Abstract
In this study, 4 spatial interpolation methods, including Thiessen polygon, inverse distance weighted (IDW), ordinary kriging (OK), and ordinary cokriging (OCK), were adopted for spatial monthly precipitation interpolation. As confirmed by the results, OCK which adopt elevation as secondary variable was superior to other methods. On the other hand, the performance of OCK was also compared with that of OK for different correlation between rainfall and elevation, and the results showed that OCK could not consistently outperform OK. When the correlation coefficient was small, OK got more accurate estimation compared with OCK, while OCK performed much better than OK as the correlativity was evident, and the gap of their estimation accuracies enlarged greatly with increasing coefficient. Finally, by integrating OK and OCK based on their respective superiority, we obtained the results with less error than those of OK and OCK respectively.
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