• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.183-183
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• 2020

Accurate quantitative precipitation estimation plays an important role in hydrological modelling and prediction. Instantaneous quantitative precipitation estimation (QPE) by utilizing the weather radar data is a great applicability for operational hydrology in a catchment. Previously, regression technique performed between reflectivity (Z) and rain intensity (R) is used commonly to obtain radar QPEs. A novel, recent approaching method which might be applied in hydrological area for QPE is Long Short-Term Memory (LSTM) Networks. LSTM networks is a development and evolution of Recurrent Neuron Networks (RNNs) method that overcomes the limited memory capacity of RNNs and allows learning of long-term input-output dependencies. The advantages of LSTM compare to RNN technique is proven by previous works. In this study, LSTM networks is used to estimate the quantitative precipitation from weather radar for an urban catchment in South Korea. Radar information and rain-gauge data are used to evaluate and verify the estimation. The estimation results figure out that LSTM approaching method shows the accuracy and outperformance compared to Z-R relationship method. This study gives us the high potential of LSTM and its applications in urban hydrology.

• 대기
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• 제24권3호
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• pp.433-444
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• 2014

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2012년도 학술발표회
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• pp.200-204
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• 2012

In engineering hydrology, an estimation of precipitation loss is one of the most important issues for successful modeling to forecast flooding or evaluate water resources for both surface and subsurface flows in a watershed. An accurate estimation of precipitation loss is required for successful implementation of rainfall-runoff models. Precipitation loss or hydrological abstraction may be defined as the portion of the precipitation that does not contribute to the direct runoff. It may consist of several loss elements or abstractions of precipitation such as infiltration, depression storage, evaporation or evapotranspiration, and interception. A composite loss rate model that combines four loss rates over time is derived as a lumped form of a continuous time function for a storm event. The composite loss rate model developed is an exponential model similar to Horton's infiltration model, but its parameters have different meanings. In this model, the initial loss rate is related to antecedent precipitation amounts prior to a storm event, and the decay factor of the loss rate is a composite decay of four losses.

• 한국습지학회지
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• 제7권3호
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• pp.41-48
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• 2005

우리나라에서 유효우량의 산정방법으로 NRCS(Natural Resources Conservation Service)의 유효우량 산정방법이 널리 사용되고 있다. 그러나 NRCS 방법은 미국내 유역의 특성을 반영하여 개발된 것으로 미국과 우리나라 유역간의 차이에 대한 별도의 검증 없이 이를 그대로 사용하는 데에는 문제가 있을 것으로 예상된다. 따라서 본 연구는 우리나라 유역에서 NRCS 방법을 적용할 때에 적합한 선행강수일에 대하여 검토해보고자 한다. 이를 위하여 본 연구는 선행강수일수를 1일부터 7일까지 변화시켜 가면서 HEC-HMS 모형을 사용해 강우-유출 모의를 수행하여 탄부 소유역에 대해 가장 적절한 선행강우일수를 추정하였다. 그 결과, 선행2일강수량이 가장 적합한 것으로 나타났으며, 결론적으로, 본 연구의 결과에 의하면 NRCS 방법을 우리나라 유역에 적용할 때에는 세심한 주의가 필요할 것으로 사료된다.

• 한국환경과학회지
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• 제13권12호
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• pp.1033-1039
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• 2004

The methodology developed by Soil Conservation Service for determination of runoff value from precipitation is applied to estimate the precipitation recharge in the Pyungchang river basin. Two small areas of the basin are selected for this study. The CN values are determined by considering the type of soil, soil cover and land use with the digital map of 1:25,000. Forest covers more than $94{\%}$ of the study area.. The CN values for the study area vary between 47 in the forest area and 94 in the bare soil under AMC 2 condition. The precipitation recharge rate is calculated for the year when the precipitation data is available since 1990. To obtain the infiltration rate, the index of CN and five day antecedent moisture conditions are applied to each precipitation event during the study period. As a result of estimation, the value of precipitation recharge ratio in the study area vary between $15.2{\%}\;and\;35.7{\%}$ for the total precipitation of the year. The average annual precipitation recharge rate is $26.4{\%}\;and\;26.8{\%}$, meaning 377.9mm/year and 397.5mm/year in each basin.

