• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.383-392
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• 2008

It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established. Therefore, uncertainty analysis are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an unexpected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.

• Journal of Korea Water Resources Association
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• v.42 no.7
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• pp.547-558
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• 2009

General reliability assessment of levees embankment is performed with safety factors for rainfall characteristics and hydrologic and hydraulic parameters, based on the results of deterministic analysis. The safety factors are widely employed in the field of engineering handling model parameters and the diversity of material properties, but cannot explain every natural phenomenon. Uncertainty of flood analysis and related parameters by introducing stochastic method rather than deterministic scheme will be required to deal with extreme weather and unprecedented flood due to recent climate change. As a consequence, stochastic-method-based measures considering parameter uncertainty and related factors are being established. In this study, a variety of dimensionless cumulative rainfall curve for typhoon and monsoon season of July to September with generation method of stochastic temporal variation is generated by introducing Monte Carlo method and applied to the risk assessment of levee embankment using reliability index. The result of this study reflecting temporal and regional characteristics of a rainfall can be used for the establishment of flood defence measures, hydraulic structure design and analysis on a watershed.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.4
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• pp.33-43
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• 2020

In order to reduce damage from farmland inundation caused by recent climate change, it is necessary to predict the risk of farmland inundation accurately. Inundation modeling should be performed by considering multiple time distributions of possible rainfalls, as digital forecasts of Korea Meteorological Administration is provided on a six-hour basis. As building multiple inputs and creating inundation models take a lot of time, it is necessary to shorten the forecast time by building a data base (DB) of farmland inundation probability. Therefore, the objective of this study is to establish a DB of farmland inundation probability in accordance with forecasted rainfalls. In this study, historical data of the digital forecasts was collected and used for time division. Inundation modeling was performed 100 times for each rainfall event. Time disaggregation of forecasted rainfall was performed by applying the Multiplicative Random Cascade (MRC) model, which uses consistency of fractal characteristics to six-hour rainfall data. To analyze the inundation of farmland, the river level was simulated using the Hydrologic Engineering Center - River Analysis System (HEC-RAS). The level of farmland was calculated by applying a simulation technique based on the water balance equation. The inundation probability was calculated by extracting the number of inundation occurrences out of the total number of simulations, and the results were stored in the DB of farmland inundation probability. The results of this study can be used to quickly predict the risk of farmland inundation, and to prepare measures to reduce damage from inundation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.439-446
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• 2013

This paper is about assessment of soil loss in irrigation reservoir based on GIS. Natural disaster caused by soil loss whose natural incidence has been rapidly reduced due to successful tree planting campaign shows high potential risk, since the latest localized heavy rain resulted from extreme weather event and artificial land development acts as direct factors for land disaster. To prevent it, various techniques and technologies have been used to predict effect of soil loss. However, reliability of techniques and technologies to predict its effect precisely is relatively low so far because the natural disaster by soil loss is taken place by complicated interaction between possible factors and direct factors. Geospatial approach is essential to examine these interactions. In this regard, this study will provide detailed plan to improve prediction reliability for soil loss of irrigation reservoir, using GIS that has Hydrologic -Topographical parameter and digital map as its input parameters.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.1
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• pp.37-45
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• 2015

Recent droughts in South Korea have had large economic and environmental impacts across the country. Changes in rainfall and hydrologic patterns due to climate change can potentially increase the occurrence of extreme droughts and affect the future availability of water resources. Therefore, it is necessary to evaluate drought vulnerability for water resources planning and management, and identify the appropriate mitigation actions to conduct a drought risk analysis in the context of climate change. The objective of this study is changes in the temporal trends of drought characteristics in South Korea to examine drought impacts under climate change. First, the changes of drought occurrence were analyzed by applying the Standardized Precipitation Evapotranspiration Index (SPEI) for meteorological data on 54 meteorological stations, and were analyzed for the past 30 years (1981-2010), and Representative Concentration Pathways (RCP) climate change scenarios (2011-2100). Second, the changes on the temporal trends of drought characteristics were performed using run theory, which was used to compare drought duration, severity, and magnitude to allow for quantitative evaluations under past and future climate conditions. These results show the high influence of climate change on drought phenomenon, and will contribute to water resources management and drought countermeasures to climate change.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.1-9
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• 2020

