The elevations recorded within digital models are known to be fraught with errors of sampling, measurement and interpolation. Reporting of these errors according to spatial data standards makes several implicit and unacceptable assumptions about the error: it has no spatial distribution, and it is statistically stationary across a region, or even a nation. The approach explored in this paper employs actual elevations measured in ground and aerial survey at higher precision than the elevations in the DEM and recorded on standard paper maps. These high precision elevations are digitized and used to establish the real statistical and spatial distribution of the error. Direct measurements could also have been taken in the field by GPS or any other means of high precision data collection. These high precision elevations are subtracted from values stored in the DEM for approximately the same locations. The distribution of errors specific to the DEM can then be explored, and can be used in the geostatistical method of conditional stochastic simulation to derive alternative realizations of the error modeled and so of the DEM. Multiple versions of the derived products can also be determined. This paper compares the results of using different methods of error modeling. The best method, which gives widely implementable and defensible results, is that based on conditional stochastic simulation.