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Paddy and Water Environment

, Volume 10, Issue 3, pp 209–222 | Cite as

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

  • Feng-Wen Chen
  • Chen-Wuing Liu
Article

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.

Keywords

Inverse distance weighting (IDW) Spatial interpolation Rainfall data Omission 

References

  1. Bedient PB, Huber WC (1992) Hydrology and floodplain analysis, 2nd edn. Addison-Wesley, ReadingGoogle Scholar
  2. Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Oxford University Press, OxfordGoogle Scholar
  3. Chu SL, Zhou ZY, Yuan L, Chen QG (2008) Study on spatial precipitation interpolation methods. Pratacult Sci 25(6):19–23Google Scholar
  4. Cressie N (1993) Statics for spatial data (revised edition). Wiley, New YorkGoogle Scholar
  5. Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice-hall, LondonGoogle Scholar
  6. Dirks KN, Hay JE, Stow CD (1998) High resolution studies of rainfall on Norfolk Island Part II: interpolation of rainfall data. J Hydrol 208:187–193CrossRefGoogle Scholar
  7. Dong XH, Bo HJ, Deng X, Su J, Wang X (2009) Rainfall spatial interpolation methods and their applications to Qingjiang river basin. J China Three Gorges Univ (Nat Sci) 31(6):6–10Google Scholar
  8. Feng ZM, Yang YZ, Ding XQ, Lin ZH (2004) Optimization of the spatial interpolation methods for climate resources. Geograph Res 23(5):357–364Google Scholar
  9. Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationship. Wiley, New YorkGoogle Scholar
  10. Garcia M, Peters-Lidard CD, Goodrich DC (2008) Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States. Water Resour Res 44:W05S13(1–14)Google Scholar
  11. Geisser S (1993) Predictive inference. Chapman and Hall, New YorkGoogle Scholar
  12. Gemmer M, Becker S, Jiang T (2004) Observed monthly precipitation trends in China 1951–2002. Theor Appl Climatol 77:39–45CrossRefGoogle Scholar
  13. Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228:113–129CrossRefGoogle Scholar
  14. Gyalistras D (2003) Development and validation of a high-resolution monthly gridded temperature and precipitation data set for Switzerland (1951–2000). Clim Res 25(1):55–83CrossRefGoogle Scholar
  15. Hsieh HH, Cheng SJ, Liou JY, Chou SC, Siao BR (2006) Characterization of spatially distributed summer daily rainfall. J Chin Agric Eng 52(1):47–55Google Scholar
  16. Jeffrey SJ, Carter JO, Moodie KB, Beswick AR (2001) Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ Model Softw 16(4):309–330CrossRefGoogle Scholar
  17. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the fourteenth international joint conference on artificial intelligence, vol 2, no 12. Morgan Kaufmann, San Mateo, pp 1137–1143Google Scholar
  18. Kohavi R, Provost F (1998) Special issue on applications of machine learning and the knowledge discovery process. Mach Learn 30(2–3):271–274Google Scholar
  19. Kong YF, Tong WW (2008) Spatial exploration and interpolation of the surface precipitation data. Geograph Res 27(5):1097–1108Google Scholar
  20. Kurtzman D, Navon S, Morin E (2009) Improving interpolation of daily precipitation for hydrologic modeling: spatial patterns of preferred interpolators. Hydrol Process 23:3281–3291CrossRefGoogle Scholar
  21. Lam NS-N (1983) Spatial interpolation methods: a review. Am Cartogr 10:129–140CrossRefGoogle Scholar
  22. Li J, Heap AD (2008) Spatial interpolation methods: a review for environmental scientists. Geoscience Australia, Record. Geoscience Australia, CanberraGoogle Scholar
  23. Li JL, Zhang J, Zhang C, Chen QG (2006) Analyze and compare the spatial interpolation methods for climate factor. Pratacult Sci 23(8):6–11Google Scholar
  24. Li B, Huang JF, Jin ZF, Liu ZY (2010) Methods for calculation precipitation spatial distribution of Zhejiang Province based on GIS. J Zhejiang Univ (Sci Ed) 27(2):239–244Google Scholar
  25. Lin XS, Yu Q (2008) Study on the spatial interpolation of agroclimatic resources in Chongqing. J Anhui Agric 36(30):13431–13463, 13470Google Scholar
  26. Lloyd CD (2005) Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain. J Hydrol 308:128–150CrossRefGoogle Scholar
  27. Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci 34(9):1044–1055CrossRefGoogle Scholar
  28. McLachlan GJ, Ambroise K-A, Do C (2004) Analyzing microarray gene expression data. Wiley, New YorkCrossRefGoogle Scholar
  29. Naoum S, Tsanis IK (2004) A multiple linear regression GIS module using spatial variables to model orographic rainfall. J Hydroinform 6:39–56Google Scholar
  30. Phogat V, Yadav AK, Malik RS, Kumar S, Cox J (2010) Simulation of salt and water movement and estimation of water productivity of rice crop irrigated with saline water. Paddy Water Environ 8:333–346CrossRefGoogle Scholar
  31. Price DT, McKenney DW, Nalder IA, Hutchison MF, Kesteven JL (2000) A comparison of two statistical methods for interpolation of Canadian monthly mean climate data. Agric Meteorol 101:81–94CrossRefGoogle Scholar
  32. Seaman RS (1983) Objective analysis accuracies of statistical interpolation and successive correction schemes. J Aust Meteorol Mag 31:225–240Google Scholar
  33. Segond M-L, Neokleous N, Makropoulos C, Onof C, Maksimovic C (2007) Simulation and spatio-temporal disaggregation of multi-site rainfall data for urban drainage applications. Hydrol Sci J 52(5):917–935CrossRefGoogle Scholar
  34. Simanton JR, Osborn HB (1980) Reciprocal-distance estimate of point rainfall. J Hydraul Eng 106(7):1242–1246Google Scholar
  35. Traore S, Wang YM, Kan CE, Kerh T, Leu JM (2010) A mixture neural methodology for computing rice consumptive water requirements in Fada N’Gourma Region, Eastern Burkina Faso. Paddy Water Environ 8:165–173CrossRefGoogle Scholar
  36. Tung YK (1983) Point rainfall estimation for a mountainous region. J Hydraul Eng 109(10):1386–1393CrossRefGoogle Scholar
  37. Wang Y, Li CK, Chen L, Zheng SN (2008) Analysis on impact of weight to spatial interpolation methods. J Hunan Univ Sci Technol (Nat Sci Ed) 23(4):77–80Google Scholar
  38. Wu L, Wu XJ, Xiao CC, Tian Y (2010) On temporal and spatial error distribution of five precipitation interpolation models. Geogr Geo-Inf Sci 26(3):19–24Google Scholar
  39. Yeh HC, Chen YC, Wei C, Chen RH (2011) Entropy and kriging approach to rainfall network design. Paddy Water Environ 9:343–355CrossRefGoogle Scholar
  40. Zhong JJ (2010) A comparative study of spatial interpolation precision of annual average precipitation based on GIS in Xinjiang. Desert Oasis Meteorol 4(4):51–54Google Scholar
  41. Zhu HY, Jia SF (2004) Uncertainty in the spatial interpolation of rainfall data. Prog Geogr 23(2):34–42Google Scholar
  42. Zhuang LW, Wang SL (2003) Spatial interpolation methods of daily weather data in Northeast China. Quart J Appl Meteorol 14(5):605–616Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of Bioenvironmental Systems EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.Agricultural Engineering Research CenterZhongliTaiwan

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