Browse > Article
http://dx.doi.org/10.5532/KJAFM.2022.24.4.337

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

Seung-Min, Lee (National Center for AgroMeteorology)
Sung-Won, Choi (National Center for AgroMeteorology)
Seung-Jae, Lee (National Center for AgroMeteorology)
Man-Il, Kim (Forest Engineering Research Institute, National Forestry Cooperative Federation)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.24, no.4, 2022 , pp. 337-345 More about this Journal
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.
Keywords
ArcGIS; R; Spatial interpolation; IDW; LAMP;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
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   DOI
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   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI