Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments |
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| Authors: | Sajal Kumar Adhikary Nitin Muttil Abdullah Gokhan Yilmaz |
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| Affiliation: | 1. College of Engineering and Science, Victoria University, Melbourne, Victoria, Australia;2. Institute for Sustainability and Innovation, Victoria University, Melbourne, Victoria, Australia;3. College of Engineering, University of Sharjah, Sharjah, United Arab Emirates |
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| Abstract: | Rainfall data in continuous space provide an essential input for most hydrological and water resources planning studies. Spatial distribution of rainfall is usually estimated using ground‐based point rainfall data from sparsely positioned rain‐gauge stations in a rain‐gauge network. Kriging has become a widely used interpolation method to estimate the spatial distribution of climate variables including rainfall. The objective of this study is to evaluate three geostatistical (ordinary kriging OK], ordinary cokriging OCK], kriging with an external drift KED]), and two deterministic (inverse distance weighting, radial basis function) interpolation methods for enhanced spatial interpolation of monthly rainfall in the Middle Yarra River catchment and the Ovens River catchment in Victoria, Australia. Historical rainfall records from existing rain‐gauge stations of the catchments during 1980–2012 period are used for the analysis. A digital elevation model of each catchment is used as the supplementary information in addition to rainfall for the OCK and kriging with an external drift methods. The prediction performance of the adopted interpolation methods is assessed through cross‐validation. Results indicate that the geostatistical methods outperform the deterministic methods for spatial interpolation of rainfall. Results also indicate that among the geostatistical methods, the OCK method is found to be the best interpolator for estimating spatial rainfall distribution in both the catchments with the lowest prediction error between the observed and estimated monthly rainfall. Thus, this study demonstrates that the use of elevation as an auxiliary variable in addition to rainfall data in the geostatistical framework can significantly enhance the estimation of rainfall over a catchment. |
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| Keywords: | cross‐variogram model digital elevation model kriging with an external drift ordinary cokriging positive‐definite condition variogram model |
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