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DIVISION S-1—SOIL PHYSICS

Two-dimensional Markov Chain Simulation of Soil Type Spatial Distribution

^a Dep. of Geography, Univ. of Wisconsin, Madison, WI 53706
^b Dep. of Geography and Geology, Univ. of Wisconsin, Whitewater, WI 53190
^c State Key Lab. of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China
^d Institute for Land and Water Management, Catholic Univ. of Leuven, B-3000 Leuven, Belgium

^* Corresponding author (li2{at}wisc.edu)

Soils typically exhibit complex spatial variation of multi-categorical variables such as soil types and soil textural classes. Quantifying and assessing soil spatial variation is necessary for land management and environmental research, especially for accurately assessing the water and solute transport processes in watershed scales. This study describes an efficient Markov chain model for two-dimensional modeling and simulation of spatial distribution of soil types (or classes). The model is tested through simulations of a simplified soil map. The application of the model for predictive soil mapping with parameters estimated from survey lines is explored. Analyses of both simulated maps and associated semi-variograms show that the model can effectively reproduce observed spatial patterns of soil types and their spatial autocorrelation given an adequate number of survey lines. This indicates that the model is a feasible method for modeling spatial distributions of soil types (or classes) and the transition probability matrices of soil types in different directions can adequately capture the spatial interdependency relationship of soil types. The model is highly efficient in terms of computer time and storage. The model also provides an approach for assessing the uncertainty of soil type spatial distribution in areas where detailed survey data are lacking. The major constraint on applications of the model at this stage is that the minor soil types are relatively underestimated when survey lines are too sparse.

Abbreviations: CMC, coupled Markov chain • TMC, triplex Markov chain • TPM, transition probability matrix

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