• Journal of Korea Water Resources Association
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• v.45 no.7
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• pp.657-674
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• 2012

This paper presents the effect of spatially-distributed rainfall on both rainfall-sediment-runoff and erosion or deposition in the experimental Cheoncheon catchment: upstream of Yongdam dam basin. The rainfall fields were generated by three rainfall interpolation techniques (Thiessen polygon: TP, Inverse Distance Weighting: IDW, Kriging) based only on ground gauges and two radar rainfall synthetic techniques (Gauge-Radar ratio: GR, Conditional Merging: CM). Each rainfall field was then assessed in terms of spatial feature and quantity and also used for rainfall-sediment-runoff and erosion-deposition simulation due to the spatial difference of rainfall fields. The results showed that all the interpolation methods based on ground gauges provided very similar hydrologic responses in spite of different spatial pattern of erosion and deposition while raw radar and GR rainfall fields led to underestimated and overestimated simulation results, respectively. The CM technique was acceptable to improve the accuracy of raw radar rainfall for hydrologic simulation even though it is more time consuming to generate spatially-distributed rainfall.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1196-1200
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• 2010

본 논문에서는 지점 강우의 결측치를 추정하기 위해 전통적인 통계학적 내삽기법을 이용한 역거리가중치법(IDWM), 역지수가중치법(IEWM), 상관계수가중치법(CCWM)과 패턴 인식의 일종인 인공신경망(ANN)기법 그리고 시공간적 강우분포의 측정이 가능한 레이더 자료를 이용해 결측치를 추정하여 각각의 방법을 비교하였다. 임진강 유역의 15개 지상관측소를 대상으로 교차검정(Cross validation) 분석을 실시해 본 결과, CCWM 방법과 ANN기법에 의한 RMSE가 0.46~1.79의 범위를 보였고, 보정레이더를 이용하여 결측치를 추정한 경우RMSE가 0.05~2.26의 범위를 보여 기존의 전통적 결측치 추정방법보다 실측치에 가까운 결과를 보였다. 이는 레이더자료가 지점 강우자료와는 달리 강우의 시공간적 변동성을 고려한 공간분포의 정보를 지니고 있기 때문인 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.37 no.3
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• pp.233-240
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• 2004

Most of hydrological analyses in the field of water resources are launched by gathering and analyzing rainfall data. Several methods have been developed to estimate areal rainfall from point rainfall data and to fill missing or ungaged data. Thiessen and Reciprocal Distance Squared(RDS) methods whose parameters are only dependent on inter-station distance are classical work in hydrology, but these techniques do not provide a continuous representation of the hydrologic process involved. In this study, kriging technique was applied to rainfall analysis in Nakdong river basin in order to complement the defects of these classical methods and to reflect spatial characteristics of regional rainfall. After spatial correlation and semi-variogram analyses were performed to perceive regional rainfall property, kriging analysis was performed to interpolate rainfall data for each grid Thus, these procedures were enable to estimate average rainfall of subbasins. In addition, poor region of rainfall observation was analyzed by spatial interpolation error for each grid and mean error for each subbasin.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1053-1064
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• 2022

The Thiessen method, which is the current area average precipitation method, has serious structural limitations in accurately calculating the average precipitation in the watershed. In addition to the observation accuracy of the precipitation meter, errors may occur in the area average precipitation calculation depending on the arrangement of the precipitation meter and the direction of the heavy rain. When the watershed is small and the station density is sparse, in both simulation and observation history, the Thiessen method showed a peculiar tendency that the average precipitation in the watershed continues to increase and decrease rapidly for 10 minutes before and after the peak. And the average precipitation in the Thiessen basin was different from the rainfall radar at the peak time. In the case where the watershed is small but the station density is relatively high, overall, the Thiessen method did not show a trend of sawtooth-shaped over-peak, and the time-dependent fluctuations were similar. However, there was a continuous time lag of about 10 minutes between the rainfall radar observations and the ground precipitation meter observations and the average precipitation in the basin. As a result of examining the ground correction effect of the rainfall radar watershed average precipitation, the correlation between the area average precipitation after correction is rather low compared to the area average precipitation before correction, indicating that the correction effect of the current rainfall radar ground correction algorithm is not high.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.100-100
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• 2019

