Estimation of semivariogram parameters and evaluation of the effects of data sparsity |
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| Authors: | Gregg Lamorey and Elizabeth Jacobson |
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| Institution: | (1) Water Resources Center, Desert Research Institute, University and Community College System of Nevada, 89512 Reno, Nevada |
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| Abstract: | Semivariogram parameters are estimated by a weighted least-squares method and a jackknife kriging method. The weighted least-squares method is investigated by differing the lag increment and maximum lag used in the fit. The jackknife kriging method minimizes the variance of the jackknifing error as a function of semivariogram parameters. The effects of data sparsity and the presence of a trend are investigated by using 400-, 200-, and 100-point synthetic data sets. When the two methods yield significantly different results, more data may be needed to determine reliably the semivariogram parameters, or a trend may be present in the data. |
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| Keywords: | semivariogram fitting sparse data drift detection jackknife kriging |
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