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포아송 클러스터 가상강우생성 웹 어플리케이션 개발 및 검증 - 우리나라에 대해서

Development and validation of poisson cluster stochastic rainfall generation web application across South Korea

  • 한재문 (홍익대학교 대학원 토목공학과) ;
  • 김동균 (홍익대학교 공과대학 토목공학과)
  • 투고 : 2016.02.05
  • 심사 : 2016.02.29
  • 발행 : 2016.04.30

초록

본 연구에서는 포아송 클러스터 강우생성모형의 하나인 MBLRP 모형의 매개변수지도를 우리나라에 대하여 제작하고 이에 기반을 둔 가상강우생성 웹 어플리케이션을 개발 및 검증하였다. 이를 위하여 우리나라의 62개 ASOS 지상 강우 관측소에서 관측된 강우자료를 기반으로 서로 다른 수문모의의 목적(홍수량 모의, 장기 유출량 모의, 일반 모의)에 따른 MBLRP 모형의 매개변수지도를 산정한 후, 이를 Ordinary Kriging 기법을 통해 공간 보간하여 우리나라에 대한 매개변수지도를 제작하였으며, 이에 기반을 두고 가상강우 시계열을 생성하는 웹 어플리케이션을 개발하였다. 검증을 위하여 웹어플리케이션을 사용하여 가상강우를 생성한 후 평균, 분산, 자기상관계수, 무강우 확률, 극한강우량 및 다양한 유역에 대한 극한홍수량과 유출량을 계산하고 이를 관측 강우에 근거하여 산출된 값과 비교하였다. 비교 결과 가상 강우의 각종 통계값은 관측강우에 근거한 값과 매우 유사하게 나타났으나, 극한강우와 극한홍수는 관측치에 근거한 값과 비교하여 16%-40% 정도 과소산정되는 경향을 보였다. 이러한 결과는 교정계수로 활용할 수 있도록 등고선도의 형태로 제공되었다. 본 연구에서 개발한 웹 어플리케이션은 모형의 매개변수 산정부터 가상 강우 시계열 생성까지 일련의 과정을 포함하고 있어 강우자료를 필요로 하는 다양한 수문 분석에 활발히 활용될 것으로 기대된다.

This study produced the parameter maps of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) stochastic rainfall generation model across South Korea and developed and validated the web application that automates the process of rainfall generation based on the produced parameter maps. To achieve this purpose, three deferent sets of parameters of the MBLRP model were estimated at 62 ground gage locations in South Korea depending on the distinct purpose of the synthetic rainfall time series to be used in hydrologic modeling (i.e. flood modeling, runoff modeling, and general purpose). The estimated parameters were spatially interpolated using the Ordinary Kriging method to produce the parameter maps across South Korea. Then, a web application has been developed to automate the process of synthetic rainfall generation based on the parameter maps. For validation, the synthetic rainfall time series has been created using the web application and then various rainfall statistics including mean, variance, autocorrelation, probability of zero rainfall, extreme rainfall, extreme flood, and runoff depth were calculated, then these values were compared to the ones based on the observed rainfall time series. The mean, variance, autocorrelation, and probability of zero rainfall of the synthetic rainfall were similar to the ones of the observed rainfall while the extreme rainfall and extreme flood value were smaller than the ones derived from the observed rainfall by the degree of 16%-40%. Lastly, the web application developed in this study automates the entire process of synthetic rainfall generation, so we expect the application to be used in a variety of hydrologic analysis needing rainfall data.

