• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.112-112
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• 2016

여름철 국지성 집중호우 및 태풍으로 인한 피해가 빈번하게 발생하고 있으며, 산지가 많은 국내에서는 산지지역 뿐만 아니라 도심지에서의 토석류 피해 또한 급증하고 있다. 2011년 집중호우로 인해 서울시 서초구에 위치한 우면산에서 동시 다발적으로 많은 토석류가 발생하였고 이로 인해 많은 인명과 재산피해가 발생하였다. 규모면에서 보다 큰 토석류가 이전에 강원지역 등에서 발생하였음에도 불구하고 우면산 산사태가 사회적 관심을 일으킨 것은 서울 도심지에서 발생하였기 때문이다. 이러한 토석류로 인한 피해를 줄이기 위해서는 토석류를 유발시키는 강우의 해석이 먼저 선행되어야 하며, 토사유출모의 결과의 확산면적의 정확성이 중요하다고 판단된다. 따라서 본 연구에서는 도심권(서초구 우면산) / 비도심권(춘천 마적산)을 대상지역으로 선정하였으며, 분포형 강우-유출모형인 S-RAT을 이용하여 빈도별(30년, 50년, 100년, 200년, PMP) 토석류 유발 강우를 산정하고, 토석류 2차원 수치모형인 RAMMS를 이용하여 정량지수(Quantitative index)분석을 통해 최적의 매개변수를 산정하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.6
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• pp.1269-1276
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• 2015

The methodology of utilizing Intensity-Duration-flood Quantity (IDQ) curve for flood alert and warning was introduced and its performance was evaluated. For this purpose the lumped parameter model was calibrated and validated for gauged basin data set and the index precipitation equivalent to alert and warning flood was estimated. The index precipitation and IDQ curves associated by three different Antecedant Moisture Conditions (AMCs) are made provision for various possible flood scenarios. The test basin is Wonju-cheon basin ($94.4km^2$) located in Gangwon province, Korea. The IDQ curves corresponding to alert (50% of design flood level) and warning (70% of design flood level) level was estimated using the Clark unit hydrograph based lumped parameter model. The performance evaluation showed 0.704 of POD (Probability of Detection), 0.136 of FAR (False Alarm Ratio), and 0.633 of CSI (Critical Success Index), which is improved from the result of IDQ with single fixed AMC.

• The Journal of Engineering Geology
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• v.28 no.1
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• pp.47-59
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• 2018

At present, there has been a wide range of studies on debris flow in Korea, more specifically, on rainfall characteristics that trigger debris flow including rainfall intensity, rainfall duration, and preceding rainfall. the prediction of landslide / debris flow relies on the criteria for landslide watch and warning by the Korea Forest Service (KFS, 2012). Despite this, it has been found that most incidents of debris flow were caused by rainfall above the level of landslide watch, maximum hourly rainfall, extensive damage was caused even under the watch level. Under these circumstances, we calculated a rainfall triggering index (RTI) using the main factors that trigger debris flow-rainfall, rainfall intensity, and cumulative rainfall-to design a more sophisticated watch / warning criteria than those by the KFS. The RTI was classified into attention, caution, alert, and evacuation, and was assessed through the application of two debris flow incidents that occurred in Umyeon Mountain, Seoul, and Cheongju, Inje, causing serious damage and casualties. Moreover, we reviewed the feasibility of the RTI by comparing it with the KFS's landslide watch / warning criteria (KFS, 2012).

• Journal of Korea Water Resources Association
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• v.39 no.1 s.162
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• pp.47-57
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• 2006

This study derives an event-based tank model with a conceptual rainfall-infiltration process, modifying conventional tank models. The model comprises two serial tanks, one parallel tank and an infiltration regulating element. The infiltration process within the element is not represented as a function of only time, but as a function of soil moisture content for three possible cases owing to the relationship between rainfall intensity and infiltration capacity. This study considers the previous soil moisture condition of a watershed by using antecedent precipitation index. Six parameters of the model are identified by using the real coded genetic algorithm. The applicability and validity of the proposed model are assessed for the observed stormwater data from the research basin of the International Hydrological Program, the Pyeongchanggang River basin, Republic of Korea. The results computed streamflows show relatively good agreement with observed ones.