Reservoir storage and water level information is essential for accurate drought monitoring and prediction. In particular, the agricultural drought has increased the risk of agricultural water shortages due to regional bias in reservoirs and water supply facilities, which are major water supply facilities for agricultural water. Therefore, it is important to evaluate the available water capacity of the reservoir, and it is necessary to determine the water surface area and water capacity. Remote sensing provides images of temporal water storage and level variations, and a combination of both measurement techniques can indicate a change in water volume. In areas of ungauged water volume, satellite remote sensing image acts as a powerful tool to measure changes in surface water level. The purpose of this study is to estimate of reservoir storage and level variations using satellite remote sensing image combined with hydrological statistical data and the Normalized Difference Water Index (NDWI). Water surface areas were estimated using the Sentinel-2 satellite images in Seosan, Chungcheongnam-do from 2016 to 2018. The remote sensing-based reservoir storage estimation algorithm from this study is general and transferable to applications for lakes and reservoirs. The data set can be used for improving the representation of water resources management for incorporating lakes into weather forecasting models and climate models, and hydrologic processes.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.781-796
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• 2019

This study attempted to simulate the flood discharge in the Duman River basin containing Hoeryong City and Musan County of North Korea where were damaged from Typhoon Lionrock on August, 2016. For hydrological modelling remotely sensed datasets were used to estimate watershed properties and hydrologic factors because the basin is ungauged where hydrological observation is not exist or sparse. For validation we applied our methodology and datasets to the Soyanggang Dam basin. It has not only similar shape factor and compactness ratio to those of the target basin but also accurate, adequate, and abundant measurements. The results showed that the flood discharge from Typhoon Lionrock corresponded to three to five years design floods in the Duman River basin. This indicate that the Duman River basin has a high risk of flood in the near future. Finally this study demonstrated that remotely sensed data and geographic information could be utilized to simulate flood discharge in an ungauged watershed.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.501-501
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• 2015

댐과 같은 수공구조물의 치수능력부족은 구조물의 파손이나 붕괴로 직결되며, 대규모 재산피해와 인명피해가 불가피하다. 또한 최근 기후변화 현상에 의해 발생되고 있는 예상치 못한 큰 강우와 홍수는 댐 안전을 저하하는 요소로 간주되며, 복합적으로 발생시 댐의 치수능력이 크게 저하되어 댐 파괴에 영향을 미칠 가능성이 크다. 또한 ICOLD, 세계대댐회 등의 조사 결과 댐 파괴에 큰 영향을 미치고 있는 수문학적 요소로서 예상치 못한 강우, 홍수 및 월류로 조사되었다. 이러한 이유로 수문학적 위험인자를 효과적으로 고려하기 위해 2000년도 이후 선진국에서는 위험도 해석 기법을 기반으로 파괴모드, 다양한 하중조건 등을 조합하여 위험도 해석을 통해 댐의 안전도 검토를 실시하고 있다. 따라서 최근 증가하는 기상변동성을 능동적으로 고려하기 위해서는 위험도 해석기반의 수공구조물 안정성 평가기법을 기반으로 하는 종합적인 위험도 해석 방안수립이 요구된다. 이러한 이유로 본 연구에서는 BN 모형 기반의 댐 위험도 적용에 앞서 실증댐을 대상으로 분석을 수행하였으며, 분석 절차는 다음과 같다. 첫째, ICOLD 및 세계대댐회 등 다양한 논문, 보고서 등을 조사하여 댐 붕괴에 가장 큰 영향을 미치는 수문학적 파괴인자를 도출하였다. 둘째, BN 모형 구축시 각 노드는 앞서 도출된 수문학적 파괴인자를 이용하였으며, 각 파괴인자에 적합한 확률분포형을 부여하였다. 마지막으로, 노드별 임계값을 부여하여 상황별 파괴인자의 변화 양상을 도출하였다. 본 연구의 결과로 인해 연구대상댐의 가장 취약한 수문학적 파괴인자 도출이 가능하며, 댐의 보수 보강시 우선순위 의사결정의 지원이 가능 할 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.982-982
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• 2012

