• Atmosphere
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• v.24 no.2
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• pp.159-171
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• 2014

This study investigated the temporal and spatial characteristics of summertime (June-August) precipitation over Korean peninsula, using Korea Meteorological Administration (KMA)is Automated Synoptic Observing System (ASOS) data for the period of 1973-2010 and Automatic Weather System (AWS) data for the period of 1998-2010.The authors looked through climatological features of the summertime precipitation, then examined the degree of locality of the precipitation, and probable precipitation amount and its return period of 100 years (i.e., an extreme precipitation event). The amount of monthly total precipitation showed increasing trends for all the summer months during the investigated 38-year period. In particular, the increasing trends were more significant for the months of July and August. The increasing trend of July was seen to be more attributable to the increase of precipitation intensity than that of frequency, while the increasing trend of August was seen to be played more importantly by the increase of the precipitation frequency. The e-folding distance, which is calculated using the correlation of the precipitation at the reference station with those at all other stations, revealed that it is August that has the highest locality of hourly precipitation, indicating higher potential of localized heavy rainfall in August compared to other summer months. More localized precipitation was observed over the western parts of the Korean peninsula where terrain is relatively smooth. Using the 38-years long series of maximum daily and hourly precipitation as input for FARD2006 (Frequency Analysis of Rainfall Data Program 2006), it was revealed that precipitation events with either 360 mm $day^{-1}$ or 80 mm $h^{-1}$ can occur with the return period of 100 years over the Korean Peninsula.

• Journal of Korea Society of Waste Management
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• v.35 no.8
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• pp.777-784
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• 2018

Since climate change increases the risk of extreme rainfall events, concerns on flood management have also increased. In order to rapidly recover from flood damages and prevent secondary damages, fast collection and treatment of flood debris are necessary. Therefore, a quick and precise estimation of flood debris generation is a crucial procedure in disaster management. Despite the importance of debris estimation, methodologies have not been well established. Given the intrinsic heterogeneity of flood debris from local conditions, a regional-scale model can increase the accuracy of the estimation. The objectives of this study are 1) to identify significant damage variables to predict the flood debris generation, 2) to ascertain the difference in the coefficients, and 3) to evaluate the accuracy of the debris estimation model. The scope of this work is flood events in Ulsan city region during 2008-2016. According to the correlation test and multicollinearity test, the number of damaged buildings, area of damaged cropland, and length of damaged roads were derived as significant parameters. Key parameters seems to be strongly dependent on regional conditions and not only selected parameters but also coefficients in this study were different from those in previous studies. The debris estimation in this study has better accuracy than previous models in nationwide scale. It can be said that the development of a regional-scale flood debris estimation model will enhance the accuracy of the prediction.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1119-1130
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• 2020

Flood disasters have been recently increasing worldwide due to climate change and extreme weather events. Since flood damage recovery has been conducted as a common coping strategy to flood disasters in the Republic of Korea, it is necessary to predict the regional flood damage costs by rainfall characteristics for a preventative measure to flood damage. Therefore, the purpose of this study is to present the regression functions for human and economic flood damage assessments for the 7 metropolises in the Republic of Korea. A comprehensive regression analysis was performed through the total 48 simple regression models on the two types of flood damage records for human and economic costs over the past two decades from 1998 to 2017 using the four kinds of nonlinear equations with each of the six rainfall variables. The damage assessment functions for each metropolis were finally selected by the evaluation of the regression results with the coefficient of determination and the statistical significance test, and then used for the human and economic flood damage assessments for 100-year rainfall in the 7 metropolises. The results of this study are expected to provide the basic information on flood damage cost assessments for flood damage mitigation measures.

• Journal of Korea Water Resources Association
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• v.50 no.2
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• pp.75-87
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• 2017

In recent decades, extreme events have been significantly increased over the Korean Peninsula due to climate variability and climate change. The potential changes in hydrologic cycle associated with the extreme events increase uncertainty in water resources planning and designing. For these reasons, a reliable changing point analysis is generally required to better understand regime changes in hydrologic time series at watershed scale. In this study, a hierarchical changing point analysis approach that can apply in a watershed scale is developed by combining the existing changing point analysis method and hierarchical Bayesian method. The proposed model was applied to the selected stations that have annual rainfall data longer than 40 years. The results showed that the proposed model can quantitatively detect the shift in precipitation in the middle of 1990s and identify the increase in annual precipitation compared to the several decades prior to the 1990s. Finally, we explored the changes in precipitation and sea level pressure in the context of large-scale climate anomalies using reanalysis data, for a given change point. It was concluded that the identified large-scale patterns were substantially different from each other.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.183-194
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• 2019

In recent years, the risk of external flooding of major national facilities has increased significantly since 2000 due to the increase in local heavy rainfall events. For important domestic national facilities, it is necessary to analyze the risk of external flooding as flooding in major sites due to heavy rain can cause functional paralysis in major facilities and ultimately lead to massive trouble events. In order to manage the safety of main facilities and its related facilities at a high level, it is necessary to analyze the degree of disaster such as flood depth, flood flow rate, flood time and flood intensity when extreme floods (LIP) are introduced. In addition, the degree of vulnerability of these related facilities should be assessed and risk assessments should be reassessed through linkage analysis that combines the degree of disaster and vulnerability. By calculating a new flood hazard curve for the flood depth and flood intensity in major national facilities under the heavy rainfall conditions through this study, it is expected to be a basis for the waterproof design of important SOC facilities, flood prevention function design, advancement of flood prevention measures and procedures and evaluation of flood mitigation functions.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.475-486
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• 2020