• Journal of Korea Water Resources Association
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• v.35 no.3
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• pp.331-344
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• 2002

This study is aimed at the development of a deterministic runoff model which can be used for flood runoff. The model is formulated by the watershed runoff model. Based on the assumptions that runoff system is nonlinear, the proposed watershed runoff model is the conceptual model. In the model structure, the conceptual model divides the runoff system into a surface structure and a subsurface structure corresponding to the surface flow, and inter flow and ground water flow respectively. The lag time effect of surface can be represented by the sub-tank of surface structure in the conceptual model. The parameter calibration of inter flow and ground water flow in the subsurface structure of the conceptual model is performed by separating the components with numeric filter The runoff coefficient($\alpha$$_2$) is expressed as the function of antecedent precipitation index(API). The parameters with the surface flow can be calibrated with the runoff coefficient($\alpha$$_1$ and $\alpha$/$_{11}$) in the conceptual model. In the conceptual model, an algorithm is developed to calibrate the parameters automatically based on efficiency criteria. The comparative study shows that simulated value from the conceptual model well agreed to observed value.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.587-596
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• 2014

Soil moisture is one of the most important interests in hydrological response and the interaction between the land surface and atmosphere. Estimation of Antecedent Wetness Conditions (AWC) which is soil moisture condition prior to a rainfall in the basin should be considered for rainfall-runoff prediction. In this study, Soil Wetness Index (SWI), Antecedent Precipitation Index ($API_5$), remotely sensed Soil Moisture ($SM_{rs}$), and 5 days ground Soil Moisture ($SM_{g5}$) were selected to estimate the AWC at four study area in the Korean Peninsula. The remotely sensed soil moisture data were taken from the AMSR-E soil moisture archive. The maximum potential retention ($S_{obs}$) was obtained from direct runoff and rainfall using Soil Conservation Service-Curve Number (SCS-CN) method by rainfall data of 2011 for each study area. Results showed the great correlations between the maximum potential retention and SWI with a mean correlation coefficient which is equal to -0.73. The results of time length representing the time scale of soil moisture showed a gap from region to region. It was due to the differences of soil types and the characteristics of study area. Since the remotely sensed soil moisture has been proved as reasonable hydrological variables to predict a wetness in the basin, it should be continuously monitored.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.37-37
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• 2022

유효우량 산정을 위하여 국내에서 주로 사용되는 모형은 NRCS-CN(Natural Resources Conservation Service - curve number) 모형으로, 유역의 유출 능력을 나타내는 유출곡선지수(runoff curve number, CN)와 같은 NRCS-CN 모형의 매개변수들은 관측 강우-유출자료 또는 토양도, 토지피복지도 등을 이용하여 유역마다 결정된 값이 사용되고 있다. 그러나 유역의 CN값은 유역의 토양 상태와 같은 환경적 조건에 따라 달라질 수 있으며, 이를 반영하기 위하여 선행토양함수조건(antecedent moisture condition, AMC)을 이용하여 CN값을 조정하는 방법이 사용되고 있으나, AMC 조건에 따른 CN 값의 갑작스런 변화는 유출량의 극단적인 변화를 가져올 수 있다. NRCS-CN 모형과 더불어 강우 손실량 산정에 많이 사용되는 모형으로 Green-Ampt 모형이 있다. Green-Ampt 모형은 유역에서 발생하는 침투현상의 물리적 과정을 고려하는 모형이라는 장점이 있으나, 모형에 활용되는 다양한 물리적인 매개변수들을 산정하기 위해서는 유역에 대한 많은 조사가 선행되어야 한다. 또한 이렇게 산정된 매개변수들은 유역 내 토양이나 식생 조건 등에 따른 여러 불확실성을 내포하고 있어 실무적용에 어려움이 있다. 따라서 본 연구에서는, 현재 사용되고 있는 강우손실 모형들의 매개변수를 추정하기 위한 방법을 제시하고자 하였다. 본 연구에서 제시하는 방법은 인공지능(AI) 기술 중 하나인 딥러닝(deep-learning) 기법을 기반으로 하고 있으며, 딥러닝 모형으로는 장단기 메모리(Long Short-Term Memory, LSTM) 모형이 활용되었다. 딥러닝 모형의 입력 데이터는 유역에서의 강우특성이나 토양수분, 증발산, 식생 특성들을 나타내는 인자이며, 모의 결과는 유역에서 발생한 총 유출량으로 강우손실 모형들의 매개변수 값들은 이들을 활용하여 도출될 수 있다. 산정된 매개변수 값들을 강우손실 모형에 적용하여 실제 유역들에서의 유효우량 산정에 활용해보았으며, 동역학파 기반의 강우-유출 모형을 사용하여 유출을 예측해보았다. 예측된 유출수문곡선을 관측 자료와 비교 시 NSE=0.5 이상으로 산정되어 유출이 적절히 예측되었음을 확인했다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.128-128
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• 2022

