Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Evaluation of GPM satellite and S-band radar rain data for flood simulation using conditional merging method and KIMSTORM2 distributed model

조건부합성 기법과 KIMSTORM2 분포형 수문모형을 이용한 GPM 위성 강우자료 및 Radar 강우자료의 홍수모의 평가

  • Kim, Se Hoon (Department of Civil, Environmental and Plant Engineering, Konkuk University) ;
  • Jung, Chung Gil (Texas A&M AgriLife Research Center at El Paso) ;
  • Jang, Won Jin (Department of Civil, Environmental and Plant Engineering, Konkuk University) ;
  • Kim, Seong Joon (Department of Civil, Environmental and Plant Engineering, Konkuk University)
  • 김세훈 (건국대학교 공과대학 사회환경플랜트공학과) ;
  • 정충길 ;
  • 장원진 (건국대학교 공과대학 사회환경플랜트공학과) ;
  • 김성준 (건국대학교 대학원 사회환경플랜트공학과)
  • Received : 2018.08.10
  • Accepted : 2018.11.05
  • Published : 2019.01.31
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Abstract

This study performed to simulate the watershed storm runoff using data of S-band dual-polarization radar rain, GPM (Global Precipitation Mission) satellite rain, and observed rainfall at 21 ground stations operated by KMA (Korea Meteorological Administration) respectively. For the 3 water level gauge stations (Sancheong, Changchon, and Namgang) of NamgangDam watershed ($2,293km^2$), the KIMSTORM2 (KIneMatic wave STOrm Runoff Model2) was applied and calibrated with parameters of initial soil moisture contents, Manning's roughness of overland and stream to the event of typhoon CHABA (82 mm in watershed aveprage) in $5^{th}$ October 2016. The radar and GPM data was corrected with CM (Conditional Merging) method such as CM-corrected Radar and CM-corrected GPM. The CM has been used for accurate rainfall estimation in water resources and meteorological field and the method combined measured ground rainfall and spatial data such as radar and satellite images by the kriging interpolation technique. For the CM-corrected Radar and CM-corrected GPM data application, the determination coefficient ($R^2$) was 0.96 respectively. The Nash-Sutcliffe efficiency (NSE) was 0.96 and the Volume Conservation Index (VCI) was 1.03 respectively. The CM-corrected data of Radar and GPM showed good results for the CHABA peak runoff and runoff volume simulation and improved all of $R^2$, NSE, and VCI comparing with the original data application. Thus, we need to use and apply the radar and satellite data to monitor the flood within the watershed.

본 연구에서는 비슬산 이중편파 Radar 자료와, GPM 위성자료 및 21개 (Korea Meteorological Administration, KMA) 지상강우자료를 활용하여 분포형 강우-유출 모형(KIneMatic wave STOrm Runoff Model2, KIMSTORM2)을 이용해 남강댐 유역($2,293km^2$)을 대상으로 유출해석을 수행하였다. 모형의 유출 해석은 2016년 10월 5일 02:00~09:00 총 8시간 동안 최대강우강도 33 mm/hr, 유역평균 총 강우량 82 mm이 발생한 태풍 차바(CHABA)를 대상으로 하였으며, Radar 및 GPM 자료와 조건부합성(Conditional Merging, CM) 기법을 적용한 Radar (CM-corrected Radar) 및 GPM (CM-corrected GPM) 자료를 각각 활용하여 결과를 비교하였다. 이 때, 공간 강우자료에 유출 검보정은 남강댐 유역 내 3개의 수위관측 지점(산청, 창촌, 남강댐)을 대상으로 실시하였으며, 모형의 매개변수 초기토양수분함량, 지표와 하천의 Manning 조도계수를 이용하여 검보정하였다. 유출 결과는 결정계수(Determination coefficient, $R^2$), Nash-Sutcliffe의 모형효율계수(NSE) 및 유출용적지수(Volume Conservation Index, VCI)를 산정하였다. 그 결과 CM-corrected Radar, GPM 자료가 평균 $R^2$는 0.96, NSE의 경우 0.96, 유출용적지수(VCI)는 1.03으로 가장 우수한 결과를 나타내었다. 최종적으로 CM 기법을 이용한 보정된 공간분포자료는 기존의 자료에 비해 시공간적으로 정확한 홍수 예측에 사용 될 것으로 판단된다.

Keywords

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Fig. 1. Flowchart of this study

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Fig. 2. Location of Namgang Dam watershed

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Fig. 3. GIS input data for KIMSTORM2 model (Jung et al., 2017)

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Fig. 4. The cumulative rainfall graphs and relationship between observed rainfall and Kriging, Radar and GPM rain data

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Fig. 5. The cumulative rainfall graphs and relationship between observed rainfall and CM-corrected Radar and CM-corrected GPM rain data

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Fig. 6. Comparison of spatial distributions of Radar, GPM, and CM results during the CHABA period (2016.10.05. 02:00~09:00)

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Fig. 7. Comparison of observed runoff and predicted hydrograph about Radar, GPM and CM method after model calibration: (a) Sancheong (SC), (b) Changcheon (CC) and (c) Namgang Dam (NR) during the CHABA period (2016.10.05. 02:00∼ 09:00)

Table 1. Selected model parameters for model calibration

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Table 2. Summary of model evaluation

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Table 3. Summary of model calibration

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