NDLI: Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints

Content Provider	Springer Nature Link
Author	Richardson, Andrew D. Williams, Mathew Hollinger, David Y. Moore, David J. P. Dail, D. Bryan Davidson, Eric A. Scott, Neal A. Evans, Robert S. Hughes, Holly Lee, John T. Rodrigues, Charles Savage, Kathleen
Copyright Year	2010
Abstract	We conducted an inverse modeling analysis, using a variety of data streams (tower-based eddy covariance measurements of net ecosystem exchange, NEE, of CO2, chamber-based measurements of soil respiration, and ancillary ecological measurements of leaf area index, litterfall, and woody biomass increment) to estimate parameters and initial carbon (C) stocks of a simple forest C-cycle model, DALEC, using Monte Carlo procedures. Our study site is the spruce-dominated Howland Forest AmeriFlux site, in central Maine, USA. Our analysis focuses on: (1) full characterization of data uncertainties, and treatment of these uncertainties in the parameter estimation; (2) evaluation of how combinations of different data streams influence posterior parameter distributions and model uncertainties; and (3) comparison of model performance (in terms of both predicted fluxes and pool dynamics) during a 4-year calibration period (1997–2000) and a 4-year validation period (“forward run”, 2001–2004). We find that woody biomass increment, and, to a lesser degree, soil respiration, measurements contribute to marked reductions in uncertainties in parameter estimates and model predictions as these provide orthogonal constraints to the tower NEE measurements. However, none of the data are effective at constraining fine root or soil C pool dynamics, suggesting that these should be targets for future measurement efforts. A key finding is that adding additional constraints not only reduces uncertainties (i.e., narrower confidence intervals) on model predictions, but at the same time also results in improved model predictions by greatly reducing bias associated with predictions during the forward run.
Starting Page	25
Ending Page	40
Page Count	16
File Format	PDF
ISSN	00298549
Journal	Oecologia
Volume Number	164
Issue Number	1
e-ISSN	14321939
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2010-04-14
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Carbon cycle Data-model fusion Eddy covariance Howland Forest Inverse modeling Parameter estimation Uncertainty Ecology Plant Sciences Hydrology/Water Resources
Content Type	Text
Resource Type	Article
Subject	Ecology, Evolution, Behavior and Systematics

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