Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan

In this article, we used the inverse distance weighting (IDW) method to estimate the rainfall distribution in the middle of Taiwan. We evaluated the relationship between interpolation accuracy and two critical parameters of IDW: power (α value), and a radius of influence (search radius). A total of...

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Published in	Paddy and water environment Vol. 10; no. 3; pp. 209 - 222
Main Authors	Chen, Feng-Wen, Liu, Chen-Wuing
Format	Journal Article
Language	English
Published	Japan Springer Japan 01.09.2012 Springer Nature B.V
Subjects	Agriculture Biomedical and Life Sciences Capacity development Correlation coefficient Dry season Ecotoxicology Environmental management Floods Geoecology/Natural Processes Geographic information systems Hydrogeology Hydrologic data Hydrology Hydrology/Water Resources Interpolation Inverse Irrigation Life Sciences Meteorology Optimization Rain Rainfall Rainfall distribution Soil Science & Conservation Stations Weighting Spatial interpolation Rainfall data Inverse distance weighting (IDW) Omission
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Abstract	In this article, we used the inverse distance weighting (IDW) method to estimate the rainfall distribution in the middle of Taiwan. We evaluated the relationship between interpolation accuracy and two critical parameters of IDW: power (α value), and a radius of influence (search radius). A total of 46 rainfall stations and rainfall data between 1981 and 2010 were used in this study, of which the 12 rainfall stations belonging to the Taichung Irrigation Association (TIA) were used for cross-validation. To obtain optimal interpolation data of rainfall, the value of the radius of influence, and the control parameter-α were determined by root mean squared error. The results show that the optimal parameters for IDW in interpolating rainfall data have a radius of influence up to 10–30 km in most cases. However, the optimal α values varied between zero and five. Rainfall data of interpolation using IDW can obtain more accurate results during the dry season than in the flood season. High correlation coefficient values of over 0.95 confirmed IDW as a suitable method of spatial interpolation to predict the probable rainfall data in the middle of Taiwan.
AbstractList	Issue Title: Special Issue on Capacity Building for Participatory Irrigation and Environmental Management In this article, we used the inverse distance weighting (IDW) method to estimate the rainfall distribution in the middle of Taiwan. We evaluated the relationship between interpolation accuracy and two critical parameters of IDW: power (α value), and a radius of influence (search radius). A total of 46 rainfall stations and rainfall data between 1981 and 2010 were used in this study, of which the 12 rainfall stations belonging to the Taichung Irrigation Association (TIA) were used for cross-validation. To obtain optimal interpolation data of rainfall, the value of the radius of influence, and the control parameter-α were determined by root mean squared error. The results show that the optimal parameters for IDW in interpolating rainfall data have a radius of influence up to 10-30 km in most cases. However, the optimal α values varied between zero and five. Rainfall data of interpolation using IDW can obtain more accurate results during the dry season than in the flood season. High correlation coefficient values of over 0.95 confirmed IDW as a suitable method of spatial interpolation to predict the probable rainfall data in the middle of Taiwan.[PUBLICATION ABSTRACT] In this article, we used the inverse distance weighting (IDW) method to estimate the rainfall distribution in the middle of Taiwan. We evaluated the relationship between interpolation accuracy and two critical parameters of IDW: power (α value), and a radius of influence (search radius). A total of 46 rainfall stations and rainfall data between 1981 and 2010 were used in this study, of which the 12 rainfall stations belonging to the Taichung Irrigation Association (TIA) were used for cross-validation. To obtain optimal interpolation data of rainfall, the value of the radius of influence, and the control parameter-α were determined by root mean squared error. The results show that the optimal parameters for IDW in interpolating rainfall data have a radius of influence up to 10–30 km in most cases. However, the optimal α values varied between zero and five. Rainfall data of interpolation using IDW can obtain more accurate results during the dry season than in the flood season. High correlation coefficient values of over 0.95 confirmed IDW as a suitable method of spatial interpolation to predict the probable rainfall data in the middle of Taiwan. In this article, we used the inverse distance weighting (IDW) method to estimate the rainfall distribution in the middle of Taiwan. We evaluated the relationship between interpolation accuracy and two critical parameters of IDW: power ( alpha value), and a radius of influence (search radius). A total of 46 rainfall stations and rainfall data between 1981 and 2010 were used in this study, of which the 12 rainfall stations belonging to the Taichung Irrigation Association (TIA) were used for cross-validation. To obtain optimal interpolation data of rainfall, the value of the radius of influence, and the control parameter- alpha were determined by root mean squared error. The results show that the optimal parameters for IDW in interpolating rainfall data have a radius of influence up to 10-30 km in most cases. However, the optimal alpha values varied between zero and five. Rainfall data of interpolation using IDW can obtain more accurate results during the dry season than in the flood season. High correlation coefficient values of over 0.95 confirmed IDW as a suitable method of spatial interpolation to predict the probable rainfall data in the middle of Taiwan.
