• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.839-850
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• 2018

The flood damage in urban areas due to torrential rain is increasing with urbanization. For this reason, accurate and rapid flooding forecasting and expected inundation maps are needed. Predicting the extent of flooding for certain rainfalls is a very important issue in preparing flood in advance. Recently, government agencies are trying to provide expected inundation maps to the public. However, there is a lack of quantifying the extent of inundation caused by a particular rainfall scenario and the real-time prediction method for flood extent within a short time. Therefore the real-time prediction of flood extent is needed based on rainfall-runoff-inundation analysis. One/two dimensional model are continued to analyize drainage network, manhole overflow and inundation propagation by rainfall condition. By applying the various rainfall scenarios considering rainfall duration/distribution and return periods, the inundation volume and depth can be estimated and stored on a database. The Rainfall-Duration-Flooding Quantity (RDF) relationship curve based on the hydraulic analysis results and the Self-Organizing Map (SOM) that conducts unsupervised learning are applied to predict flooded area with particular rainfall condition. The validity of the proposed methodology was examined by comparing the results of the expected flood map with the 2-dimensional hydraulic model. Based on the result of the study, it is judged that this methodology will be useful to provide an unknown flood map according to medium-sized rainfall or frequency scenario. Furthermore, it will be used as a fundamental data for flood forecast by establishing the RDF curve which the relationship of rainfall-outflow-flood is considered and the database of expected inundation maps.

• Journal of Environmental Impact Assessment
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• v.25 no.5
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• pp.345-356
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• 2016

In this study, auto-calibration method for water quality model was compared and analyzed using QUAL2Kw, which can estimate the optimum parameters through the integration of genetic algorithm and QUAL2K. The QUAL2Kw was applied to the Sum River which is greatly affected by the pollution loads of Wonju city. Two auto-calibration methods were examined: single parameter application for the whole river reach and separate parameter application for each reach of multiple reaches. The analysis about CV(RMSE) and fitness of the GA show that the separate parameter auto-calibration method is better than the single parameter method in the degree of precision. Thus the separate parameter auto-calibration method is applied to the water quality modelling of this study. The calibrated QUAL2Kw was used for the three scenarios for the water quality management of the Sum River, and the water quality impact on the river was analyzed. In scenario 1, which improve the effluent water quality of Wonju WWTP, BOD and TP concentrations of the Sum River 4-1 station which is representative one of Mid-Watershed, are decreased 17.7% and 29.1%, respectively. And immediately after joining the Wonjucheon, BOD and TP concentrations are decreased 50.4% and 40.5%, respectively. In scenario 2, Wonju water supply intake is closed and multi-regional water supply, which come from other watershed except the Sum River, is provided. The Sum River water quality in scenario 2 is slightly improved as the flow of the river is increased. Immediately after joining the Wonjucheon, BOD and TP concentrations are decreased 0.18mg/L and 0.0063mg/L, respectively. In scenario 3, the water quality management alternatives of scenario 1 and 2 are planned simultaneously, the Sum River water quality is slightly more improved than scenario 1. Water quality prediction of the three scenarios indicates that effluent water quality improvement of Wonju WWTP is the most efficient alternative in water quality management of the Sum River. Particularly the Sum River water quality immediately after joining the Wonjucheon is greatly improved. When Wonju water supply intake is closed and multi-regional water supply is provided, the Sum River water quality is slightly improved.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.106-117
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• 2021

The purpose of this study is prediction of landslide occurrence reflecting the subsurface flow characteristics within the soil layer in the future due to climate change in a large scale watershed. To do this, we considered the infinite slope stability theory to evaluate the landslide occurrence with predicted soil moisture content by SWAT model based on monitored data (rainfall-soil moisture-discharge). The correlation between the SWAT model and the monitoring data was performed using the coefficient of determination (R²) and the model's efficiency index (Nash and Sutcliffe model efficiency; NSE) and, an accuracy analysis of landslide prediction was performed using auROC (area under Receiver Operating Curve) analysis. In results comparing with the calculated discharge-soil moisture content by SWAT model vs. actual observation data, R² was 0.9 and NSE was 0.91 in discharge and, R² was 0.7 and NSE was 0.79 in soil moisture, respectively. As a result of performing infinite slope stability analysis in the area where landslides occurred in the past based on simulated data (SWAT analysis result of 0.7~0.8), AuROC showed 0.98, indicating that the suggested prediction method was resonable. Based on this, as a result of predicting the characteristics of landslide occurrence by 2050 using climate change scenario (RCP 8.5) data, it was calculated that four landslides could occur with a soil moisture content of more than 75% and rainfall over 250 mm/day during simulation. Although this study needs to be evaluated in various regions because of a case study, it was possible to determine the possibility of prediction through modeling of subsurface flow mechanism, one of the most important attributes in landslide occurrence.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.2
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• pp.114-128
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• 2013

Rapid industrialization and economic growth have led to serious problems including reduced open space, environmental degradation, traffic congestion, and urban sprawl. These problems have been exacerbated by the absence of effective conservation and governance, and have resulted in various social conflicts. In response to these challenges, many scholar and government hope to achieve sustainable development through the establishment and management of environment-friendly planning. For this purpose, we would like to analyze functional change for ecosystem by future land-use/cover changes in South Korea. Toward this goal, we predicted land-use/cover changes from 2010 to 2060 using the future population of Statistics Korea and urban growth probability map created by logistic regression analysis and analyzed ecosystem service value using costanza's coefficient. In the case of scenario 1, ecosystem service value represented 6,783~7,092 million USD. In the case of scenario 2, ecosystem represented 6,775~7,089 million USD, 2.9~7.6 million USD decreased compared by scenario 1. This was the result of area reduction for farmland and wetland which have high environmental value relatively according to urban growth by development point of view. The results of this analysis indicate that environmentally sustainable systems and urban development must be applied to achieve sustainable development and environmental protection. Quantitative analysis of environmental values in accordance with environmental policy can help inform the decisions of policy makers and urban developers. Furthermore, forecasting urban growth based on future demand will provide more precise predictive analysis.

