Scaling an Instantaneous Model of Tundra NEE to the Arctic Landscape

TitleScaling an Instantaneous Model of Tundra NEE to the Arctic Landscape
Publication TypeJournal Article
Year of Publication2011
AuthorsLoranty M.M, Goetz S.J, Rastetter E.B, Rocha A.V, Shaver GR, Humphreys E.R, Lafleur P.M
Date PublishedJan
ISBN Number1432-9840
Accession NumberISI:000288172000006
Keywordsalaskan tundra, arctic carbon exchange, carbon exchange, climate-change, co2 flux, ecosystem co2 production, functional convergence, leaf-area index, modeling, nee, permafrost thaw, soils, tundra, upscaling, vegetation type, wet sedge tundra

We scale a model of net ecosystem CO2 exchange (NEE) for tundra ecosystems and assess model performance using eddy covariance measurements at three tundra sites. The model, initially developed using instantaneous (seconds-minutes) chamber flux (similar to m(2)) observations, independently represents ecosystem respiration (ER) and gross primary production (GPP), and requires only temperature (T), photosynthetic photon flux density (I-0), and leaf area index (L) as inputs. We used a synthetic data set to parameterize the model so that available in situ observations could be used to assess the model. The model was then scaled temporally to daily resolution and spatially to about 1 km(2) resolution, and predicted values of NEE, and associated input variables, were compared to observations obtained from eddy covariance measurements at three flux tower sites over several growing seasons. We compared observations to modeled NEE calculated using T and I-0 measured at the towers, and L derived from MODIS data. Cumulative NEE estimates were within 17 and 11% of instrumentation period and growing season observations, respectively. Predictions improved when one site-year experiencing anomalously dry conditions was excluded, indicating the potential importance of stomatal control on GPP and/or soil moisture on ER. Notable differences in model performance resulted from ER model formulations and differences in how L was estimated. Additional work is needed to gain better predictive ability in terms of ER and L. However, our results demonstrate the potential of this model to permit landscape scale estimates of NEE using relatively few and simple driving variables that are easily obtained via satellite remote sensing.

Short TitleEcosystemsEcosystems
Alternate JournalEcosystems