• Water for future
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• v.29 no.3
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• pp.187-195
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• 1996

In this study, a methodology for the hourly prediction of rainfall surfaces was applied to the Pyungchang river basin at the upstream of South Han river with meteorological radar and ground rainfall data. The methods for the exclusion of abnormal echoes, and suppression of ground clutter, and the augmentation of attenuation effects associated with rainfall phenomena were reviewed, and the relationship between radar reflectivity (Z) and rainfall rate (R) was analyzed. The transformation of augmented radar reflectivities into the rdar rainfall surfaces was carried out, and afterward they were synthesized with the ground rainfall data generating the hourly rainfall surfaces. For the prediction of hourly rainfall surface, the moving factors of rainfall field estimated by the cross correlation coefficient method and the temporal variation of radar rainfall intensities were considered. The synthesized hourly rainfall surfaces were used to predict the hourly rainfall surfaces up to 3 hours in advance and subsequently the results were compared with the measured and the synthesized. It seems that the prediction method need to be verified with more data and be complemented further to consider the physical characteristics of rainfall field and the topography of the basin.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.116-116
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• 2023

대규모 댐과 같은 수공구조물의 파괴시 상당한 피해가 발생하므로 구조물설계시 가능최대강수량(PMP) 기준이 적용된다. 포락선 방법은 가장 극심했던 강우량의 포락선을 작성하여 PMP를 산정하는 방법으로 기상 및 강수량자료가 부족시 PMP 추정이 어려운 경우에 사용한다. 포락선의 근사식은 지속시간의 거듭제곱인 멱함수 형태로 나타내며, 우리나라의 경우 1일을 전후로 계수와 차수가 다른 식을 사용한다. 이러한 근사식은 우리나라의 이상홍수 발생빈도 및 규모가 커짐에 따라 검토될 필요성이 있다. 또한, PMP 산정시 활용하는 제한된 수의 지상관측자료는 시공간적 변동성을 완전히 포착할 수 없어 한계가 있다. 본 연구는 이러한 한계를 극복하기 위하여 기상레이더 자료를 기반으로 우리나라 전역의 최대 강우깊이-지속시간 관계를 분석 및 새로운 PMP 포락선을 제시한다. 활용한 레이더는 CMAX(Column Maximum)로 2009~2018년간 10분 단위자료를 수집하였다. 레이더 자료와 비교하기 위하여 지상관측자료 AWS를 함께 수집하였다. AWS는 1997~2022년간 1분 단위자료로 우리나라 전역의 547개 지점관측자료를 활용하였다. 레이더자료는 Z-R 관계식으로 변환하여 가외치(outlier)를 제거 및 보정하였다. 그 후, 정규 크리깅기법으로 생성한 지상관측 강우장과 병합하는 CM(Conditional Merging)기법을 적용하였다. 우리나라 최대 강우깊이-지속시간 관계를 산정한 결과, 기존 포락선의 값이 낮게 산정되었음을 확인하였다. 이는 기후변화 등에 따라 최근 극한 호우가 발생한 것으로 판단된다. 또한, 실제 근사식은 멱함수 거동에서 벗어난 형태로 나타났고, 지점관측자료가 기상레이더 값보다 과소추정되는 경향을 확인하였다. 특히 같은 기간에서 확인하였을 때, 강우지속시간이 짧을수록 AWS값과 레이더자료의 강수량이 2배 정도 차이를 보여 지점관측소가 없는 지역의 국지성 호우 존재를 확인할 수 있었다. 추후, 미래에 더 긴 레이더 시계열을 사용한다면, 더욱 신뢰성 있는 자료로 활용할 수 있을 것으로 판단한다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.3
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• pp.348-356
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• 2014

The existing effective path-length model of ITU-R has some drawbacks: The prediction error is quite large compared to domestic measurement data and it is an empirical model in which the physical characteristics of rain cells are not considered. In this paper, a theoretical model for effective path-length using the rain-cell concept was proposed and its validity was verified using the measurement data. To analyze the statistical characteristics of rain cell parameters, the weather-radar data(CAPPI) measured by Korea Meterological Administration were analyzed and the correction factor was properly introduced to fit the Chollian beacon measurement data of ETRI(Electronics and Telecommunications Research Institute). To verify the proposed effective path-length model, it was compared with the Mugunghwa No. 5 beacon data measured in Chungnam National University with the support of ADD(Agency for Defense Development). It was confirmed that the prediction results of the proposed model are in good agreement with the measurement data.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.119-133
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• 2014

