Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Evaluation of High-Resolution QPE data for Urban Runoff Analysis

고해상도 QPE 자료의 도시유출해석 적용성 평가

  • Choi, Sumin (WISE Institute, Hankuk University of Foreign Studies) ;
  • Yoon, Seongsim (WISE Institute, Hankuk University of Foreign Studies) ;
  • Lee, Byongju (WISE Institute, Hankuk University of Foreign Studies) ;
  • Choi, Youngjean (WISE Institute, Hankuk University of Foreign Studies)
  • 최수민 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 윤성심 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 이병주 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 최영진 (한국외국어대학교 차세대도시농림융합기상사업단)
  • Received : 2015.02.25
  • Accepted : 2015.07.20
  • Published : 2015.09.30
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Abstract

In this study, urban runoff analyses were performed using high resolution Quantitative Precipitation Estimation (QPE), and variation of rainfall and runoff were analyzed to evaluate QPE data for urban runoff analysis. The five drainage districts (Seocho3, 4, 5, Yeoksam and Nonhyun) around Gangnam station were chosen as study area, the area is $7.4km^2$. Rainfall data from KMA AWS (34 stations), SKP AWS (156 stations) and Gwanduk radar were used for QPEs in Seoul area. Four types of QPE(QPE1: KMA AWS, QPE2: KMA+ SKP AWS, QPE3: Gwangduk radar, QPE4: QPE2+QPE3) of 6 events in July 2013 were generated by using Krigging and conditional merging. The temporal and spatial resolution of QPEs are 10 minutes and 250 m, respectively. The complex pipe network were treated as 773 manholes, 772 sub-drainage districts and 1,059 pipelines for urban runoff analysis as input data. QPE2 and QPE4 show spatial variation of rainfall by sub-drainage districts as 1.9 times bigger than QPE1. The peak runoff of QPE2 and QPE4 also show spatial variation as 6 times bigger than Gangnam and Seocho AWS. Thus, the spatial variation of rainfall and runoff could exist in small area such as this study area, and using high-resolution rainfall data is desirable for accurate urban runoff analysis.

본 연구의 목적은 고해상도 정량적강수추정치(QPE)를 이용하여 도시유출해석을 수행하고 소배수분구별 강우와 유출량의 공간 변동성을 분석하여 적용성을 평가하는 것이다. 대상유역은 강남역을 중심으로 하는5개 배수분구(서초3, 4, 5, 역삼, 논현)을 선정하였으며, 유역면적은 $7.4km^2$이다. QPE 생산을 위해 KMA AWS (34소), SKP AWS (156소), 광덕산 레이더 자료를 통해 서울지역의 강우자료를 구축하였으며 크리깅 기법과 조건부합성 방법을 적용하여 4가지 QPE(QPE1: KMA AWS, QPE2: KMA+SKP AWS, QPE3: 광덕산 레이더, QPE4: QPE2+QPE3)를 생산하였다. 시공간 해상도는 10분, 250 m이며, 2013년 7월에 발생한 6개 호우를 대상으로 하였다. 복잡한 실제 관망을 도시유출해석모형에 입력하기 위해 773개 맨홀과 772개 소배수분구, 1,059개 하수관거로 재구성하여 분석을 수행하였다. QPE2와 QPE4는 QPE1에 비해 소배수구역별 면적강우량의 변동폭이 최대 1.9배까지 차이가 나타나 작은 유역에서도 강우공간변동성이 있음을 확인하였다. 또한 소배수 구역별 첨두유량 분석결과에서도 강남과 서초 AWS에 비해 QPE2와 QPE4의 변동폭이 최대 6배 큰 것으로 분석되었다. 따라서 본 연구의 대상지역과 같이 수 km2 이하의 도시유역에서도 강우와 첨두유량의 공간변동성이 발생함을 알 수 있었으며, 정확한 도시유출해석을 위해서는 고해상도 강우자료를 활용하는 것이 바람직한 것으로 판단된다.