최근 대기 중 미세먼지의 농도가 높은 일수가 급증하면서, 미세먼지를 저감하고자 하는 연구가 활발히 이루어지고 있다. 미세먼지는 주로 자동차 혹은 공장 등 인간 활동에 의한 오염물질 배출에 의해 발생하는 것으로 알려져 있으며, 태양복사에너지, 토양수분, 강우, 풍속 등의 수문기상학적 인자에 의해 발생, 이동, 소멸의 과정을 거친다. 현재 우리나라에서는 미세먼지 농도를 관측하기 위해 지점 기반의 관측소를 운영하고 있으며, 관측소가 위치하지 않은 지역의 미세먼지 농도는 선형 보간법 등을 활용한 내삽 기법을 통해 제공하고 있다. 그러나 미세먼지 농도는 다양한 수문기상인자들의 영향에 의한 차이가 크게 나타나기 때문에 지점 기반의 자료로는 해당 지역의 미세먼지 농도를 추정하는 데 어려움이 많다. 본 연구에서는 미세먼지의 공간적인 분포를 추정하고자 MODerate resolution Imaging Spectroradiometer (MODIS) 에어로졸 자료와 Global Land Data Assimilation System (GLDAS) 수문기상인자를 활용하여 미세먼지 농도에 영향을 주는 것으로 판단되는 다양한 수문기상인자들과의 상관성을 분석하였다. 미세먼지와 각 인자간의 상관성을 분석하여 높은 상관성을 갖는 수문기상인자들을 도출하고 최적의 선형회귀분석 모델을 구축하기 위해 베이지안 모델 평균(Bayesian Model Averaging, BMA)을 사용하였으며, 지점 데이터와의 비교를 통해 활용성을 검증하였다. 전체적으로 수문기상인자를 사용한 선형회귀분석 결과에서는 미세먼지농도 변화의 경향을 반영하고 있는 것을 확인할 수 있었으나, 계절별, 지역별 등 대기 특성을 고려하지 않아 각 기간의 급격한 농도 변화를 감지하기에 어려움이 있었다. 이러한 연구를 바탕으로 수문기상인자와 미세먼지 농도의 패턴이 더욱 정확히 분석된다면, 미세먼지 농도 모니터링과 정확한 예보 시스템의 구축에 효과적으로 활용 될 것으로 기대된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.39-58
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• 2019

This study performed the dam watershed storm runoff modeling using GPM(Global Precipitation Measurement) satellite rain and KIMSTORM2(KIneMatic wave STOrm Runoff Model 2) distributed model. For YongdamDam watershed(930㎢), three heavy rain events of 25th August 2014, 11th September 2017, and 26th June 2018 were selected and tested for 4 cases of spatial rainfalls such as (a) Kriging interpolated data using ground observed data at 7 stations, (b) original GPM data, (c) GPM corrected by CM(Conditional Merging), and GPM corrected by GDA(Geographical Differential Analysis). For the 4 kinds of data(Kriging, GPM, CM-GPM, and GDA-GPM), the KIMSTORM2 was calibrated respectively using the observed flood discharges at 3 water level gauge stations(Cheoncheon, Donghyang, and Yongdam) with parameters of initial soil moisture contents, stream Manning's roughness coefficient, and effective hydraulic conductivity. The total average Nash-Sutcliffe efficiency(NSE) for the 3 events and 3 stations was 0.94, 0.90, 0.94, and 0.94, determination coefficient(R²) was 0.96, 0.92, 0.97 and 0.96, the volume conservation index(VCI) was 1.03, 1.01, 1.03 and 1.02 for Kriging, GPM, CM-GPM, and GDA-GPM applications respectively. The CM-GPM and GDA-GPM showed better results than the original GPM application for peak runoff and runoff volume simulations, and they improved NSE, R², and VCI results.