Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Application of High Resolution Multi-satellite Precipitation Products and a Distributed Hydrological Modeling for Daily Runoff Simulation

고해상도 다중위성 강수자료와 분포형 수문모형의 유출모의 적용

  • Kim, Jong Pil (Climate Research Department, APEC Climate Center) ;
  • Park, Kyung-Won (Climate Research Department, APEC Climate Center) ;
  • Jung, Il-Won (Climate Research Department, APEC Climate Center) ;
  • Han, Kyung-Soo (Department of Spatial Information Engineering, Pukyong National University) ;
  • Kim, Gwangseob (School of Architecture and Civil Engineering, Kyungpook National University)
  • 김종필 ((재) APEC 기후센터 연구본부) ;
  • 박경원 ((재) APEC 기후센터 연구본부) ;
  • 정일원 ((재) APEC 기후센터 연구본부) ;
  • 한경수 (부경대학교 공간정보시스템공학과) ;
  • 김광섭 (경북대학교 건축토목공학부)
  • Received : 2013.04.18
  • Accepted : 2013.04.23
  • Published : 2013.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this study we evaluated the hydrological applicability of multi-satellite precipitation estimates. Three high-resolution global multi-satellite precipitation products, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), the Global Satellite Mapping of Precipitation (GSMaP), and the Climate Precipitation Center (CPC) Morphing technique (CMORPH), were applied to the Coupled Routing and Excess Storage (CREST) model for the evaluation of their hydrological utility. The CREST model was calibrated from 2002 to 2005 and validated from 2006 to 2009 in the Chungju Dam watershed, including two years of warm-up periods (2002-2003 and 2006-2007). Areal-averaged precipitation time series of the multi-satellite data were compared with those of the ground records. The results indicate that the multi-satellite precipitation can reflect the seasonal variation of precipitation in the Chungju Dam watershed. However, TMPA overestimates the amount of annual and monthly precipitation while GSMaP and CMORPH underestimate the precipitation during the period from 2002 to 2009. These biases of multi-satellite precipitation products induce poor performances in hydrological simulation, although TMPA is better than both of GSMaP and CMORPH. Our results indicate that advanced rainfall algorithms may be required to improve its hydrological applicability in South Korea.

본 연구에서는 다중위성 강수자료의 수문학적 적용성을 평가하기 위하여 Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Center (CPC) Morphing technique(CMORPH) 등 전 지구 규모의 고해상도 다중위성 강수자료와 분포형 수문모형을 이용하여 유출모의를 수행하였다. 충주댐 유역에 대하여 2002년 1월 1일부터 2009년 12월 31일까지의 기간에 대하여 Coupled Routing and Excess Storage (CREST) 모형을 적용하였다. 분석기간은 준비기간(2002-2003년, 2006-2007년), 보정기간(2004-2005년), 그리고 검증기간(2008-2009년)으로 구분하여 모의를 수행하였다. 각 다중위성 강수자료를 지상관측자료와 비교결과, 강수의 계절적 변동특성은 잘 반영하고 있으나 연강수량합계 및 월평균강수량에서 TMPA는 과대추정을, GSMaP과 CMORPH는 과소추정하는 경향을 보여주었다. 또한 유출분석결과, TMPA를 제외한 GSMaP과 CMORPH의 충주댐 유역에 대한 수문학적 적용성이 매우 낮은 것을 알 수 있었으며, 향후 다중위성 강수자료의 활용에 앞서 통계적 보정이나 강수알고리즘에 대한 개선이 필요한 것으로 판단된다.

