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|Title:||Deciphering the components of regional net ecosystem fluxes following a bottom-up approach for the Iberian Peninsula||Authors:||Carvalhais, N.
Collatz, G. J.
Mahecha, M. D.
Neigh, C. S. R.
Benali, A. A.
|Keywords:||Organic-matter decomposition;Eddy covariance measurements;Carbon-dioxide exchange;Light-Use Efficiency;Terrestrial Carbon;Soil Carbon;Temperature-Dependence;Global Satellite;Atmospheric CO2;Process Model||Issue Date:||2010||Publisher:||Copernicus publications||Source:||Carvalhais, N. et al. 2010. Deciphering the components of regional net ecosystem fluxes following a bottom-up approach for the Iberian Peninsula. "Biogeosciences" 7 (11): 3707-3729||Project:||info:eu-repo/grantAgreement/EC/FP7/226701 ItemCrisRefDisplayStrategy.project.deleted.icon||Abstract:||
Quantification of ecosystem carbon pools is a fundamental requirement for estimating carbon fluxes and for addressing the dynamics and responses of the terrestrial carbon cycle to environmental drivers. The initial estimates of carbon pools in terrestrial carbon cycle models often rely on the ecosystem steady state assumption, leading to initial equilibrium conditions. In this study, we investigate how trends and inter-annual variability of net ecosystem fluxes are affected by initial non-steady state conditions. Further, we examine how modeled ecosystem responses induced exclusively by the model drivers can be separated from the initial conditions. For this, the Carnegie-Ames-Stanford Approach (CASA) model is optimized at set of European eddy covariance sites, which support the parameterization of regional simulations of ecosystem fluxes for the Iberian Peninsula, between 1982 and 2006.
The presented analysis stands on a credible model performance for a set of sites, that represent generally well the plant functional types and selected descriptors of climate and phenology present in the Iberian region - except for a limited Northwestern area. The effects of initial conditions on inter-annual variability and on trends, results mostly from the recovery of pools to equilibrium conditions; which control most of the inter-annual variability (IAV) and both the magnitude and sign of most of the trends. However, by removing the time series of pure model recovery from the time series of the overall fluxes, we are able to retrieve estimates of interannual variability and trends in net ecosystem fluxes that are quasi-independent from the initial conditions. This approach reduced the sensitivity of the net fluxes to initial conditions from 47% and 174% to -3% and 7%, for strong initial sink and source conditions, respectively.
With the aim to identify and improve understanding of the component fluxes that drive the observed trends, the net ecosystem production (NEP) trends are decomposed into net primary production (NPP) and heterotrophic respiration (R(H)) trends. The majority (similar to 97%) of the positive trends in NEP is observed in regions where both NPP and RH fluxes show significant increases, although the magnitude of NPP trends is higher. Analogously, similar to 83% of the negative trends in NEP are also associated with negative trends in NPP. The spatial patterns of NPP trends are mainly explained by the trends in fAPAR (r = 0.79) and are only marginally explained by trends in temperature and water stress scalars (r = 0.10 and r = 0.25, respectively). Further, we observe the significant role of substrate availability (r = 0.25) and temperature (r = 0.23) in explaining the spatial patterns of trends in R(H). These results highlight the role of primary production in driving ecosystem fluxes.
Overall, our study illustrates an approach for removing the confounding effects of initial conditions and emphasizes the need to decompose the ecosystem fluxes into its components and drivers for more mechanistic interpretations of modeling results. We expect that our results are not only specific for the CASA model since it incorporates concepts of ecosystem functioning and modeling assumptions common to biogeochemical models. A direct implication of these results is the ability of this approach to detect climate and phenology induced trends regardless of the initial conditions.
|Appears in Collections:||DiSAFRi - Archivio della produzione scientifica|
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checked on Nov 27, 2020
checked on Nov 27, 2020
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