Climate change scenarios are the basis of research to cope with climate change, and consist of large-scale spatio-temporal data. From the data point of view, one scenario has a large capacity of about 83 gigabytes or more, and the data format is semi-structured, making it difficult to utilize the data through means such as search, extraction, archiving and analysis. In this study, a tool for analyzing extreme climate events based on spatial information is developed to improve the usability of large-scale, multi-period climate change scenarios. In addition, a pilot analysis is conducted on the time and space in which the heavy rain thresholds that occurred in the past can occur in the future, by applying the developed tool to the RCP8.5 climate change scenario. As a result, the days with a cumulative rainfall of more than 587.6 mm over three days would account for about 76 days in the 2080s, and localized heavy rains would occur. The developed analysis tool was designed to facilitate the entire process from the initial setting through to deriving analysis results on a single platform, and enabled the results of the analysis to be implemented in various formats without using specific commercial software: web document format (HTML), image (PNG), climate change scenario (ESR), statistics (XLS). Therefore, the utilization of this analysis tool is considered to be useful for determining future prospects for climate change or vulnerability assessment, etc., and it is expected to be used to develop an analysis tool for climate change scenarios based on climate change reports to be presented in the future.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.139-152
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• 2015

Along with climate change, it is reported that the scale and frequency of extreme climate events show unstable tendency of increase. Thus, to comprehend the change characteristics of precipitation data, it is needed to consider non-stationary. The main objectives of this study were to estimate future design floods for Wonpyeongcheon watershed based on RCP (Representative Concentration Pathways) scenario. Wonpyeongcheon located in the Keum River watershed was selected as the study area. Historical precipitation data of the past 35 years (1976~2010) were collected from the Jeonju meteorological station. Future precipitation data based on RCP4.5 were also obtained for the period of 2011~2100. Systematic bias between observed and simulated data were corrected using the quantile mapping (QM) method. The parameters for the bias-correction were estimated by non-parametric method. A non-stationary frequency analysis was conducted with moving average method which derives change characteristics of generalized extreme value (GEV) distribution parameters. Design floods for different durations and frequencies were estimated using rational formula. As the result, the GEV parameters (location and scale) showed an upward tendency indicating the increase of quantity and fluctuation of an extreme precipitation in the future. The probable rainfall and design flood based on non-stationarity showed higher values than those of stationarity assumption by 1.2%~54.9% and 3.6%~54.9%, respectively, thus empathizing the necessity of non-stationary frequency analysis. The study findings are expected to be used as a basis to analyze the impacts of climate change and to reconsider the future design criteria of Wonpyeongcheon watershed.

• Journal of Wetlands Research
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• v.15 no.2
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• pp.223-231
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• 2013

Excessive precipitation, drought, heat waves, strong typhoons and rising sea levels are just some of the common indicators of climate change. In the Philippines, excessive precipitation never failed to devastate and drown the streets of Metro Manila, a highly urbanized and flood-prone area; such problems are expected to occur frequently. Moreover, the water supply of Metro Manila is dependent only to Angat Reservoir. Rainwater harvesting can serve as an alternative source of raw water and it can mitigate the effects of flooding. The harvested rainwater can be used for: potable consumption if filtered and disinfected; and non-potable consumptions (e.g., irrigation, flushing toilets, carwash, gardening, etc.) if used untreated. The rainfall data were gathered from all 5 rainfall stations located in Metro Manila namely: Science Garden, Port Area, Polo, Nangka and Napindan rain gauge stations. To be able to determine the potential volume of rainwater harvested and the potentiality of rainwater harvesting system as an alternate source of raw water; in this study, three different climatic conditions were considered, the dry, median and wet rainfall years. The frequent occurrence of cyclonic events in the Philippines brought significant amount of rainwater that causes flooding in the highly urbanized region of Metro Manila. Based from the results of this study, the utilization of rainwater harvesting system can serve as an alternative source of non-potable water for the community; and could also reduce the amount of surface runoff that could result to extreme flooding.

• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.85-95
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• 2020

According to the standard guidelines of design flood (MLTM, 2012; MOE, 2019), the design flood is calculated based on past precipitation. However, due to climate change, the frequency of extreme rainfall events is increasing. Therefore, it is necessary to analyze future floods' volume by using climate change scenarios. Meanwhile, the standard guideline was revised by MOE (Ministry of Environment) recently. MOE proposed modified Huff distribution and new CN (Curve Number) value of forest and paddy. The objective of this study was to analyze the change of flood volume by applying the modified Huff and newly proposed CN to the probabilistic precipitation based on SSP and RCP scenarios. The probabilistic rainfall under climate change was calculated through RCP 4.5/8.5 scenarios and SSP 245/585 scenarios. HEC-HMS (Hydrologic Engineering Center - Hydrologic Modeling System) was simulated for evaluating the flood volume. When RCP 4.5/8.5 scenario was changed to SSP 245/585 scenario, the average flood volume increased by 627 ㎥/s (15%) and 523 ㎥/s (13%), respectively. By the modified Huff distribution, the flood volume increased by 139 ㎥/s (3.76%) on a 200-yr frequency and 171 ㎥/s (4.05%) on a 500-yr frequency. The newly proposed CN made the future flood value increase by 9.5 ㎥/s (0.30%) on a 200-yr frequency and 8.5 ㎥/s (0.25%) on a 500-yr frequency. The selection of climate change scenario was the biggest factor that made the flood volume to transform. Also, the impact of change in Huff was larger than that of CN about 13-16 times.