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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 13 Dec 2018

Development and technical paper | 13 Dec 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Optimizing shrub parameters to estimate gross primary production of the sagebrush ecosystem using the Ecosystem Demography (EDv2.2) model

Karun Pandit1, Hamid Dashti1, Nancy F. Glenn1, Alejandro N. Flores1, Kaitlin C. Maguire2, Douglas J. Shinneman2, Gerald N. Flerchinger3, and Aaron W. Fellows3 Karun Pandit et al.
  • 1Department of Geosciences, Boise State University, 1910 University Dr., Boise, ID 83725-1535 USA
  • 2United States Geological Survey, Forest and Rangeland Ecosystem Science Center, 970 Lusk St., Boise, ID 83706
  • 3Uni ted States Department of Agriculture, Agricultural Research Service, 800 Park Blvd., Suite 105, Boise, ID 83712

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 (EDv2.2), 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 Functional Type (PFT) for use in the EDv2.2 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 fitted allometric relationships for sagebrush using field-collected data, gathered information from existing sagebrush literature, and borrowed values from other PFTs in EDv2.2. 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.

Karun Pandit et al.
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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Karun Pandit et al.
Data sets

Input data used for sagebrush PFT in ED2 K. Pandit

Model code and software

Codes for sagebrush PFT in ED2 K. Pandit

Karun Pandit et al.
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Short summary
We identified PFT parameters for sagebrush (shrub) in Ecosystem Demography (ED2) model based on field data and existing literature. We also performed sensitivity and optimization analysis using simulated Gross Primary Production (GPP) with that observed from two EC tower sites. Predicted GPPs were comparable with the observed data from both EC sites, with some negative bias. Using these sagebrush parameters in ED2, we can assess effects of restoration on sagebrush ecosystem at regional scale.
We identified PFT parameters for sagebrush (shrub) in Ecosystem Demography (ED2) model based on...