DOI QR코드

DOI QR Code

Applicability of VariousInterpolation Approaches for High Resolution Spatial Mapping of Climate Data in Korea

남한 지역 고해상도 기후지도 작성을 위한 공간화 기법 연구

  • Jo, Ayeong (Department of Lifescience, Korea University) ;
  • Ryu, Jieun (Environmental GIS/RS Center, Korea University) ;
  • Chung, Hyein (Department of Environmental Science & Ecological Engineering, Korea University) ;
  • Choi, Yuyoung (Department of Environmental Science & Ecological Engineering, Korea University) ;
  • Jeon, Seongwoo (Department of Environmental Science & Ecological Engineering, Korea University)
  • 조아영 (고려대학교 생명과학과) ;
  • 류지은 (고려대학교 환경GIS/RS 센터) ;
  • 정혜인 (고려대학교 환경생태공학과) ;
  • 최유영 (고려대학교 환경생태공학과) ;
  • 전성우 (고려대학교 환경생태공학과)
  • Received : 2018.07.02
  • Accepted : 2018.10.02
  • Published : 2018.10.31

Abstract

The purpose of this study is to build a new dataset of spatially interpolated climate data of South Korea by performing various geo-statistical interpolation techniques for comparison with the LDAPS grid data of KMA. Among 595 observation data in 2017, 80 % of the total points and remaining 117 points were used for spatial mapping and quantification,respectively. IDW, cokriging, and kriging were performed via the ArcGIS10.3.1 software and Python3.6.4, and each result was then divided into three clusters and four watersheds for statistical verification. As a result, cokriging produced the most suitable grid climate data for instantaneous temperature. For 1-hr accumulated precipitation, IDW was most suitable for expressing local rainfall effects.

본 연구의 목적은 다양한 지리통계학적 공간화 기법을 적용한 격자기후자료와 기상청에서 제공하는 국지예보모델(Local Data Assimilation and Prediction System, LDAPS) 격자기후자료를 비교 분석하여 남한 지역의 고해상도 격자기후지도 작성 방안을 모색하는 것이다. 2017년의 595개 기후관측자료 중, 80%의 지점자료를 이용하여 순간 온도와 1시간 누적강수량에 대한 격자기후자료를 생성하였고 나머지 117개의 지점자료를 검증에 이용하였다. ArcGIS10.3.1과 Python3.6.4을 이용하여 관측자료 및 DEM을 IDW, 공동크리깅, 크리깅에 적용한 후, 공간보간 결과를 3개 군집으로 나누어 검증하였으며 LDAPS 격자기후자료를 바탕으로 유역 별 패턴 비교를 수행하였다. 결과적으로 순간 온도의 공간화에는 고도를 부변수로 추가한 공동크리깅이, 1시간 누적강수량 공간화에는 IDW가 가장 적합하였다.

