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Advances in Meteorology
Volume 2015, Article ID 563629, 12 pages
Research Article

Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region

1New South Wales Office of Environment and Heritage, P.O. Box 3720, Parramatta, NSW 2150, Australia
2Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
3NSW Department of Industry, Skills & Regional Development, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
4Graham Centre for Agricultural Innovation (an Alliance between NSW Department of Industry and Charles Sturt University), Wagga Wagga, NSW 2650, Australia
5New South Wales Office of Environment and Heritage, P.O. Box 733, Queanbeyan, NSW 2620, Australia

Received 7 April 2015; Revised 15 June 2015; Accepted 16 June 2015

Academic Editor: Pedro Jiménez-Guerrero

Copyright © 2015 Xihua Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents spatial interpolation techniques to produce finer-scale daily rainfall data from regional climate modeling. Four common interpolation techniques (ANUDEM, Spline, IDW, and Kriging) were compared and assessed against station rainfall data and modeled rainfall. The performance was assessed by the mean absolute error (MAE), mean relative error (MRE), root mean squared error (RMSE), and the spatial and temporal distributions. The results indicate that Inverse Distance Weighting (IDW) method is slightly better than the other three methods and it is also easy to implement in a geographic information system (GIS). The IDW method was then used to produce forty-year (1990–2009 and 2040–2059) time series rainfall data at daily, monthly, and annual time scales at a ground resolution of 100 m for the Greater Sydney Region (GSR). The downscaled daily rainfall data have been further utilized to predict rainfall erosivity and soil erosion risk and their future changes in GSR to support assessments and planning of climate change impact and adaptation in local scale.