• Journal of the Korean Geographical Society
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• v.51 no.5
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• pp.633-649
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• 2016

Exposure to high temperatures during the reproductive period of crops decreases their productivity. The Intergovernmental Panel on Climate Change's (IPCC) fifth Assessment Report predicts that the frequency of high temperatures will continue to increase in the future, resulting in significant impacts on the world's food supply. This study evaluate climate change-induced heat stress on four major agricultural crops (rice, maize, soybean, and wheat) at a global level, using the coupled atmosphere-ocean model of Hadley Centre Global Environmental Model version 2 (HadGEM2-AO) and FAO/IIASA Global Agro-Ecological Zone (GAEZ) model data. The maximum temperature rise ($1.8-3.5^{\circ}C$) during the thermal-sensitive period (TSP) from the baseline (1961-1990) to the future (2070-2090) is expected to be larger under a Representative Concentration Pathway (RCP) 8.5 climate scenario than under a RCP2.6 climate scenario, with substantial heat stress-related damage to productivity. In particular, heat stress is expected to cause severe damage to crop production regions located between 30 and $50^{\circ}N$ in the Northern Hemisphere. According to the RCP8.5 scenario, approximately 20% of the total cultivation area for all crops will experience unprecedented, extreme heat stress in the future. Adverse effects on the productivity of rice and soybean are expected to be particularly severe in North America. In Korea, grain demands are heavily dependent on imports, with the share of imports from the U.S. at a particularly high level today. Hence, it is necessary to conduct continuous prediction on food security level following the climate change, as well as to develop adaptation strategy and proper agricultural policy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.284-301
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• 2023

The changes in rice climatic yield potential (CYP) across the Korean Peninsula are evaluated based on the new climate change scenario produced by the National Institute of Agricultural Sciences with 18 ensemble members at 1 km resolution under a Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathways (RCP) emission scenarios. To overcome the data availability, we utilize solar radiation f or CYP instead of sunshine duration which is relatively uncommon in the climate prediction f ield. The result show that maximum CYP(CYPmax) decreased, and the optimal heading date is progressively delayed under warmer temperature conditions compared to the current climate. This trend is particularly pronounced in the SSP5-85 scenario, indicating faster warming, except for the northeastern mountainous regions of North Korea. This shows the benef its of lower emission scenarios and pursuing more efforts to limit greenhouse gas emissions. On the other hand, the CYPmax shows a wide range of feasible futures, which shows inherent uncertainties in f uture climate projections and the risks when analyzing a single model or a small number of model results, highlighting the importance of the ensemble approach. The f indings of this study on changes in rice productivity and uncertainties in temperature and solar radiation during the 21st century, based on climate change scenarios, hold value as f undamental information for climate change adaptation efforts.

• Journal of Platform Technology
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• v.12 no.1
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• pp.91-105
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• 2024

As the number of cases of applying IT systems to the existing isolated ICS (Industrial Control System) network environment continues to increase, security threats in the ICS environment have rapidly increased. Security threat scenarios help to design security strategies in cybersecurity training, including analysis, prediction, and response to cyberattacks. For successful cybersecurity training, research is needed to develop valid and reliable security threat scenarios for meaningful training. Therefore, this paper proposes a case-based security threat scenario development methodology for cybersecurity training in the ICS environment. To this end, we develop a methodology consisting of five steps based on analyzing actual cybersecurity incident cases targeting ICS. Threat techniques are standardized in the same form using objective data based on the MITER ATT&CK framework, and then a list of CVEs and CWEs corresponding to the threat technique is identified. Additionally, it analyzes and identifies vulnerable functions in programming used in CWE and ICS assets. Based on the data generated up to the previous stage, develop security threat scenarios for cybersecurity training for new ICS. As a result of verification through a comparative analysis between the proposed methodology and existing research confirmed that the proposed method was more effective than the existing method regarding scenario validity, appropriateness of evidence, and development of various scenarios.

• Journal of Korean Society on Water Environment
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• v.26 no.2
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• pp.200-207
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• 2010

The sewage treatment plants to be built to improve the water quality of the Mankyung River will total 11, of which combined capacity will reach $39,850m^3/day$, and saying in detail, 5 at Gunsan city, 2 at Iksan city, 1 at Kimje city and 3 at Wanju gun, The scenario for water quality improvement was developed, considering the conditions of plant operation ratio and the accomplishment of the water quality target (BOD 4.4 mg/L, T-P 0.356 mg/L) at the end of the watershed of Mankyung B was predicted, making use of QUAL2E model. As a result of prediction using QUAL2E model based on scenarios with 70% and 100% of operation ratio, respectively, at 11 plants in 2010, the water quality at the watershed of Mankyung B was estimated at 4.322 mg/L which was lower than the target of BOD 4.4 mg/L, indicating the target water quality was achieved, when it comes to 70% of operation ratio, But in case of T-P, it was estimated at 0.565 mg/L, which was higher than the target. When it comes to 100% of operation ratio, T-P also was 0.563 mg/L which exceeded the target, 0.356 mg/L. As indicated above, the effect of water quality improvement appeared very insignificant, which was attributable to the limit of small scale sewage treatment plant in total reduction capacity. Hence, the measures for additional reduction in a bid to achieve the target water quality of T-P at the designated location need to be taken, and the measures to build the Sewage treatment facilities at the place where the pollution is significantly caused by T-P appeared to be required as well.