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Optimization of PRISM Parameters and Digital Elevation Model Resolution for Estimating the Spatial Distribution of Precipitation in South Korea

남한 강수량 분포 추정을 위한 PRISM 매개변수 및 수치표고모형 최적화

  • 박종철 (포트랜드주립대학교 지리학과) ;
  • 정일원 (포트랜드주립대학교 지리학과) ;
  • 장희준 (포트랜드주립대학교 지리학과) ;
  • 김만규 (공주대학교 지리학과)
  • Received : 2012.05.22
  • Accepted : 2012.08.02
  • Published : 2012.09.30

Abstract

The demand for a climatological dataset with a regular spaced grid is increasing in diverse fields such as ecological and hydrological modeling as well as regional climate impact studies. PRISM(Precipitation-Elevation Regressions on Independent Slopes Model) is a useful method to estimate high-altitude precipitation. However, it is not well discussed over the optimization of PRISM parameters and DEM(Digital Elevation Model) resolution in South Korea. This study developed the PRISM and then optimized parameters of the model and DEM resolution for producing a gridded annual average precipitation data of South Korea with 1km spatial resolution during the period 2000-2005. SCE-UA (Shuffled Complex Evolution-University of Arizona) method employed for the optimization. In addition, sensitivity analysis investigates the change in the model output with respect to the parameter and the DEM spatial resolution variations. The study result shows that maximum radius within which station search will be conducted is 67km. Minimum radius within which all stations are included is 31km. Minimum number of stations required for cell precipitation and elevation regression calculation is four. Optimizing DEM resolution is $1{\times}1km$. This study also shows that the PRISM output very sensitive to DEM spatial resolution variations. This study contributes to improving the accuracy of PRISM technique as it applies to South Korea.

생태환경모델링, 수문모델링, 기후변화 영향평가 등 다양한 분야에서 정규 격자 형태의 기후자료에 대한 요구가 증가하고 있다. PRISM(Precipitation-Elevation Regressions on Independent Slopes Model)은 다양한 격자형태의 기후자료 생산방법 중 고지대의 강수량 추정에 유용한 방법이다. 그러나 국내에서는 이 모델의 매개변수 및 모델에 사용되는 수치표고모형의 공간해상도 최적화에 대한 논의가 충분하지 않았다. 이에 본 연구에서는 PRISM을 개발하였다. 그리고 SCE-UA(Shuffled Complex Evolution-University of Arizona) 기법을 이용하여 2000-2005년 1km 공간해상도의 남한 연평균 강수 격자자료를 생산하는 데 필요한 PRISM 매개변수 최적값 및 DEM의 적정 공간해상도를 추정하였다. 아울러 매개변수와 수치표고모형에 대한 PRISM의 민감도 분석을 수행하였다. 그 결과 PRISM 모델에서 관측소 최대 탐색반경(67km)과 최소반경(31km), 지형고도-강수량의 선형회귀식 산정에 필요한 최소 관측소 개수(4개), 수치표고모형의 적정 공간해상도($1{\times}1km$) 등을 결정하였다. 그리고 PRISM 모의 결과가 수치표고모형의 공간해상도에 매우 민감하다는 것을 확인하였다. 본 연구결과는 PRISM 기법을 국내에 적용할 때 정확도를 향상시키는데 기여할 것으로 기대된다.

