Improved Soil Temperature Modeling Using Spatially Explicit Solar Energy Drivers
Jonathan J. Halama,
Bradley L. Barnhart,
Robert E. Kennedy,
Robert B. McKane,
James J. Graham,
Paul P. Pettus,
Allen F. Brookes,
Kevin S. Djang,
Ronald S. Waschmann
Affiliations
Jonathan J. Halama
Western Ecology Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Corvallis, OR 97330, USA
Bradley L. Barnhart
Western Ecology Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Corvallis, OR 97330, USA
Robert E. Kennedy
College of Earth and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA
Robert B. McKane
Western Ecology Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Corvallis, OR 97330, USA
James J. Graham
Environmental Science & Management, Humboldt State University, Arcata, CA 95521, USA
Paul P. Pettus
Western Ecology Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Corvallis, OR 97330, USA
Allen F. Brookes
Western Ecology Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Corvallis, OR 97330, USA
Kevin S. Djang
Inoventures (LLC), Western Ecology Division, National Health and Environmental Effects Research Laboratory, c/o U.S. Environmental Protection Agency, Corvallis, OR 97330, USA
Ronald S. Waschmann
Western Ecology Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Corvallis, OR 97330, USA
Modeling the spatial and temporal dynamics of soil temperature is deterministically complex due to the wide variability of several influential environmental variables, including soil column composition, soil moisture, air temperature, and solar energy. Landscape incident solar radiation is a significant environmental driver that affects both air temperature and ground-level soil energy loading; therefore, inclusion of solar energy is important for generating accurate representations of soil temperature. We used the U.S. Environmental Protection Agency’s Oregon Crest-to-Coast (O’CCMoN) Environmental Monitoring Transect dataset to develop and test the inclusion of ground-level solar energy driver data within an existing soil temperature model currently utilized within an ecohydrology model called Visualizing Ecosystem Land Management Assessments (VELMA). The O’CCMoN site data elucidate how localized ground-level solar energy between open and forested landscapes greatly influence the resulting soil temperature. We demonstrate how the inclusion of local ground-level solar energy significantly improves the ability to deterministically model soil temperature at two depths. These results suggest that landscape and watershed-scale models should incorporate spatially distributed solar energy to improve spatial and temporal simulations of soil temperature.
