Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the northern Great Basin using the Ecosystem Demography (EDv2.2) model

Authors: PANDIT, KARUN - Boise State University; DASHITI, HAMID - Boise State University; GLENN, NANCY - Boise State University; FLORES, ALEJANDRO - Boise State University; SHINNEMAN, DOUGLAS - Us Geological Survey (USGS); MAGUIRE, KAITLIN - Us Geological Survey (USGS); Flerchinger, Gerald; Fellows, Aaron

Submitted to: Geoscientific Model Development
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/16/2019
Publication Date: 11/5/2019
Citation: Pandit, K., Dashiti, H., Glenn, N., Flores, A., Shinneman, D., Maguire, K., Flerchinger, G.N., Fellows, A.W. 2019. Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the northern Great Basin using the Ecosystem Demography (EDv2.2) model. Geoscientific Model Development. 12(11):4585-4601. https://doi.org/10.5194/gmd-12-4585-2019.
DOI: https://doi.org/10.5194/gmd-12-4585-2019

Interpretive Summary: Gross primary production (GPP) is one of the most critical processes in the global carbon cycle, but is difficult to quantify in part because of its high spatiotemporal variability. Direct techniques to quantify GPP are lacking, thus, researchers rely on data inferred from eddy covariance (EC) towers and/or ecosystem dynamic models. The latter are useful to quantify GPP over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. However, such models have also been associated with internal uncertainties and complexities arising from distinct qualities of the ecosystem being analyzed. Widely distributed sagebrush-steppe ecosystems in western North America are threatened by anthropogenic disturbance, invasive species, climate change, and altered fire regimes. Although land managers have focused on different restoration techniques, the effects of these activities and their interactions with fire, climate change, and invasive species on ecosystem dynamics are poorly understood. In this study, we applied an ecosystem dynamic model, Ecosystem Demography (ED2), to parameterize and predict GPP for sagebrush-steppe ecosystems in the Reynolds Creek Experimental Watershed (RCEW), located in the northern Great Basin. Our primary objective was to develop and parameterize a sagebrush (Artemisia spp.) shrubland Plant Function Type (PFT) for use in the ED2 model, which will support future studies to model estimates of GPP under different climate and management scenarios. To accomplish this, we employed a three-tiered approach. First, to parameterize the sagebrush PFT, we developed innovative allometric relationships for sagebrush using field-collected data, gathered information from existing sagebrush literature, and borrowed values from other PFTs in ED2. Second, we identified the five most sensitive parameters out of thirteen that were found to be influential in GPP prediction based on previous studies. Third, we optimized the five parameters using an exhaustive search method to predict GPP, and performed validation using observations from two EC sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent. We expect that, with further refinement, the resulting sagebrush PFT will permit explicit scenario testing of potential anthropogenic modifications of GPP in sagebrush ecosystems, and will contribute to a better understanding of the role of sagebrush ecosystems in shaping global carbon cycles.

Technical Abstract: Gross primary production (GPP) is one of the most critical processes in the global carbon cycle, but is difficult to quantify in part because of its high spatiotemporal variability. Direct techniques to quantify GPP are lacking, thus, researchers rely on data inferred from eddy covariance (EC) towers and/or ecosystem dynamic models. The latter are useful to quantify GPP over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. However, such models have also been associated with internal uncertainties and complexities arising from distinct qualities of the ecosystem being analyzed. Widely distributed sagebrush-steppe ecosystems in western North America are threatened by anthropogenic disturbance, invasive species, climate change, and altered fire regimes. Although land managers have focused on different restoration techniques, the effects of these activities and their interactions with fire, climate change, and invasive species on ecosystem dynamics are poorly understood. In this study, we applied an ecosystem dynamic model, Ecosystem Demography (ED2), to parameterize and predict GPP for sagebrush-steppe ecosystems in the Reynolds Creek Experimental Watershed (RCEW), located in the northern Great Basin. Our primary objective was to develop and parameterize a sagebrush (Artemisia spp.) shrubland Plant Function Type (PFT) for use in the ED2 model, which will support future studies to model estimates of GPP under different climate and management scenarios. To accomplish this, we employed a three-tiered approach. First, to parameterize the sagebrush PFT, we developed innovative allometric relationships for sagebrush using field-collected data, gathered information from existing sagebrush literature, and borrowed values from other PFTs in ED2. Second, we identified the five most sensitive parameters out of thirteen that were found to be influential in GPP prediction based on previous studies. Third, we optimized the five parameters using an exhaustive search method to predict GPP, and performed validation using observations from two EC sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent. We expect that, with further refinement, the resulting sagebrush PFT will permit explicit scenario testing of potential anthropogenic modifications of GPP in sagebrush ecosystems, and will contribute to a better understanding of the role of sagebrush ecosystems in shaping global carbon cycles.