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Title: Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

Abstract

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. Here, the result indicates that a heteroscedastic, correlated, Gaussian error model ismore » appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [3]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  3. Harvard Univ., Cambridge, MA (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1399968
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Biogeosciences (Online)
Additional Journal Information:
Journal Name: Biogeosciences (Online); Journal Volume: 14; Journal Issue: 18; Journal ID: ISSN 1726-4189
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Lu, Dan, Ricciuto, Daniel M., Walker, Anthony P., Safta, Cosmin, and Munger, William. Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods. United States: N. p., 2017. Web. doi:10.5194/bg-14-4295-2017.
Lu, Dan, Ricciuto, Daniel M., Walker, Anthony P., Safta, Cosmin, & Munger, William. Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods. United States. doi:10.5194/bg-14-4295-2017.
Lu, Dan, Ricciuto, Daniel M., Walker, Anthony P., Safta, Cosmin, and Munger, William. Wed . "Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods". United States. doi:10.5194/bg-14-4295-2017. https://www.osti.gov/servlets/purl/1399968.
@article{osti_1399968,
title = {Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods},
author = {Lu, Dan and Ricciuto, Daniel M. and Walker, Anthony P. and Safta, Cosmin and Munger, William},
abstractNote = {Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. Here, the result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.},
doi = {10.5194/bg-14-4295-2017},
journal = {Biogeosciences (Online)},
number = 18,
volume = 14,
place = {United States},
year = {2017},
month = {9}
}

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