Abstract
Two different Markov models for cross-covariance and coregionalization modeling are proposed and compared in cokriging and stochastic simulation modes. The newly introduced Markov model 2 performs better in cases where the secondary data are defined on a larger support volume than the primary variable being estimated or simulated. Incorrect adoption of the more traditional Markov model 1 may result in cokriging estimated maps that are artificially too close to the secondary data map and in simulated realizations with too high nugget effect.
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