Modeling of Soil Temperature and Water Content at Selected Almnet Sites.

Knowledge of the soil temperature and moisture distributions is necessary to understanding many physical, chemical and biological processes in soil and plant science. It has been long recognized that the amount of water present in the soil and the soil temperature affect soil microbial activity. Degradation of agricultural pesticides by microbes is influenced by soil temperature and water content. The soil temperature and water content also affect the emission of greenhouses gases from soil. Perhaps, the largest influence soil temperature and soil moisture have is on plant growth and development. Seed germination is extremely dependent on soil temperature and soil moisture. The soil temperature can also influence the mechanism by which water is transported to seeds during the germination process. Plant growth is also dependent on the available soil moisture, which can vary with soil and atmospheric conditions. Therefore, predictions of soil temperature and soil moisture distributions are useful for predicting the impact of climate conditions on crop growth, development, and yield.  Several models exist for simulating soil temperature and soil moisture.  However, these models require significantly large inputs.  The Soil Temperature and Moisture Model (STM2) (USDA-ARS), an empirical model, was developed to require little input to simulate soil temperature and moisture distributions.  The input required consists of basic soil physical properties.  However, limited studies exist in which the model has been used to compare predicted and observed soil moisture and temperature observations.  Therefore, the objective of this study is to evaluate the ability of the STM2 model to predict soil moisture and soil temperature at select sites within the Alabama MesoNet (ALMNet).  Sites that represent a range of soil textures were selected.  Soil temperature and moisture distributions at the selected sites will be compared to modeled soil temperature and moisture distributions using the STM2 model.  The performance of the STM2 model will be reported.