Improved soil temperature modeling using spatially explicit solar energy drivers
Citation:
Halama, J., B. Barnhart, R. Kennedy, Bob Mckane, J. Graham, P. Pettus, A. Brookes, K. Djang, AND Ron Waschmann. Improved soil temperature modeling using spatially explicit solar energy drivers. WATER. MDPI, Basel, Switzerland, 10(10):1398, (2018). https://doi.org/10.3390/w10101398
Impact/Purpose:
This is a journal article to be submitted to Water. The journal is especially interested in “vulnerabilities and resiliency to changes in landscape”. This special issue release is focusing on how changing landscapes may impact water availability, or degradation to water quality. They explicitly mention decision analysis tools to better understand the impact due to landscape change. The research behind this manuscript aimed to improve our ability to model spatially distributed soil temperature due to landscape change. The model Visualizing Ecosystem Land Management Assessments (VELMA) is an existing watershed model capable of assessing the impacts of land management on an ecosystem. This research helped improve the soil temperature modeling component within VELMA by creating a new soil temperature modeling approach from VELMA’s original soil temperature sub-model. This new approach that leveraged spatially distributed solar energy. We tested both the original and new approach against the observed data collected through the US EPA Crest to Coast monitoring project. The testing revealed the original method functioned well, but also that the new approach out preformed the original soil modeling approach at 7 of the 8 sites. This information will lead to more integrated modeling systems that better inform stakeholders such as: watershed councils, tribes, local, state, and federal decision makers interested in quantifying effects of land use and impacts due to local climate shifts. Regarding soil temperature, VELMA can better inform models influencing future land management by making more evident the full consequential impacts of management decisions on human and natural systems.
Description:
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. Here we used the 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). These site data expose how localized ground-level solar energy between open and forested landscapes greatly influence the resulting soil temperature. Here 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.
URLs/Downloads:
DOI: Improved soil temperature modeling using spatially explicit solar energy drivers