Keywords

References

  1. 김규범, 손영철, 김종욱, 이장룡. 2005. ArcView GIS의 Avenue(TM) Language를 활용한 수문지질도 작성 알고리즘 개발 및 적용 사례 연구. 한국지리정보학회지 8(3):107-120.
  2. 김맹기, 한명수, 장동호, 백승균, 이우섭, 김연희, 김성. 2012. 1km 해상도의 관측 격자자료 생산 기술. 기후연구 7(1):55-68.
  3. 김호용. 2010a. 공간통계기법을 이용한 도시교통량 예측의 정확성 향상. 한국지리정보학회지 13(4):138-147.
  4. 김호용. 2010b. 공간통계기법을 이용한 태양광 발전시설 입지 정확성 향상 방안. 한국지리정보학회지 13(2):146-156.
  5. 박노욱, 장동호. 2008. 수치표고모델과 다변량 크리깅을 이용한 기온 및 강수 분포도 작성. 대한지리학회지 43(6):1002-1015.
  6. 박종철, 김만규. 2009. 공동 크리깅을 이용한 강수 분포도 작성에서 지형 사면방향 변수 사용에 대한 연구: 제주도를 사례지역으로. 한국지형학회지 16(3):59-66.
  7. 박현주, 신휴석, 노영희, 김경민, 박기호. 2012. 크리깅 기법을 이용한 단양군의 산림 탄소저장량 추정-지상부 바이오매스를 대상으로-. 한국지리정보학회지 15(1):16-33. https://doi.org/10.11108/kagis.2012.15.1.016
  8. 신성철, 김맹기, 서명석, 나득균, 장동호, 김찬수, 이우섭, 김연희. 2008. GIS와 PRISM을 이용한 고해상도 격자형 강수자료 추정. 대기 18(1):71-81.
  9. 이상훈. 2008. 클러스터링과 지구통계학 기법을 이용한 지하공간정보 모델 생성시스템 개발. 한국지리정보학회지 11(4):64-75.
  10. 이재봉. 이홍로. 2005. 객체지향 데이터 모델에 기반한 해양환경 분석에 따른 어장 등급분류. 한국지리정보학회지 8(1):40-48.
  11. 이형석. 2010. 공간보간법의 매개변수 설정에 따른 평균제곱근 비교 및 평가. 한국지리정보학회지 13(3):29-41.
  12. 조홍래, 정종철. 2006. 강우자료에 대한 공간보간 기법의 적용. 한국GIS학회 14(1):29-41.
  13. Beven, K.J. and A.M. Binley. 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 6(3):279-298. https://doi.org/10.1002/hyp.3360060305
  14. Briggs, P. and J. Cogley. 1996. Topographic bias in mesoscale precipitation networks. Journal of Climatology 9:205-218. https://doi.org/10.1175/1520-0442(1996)009<0205:TBIMPN>2.0.CO;2
  15. Chang, H. and I-W. Jung. 2010. Spatial and temporal changes in runoff caused by climate change in a complex large river basin in Oregon. Journal of Hydrology 388(3-4):186-207. https://doi.org/10.1016/j.jhydrol.2010.04.040
  16. Daly, C., G.H. Taylor, W.P. Gibson, T.W. Parzybok, G.L. Johnson and P. Pasteris. 2001. High-quality spatial climate data sets for the United States and beyond. Transactions of the American Society of Agricultural Engineers 43:1957-1962.
  17. Daly, C. and G.L. Johnson. 1998. PRISM spatial climate layers: Climate mapping with PRISM. pp.1-49.
  18. Daly, C., J. Smith and R. McKane. 2007. High-resolution spatial modeling of daily weather elements for a catchment in the Oregon Cascade Mountains, USA. Journal of Applied Meteorology and Climatology 46:1565-1586. https://doi.org/10.1175/JAM2548.1
  19. Daly, C., R.P. Neilson and D.L. Phillips. 1994. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology 33(2):140-158. https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2
  20. Daly, C., T.G.F. Kittel, A. McNab, W.P. Gibson, J.A. Royle, D. Nychka, T. Parzybok, N. Rosenbloom and G. Taylor. 2000. Development of a 103-year highresolution climate data set for the conterminous United States. Proceedings of the 12th AMS Conference on Applied Climatology. American Meteorological Society: Asheville, NC, May 8-11, pp.249-252.
  21. Duan, Q., S. Sorooshian and V.K. Gupta. 1992. Effective and efficient global optimization for conceptual rainfallrunoff models. Water Resource Research 28(4):1015-1031. https://doi.org/10.1029/91WR02985
  22. Duan, Q., S. Sorooshian and V.K. Gupta. 1994. Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology 158:265-284. https://doi.org/10.1016/0022-1694(94)90057-4
  23. Gan, T.Y. and G.F. Biftu. 1996. Automatic calibration of conceptual rainfall-runoff models: optimization algorithms, catchment conditions, and model structure. Water Resource Research 32(12):3513-3524. https://doi.org/10.1029/96WR02195