Keywords

References

  1. Ann C, Ewa D, Carl K, Bill A, Ian F, Veronika N, Jason L, Alex S, Arie S, Bob D, Dean W, Don M. 2003. High-performance remote access to climate simulation data: a challenge problem for data grid technologies. Parallel Computing. 29: 1335-1356. https://doi.org/10.1016/j.parco.2003.06.001
  2. Baek GH, Lee MG, Kang BJ. 2011. Development of Spatial Statistical Downscaling Method for KMA-RCM by Using GIS. The Korean Association of Geographic Information Studies. 14(3): 136-149. [Korean Literature] https://doi.org/10.11108/kagis.2011.14.3.136
  3. Baek SG, Jang DH. 2011. Evaluation for Applicability of Cokriging for High Resolution Spatial Mapping of Temperature and Rainfall, Climate Research. 6(3): 242-253. [Korean Literature]
  4. Bruno AW, Joslin LM. 2005. The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography. 28: 815-829. https://doi.org/10.1111/j.2005.0906-7590.04112.x
  5. Chun SH, Kim CB, Kim WR, Park SG, Chae SK. 2015. Analysis of Stream Environmental Assessment Systems in Korea : Focus on the Biological Aspect, Ecology and Resilient Infrastructure. 2(2): 108-117. https://doi.org/10.17820/eri.2015.2.2.108
  6. Creutin JD, Delrieu G, Lebel T. 1988. Rain measurement by raingage-radar combination: a geostatistical approach. Journal of Atmospheric and Pceanic Technologies. 5: 102-115. https://doi.org/10.1175/1520-0426(1988)005<0102:RMBRRC>2.0.CO;2
  7. Daly C, Helmer EH, Maya Q. 2003. Mapping the Climate of Puerto Rico, Vieques and Culebra. International Journal of climatology. 23: 1359-1381. https://doi.org/10.1002/joc.937
  8. Daly C. 2006. Guidelines for assessing the suitability of spatial climate data sets. Int. J. Climatol. 26: 707-721. https://doi.org/10.1002/joc.1322
  9. Girish P, David WS. 1983. Cluster Analysis in Marketing Research: Review and Suggestions for Application. Journal of Marketing Research. 20(2): 134-148. https://doi.org/10.1177/002224378302000204
  10. Hong KO, Suh MS, Rah DK, Chang DH, Kim CS, Kim MK. 2007. Estimation of High Resolution Gridded Temperature Using GIS and PRISM. Atmosphere. 17(3): 255-268. [Korean Literature]
  11. Hwang SH, Ham DH. 2013. Evaluation of Spatial Downscaling Methods for Enhancement of Spatial Precipitation Estimation. Journal of KOSHAN. 13(4): 149-163. [Korean Literature]
  12. Jang DH, Wi NS, Park NW. 2015. High-resolution Spatial Mapping and Evaluation of Temperature and Rainfall in South Korea using a Simple Kriging with Local Mean. Climate Research. 10(2): 165-182. [Korean Literature] https://doi.org/10.14383/cri.2015.10.2.165
  13. Jeong JJ, Choi YG. 2011. Study on Interpolation Methods to Generate GIS-based Climate Maps. Climate Research. 6(2): 159-170. [Korean Literature]
  14. Jin MJ, Park SY. 2015. Temperature Changes of Climatic Solar Terms and Their Spatiotemporal Characteristics in South Korea. The Korean Geographical Society. 50(1): 23-36. [Korean Literature]
  15. Kim JP, Lee WS, Cho HG, Kim GS. 2014. Estimation of High Resolution Daily Precipitation Using a Modified PRISM Model. Korean Society of Civil Engineers. 34(4): 1139-1150. [Korean Literature] https://doi.org/10.12652/Ksce.2014.34.4.1139
  16. KMA. 2017. 11-1360395-000252-01
  17. KMA. 2017. 11-1360000-000002-06
  18. Kim YS, Shim KM, Jung MP, Choi IT. 2014. Accuracy Comparison of Air Temperature Estimation using Spatial Interpolation Methods according to Application of Temperature Lapse Rate Effect. The Korean Society of Climate Change Research. 5(4): 323-329. [Korean Literature] https://doi.org/10.15531/ksccr.2014.5.4.323
  19. Miquel N, Xavier P, Joan MR. 2007. Objective air temperature mapping for the Iberian Peninsula using spatial interpolation and GIS. Int. J. Climatol. 27: 1231-1242. https://doi.org/10.1002/joc.1462
  20. Park JC, Kim MK. 2009, A Study on the Use of a Terrain Aspect Variable in Producing the Precipitation Distribution Map applying Cokriging: A Case of Jeju Island. Journal of the Korean Geomorphological Association. 16(3): 59-66. [Korean Literature]
  21. Park JC, Kim MK. 2013. Comparison of Precipitation Distributions in Precipitation Data Sets Representing 1km Spatial Resolution over South Korea Produced by PRISM, IDW, and Cokriging. Journal of the Korean Association of Geographic Information Studies. 16(3): 147-163. [Korean Literature] https://doi.org/10.11108/kagis.2013.16.3.147
  22. Park NW, Jang DH. 2008. Mapping of Temperature and Rainfall Using DEM and Multivariate Kriging. The Korean Geographical Society. 43(6): 1002-1015. [Korean Literature]
  23. Park NW, Jang DH. 2011. Use of Space-time Autocorrelation Information in Time-series Temperature Mapping. The Korean Association of Regional Geographers. 17(4): 432-442. [Korean Literature]
  24. Park SJ. 2014. Generality and Specificity of Landforms of the Korean Peninsula, and Its Sustainability. The Korean Geographical Society. 49(5): 656-674. [Korean Literature]
  25. Stephen EF, Robert JH. 2017. WorldClim2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37: 4032-4315.