|Title||Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints|
|Publication Type||Journal Article|
|Year of Publication||2010|
|Authors||Richardson, AD, Williams, M, Hollinger, DY, Moore, DJP, Dail, DB, Davidson, EA, Scott, NA, Evans, RS, Hughes, H, Lee, JT, Rodrigues, C, Savage, K|
|Pagination||25 - 40|
|Keywords||BOREAL FOREST, Carbon cycle, CARBON-DIOXIDE EXCHANGE, CO2 EXCHANGE, DATA ASSIMILATION, Data-model fusion, Eddy covariance, EDDY-COVARIANCE MEASUREMENTS, ENSEMBLE KALMAN FILTER, Howland Forest, Inverse modeling, LAND-SURFACE MODEL, Parameter estimation, SOIL RESPIRATION MEASUREMENTS, TERRESTRIAL ECOSYSTEMS|
We conducted an inverse modeling analysis, using a variety of data streams (tower-based eddy covariance measurements of net ecosystem exchange, NEE, of CO(2), 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.