  24. Goovaerts, P. 2000. Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology 228:113-129. https://doi.org/10.1016/S0022-1694(00)00144-X
  25. Hevesi, J., J. Istok and A. Flint. 1992. Precipitation estimation in mountainous terrain using multivariate geostatistics. Part I: structural analysis. Journal of Applied Meteorology 31:661-676. https://doi.org/10.1175/1520-0450(1992)031<0661:PEIMTU>2.0.CO;2
  26. Holland, J.H. 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. pp.1-183.
  27. Hooke, R. and T.A. Jeeves. 1961. Direct search solutions of numerical and statistical problems. Journal of Association for Computing Machinery 8(2):212-229. https://doi.org/10.1145/321062.321069
  28. Johnson, G.L., C. Daly, G.H. Taylor, C.L. Hanson and Y.Y. Lu, 1997. GEM model temperature and precipitation parameter variability, and distribution using PRISM. Proceeding of the 10th AMS Conforence. pp.210-214.
  29. Kittel, T.G.F., J.A. Royle, C. Daly, N.A. Rosenbloom, W.P. Gibson, H.H. Fisher, D.S. Schimel, L.M. Berliner and VEMAP2 Participants. 1997. A gridded historical (1895-1993) bioclimate dataset for the conterminous United States. Proceeding of the 10th AMS Conforence. pp.219-222.
  30. Krige, D.G. 1951. A statistical approach to some mine valuations and allied problems at the Witwatersrand, Master Thesis, Univ. of Witwatersrand. 272pp.
  31. Lee, E.K. 2003. A Space Model to Annual Rainfall in South Korea. The Korean Communications in statistics 10(2):445-456. https://doi.org/10.5351/CKSS.2003.10.2.445
  32. Lettenmaier, D., D. Major, L. Poff and S. Running. 2008. Water Resources. In: M. Walsh et al. The Effects of Climate Change on Agriculture, Land Resources, Water Resources, and Biodiversity in the United States. U.S. Climate Change Science Program and the subcommittee on Global Change Research, Washington, DC. pp.362.
  33. Moran, P.A.P. 1950. Notes on continuous stochastic phenomena. Biometrika 37(1) :17-23. https://doi.org/10.1093/biomet/37.1-2.17
  34. Nelder, J.A. and R. Mead. 1965. A Simplex method for function minimization. Computer Journal 7:308-313. https://doi.org/10.1093/comjnl/7.4.308
  35. Pronzato, U., E. Walter, A. Venot and J.F. Lebruchec. 1984. A general purpose global optimizer: implementation and applications. Mathematics and Computers in Simulation 26:412-422. https://doi.org/10.1016/0378-4754(84)90105-8
  36. Shepard, D. 1968. A two-dimensional interpolation function for irregularlyspaced data. Proceedings of the 1968 23rd ACM National Conference. New York, NY, USA. pp.517-524.
  37. Simpson, J.J., G.L. Hufford, C. Daly, J.S. Berg and M.D. Fleming. 2005. Comparing maps of mean monthly surface temperature and precipitation for Alaska and adjacent areas of Canada produced by two different methods. Arctic 58(2):137-161.
  38. Thiessen, A.H. 1911. Precipitation averages for large area. Monthly Weather Review 39:1082-1084.
  39. Vrugt, J.A., C.J.F. ter Braak, M.P. Clark, J.M. Hyman and B.A. Robinson. 2008. Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with markov chain monte carlo simulation. Water Resource Research 44:W00B09:1-15.
  40. Yates, S.R. and A.W. Warrick. 1987. Estimating soil water content using cokriging. Soil Soil Science Society of America Journal 51:23-30. https://doi.org/10.2136/sssaj1987.03615995005100010005x

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