Uncertainty analysis for soil‐terrain models

Abstract

The aim of the study was to examine how robust soil‐terrain models are to uncertainty in the source elevation data. The study site was a 74 ha agricultural field in Australia. A global positioning system was used to measure elevation and the uncertainty of the measurement, therefore allowing maps of elevation and its uncertainty to be created. Monte‐Carlo simulation with a modified version of Latin Hypercube Sampling was used to create 100 realizations of a slope map. Clay content was measured at 111 sites, and kriging with external drift was used to map clay content where each slope realization was used as a secondary information source. Maps of the mean and standard deviation of clay content across all realizations were created. The standard deviations of clay content were generally small (<4 dag kg⁻¹) and in most parts of the field less than the analytical accuracy of the hydrometer method which was used to measure soil‐clay content in the laboratory. The values in the map of elevation uncertainty were multiplied by 5 and the entire error propagation process was repeated to create a second set of 100 realizations of the clay content. The ratio of the uncertainty in the original DEM was 5:1 when compared with that in the perturbed DEM, i.e. it was multiplied by 5. The ratio between the standard deviation in the two clay‐content maps was 3.79:1, which indicates a reduction in uncertainty through the modelling process. The results showed that the soil‐terrain model performs well for the study area, and it is not very sensitive to DEM errors. We conclude that input uncertainty tests as shown in this study should accompany soil mapping studies where secondary information is used in the prediction mdoel.

Keywords:

• Uncertainty analysis
• Pedometrics, Soil mapping
• Terrain modelling

Acknowledgements

The authors would like to thank Tamara Shatar and James Cupitt, for assistance with the field and laboratory work, and the Boydell family, for access to their farm.