A physically-based computer model is developed for estimating soil temperatures at 0.05, 0.1, 0.2, and 0.5 m in sandy, forested soils, using only daily precipitation and minimum and maximum air temperatures as input data. The algorithm is evaluated by comparing its output to 490 soil temperature observations for 1990-1993 at 10 sites in Michigan, USA. The mean bias of the errors of the soil temperature estimates ranges from +0.6° to- .6°C, depending upon depth and season, thus comparing favorably to models requiring more detailed input. The model is applied to 14 stations across lower Michigan, using 1951-1991 US National Weather Service data, in order to examine regional trends in soil temperature and freezing and to compare these trends to patterns of snow thickness and air temperature. Simulations reveal that soils seldom freeze within the deep lake-effect snow belt of southern Michigan and along the coast of Lake Michigan. Soil freezing is more common at non-snowbelt, interior locations. Based on simulation data, new "potential freeze day" indices are presented that correlate better with annual minimum soil temperatures and "number of days frozen" than do other, more commonly used indices of freezing not specifically formulated for soils.
All Science Journal Classification (ASJC) codes
- Geotechnical Engineering and Engineering Geology
- Earth and Planetary Sciences(all)