• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.208-208
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• 2022

Recently, the frequency of abnormal weather due to complex factors such as global warming is increasing frequently. From the past rainfall patterns, it is evident that climate change is causing irregular rainfall patterns. This phenomenon causes difficulty in predicting rainfall and makes it difficult to prevent and cope with natural disasters, casuing human and property damages. Therefore, accurate rainfall estimation and rainfall occurrence time prediction could be one of the ways to prevent and mitigate damage caused by flood and drought disasters. However, rainfall prediction has a lot of uncertainty, so it is necessary to understand and reduce this uncertainty. In addition, when accurate rainfall prediction is applied to the rainfall-runoff model, the accuracy of the runoff prediction can be improved. In this regard, this study aims to increase the reliability of rainfall prediction by analyzing the uncertainty of the Korean rainfall ensemble prediction data and the outflow analysis model using the Limited Area ENsemble (LENS) and the Grid based Rainfall-runoff Model (GRM) models. First, the possibility of improving rainfall prediction ability is reviewed using the QM (Quantile Mapping) technique among the bias correction techniques. Then, the GRM parameter calibration was performed twice, and the likelihood-parameter applicability evaluation and uncertainty analysis were performed using R², NSE, PBIAS, and Log-normal. The rainfall prediction data were applied to the rainfall-runoff model and evaluated before and after calibration. It is expected that more reliable flood prediction will be possible by reducing uncertainty in rainfall ensemble data when applying to the runoff model in selecting behavioral models for user uncertainty analysis. Also, it can be used as a basis of flood prediction research by integrating other parameters such as geological characteristics and rainfall events.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.151-163
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• 2018

The objective of this study was to estimate LOADEST (LOAD Estimator) coefficients for simulating pollutant loads in ungauged watersheds. Regression models of LOADEST were used to simulate pollutant loads, and the multiple linear regression (MLR) was used for coefficients estimation on watershed characteristics. The fifth and third model of LOADEST were selected to simulate T-N (Total-Nitrogen) and T-P (Total-Phosphorous) loads, respectively. The results and statistics indicated that regression models based on LOADEST simulated pollutant loads reasonably and model coefficients were reliable. However, the results also indicated that LOADEST underestimated pollutant loads and had a bias. For this reason, simulated loads were corrected the bias by a quantile mapping method in this study. Corrected loads indicated that the bias correction was effective. Using multiple regression analysis, a coefficient estimation methods according to the watershed characteristic were developed. Coefficients which calculated by MLR were used in models. The simulated result and statistics indicated that MLR estimated the model coefficients reasonably. Regression models developed in this study would help simulate pollutant loads for ungauged watersheds and be a screen model for policy decision.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.1
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• pp.47-57
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• 2015

Along with climate change, the occurrence and severity of natural disasters have been increased globally. In particular, the increase of localized heavy rainfalls have caused severe flood damage. Thus, it is needed to consider climate change into the estimation of design flood, a principal design factor. The main objective of this study was to estimate design floods for an agricultural reservoir watershed based on the RCP (Representative Concentration Pathways) scenarios. Gyeryong Reservoir located in the Geum River watershed was selected as the study area. Precipitation data of the past 30 years (1981~2010; 1995s) were collected from the Daejeon meteorological station. Future precipitation data based on RCP2.6, 4.5, 6.0, 8.5 scenarios were also obtained and corrected their bias using the quantile mapping method. Probability rainfalls of 200-year frequency and PMPs were calculated for three different future spans, i.e. 2011~2040; 2025s, 2041~2070; 2055s, 2071~2100; 2085s. Design floods for different probability rainfalls were calculated using HEC-HMS. As the result, future probability rainfalls increased by 9.5 %, 7.8 % and 22.0 %, also design floods increased by 20.7 %, 5.0 % and 26.9 %, respectively, as compared to the past 1995s and tend to increase over those of 1995s. RCP4.5 scenario, especially, resulted in the greatest increase in design floods, 37.3 %, 36.5 % and 47.1 %, respectively, as compared to the past 1995s. The study findings are expected to be used as a basis to reduce damage caused by climate change and to establish adaptation policies in the future.

• Journal of Korean Society of Disaster and Security
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• v.8 no.1
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• pp.21-27
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• 2015

Recent researches show that climate change has impact on the rainfall process at different temporal and spatial scales. The present paper is focused on climate change impact on sub-daily rainfall quantile of Han River basin in South Korea. Climate change simulation outputs from ECHO-G GCM under the A2 scenario were used to estimate daily extreme rainfall. Sub-daily extreme rainfall was estimated using the scale invariance concept. In order to assess sub-daily extreme rainfall from climate change simulation outputs, precipitation time series were generated based on NSRPM (Neyman-Scott Rectangular Pulse Model) and modified using the ratio of rainfall over projection periods to historical one. Sub-daily extreme rainfall was then estimated from those series. It was found that sub-daily extreme rainfall in the future displayed increasing or decreasing trends for estimation methods and different periods.

• 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 Korea Water Resources Association
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• v.41 no.5
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• pp.517-525
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• 2008

Regional rainfall quantile depends on the identification of hydrologically homogeneous regions. Various variables relevant to precipitation can be used to form regions. Since the type and number of variables may lead to improve the efficiency of partitioning, it is important to select those precipitation related variables, which represent most of the information from all candidate variables. Multivariate analysis techniques can be used for this purpose. Procrustes analysis which can decrease the dimension of variables based on their correlations, are applied in this study. 42 rainfall related variables are decreased into 21 ones by Procrustes analysis. Factor analysis is applied to those selected variables and then 5 factors are extracted. Fuzzy-c means technique classifies 68 stations into 6 regions. As a result, the GEV distributions are fitted to 6 regions while the lognormal and generalized logistic distributions are fitted to 5 regions. For the comparison purpose with previous results, rainfall quantiles based on generalized logistic distribution are estimated by at-site frequency analysis, index flood method, and regional shape estimation method.