 98 R2 and 8. 9 RMSE. This study can serve as a foundation for the use of FLMs to other applications such as comparing alternative forest management scenarios and natural disturbance effects.
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