DOI QR코드

DOI QR Code

Comparison of Spatial Interpolation Processing Environments for Numerical Model Rainfall and Soil Moisture Data

수치모델 강우 및 토양수분 자료의 공간보간 처리환경의 비교

  • Received : 2022.11.29
  • Accepted : 2022.12.29
  • Published : 2022.12.30

Abstract

For data such as rainfall and soil moisture, it is important to obtain the values of all points required as geostatistical data. Spatial interpolation is generally performed in this process, and commercial software such as ArcGIS is often used. However, commercial software has fatal drawbacks due to its high expertise and cost. In this study, R, an open source-based environment with ArcGIS, a commercial software, was used to compare the differences according to the processing environment when performing spatial interpolation. The data for spatial interpolation was weather forecast data calculated through Land-Atmosphere Modeling Package (LAMP)-WRF model, and soil moisture data calculated for each cumulative rainfall scenario. There was no difference in the output value in the two environments, but there was a difference in user interface and calculation time. The results of spatial interpolation work in the test bed showed that the average time required for R was 5 hours and 1 minute, and for ArcGIS, the average time required was 4 hours and 40 minutes, respectively, showing a difference of 7.5%. The results of this study are meaningful in that researchers can derive the same results in a commercial software environment and an open source-based environment, and can choose according to the researcher's environment and level.

강우와 토양수분과 같은 자료는 지구통계자료로서, 필요로 하는 모든 지점의 값을 구하는 것이 중요하다. 이 과정에서는 일반적으로 공간보간이 수행되며, ArcGIS와 같은 상용 소프트웨어를 이용하는 경우가 많다. 하지만 상용 소프트웨어는 높은 전문성과 비용으로 인한 치명적 단점이 존재한다. 본 연구에서는 공간 보간을 수행하는데 있어서 처리환경에 따른 차이점을 비교하기 위해 상용 소프트웨어인 ArcGIS와 오픈소스기반 환경인 R을 활용하였다. 공간보간에 사용된 자료는 LAMP WRF에서 생산된 기상예측 자료를 기반으로 누적강우 시나리오에 따라 산출된 토양수분 자료를 사용하였다. 두 가지 환경에서 산출물의 결과값은 차이가 없었지만 사용자 인터페이스와 계산소요 시간 등에 차이가 있었다. 테스트 베드에서의 공간보간 작업 결과는 R의 경우 평균 소요시간이 5시간 1분으로 나타났고, ArcGIS의 경우 평균 소요시간이 4시간 40분으로 각각 나타나서, 7.5%의 차이를 보였다. 본 연구의 결과는 연구자가 상용 소프트웨어 환경과 오픈소스 기반 환경에서 동일한 결과를 도출할 수 있으며, 연구자의 환경과 수준에 따라 선택해야 함을 실례를 들어 제시한 데 의의가 있다.

Keywords

Acknowledgement

본 연구는 산림청(한국임업진흥원) 산림과학기술연구개발사업 (2021341B10-2223-CD01)의 지원으로 수행되었습니다.

References

  1. Chen, F. W., and C. W. Liu, 2012: Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 10, 209-222.
  2. Cho, H. L., and J. C. Jeong, 2006: Application of spatial interpolation to rainfall data. The journal of geographic information system association of Korea 14(1). 29-41.
  3. George, Y., D. Lu., and W. Wong, 2008: An adaptive inverse-distance weighting spatial interpolation technique. Computers & Geosciences 34(9). 1044-1055 https://doi.org/10.1016/j.cageo.2007.07.010
  4. Friedrich, C., 2014: Comparison of ArcGIS and QGIS for application of sustainable planning. Master of Sciences. University of Wien: https://doi.org/10.25365/thesis.35758
  5. Jang, H. S., N. R. Kang, H. S. Noh, D. R. Lee, C. H. Choi, and H. S. Kim, 2015: Applicability of spatial interpolation methods for the estimation of rainfall field. Journal of Wetlands Research 17(4), 370-379. https://doi.org/10.17663/JWR.2015.17.4.370
  6. Kang, J. H., S. Lee, S.-J. Lee, and J. H. Lee, 2022: Comparative analysis of spatial interpolation methods of PM10 observation data in South Korea. Korean Journal of Agricultural and Forest Meteorology 24(2), 124-132.
  7. Keblouti, M., L. Ouerdachi, and H. Boutaghane, 2012: Spatial interpolation of annual precipitation in Annaba-Algeria - Comparison and evaluation of methods. Energy Procedia 18, 468-475. https://doi.org/10.1016/j.egypro.2012.05.058
  8. Kwak, J. H., M.-I. Kim, and S.-J. Lee, 2018: Landslide susceptibility assessment considering the saturation depth ratio by rainfall change. The Journal of Engineering Geology 28(4), 687-699.
  9. Kim, J.-W., and H.-S. Shin, 2016: Slope stability assessment on a landslide risk area in Ulsan during rainfall. Journal of the Korean Geotechnical Society 32(6), 27-40.
  10. Ko, S. M., S. W. Lee, C. Y. Yune, and G. H. Kim, 2014: Topographic analysis of landslides in Umyeonsan. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 32(1), 55-62. https://doi.org/10.7848/ksgpc.2014.32.1.55
  11. Lee, S.-J., J. Song, and Y. J. Kim, 2016: The NCAM Land-Atmosphere Modeling Package (LAMP) version 1: Implementation and evaluation. Korean Journal of Agricultural and Forest Meteorology 18(4). 307-319. https://doi.org/10.5532/KJAFM.2016.18.4.307
  12. Lee, S., and M.-J. Lee, 2017: Susceptibility mapping of Umyeonsan using Logistic Regression (LR) model and post-validation through field investigation. Korean Journal of Remote Sensing 33(6_2), 1047-1060. https://doi.org/10.7780/KJRS.2017.33.6.2.2
  13. Matejicek, L., 2005: Spatial modeling of air pollution in urban areas with GIS: a case study on integrated database development. Advances in Geosciences 4, 63-68. https://doi.org/10.5194/adgeo-4-63-2005
  14. Ristanovic, B., M. Cimbaljevic, D. Miljkovic, M. Ostojic, and R. Fekete, 2019: GIS application for determining geographical factors on intensity of erosion in Serbian river Basins. Case study: The river Basin of Likodra. Atmosphere 10(9), 526. https://doi.org/10.3390/atmos10090526
  15. So, Y. Y., S. J. Lee, S. W. Choi, and S.-J. Lee, 2020: Construction of NCAM-LAMP precipitation and soil moisture database to support landslide prediction. Korean Journal of Agricultural and Forest Meteorology 22(3), 152-163.
  16. Vizcaino, P., and A. Pistocchi, 2014: Use of a simple GIS-based model in mapping the atmospheric concentration of γ-HCH in Europe. Atmosphere 5(4). 720-736. https://doi.org/10.3390/atmos5040720