Predicting Soil Temperature and Moisture beneath Granular-Surfaced Roadways Using Regional Weather Data Network

Abstract

The performance of granular-surfaced roadways is greatly affected by annual freeze-thaw cycles, which can cause severe structural damage to the road surface. The collection of in-situ subgrade soil temperature and moisture data as well as local weather data is very important to improve our ability to understand and predict subgrade behavior during freeze-thaw cycles. In this study, the use of data from a weather station installed adjacent to a granular road test section is compared to data interpolated from the two nearest weather stations of a statewide network, to compare their performance as inputs for freeze-thaw simulations using the Simultaneous Heat and Water (SHAW) model. The subgrade temperature and moisture content predictions from the simulations are compared to those measured by sensors installed at several depths below the soil surface at the center of the test section. The results suggest that using weather input data from the statewide weather station network may result in reasonably accurate predictions from SHAW simulations.