• 한국수자원학회논문집
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• 제42권11호
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• pp.1017-1028
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• 2009

SRPM (Neyman-Scott Rectangular Pulse Model)은 수문학분야에서 널리 쓰이고 있는 강수생성모형이다. NSRPM을 구축하기 위해서는 총 5개의 매개변수를 추정하여야 한다. 일반적으로 사용되는 모멘트를 이용하여 매개변수를 추정할 경우, 사용되는 목적함수의 증가에 따라 추정되는 매개변수의 결과가 평탄해지고 목적함수를 추가하거나 조정하기 위해서는 복잡한 수식을 다시 계산해야 하며 추정된 매개변수가 무작위변수 생성 모형에 따라 상이한 결과를 나타내는 단점이 있다. 본 연구에서는 직접적인 매개변수 추정방법을 제시하여 모멘트를 이용한 매개변수 추정의 단점을 극복하고자 하였다. 직접적인 추정방법을 적용하기 위하여 NSRPM의 강수 생성 개수에 따른 통계치 변화를 모의하여 직접적인 추정을 위한 모형을 구축하였다. 기상청 청주 지상관측소의 관측 강수 자료를 사용하여 모멘트를 이용하여 추정된 매개변수와 직접적인 방법을 이용하여 매개변수를 추정하였다. 총 4 개의 무작위변수 알고리즘을 적용하여 강수를 생성하였고, 관측 강수 시계열을 이용하여 정확도를 비교하였다. 비교 결과 직접적인 추정방법이 모멘트를 이용한 매개변수 추정방법보다 안정적이고 높은 정확도를 보이는 매개변수를 추정하는 것으로 나타났다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• Journal of Multimedia Information System
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• 제5권4호
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• pp.245-256
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• 2018

This study estimated rainfall information more effectively by image signals through the information system of weather radar. Based on this, we suggest the way to estimate quantitative precipitation utilizing overlapped observation area of radars. We used the overlapped observation range of ground hyetometer observation network and radar observation network which are dense in our country. We chose the southern coast where precipitation entered from seaside is quite frequent and used Sungsan radar installed in Jeju island and Gudoksan radar installed in the southern coast area. We used the rainy season data generated in 2010 as the precipitation data. As a result, we found a reflectivity bias between two radar located in different area and developed the new quantitative precipitation estimation method using the bias. Estimated radar rainfall from this method showed the apt radar rainfall estimate than the other results from conventional method at overall rainfall field.

• 한국수자원학회논문집
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• 제44권2호
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• pp.135-144
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• 2011

직접적인 매개변수 추정방법의 다양한 Neyman-Scott 구형펄스모형(NSRPM) 기반 모형에 대한 적용성 검토와 정규분포를 이용한 새로운 NSRPM(NMNSRPM)의 개발 연구를 수행하였다. 기상청 서울 유인관측소에서 제공하는 49년의 관측 강수자료를 사용하여 매개변수를 추정하였으며, 추정된 매개변수들의 정확도를 판단하고자 생성된 강수자료의 통계값, 유강수일 비율, 강수분포를 비교하였다. 통계값을 비교해본 결과 NSRPM과 수정 NSRPM(MNSRPM)은 7-9월의 강수자료 통계값의 절대상대오차가 커지는 것을 확인할 수 있었으며, 절대상대오차가 10.11%로 NMNSRPM이 강수자료의 통계값를 가장 잘 모의한 것으로 나타났다. 유강수일 비율을 비교해본 결과 MNSRPM의 절대상대오차 평균이 16.35%로 가장 작은 절대상대오차 값을 보였고 그래프를 이용한 도시적인 분석법을 통하여 세 모형이 유강수일 비율을 과소추정하는 것을 확인하였다. 강수분포를 비교해본 결과 세 모형이 약 2% 내외의 절대상대오차를 보여 세 모형 모두 강수분포를 잘 모의하는 것을 확인 하였다. 직접적인 매개변수 추정방법으로 NSRPM, MNSRPM, NMNSRPM의 매개변수를 추정 할 수 있는 것을 확인 하였으며, 직접적인 매개변수 추정방법이 NSRPM 뿐만 아니라 이를 기반으로 한 다른 모형들의 매개변수도 추정할 수 있다는 것을 확인하였다. NMNSRPM의 모의 정확도를 비교한 결과 직접적인 매개변수 추정방법을 통한 NSRPM 기반의 새로운 모형에 대한 개발이 가능하다는 것을 확인할 수 있었으며, 모형의 성능이 기존 모형들과 비슷한 수준임을 확인하였다.

• 물과 미래
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• 제11권1호
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• pp.59-68
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• 1978

According to the simplified Gringorten's method of extreme values from data samples, daily maximum precipitation and return period at several stations in the central part of Korea were estimated. And also, it was known that the distribution of daily maximum precipitation of Sogcho, Chuncheon, Kangreung, Seoul, Inchon, Suwon, Seosan, Cheongju and Daejeon area belong to an exponential type of distribution.