The study adopted extended Kalman filter technique in an effort to predict Z-R relationship parameter as a stable value in real-time. Toward this end, a parameter estimation model was established based on extended Kalman filter in consideration of non-linearity of Z-R relationship. A state-space model was established based on a study that was conducted by Adamowski and Muir (1989). Two parameters of Z-R relationship were set as state variables of the state-space model. As a result, a stable model where a divergence of Kalman gain and state variables are not generated was established. It is noteworthy that overestimated or underestimated parameters based on a conventional method were filtered and removed. As application of inappropriate parameters might cause physically unrealistic rain rate estimation, it can be more effective in terms of quantitative precipitation estimation. As a result of estimation on radar rainfall based on parameters predicted with the extended Kalman filter, the mean field bias correction factor turned out to be around 1.0 indicating that there was a minor difference from the gauge rain rate without the mean field bias correction. In addition, it turned out that it was possible to conduct more accurate estimation on radar rainfall compared to the conventional method.

• Journal of Korea Water Resources Association
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• v.44 no.1
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• pp.51-64
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• 2011

The objective of this study is to develop a continuous rainfall-runoff model for flood prediction on a large-scale basin. For this study, the hourly surface runoff estimation method based on the variable retention parameter and runoff curve number is developed. This model is composed that the soil moisture to continuous rainfall can be simulated with applying the hydrologic components to the continuous equation for soil moisture. The runoff can be simulated by linking the hydrologic components with the storage function model continuously. The runoff simulation to large basins can be performed by using channel storage function model. Nakdong river basin is selected as the study area. The model accuracy is evaluated at the 8 measurement sites during flood season in 2006 (calibration period) and 2007~2008 (verification period). The calibrated model simulations are well fitted to the observations. Nash and Sutcliffe model efficiencies in the calibration and verification periods exist in the range of 0.81 to 0.95 and 0.70 to 0.94, respectively. The behavior of soil moisture depending on the rainfall and the annual loadings of simulated hydrologic components are rational. From this results, continuous rainfall-runoff model developed in this study can be used to predict the discharge on large basins.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.383-383
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• 2018

최근 기후변화와 도시화로 인해 국지성 집중호우 및 불투수면적이 증가하고 있는 실정이며, 도시 지역 내의 첨두유량, 도달시간, 지체시간 등과 같은 수문학적 인자가 변화함에 따라 재산피해, 인명피해가 발생하고 있다. 저영향개발(Low Impact Development, LID) 기법은 수리수문학적 및 환경생태학적 문제를 저감하는 방안 중 하나로써 도시지역에서 수환경을 자연상태로 복원하는 대안으로 제시되고 있다. LID 기법 중 하나인 옥상녹화는 도시 내의 불투수면 증가로 인한 초과 지표면유출을 저감시켜 물관리를 하는 기술이다. 본 연구는 경남 양산시 부산대학교 제 2 캠퍼스에 조성된 옥상녹화 장치를 이용하여 정량적으로 유출량을 분석하였다. 비식생구와 식생구를 설치하고 실험의 시나리오는 강우강도를 25, 50, 75, 100 mm/hr로 설정하여 측정된 데이터 값을 바탕으로 SWMM(Storm Water Management Model) 모델링을 수행하였다. 유출량 값은 SWMM 5의 매개변수 추정지원 시스템인 SWMM-SCE를 이용하여 모형을 자동보정하였다. 보정된 모의유량은 실측유량과 0.28~3.81% 만큼의 오차를 보였고 각 시나리오에 따라 검증한 결과 상관계수가 0.82 이상으로서 실측값과 높은 상관성을 나타내었다. 옥상녹화 실험의 경우, 강우강도 75mm/hr일 때 첨두유출저감율과 지연시간은 각각 15.45% 감소, 15초 지연으로 최적의 효율이 나타났으며 강우강도 25mm/hr일 때 첨두유출저감율과 지연시간은 각각 1.36% 감소, 4초 지연으로 최저의 효율이 나타났다. SWMM 모의 결과는 강우강도 75mm/hr일 때 첨두유출저감율과 지연시간은 각각 15.45% 감소, 16초 지연으로 최적의 효율이 나타났으며 강우강도 25mm/hr일 때 첨두유출저감율과 지연시간은 각각 2.73% 감소, 4초 지연으로 최저의 효율이 나타났다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.114-114
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• 2021