키워드

참고문헌

  1. Abaurrea, J., and Cebrian, A.C. (2002). "Drought analysis based on a cluster Poisson model: distribution of the most severe drought." Climate Research. Vol. 22, No. 3, pp. 227-235. https://doi.org/10.3354/cr022227
  2. Bathurst, J.C., Moretti, G., El-Hames, A., Moaven-Hashemi, A., and Burton, A. (2005). "Scenario modelling of basin-scale, shallow landslide sediment yield, Valsassina, Italian Southern Alps." Natural Hazards and Earth System Science, Vol. 5, No. 2, pp. 189-202. https://doi.org/10.5194/nhess-5-189-2005
  3. Blazkov, S., and Beven, K. (1997). "Flood frequency prediction for data limited catchments in the Czech Republic using a stochastic rainfall model and TOPMODEL." Journal of Hydrology. Vol. 95, No. 1, pp. 256-278.
  4. Bo, Z., Islam, S., and Eltahir, E.A.B. (1994). "Aggregation-disaggregation properties of a stochastic rainfall model." Water Resources Research. Vol. 30, No. 12, pp. 3423-3435. https://doi.org/10.1029/94WR02026
  5. Burton, A., Fowler, H.J., Blenkinsop, S., and Kilsby, C.G. (2010). "Downscaling transient climate change using a Neyman-Scott Rectangular Pulses stochastic rainfall model." Journal of Hydrology. Vol. 381, No. 1, pp. 18-32. https://doi.org/10.1016/j.jhydrol.2009.10.031
  6. Burton, A., Kilsby, C.G., Fowler, H.J., Cowpertwait, P.S.P., and O'Connell, P.E. (2008). "RainSim: A spatial-temporal stochastic rainfall modelling system." Environmental Modelling & Software. Vol. 23, No. 12, pp. 1356-1369. https://doi.org/10.1016/j.envsoft.2008.04.003
  7. Camici, S., Tarpanelli, A., Brocca, L., Melone, F., and Moramarco, T. (2011). "Design soil moisture estimation by comparing continuous and storm-based rainfall-runoff modeling." Water Resources Research. Vol. 47, No. 5,
  8. Cho, H., Kim, D., Olivera, F., and Guikema, S.D. (2011). "Enhanced speciation in particle swarm optimization for multi-modal problems." European Journal of Operational Research. Vol. 231, No. 1, pp. 15-23.
  9. Chun, K.P., Wheater, H.S., and Onof, C. (2013). "Comparison of drought projections using two UK weather generators." Hydrological Sciences Journal. Vol. 58, No. 2, pp. 295-309. https://doi.org/10.1080/02626667.2012.754544
  10. Chung, C.H., and Salas, J.D. (2000). "Drought occurrence probabilities and risks of dependent hydrologic processes." Journal of Hydrologic Engineering. Vol. 5, No. 3, pp. 259-268. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:3(259)
  11. Cowden, J.R., Watkins Jr, D.W., and Mihelcic, J.R. (2008). "Stochastic rainfall modeling in West Africa: parsimonious approaches for domestic rainwater harvesting assessment." Journal of hydrology. Vol. 361, No. 1, pp. 64-77. https://doi.org/10.1016/j.jhydrol.2008.07.025
  12. Cowpertwait, P.S.P., O'Connell, P.E., Metcalfe, A.V., and Mawdsley, J.A. (1996). "Stochastic point process modelling of rainfall. I. Single-site fitting and validation." Journal of Hydrology. Vol. 175, No. 1, pp. 17-46. https://doi.org/10.1016/S0022-1694(96)80004-7
  13. Fatichi, S., Ivanov, V.Y., and Caporali, E. (2011). "Simulation of future climate scenarios with a weather generator." Advances in Water Resources. Vol. 34, No. 4, pp. 448-467. https://doi.org/10.1016/j.advwatres.2010.12.013
  14. Glasbey, C.A., Cooper, G., and McGechan, M.B. (1995). "Disaggregation of daily rainfall by conditional simulation from a point-process model." Journal of Hydrology. Vol. 165, No. 1, pp. 1-9. https://doi.org/10.1016/0022-1694(94)02598-6
  15. Hanaish, I.S., Ibrahim, K., and Jemain, A.A. (2013). "On the Applicability of Bartlett Lewis Model: With Reference to Missing Data." MATEMATIKA. Vol. 29, pp. 53-65.
