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http://dx.doi.org/10.5322/JESI.2013.22.3.303

Development of a Model Combining Covariance Matrices Derived from Spatial and Temporal Data to Estimate Missing Rainfall Data  

Sung, Chan Yong (Department of Environmental Planning, Keimyung University)
Publication Information
Journal of Environmental Science International / v.22, no.3, 2013 , pp. 303-308 More about this Journal
Abstract
This paper proposed a new method for estimating missing values in time series rainfall data. The proposed method integrated the two most widely used estimation methods, general linear model(GLM) and ordinary kriging(OK), by taking a weighted average of covariance matrices derived from each of the two methods. The proposed method was cross-validated using daily rainfall data at thirteen rain gauges in the Hyeong-san River basin. The goodness-of-fit of the proposed method was higher than those of GLM and OK, which can be attributed to the weighting algorithm that was designed to minimize errors caused by violations of assumptions of the two existing methods. This result suggests that the proposed method is more accurate in missing values in time series rainfall data, especially in a region where the assumptions of existing methods are not met, i.e., rainfall varies by season and topography is heterogeneous.
Keywords
Missing rainfall data; Geostatistics; General linear model; Ordinary kriging;
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