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Re: AI-GEOSTATS: co-kriging on data with spatial trends

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  • sebastiano trevisani
    I think that you can try to avoid cokriging. Clearly the relation is between k and the mean values of clay content. Instead of using a pure cokriging you could
    Message 1 of 3 , Nov 27, 2003
      I think that you can try to avoid cokriging. Clearly the relation is
      between k and the mean values of clay content. Instead of using a pure
      cokriging you could use a kriging with external drift in which the mean
      value of k is related to the map with clay content. Goovaert'book
      Geostatistic for Natural Resources Evaluation chapter 6 could give you an
      hand. Gstat code permit you easily to perform kriging with extrernal drift.
      Sincerely
      Sebastiano Trevisani



      At 04:35 PM 11/27/2003 +0100, Sigrun kvarno wrote:
      >Dear AI-GEOSTATS members,
      >
      >I tried co-kriging for the first time yesterday, and now I have some
      >questions:
      >
      >I have a dataset with 42 measurements of saturated hydraulic conductivity
      >(Kfs) within a grid where I have measured soil texture in 256 points with
      >10 m spacing. Kfs measurements are irregularly spaced. Kfs is
      >approximately log-normal, and there is a significant correlation between
      >log(Kfs) and log(clay content), R2 = 0.61.
      >
      >I know that there is a significant spatial trend in clay content. Earlier,
      >when using geostatistics on the clay content alone, I computed the
      >variogram and kriged on the residuals, and then added back the trend
      >function to the kriged estimates.
      >
      >Considering co-kriging, I need a variogram for both the primary variate
      >(Kfs) and the covariate (log(clay content)), but due to the trend in clay
      >content I have to compute the variogram on the residuals (this is
      >important - a variogram on the original data gives a range of about 600 m
      >(when using the GS+ program), whilst a variogram on the residuals reduces
      >the range to about 55 m). But the correlation between residuals and logKfs
      >is not so good. It is significant, and R2 = 0.20, which is not too good
      >compared to 0.61 for the original data. What is the correct way to proceed
      >in this case? Are there any rules of thumb for such problems?
      >
      >I'd also like to know which search radius should be used - the range of
      >the logKfs data is about 40 m, the effective range (exponential model) of
      >logclay is 600 m for original data (55 if residuals can be used), and the
      >range of the cross-variogram is 53 m. I've learnt that the search radius
      >should be approximately the same as the (effective) range when performing
      >kriging. Is it the range of the cross-variogram I should use in this case?
      >
      >I hope there are some simple solutions out there!
      >
      >Kind regards,
      >Sigrun
      >
      >
      >
      >
      >
      >******************************************************************************
      >Miss Sigrun H. Kværnø
      >Arealressursavd./Dept. of Land Resources
      >Jordforsk - Norwegian Centre for Soil and Environmental Research
      >Frederik A. Dahls vei 20
      >N-1432 Ås
      >NORWAY
      >phone: +47 64948159
      >fax: +47 64948110
      >******************************************************************************


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