Keywords

References

  1. Bedient, P.B., Hoblit, B.C., Gladwell, D.C., and Vieux, B.E. (2000). "NEXRAD radar for flood prediction in Houston." Journal of Hydrologic Engineering, ASCE, Vol. 5, No. 3, pp. 269-277. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:3(269)
  2. Clarke, B., Kudym, C., and Rindahl, B. (2009). "Gaugeadjusted radar rainfall estimation and basin averaged rainfall for use in local flash flood prediction and runoff modeling." Proceedings 23rd Conference on Hydrology, American Meteorological Society, Phoenix, A.Z.
  3. Kang, N.R., Noh, H.S., Lee, J.S., Lim, S.H., and Kim, H.S. (2013). "Runoff simulation of an urban drainage system using radar rainfall data." Journal of Wetlands Research, Korean Wetlands Society, Vol. 15, No. 3, pp. 413-422. https://doi.org/10.17663/JWR.2013.15.3.413
  4. Lee, B.J., Bae, D.H., and Jeong, C.S. (2003) "Runoff Curve Number Estimation for Cover and Treatment Classification of Satellite Image (II):-Application and Verification-." Journal of Korea Water Resources Association, KWRA, Vol. 36, No. 6, pp. 999-1012. https://doi.org/10.3741/JKWRA.2003.36.6.999
  5. National Emergency Management Agency (2011). Annual report of disasters in 2010.
  6. National Emergency Management Agency (2012). Annual report of disasters in 2011.
  7. Noh, H.S., Kang, N.R., Kim, B.S., and Kim, H.S. (2012). "Flood simulation using Vflo and radar rainfall adjustment data by statistical objective analysis." Journal of Wetlands Research, Korean Wetlands Society, Vol. 14, No. 2, pp. 243-254. https://doi.org/10.17663/JWR.2012.14.2.243
  8. Park, J.H., Kang, H.S., Lee, G.S., and Lee, E.R. (2007). "Flood runoff analysis using radar rainfall and Vflo model for Namgang dam watershed." Journal of the Korean Association of Geographic Information Studies, Korean Association of Geographic Information Studies, Vol. 10, No. 3, pp. 13-21.
  9. Sinclair, S., and Pegram, G. (2005). "Combining radar and rain gauge rainfall estimates using conditional merging." Atmospheric Science Letters, Royal Meteorological Society, Vol. 6, pp. 19-22. https://doi.org/10.1002/asl.85
  10. Vieux, B.E., Bedient, P.B., and Mazroi, E. (2005) "Realtime urban runoff simulation using radar rainfall and physics-based distributed modeling for site-specific forecasts." 10th International Conference on Urban Drainage, IAHR/IWA, Copenhagen, Denmark, Vol. 1, pp. 1-8.
  11. Yoon, S.S., and Bae, D.H. (2013) "Optimal Rainfall Estimation by Considering Elevation at the Han River Basin, South Korea." Journal of Applied Meteorology and Climatology, American Meteorological Society, Vol. 52, pp. 802-818. https://doi.org/10.1175/JAMC-D-11-0147.1
  12. Yoon, S.S., Bae, D.H., and Choi, Y.J. (2014) "Urban inundation forecasting using predicted radar rainfall: case study." Journal of Korean Society of Hazard Mitigation, Korean Society of Hazard Mitigation, Vol. 14, No. 3, pp. 117-126. https://doi.org/10.9798/KOSHAM.2014.14.3.117
  13. Yoon, S.S., Lee, B.J., and Choi, Y.J. (2015a) "Deduction of data quality control strategy for high density rain gauge network in Seoul area." Journal of Korea Water Resources Association, KWRA. Vol. 48, No. 4, pp. 245-255.
  14. Yoon S.S., Lee, B.J., and Choi, Y.J. (2015b) "Quantitative precipitation estimation using high density rain gauge network in Seoul area." Atmosphere, Korean Meteorological Society, Vol. 25, No. 2, pp. 283-294. https://doi.org/10.14191/Atmos.2015.25.2.283

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