본 연구에서는 Sentinel-1 SAR 센서 기반 이미지자료와 딥러닝기법을 이용하여 고해상도 토양수분을 산정하였다. 입력자료는 지표특성(모래함량, 점토함량, 경사도), 인공위성 기반의 강우와 LANDSAT 기반의 이미지자료(NDVI, LST, 공간분포 토양수분)를 사용하였다. 강우자료의 경우 GPM(Global Precipitation Measurement) 일강우 자료를 사용하였으며, 관측일 기준으로 5일전까지의 강우자료와 5일평균강우를 구분하여 사용하였다. LANDSAT 기반의 토양수분 이미지자료와 지점관측 토양수분을 이용하여 검·보정 이후 딥러닝 모형의 입력자료로 사용하였다. 입력자료는 30m × 30m 해상도로 Resample 하여 딥러닝 모형의 학습을 진행하였으며, 학습에 사용된 모형을 이용하여 Sentinel-1 기반의 고해상도(10m × 10m) 토양수분이미지를 산정하였다. 검증지점은 거창군 거창읍, 계룡시 두마면, 장수군 장수읍 및 무주군 무주읍 토양수분 관측지점을 선정하였다. 거창군 거창읍의 산정결과, LANDSAT 기반의 토양수분 이미지와 DNN 기반의 토양수분 이미지가 매우 유사하게 나타났으며, 모의값(DNN 기반 토양수분)이 실측값(LANDSAT 기반의 토양수분)을 잘 반영한 것(R: 0.875 ; RMSE: 0.013)으로 나타났다. 또한 학습모형을 토지피복이 유사한 지역에 적용하여 토양수분을 산정한 결과 검증지점 계룡시(R: 0.897 ; RMSE: 0.014), 장수군(R: 0.770 ; RMSE: 0.024) 및 무주군(R: 0.909 ; RMSE: 0.012)의 모의값이 실측값과 매우 유사한 것으로 나타났다. 이를 바탕으로 Seninel-1 SAR센서 이미지자료와 딥러닝기법을 연계한 고해상도 토양수분자료가 농업, 수문, 환경 등 다양한 분야에서 활용될 수 있을 것으로 판단된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.375-381
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• 2008

In this research, a physics based grid-multi layer distributed flood runoff model was developed to analyze discharge for the Namgang Dam Watershed ($2,293km^2$) and applied for sensitivity analysis for estimation of parameters, mainly initial soil moisture condition and saturate infiltration coefficient, which have a strong influence on discharge. Capability of the model was evaluated using VER and QER from the results of rainfall-runoff analysis and showed enhanced results of 6% compared to parameters before calibration. As the result with the sensitivity analysis of parameters, the part of the most influence on the runoff was the infiltration coefficient and ratio of layer partition. The total discharge and peak time showed comparatively precise runoff results without the initial calibration of the parameters.

• The Journal of Engineering Geology
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• v.23 no.2
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• pp.137-147
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• 2013

For the effective management of groundwater resources, it is necessary to predict groundwater level fluctuations in response to rainfall events. In the present study, time series models using artificial neural networks (ANNs) and support vector machines (SVMs) have been developed and applied to groundwater level data from the Gasan, Shingwang, and Cheongseong stations of the National Groundwater Monitoring Network. We designed four types of model according to input structure and compared their performances. The results show that the rainfall input model is not effective, especially for the prediction of groundwater recession behavior; however, the rainfall-groundwater input model is effective for the entire prediction stage, yielding a high model accuracy. Recursive prediction models were also effective, yielding correlation coefficients of 0.75-0.95 with observed values. The prediction errors were highest for Shingwang station, where the cross-correlation coefficient is lowest among the stations. Overall, the model performance of SVM models was slightly higher than that of ANN models for all cases. Assessment of the model parameter uncertainty of the recursive prediction models, using the ratio of errors in the validation stage to that in the calibration stage, showed that the range of the ratio is much narrower for the SVM models than for the ANN models, which implies that the SVM models are more stable and effective for the present case studies.

• Journal of Korea Water Resources Association
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• v.50 no.5
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• pp.303-313
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• 2017

In environmental impact assessments for large urban development projects, the Korean government requires analysis of stormwater runoff before, during and after the projects. Though hydrological models are widely used to analyze and prepare for surface runoff during storm events, accuracy of the predicted results have been in question due to limited amount of field data for model calibrations. Intensive field measurements have been made for storm events between July 2015 and July 2016 at a sub-basin of the Gwanpyung-cheon, Daejeon, Republic of Korea using an automatic monitoring system and also additional manual measurements. Continuous precipitation and surface runoff data used for utilization of SWMM model to predict surface runoff during storm events with improved accuracy. The optimal values for Manning's roughness coefficient and values for depression storage were estimated for pervious and impervious surfaces using three representative infiltration methods; the Curve Number Methods, the Horton's Method and the Green-Ampt Methods. The results of the research is expected to be used more efficiently for urban development projects in Korea.