최근 호우의 빈도와 규모는 증가하는 추세이며 이에 따른 홍수 피해는 많은 피해를 야기하고 있다. 이러한 관점에서 홍수 피해에 대한 선제적 대응을 위한 요소로써 초단시간 강우예측 정보의 중요성은 매우 높다. 특히, 레이더 자료 기반의 강우예측은 수치예보모델과 비교하여 3시간 이내의 짧은 선행시간 이내의 높은 정확도를 갖고 있어 홍수예보에 다수 활용되고 있다. 최근에는 강우자료의 복잡한 관계와 특징을 고려하기 위해 딥러닝 기반의 강우예측 활용 사례가 증가하고 있으나 국내 적용 사례는 적어 관련 연구가 요구되는 실정이다. 본 연구에서는 레이더 강우를 활용한 딥러닝 기반의 강우예측 기법을 제안하고 이에 대한 적용성을 평가하고자 한다. 2차원 레이더 강우자료의 특징과 시계열 특성을 고려하기 위한 심층신경망 구조를 제안하였으며 기존 딥러닝 모형과의 비교를 통해 활용 가능성을 제시하고자 하였다. 적용 대상지역은 한강 유역으로 선정하였다. 정성적 평가를 위해 임계성공지수(CSI)를 활용하여 예측 강우에 대한 정확도를 평가하였으며 정량적 평가를 위해 예측 강우와 관측 강우의 상관관계를 분석하였다. 평가 결과, 제안하는 방법이 기존 모형과 비교하여 예측오차의 범위가 적고 강우의 위치 변화를 잘 반영하는 것으로 나타났다. 본 연구결과는 초단기간 강우예측 자료를 활용하는 홍수예보의 정확도 향상에 기여할 것으로 기대된다.

• Journal of Wetlands Research
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• v.14 no.4
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• pp.519-535
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• 2012

In order to establish a rainfall-runoff model, calibration of hydrological parameters for the model is very important. Especially, Curve Number(CN), estimated by NRCS method, is a main factor to apply unit hydrograph theory to calculation of peak discharge. For using NRCS method, it is needed selecting AMC because CN is strongly connected with that. In this study, we focus our concern on finding a applicable standard for selecting AMC for CN. For this, three dams which are Boryeong, Habchon, Namgang are selected as target basins to use observed data including rainfall and dam inflow. As a result of this research, it is found that CN must be included as a calibrated parameter to calculate effective rainfall for the rainfall-runoff model. Also, it is preferred to use PWRMSE of HEC-HMS program as a objective function for optimizing hydrological parameters. From the analyzing result of variation of AMC for peak discharge, it is recommended to apply AMC-III to estimation of CN for calculating effective rainfall of design hydrograph.

• Journal of Wetlands Research
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• v.13 no.1
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• pp.35-44
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• 2011

In order to explore vegetation adaptation to climate variability and the impacts on water balance dynamics, the inter-regional and the inter-annual variability of both water availability and vegetation productivity are investigated. The Horton index, which is the ratio between actual evapotranspiration and catchment wetting as a measure of vegetation water use at catchment-scale, is revisited to quantify the effects of growing-season water availability on hydrologic partitioning at catchment scale. It is shown that the estimated Horton index is relatively constant irrespective of inter-annual climate variability. In addition, the Horton index is compared with catchment-scale vegetation rain use efficiency. The results show that there is an interesting pattern in the response of vegetation water use to water availability. When water becomes the limiting factor for vegetation productivity, the catchment-scale vegetation rain use efficiency converges to a common maximum value in agreement with earlier findings at the ecosystem level.