  16. Islam, S., Entekhabi, D., Bras, R.L., and Rodriguez-Iturbe, I. (1990). "Parameter estimation and sensitivity analysis for the modified Bartlett-Lewis rectangular pulses model of rainfall." Journal of Geophysical Research: Atmospheres (1984-2012). Vol. 95, No. D3, pp. 2093-2100. https://doi.org/10.1029/JD095iD03p02093
  17. Journel, A.G., Huijbregts, C.J., (1978). Mining Geostatistics, Academic Press, London.
  18. Kavvas, M.L., and Delleur, J.W. (1975). "The stochastic and chronologic structure of rainfall sequences: Application to Indiana." Indiana: Purdue University.
  19. Kigobe, M., McIntyre, N., Wheater, H., and Chandler, R. (2011). "Multi-site stochastic modelling of daily rainfall in Uganda." Hydrological sciences journal. Vol. 56, No. 1, pp. 17-33. https://doi.org/10.1080/02626667.2010.536548
  20. Kim, D., and Olivera, F. (2012). Relative importance of the different rainfall statistics in the calibration of stochastic rainfall generation models. Journal of Hydrologic Engineering, Vol. 17, No. 3, pp. 368-376. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000453
  21. Kim, D., Shin, J., Lee, S., and Kim, T. (2013a). "The Application of the Poisson Cluster Rainfall Generation Model to the Flood Analysis." Journal of Korea Water Resources Association. Vol. 46, No. 5, pp. 439-447. https://doi.org/10.3741/JKWRA.2013.46.5.439
  22. Kim, D., Olivera, F., Cho, H., and Socolofsky, S.A. (2013b). "Regionalization of the Modified Bartlett-Lewis Rectangular Pulse Stochastic Rainfall Model." Terrestrial, Atmospheric & Oceanic Sciences. Vol. 24, No. 3, pp. 421-436. https://doi.org/10.3319/TAO.2012.11.12.01(Hy)
  23. Korea Meteorological Administration. (2015). 2014 Climate Change Report.
  24. Ministry of Public Safety and Security. (2015). 2014 Disaster Yearbook.
  25. Onof, C., and Arnbjerg-Nielsen, K. (2009). "Quantification of anticipated future changes in high resolution design rainfall for urban areas." Atmospheric research. Vol. 92, No. 3, pp. 350-363. https://doi.org/10.1016/j.atmosres.2009.01.014
  26. Onof, C., and Wheater, H.S. (1994). "Improvements to the modelling of British rainfall using a modified random parameter Bartlett-Lewis rectangular pulse model." Journal of Hydrology. Vol. 157, No. 1, pp. 177-195. https://doi.org/10.1016/0022-1694(94)90104-X
  27. Onof, C., Faulkner, D., and Wheater, H.S. (1996). "Design rainfall modelling in the Thames catchment." Hydrological sciences journal. Vol. 41, No. 5, pp. 715-733. https://doi.org/10.1080/02626669609491541
  28. Park, H., Yang. J., Han. J., and Kim. D. (2015). "Application of the Poisson Cluster Rainfall Generation Model to the Urban Flood Analysis." Journal of Korea Water Resources Association, Vol. 48, No. 9, pp. 729-741. https://doi.org/10.3741/JKWRA.2015.48.9.729
  29. Rodriguez-Iturbe, I., Cox, D.R., and Isham, V. (1988). "A point process model for rainfall: further developments." Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences. Vol. 417, No. 1853, pp. 283-298. https://doi.org/10.1098/rspa.1988.0061
  30. Tucker, G.E. (2004). "Drainage basin sensitivity to tectonic and climatic forcing: Implications of a stochastic model for the role of entrainment and erosion thresholds." Earth Surface Processes and Landforms. Vol. 29, No. 2, pp. 185-205. https://doi.org/10.1002/esp.1020
  31. Tucker, G.E., and Bras, R.L. (2000). "A stochastic approach to modeling the role of rainfall variability in drainage basin evolution." Water Resources Research. Vol. 36, No. 7, pp. 1953-1964. https://doi.org/10.1029/2000WR900065
  32. Wheater, H.S., Chandler, R.E., Onof, C.J., Isham, V.S., Bellone, E., Yang, C., Lekkas, D., Lourmas, G., and Segond, M.L. (2005). "Spatial-temporal rainfall modelling for flood risk estimation." Stochastic Environmental Research and Risk Assessment. Vol. 19, No. , pp. 403-416. https://doi.org/10.1007/s00477-005-0011-8