본 연구에서는 우리나라의 중 소규모 유역의 수문학적 위험도 분석을 위하여 한강유역을 대상으로 통합위험지수(IRI: Integrated Risk Index)를 산정하였으며, El Ni$\tilde{n}$o-Southern Oscillation (ENSO)에 의한 대규모 대기순환 패턴의 변화가 한강 유역의 통합위험지수 변화에 미치는 영향을 평가하였다. ENSO자료는 전통적인 El Ni$\tilde{n}$o에 해당하는 Cold-tongue (CT) El Ni$\tilde{n}$o와 중앙태평양 부근의 이상적인 해수면 온도 상승에 의한 Warm-pool (WP) El Ni$\tilde{n}$o, 그리고 해수면 온도가 이상적으로 낮게 관측되는 La Ni$\tilde{n}$a 기간으로 구분하였으며, 각 기간 중 가장 강한 ENSO가 발생한 해(CT El Ni$\tilde{n}$o, 1998; WP El Ni$\tilde{n}$o, 2005; La Ni$\tilde{n}$a, 2000)를 대상으로 통합위험지수를 산정하였다. 통합위험지수는 수문학적 요인(Hydrologic Components), 사회 경제적 요인(Socio-Economic Components)과 생태적 요인(Ecological Components)으로 구분하였고, 엔트로피(entropy) 기법을 통하여 각 인자와 요인별 가중치를 적용하였다. 중권역별 통합위험지수의 평가는 5개의 계급구간(Very High, High, Medium, Low, Very Low)으로 구분하였다. 분석결과, CT El Ni$\tilde{n}$o해의 유역평균 IRI 값은 0.58, WP El Ni$\tilde{n}$o해의 IRI 값은 0.57로 비슷한 결과를 보였으나, La Ni$\tilde{n}$a해에는 IRI 값이 0.41로 낮게 나타났다. CT와 WP El Ni$\tilde{n}$o해에는 한강 서쪽일부 중권역에서 통합위험지수가 높게 나타났으며, La Ni$\tilde{n}$a해에는 한강 중 동부 대부분 유역에서 낮게 분석되었다. 향후 유역별 통합위험지수 산정과 더불어 서로 다른 형태의 ENSO에 따른 수자원 변동 예측이 이루어진다면, 수자원의 효율적인 관리와 안정적인 용수공급에 도움을 줄 것으로 사료되며, 이는 유역별 수자원의 취약성 평가 및 위험도 분석을 위한 기초자료로 활용이 가능하리라 사료된다.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.1-11
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• 2021

Recently, in Korea, the risk of meteorological disasters is increasing due to climate change, and the damage caused by rainfall is being emphasized continuously. Although the current weather forecast provides quantitative rainfall, there are several difficulties in predicting the extent of damage. Therefore, in order to understand the impact of damage, the threshold rainfall for each watershed is required. The damage caused by rainfall occurs differently by region, and there are limitations in the analysis considering the characteristic factors of each watershed. In addition, whenever rainfall comes, the analysis of rainfall-runoff through the hydrological model consumes a lot of time and is often analyzed using only simple rainfall data. This study used GIS data and calculated the threshold rainfall from the threshold runoff causing flooding by coupling two hydrologic models. The calculation result was verified by comparing it with the actual case, and it was analyzed that damage occurred in the dangerous area in general. In the future, through this study, it will be possible to prepare for flood risk areas in advance, and it is expected that the accuracy will increase if machine learning analysis methods are added.