Author	Chen, Feng-Wen Liu, Chen-Wuing
Author_xml	– sequence: 1 givenname: Feng-Wen surname: Chen fullname: Chen, Feng-Wen organization: Department of Bioenvironmental Systems Engineering, National Taiwan University, Agricultural Engineering Research Center – sequence: 2 givenname: Chen-Wuing surname: Liu fullname: Liu, Chen-Wuing email: cwliu@ntu.edu.tw organization: Department of Bioenvironmental Systems Engineering, National Taiwan University
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Cites_doi	10.1016/S0168-1923(99)00169-0 10.1016/S0022-1694(00)00144-X 10.3354/cr025055 10.1623/hysj.52.5.917 10.1007/978-1-4899-4467-2 10.1029/2006WR005788 10.1007/s10333-009-0195-5 10.1016/S1364-8152(01)00008-1 10.2166/hydro.2004.0004 10.1016/j.cageo.2007.07.010 10.1007/s00704-003-0018-3 10.1016/j.jhydrol.2004.10.026 10.1061/(ASCE)0733-9429(1983)109:10(1386) 10.1002/hyp.7442 10.1007/s10333-010-0213-7 10.1559/152304083783914958 10.1016/S0022-1694(98)00155-3 10.1007/s10333-010-0247-x 10.1002/9781119115151 10.1002/047172842X
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Wiley, New York BedientPBHuberWCHydrology and floodplain analysis19922ReadingAddison-Wesley SegondM-LNeokleousNMakropoulosCOnofCMaksimovicCSimulation and spatio-temporal disaggregation of multi-site rainfall data for urban drainage applicationsHydrol Sci J200752591793510.1623/hysj.52.5.917 TungYKPoint rainfall estimation for a mountainous regionJ Hydraul Eng1983109101386139310.1061/(ASCE)0733-9429(1983)109:10(1386) PriceDTMcKenneyDWNalderIAHutchisonMFKestevenJLA comparison of two statistical methods for interpolation of Canadian monthly mean climate dataAgric Meteorol2000101819410.1016/S0168-1923(99)00169-0 YehHCChenYCWeiCChenRHEntropy and kriging approach to rainfall network designPaddy Water Environ2011934335510.1007/s10333-010-0247-x DirksKNHayJEStowCDHigh resolution studies of rainfall on Norfolk Island Part II: interpolation of rainfall dataJ Hydrol199820818719310.1016/S0022-1694(98)00155-3 JeffreySJCarterJOMoodieKBBeswickARUsing spatial interpolation to construct a comprehensive archive of Australian climate dataEnviron Model Softw200116430933010.1016/S1364-8152(01)00008-1 LloydCDAssessing the effect of integrating elevation data into the estimation of monthly precipitation in Great BritainJ Hydrol200530812815010.1016/j.jhydrol.2004.10.026 KohaviRProvostFSpecial issue on applications of machine learning and the knowledge discovery processMach Learn1998302–3271274 ZhuangLWWangSLSpatial interpolation methods of daily weather data in Northeast ChinaQuart J Appl Meteorol2003145605616 GoovaertsPGeostatistical approaches for incorporating elevation into the spatial interpolation of rainfallJ Hydrol200022811312910.1016/S0022-1694(00)00144-X SeamanRSObjective analysis accuracies of statistical interpolation and successive correction schemesJ Aust Meteorol Mag198331225240 PhogatVYadavAKMalikRSKumarSCoxJSimulation of salt and water movement and estimation of water productivity of rice crop irrigated with saline waterPaddy Water Environ2010833334610.1007/s10333-010-0213-7 KongYFTongWWSpatial exploration and interpolation of the