Keywords

References

  1. Allen, R., L.S. Pereira, D. Raes, and M. Smith, 1998. Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56, Rome, Italy.
  2. Bitew, M.M. and M. Gebremichael, 2011. Assessment of satellite rainfall products for streamflow simulation in medium watersheds of the Ethiopia highlands, Hydrology and Earth System Sciences, 15: 1147-1155. https://doi.org/10.5194/hess-15-1147-2011
  3. Bonnifait, L., G. Delrieu, M.L. Lay, B. Boudevillain, A. Masson, P. Belleudy, E. Gaume, and G.M. Saulnier, 2009. Distributed hydrologic and hydraulic modelling with radar rainfall input: Reconstruction of the 8-9 September 2002 catastrophic flood event in the Gard region, France, Advances in Water Resources, 32: 1077-1089. https://doi.org/10.1016/j.advwatres.2009.03.007
  4. Hong, Y., R.F. Adler, F. Hossain, S. Curtis, and G.J. Huffman, 2007. A first approach to global runoff simulation using satellite rainfall estimation, Water Resources Research, 43, W08502, doi: 10.1029/2006WR005739.
  5. Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P. Bowman, Y. Hong, E.F. Stocker, and D.B. Wolff, 2007. The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales, Journal of Hydrolometeorology, 8: 38-55. https://doi.org/10.1175/JHM560.1
  6. Jee, J.-B. and K.-T. Lee, 2010. Estimation of rainfall intensity for MTSAT-1R data using microwave rainfall, Korean Journal of Remote Sensing, 26(5): 511-525. https://doi.org/10.7780/kjrs.2010.26.5.511
  7. Joyce, R.J., J.E. Janowiak, P.A. Arkin, and P. Xie, 2004. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution, Journal of Hydrolometeorology, 5: 487-503. https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2
  8. Khan, S.I., Y. Hong, J. Wang, K.K. Yilmaz, J.J. Gourley, R.F. Adler, G.R. Brakenridge, F. Policelli, S. Habib, and D. Irwin, 2011. Satellite remote sensing and hydrologic modeling for flood inundation mapping in Lake Victoria Basin: Implications for hydrologic prediction in ungauged basin, Transactions on Geoscience and Remote Sensing, 49(1): 85-95. https://doi.org/10.1109/TGRS.2010.2057513
  9. Kubota, T., S. Shige, H. Hashizume, K. Aonashi, N. Takahashi, S. Seto, M. Hirose, Y.N. Takayabu, K. Nakagawa, K. Iwanami, T. Ushino, M. Kachi, and K. Okamoto, 2007. Global precipitation map using satelliteborne microwave radiometers by the GSMaP project: Production and validation, IEEE Transactions on Geoscience and Remote Sensing, 45(7): 2259-2275. https://doi.org/10.1109/TGRS.2007.895337
  10. Kummerow, C., W.S. Olson, and L. Giglio, 1996. A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors, IEEE Transactions on Geoscience and Remote Sensing, 34: 1213-1232. https://doi.org/10.1109/36.536538
  11. Li, X.H., Q. Zhang, and C.Y. Xu, 2012. Suitability of the TRMM satellite rainfalls in driving a distributed hydrological model for water balance computations in Xinjiang catchment, Poyang lake basin, Journal of Hydrology, 426-427: 28-38. https://doi.org/10.1016/j.jhydrol.2012.01.013
  12. Liang, X., D.P. Lettenmaier, E.F. Wood, and S.J. Burges, 1994. A simple hydrologically based model of land surface water and energy fluxes for general circulation models, Journal of Geophysical Research, 99(D7): 14415-14428. https://doi.org/10.1029/94JD00483
  13. Likolopoulos, E.I., E.N. Anagnostou, and M. Borga, 2013. Using high-resolution satellite rainfall products to simulate a major flash flood event in Northern Italy, Journal of Hydrolometeorology, 14: 171-185. https://doi.org/10.1175/JHM-D-12-09.1
  14. Monteith, J.L., 1985, Evaporation from land surfaces: progress in analysis and prediction since 1948, Advances in Evapotranspiration, Proceedings of the ASAE Conference on Evapotranspiration, ASAE, St. Joseph, Michigan: Chicago: 4-12.
  15. Olson, W.S., C. Kummerow, Y. Hong, and W.-K. Tao, 1999. Atmospheric latent heating distributions in the Tropics derived from satellite passive microwave radiometer measurements, Journal of Applied Meteorology, 38: 633-664. https://doi.org/10.1175/1520-0450(1999)038<0633:ALHDIT>2.0.CO;2
  16. Stigen, S., and I. Sandholt, 2010. Evaluation of remotesensing- based rainfall products through predictive capability in hydrological runoff modelling, Hydrological Processes, 24: 879-891. https://doi.org/10.1002/hyp.7529
  17. Thornthwaite, C. W., 1948. An approach towards a rational classification of climate, Geographical Review, 38: 55-94. https://doi.org/10.2307/210739
  18. Tobin, K.J. and M.E. Bennett, 2010. Adjusting satellite precipitation data to facilitate hydrologic modeling, Journal of Hydrometeorology, 11: 966-978. https://doi.org/10.1175/2010JHM1206.1
  19. Ushino, T., T. Kubota, S. Shige, K. Okamoto, K. Aonashi, T. Inouge, N. Takahashi, T. Iguchi, M. Kachi, R. Oki, T. Morimoto, and Z. Kawasaki, 2009. A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data, Journal of the Meteorological Society of Japan, 87(A): 137-151.
  20. Vieux, B.E. 2004. Distributed Hydrologic Modeling using GIS, ISBN 0-7923-7002-3, Kluwer Academic Publisher, Norwell, Massachusetts, Water Science and Technology Library, 48: 293.
  21. Wang, J., Y. Hong, L. Li, J.J. Gourley, S.I. Khan, K.K. Yilmaz, R.F. Adler, F.S. Policeli, S. Habib, D. Irwin, A.S. Limaye, T. Korme, and L. Okello, 2011. The coupled routing and excess storage (CREST) distributed hydrological model, Hydrological Science Journal, 56(1): 84-98. https://doi.org/10.1080/02626667.2010.543087
  22. Weng, F., L. Zhao, R. Ferraro, G. Poe, X. Li, and N. Grody, 2003. Advanced microwave sounding unit cloud and precipitation algorithm, Radio Science, 38: 8068-8079.
  23. Wu, H., R.F. Adler, Y. Hong, Y. Tian, and F. Policelli, 2012. Evaluation of global flood detection using satellite-based rainfall and a hydrologic model, Journal of Hydrometeorology, 13: 1268-1284. https://doi.org/10.1175/JHM-D-11-087.1
  24. Zhao, R.J. and X.R. Liu, 1995. The Xinanjiang model, Computer Models of Watershed Hydrology, V.P. Singh, Ed., Water Resources Publications: 215-232.
  25. Zhao, L. and F. Weng, 2002. Retrieval of ice cloud parameters using the Advanced Microwave Sounding Unit, Journal of Applied Meteorology, 41: 384-395. https://doi.org/10.1175/1520-0450(2002)041<0384:ROICPU>2.0.CO;2

