Quantification of terrestrial ecosystem carbon dynamics in the conterminous United States combining a process-based biogeochemical model and MODIS and AmeriFlux data
Abstract. Satellite remote sensing provides continuous temporal and spatial information of terrestrial ecosystems. Using these remote sensing data and eddy flux measurements and biogeochemical models, such as the Terrestrial Ecosystem Model (TEM), should provide a more adequate quantification of carbon dynamics of terrestrial ecosystems. Here we use Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI) and carbon flux data of AmeriFlux to conduct such a study. We first modify the gross primary production (GPP) modeling in TEM by incorporating EVI and LSWI to account for the effects of the changes of canopy photosynthetic capacity, phenology and water stress. Second, we parameterize and verify the new version of TEM with eddy flux data. We then apply the model to the conterminous United States over the period 2000–2005 at a 0.05° × 0.05° spatial resolution. We find that the new version of TEM made improvement over the previous version and generally captured the expected temporal and spatial patterns of regional carbon dynamics. We estimate that regional GPP is between 7.02 and 7.78 Pg C yr−1 and net primary production (NPP) ranges from 3.81 to 4.38 Pg C yr−1 and net ecosystem production (NEP) varies within 0.08–0.73 Pg C yr−1 over the period 2000–2005 for the conterminous United States. The uncertainty due to parameterization is 0.34, 0.65 and 0.18 Pg C yr−1 for the regional estimates of GPP, NPP and NEP, respectively. The effects of extreme climate and disturbances such as severe drought in 2002 and destructive Hurricane Katrina in 2005 were captured by the model. Our study provides a new independent and more adequate measure of carbon fluxes for the conterminous United States, which will benefit studies of carbon-climate feedback and facilitate policy-making of carbon management and climate.