surface precipitation dataGeograph Res200827510971108 KurtzmanDNavonSMorinEImproving interpolation of daily precipitation for hydrologic modeling: spatial patterns of preferred interpolatorsHydrol Process2009233281329110.1002/hyp.7442 ZhuHYJiaSFUncertainty in the spatial interpolation of rainfall dataProg Geogr20042323442 BurroughPAMcDonnellRAPrinciples of geographical information systems1998OxfordOxford University Press LamNS-NSpatial interpolation methods: a reviewAm Cartogr19831012914010.1559/152304083783914958 NaoumSTsanisIKA multiple linear regression GIS module using spatial variables to model orographic rainfallJ Hydroinform200463956 TraoreSWangYMKanCEKerhTLeuJMA mixture neural methodology for computing rice consumptive water requirements in Fada N’Gourma Region, Eastern Burkina FasoPaddy Water Environ2010816517310.1007/s10333-009-0195-5 LiJLZhangJZhangCChenQGAnalyze and compare the spatial interpolation methods for climate factorPratacult Sci2006238611 LuGYWongDWAn adaptive inverse-distance weighting spatial interpolation techniqueComput Geosci20083491044105510.1016/j.cageo.2007.07.010 ZhongJJA comparative study of spatial interpolation precision of annual average precipitation based on GIS in XinjiangDesert Oasis Meteorol2010445154 DongXHBoHJDengXSuJWangXRainfall spatial interpolation methods and their applications to Qingjiang river basinJ China Three Gorges Univ (Nat Sci)2009316610 HsiehHHChengSJLiouJYChouSCSiaoBRCharacterization of spatially distributed summer daily rainfallJ Chin Agric Eng20065214755 ChuSLZhouZYYuanLChenQGStudy on spatial precipitation interpolation methodsPratacult Sci20082561923 WangYLiCKChenLZhengSNAnalysis on impact of weight to spatial interpolation methodsJ Hunan Univ Sci Technol (Nat Sci Ed)200823477801:CAS:528:DC%2BD1cXptV2qsr8%3D GeisserSPredictive inference1993New YorkChapman and Hall FengZMYangYZDingXQLinZHOptimization of the spatial interpolation methods for climate resourcesGeograph Res2004235357364 CressieNStatics for spatial data (revised edition)1993New YorkWiley LiJHeapADSpatial interpolation methods: a review for environmental scientists. 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Snippet	In this article, we used the inverse distance weighting (IDW) method to estimate the rainfall distribution in the middle of Taiwan. We evaluated the... Issue Title: Special Issue on Capacity Building for Participatory Irrigation and Environmental Management In this article, we used the inverse distance...
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SubjectTerms	Agriculture Biomedical and Life Sciences Capacity development Correlation coefficient Dry season Ecotoxicology Environmental management Floods Geoecology/Natural Processes Geographic information systems Hydrogeology Hydrologic data Hydrology Hydrology/Water Resources Interpolation Inverse Irrigation Life Sciences Meteorology Optimization Rain Rainfall Rainfall distribution Soil Science & Conservation Stations Weighting
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Title	Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan
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