Physiological ecology - Original Paper


, Volume 164, Issue 1, pp 25-40

First online:

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

  • Andrew D. RichardsonAffiliated withDepartment of Organismic and Evolutionary Biology, Harvard University Herbaria, Harvard University Email author 
  • , Mathew WilliamsAffiliated withSchool of GeoSciences, University of Edinburgh
  • , David Y. HollingerAffiliated withNorthern Research Station, USDA Forest Service
  • , David J. P. MooreAffiliated withDepartment of Geography, Environmental Monitoring and Modelling Research Group, King’s College London
  • , D. Bryan DailAffiliated withDepartment of Plant, Soil, and Environmental Sciences, University of Maine
  • , Eric A. DavidsonAffiliated withWoods Hole Research Center
  • , Neal A. ScottAffiliated withDepartment of Geography, Queen’s University
  • , Robert S. EvansAffiliated withNorthern Research Station, USDA Forest Service
  • , Holly HughesAffiliated withWoods Hole Research Center
    • , John T. LeeAffiliated withDepartment of Plant, Soil, and Environmental Sciences, University of Maine
    • , Charles RodriguesAffiliated withDepartment of Plant, Soil, and Environmental Sciences, University of Maine
    • , Kathleen SavageAffiliated withWoods Hole Research Center

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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.


Carbon cycle Data-model fusion Eddy covariance Howland Forest Inverse modeling Parameter estimation Uncertainty