Oecologia

, Volume 164, Issue 1, pp 25–40

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

  • Andrew D. Richardson
  • Mathew Williams
  • David Y. Hollinger
  • David J. P. Moore
  • D. Bryan Dail
  • Eric A. Davidson
  • Neal A. Scott
  • Robert S. Evans
  • Holly Hughes
  • John T. Lee
  • Charles Rodrigues
  • Kathleen Savage
Physiological ecology - Original Paper

DOI: 10.1007/s00442-010-1628-y

Cite this article as:
Richardson, A.D., Williams, M., Hollinger, D.Y. et al. Oecologia (2010) 164: 25. doi:10.1007/s00442-010-1628-y

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.

Keywords

Carbon cycleData-model fusionEddy covarianceHowland ForestInverse modelingParameter estimationUncertainty

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andrew D. Richardson
    • 1
  • Mathew Williams
    • 2
  • David Y. Hollinger
    • 3
  • David J. P. Moore
    • 4
  • D. Bryan Dail
    • 5
  • Eric A. Davidson
    • 6
  • Neal A. Scott
    • 7
  • Robert S. Evans
    • 3
  • Holly Hughes
    • 6
  • John T. Lee
    • 5
  • Charles Rodrigues
    • 5
  • Kathleen Savage
    • 6
  1. 1.Department of Organismic and Evolutionary Biology, Harvard University HerbariaHarvard UniversityCambridgeUSA
  2. 2.School of GeoSciencesUniversity of EdinburghEdinburghUK
  3. 3.Northern Research StationUSDA Forest ServiceDurhamUSA
  4. 4.Department of Geography, Environmental Monitoring and Modelling Research GroupKing’s College LondonLondonUK
  5. 5.Department of Plant, Soil, and Environmental SciencesUniversity of MaineOronoUSA
  6. 6.Woods Hole Research CenterFalmouthUSA
  7. 7.Department of GeographyQueen’s UniversityKingstonCanada