Cited by

  1. Validation of Multi-satellite Precipitation Products for Assessing their Potential Utility of Hydrological Application Over South Korea vol.14, pp.2, 2014, https://doi.org/10.9798/KOSHAM.2014.14.2.85
  2. Urbanization and Quality of Stormwater Runoff: Remote Sensing Measurements of Land Cover in an Arid City vol.30, pp.3, 2014, https://doi.org/10.7780/kjrs.2014.30.3.6
  3. Validation of Extreme Rainfall Estimation in an Urban Area derived from Satellite Data : A Case Study on the Heavy Rainfall Event in July, 2011 vol.47, pp.4, 2014, https://doi.org/10.3741/JKWRA.2014.47.4.371
  4. Combining rainfall data from rain gauges and TRMM in hydrological modelling of Laotian data-sparse basins vol.7, pp.3, 2017, https://doi.org/10.1007/s13201-015-0330-y
  5. Hydrological Utility and Uncertainty of Multi-Satellite Precipitation Products in the Mountainous Region of South Korea vol.8, pp.7, 2016, https://doi.org/10.3390/rs8070608
  6. 다중 위성 강우자료를 이용한 유출 평가 vol.17, pp.1, 2013, https://doi.org/10.11108/kagis.2014.17.1.107
  7. 위성 강우자료를 이용한 북한지역 홍수량 추정 vol.18, pp.4, 2013, https://doi.org/10.11108/kagis.2015.18.4.031
  8. Impact of Trend Estimates on Predictive Performance in Model Evaluation for Spatial Downscaling of Satellite-based Precipitation Data vol.33, pp.1, 2017, https://doi.org/10.7780/kjrs.2017.33.1.3
  9. 위성 강우자료를 이용한 해외 유역 홍수량 추정 - 모로코 세부강 유역을 대상으로 - vol.20, pp.3, 2017, https://doi.org/10.11108/kagis.2017.20.3.141