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

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 increme...

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Vydané v:	Oecologia Ročník 164; číslo 1; s. 25 - 40
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 01.09.2010 Springer Springer Berlin Heidelberg Springer Nature B.V
Predmet:	Analysis Animal and plant ecology Animal, plant and microbial ecology biogeochemical cycles Biological and medical sciences Biomass Biomedical and Life Sciences Calibration Carbon Carbon content Carbon Cycle Carbon cycle (Biogeochemistry) Carbon dioxide Carbon Dioxide - metabolism carbon sinks Cell Respiration confidence interval Data processing Data-model fusion Ecology Ecosystem Ecosystem models Ecosystems Eddy covariance fine roots Forest ecology Forest ecosystems Forestry Forests Fundamental and applied biological sciences. Psychology General aspects General forest ecology Generalities. Production, biomass. Quality of wood and forest products. General forest ecology growth & development Howland Forest Hydrology/Water Resources Inverse modeling Leaf area leaf area index Life Sciences Maine Mathematical independent variables Measurement metabolism model uncertainty model validation Modeling Models, Biological Net ecosystem exchange Parameter estimation Parametric models PHYSIOLOGICAL ECOLOGY - ORIGINAL PAPER Picea Picea - metabolism Plant Leaves Plant Leaves - growth & development Plant Sciences prediction Respiration Roots Soil Soil respiration Soils Uncertainty Maine Carbon cycle Parameter estimation Uncertainty Eddy covariance Data-model fusion Howland Forest Inverse modeling Forests Forest zone Modeling Data fusion Models Stock
ISSN:	0029-8549, 1432-1939, 1432-1939
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Popis
Shrnutí:	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.
Bibliografia:	http://dx.doi.org/10.1007/s00442-010-1628-y ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	0029-8549 1432-1939 1432-1939
DOI:	10.1007/s00442-010-1628-y