Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 10/9/1998
Publication Date: N/A
Citation: Interpretive Summary:
Technical Abstract: Four soil frost models were examined for their ability to simulate snow cover, soil water content, and soil frost depth. Snow cover, soil water content, and frost depth were measured in plots varying in corn residue cover over two winters in Minnesota. These measurements were compared to those simulated by the SHAW, SOIL, Benoit, and Gusev models. The SHAW and SOIL models use finite difference equations to predict temperature and water profiles in the soil whereas the other two models estimate soil frost depth by balancing heat flow through the snow-residue-soil system. Snow cover is simulated by the SHAW, SOIL, and Benoit models and only the Benoit model mimics snow drifting. The SHAW and SOIL models performed better at simulating snow dynamics than the other two models. The SHAW model was more accurate in estimating soil frost depth than the SOIL model, partly due to a better simulation of snow cover. The SOIL model, however, mimicked changes in surface soil water content better than the SHAW model. The Benoit model performed poorly in estimating snow cover due largely to a rapid snowmelt. The Benoit model, using measured and not simulated snow depths, performed better than the Gusev model in estimating the depth of soil freezing, but was more sensitive to changes in snow density. An increase in density from 250 to 350 kg m-3 in the Benoit model caused by a 150% deeper penetration of soil frost whereas this same increase in the Gusev model resulted in a 20% deeper penetration of frost. This study suggests that accurately simulating snow cover in soil frost models is critical to their performance in the field.