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

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 study, a Differential Evolution Adaptive Metropolis (DREAM) algorithm was 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 DREAM is a multi-chain method and uses differential evolution technique for chain movement, allowing it to be efficiently applied to high-dimensional problems, and can reliably estimate heavy-tailed and multimodal distributions that are difficult for single-chain schemes using a Gaussian proposal distribution. The results were evaluated against the popular Adaptive Metropolis (AM) scheme. DREAM indicated that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identified one mode. The calibration of DREAM resulted in a better model fit and predictive performance compared to the AM. DREAM provides means for a good exploration of the posterior distributions of model parameters.more » Lastly, it reduces the risk of false convergence to a local optimum and potentially improves the predictive performance of the calibrated model.« less
Authors:
 [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:
Report Number(s):
SAND-2017-1510J
Journal ID: ISSN 1810-6285; 654249
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
Biogeosciences Discussions (Online)
Additional Journal Information:
Journal Name: Biogeosciences Discussions (Online); Journal Volume: 14; Journal Issue: 18; Journal ID: ISSN 1810-6285
Publisher:
European Geosciences Union
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Bayesian calibration; MCMC sampling; AM algorithm; DREAM algorithm; DALEC model; multimodality; terrestrial ecosystem models
OSTI Identifier:
1365814

Lu, Dan, Ricciuto, Daniel, Walker, Anthony, Safta, Cosmin, and Munger, William. Bayesian calibration of terrestrial ecosystem models: A study of advanced Markov chain Monte Carlo methods. United States: N. p., Web. doi:10.5194/bg-2017-41.
Lu, Dan, Ricciuto, Daniel, Walker, Anthony, 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-2017-41.
Lu, Dan, Ricciuto, Daniel, Walker, Anthony, Safta, Cosmin, and Munger, William. 2017. "Bayesian calibration of terrestrial ecosystem models: A study of advanced Markov chain Monte Carlo methods". United States. doi:10.5194/bg-2017-41. https://www.osti.gov/servlets/purl/1365814.
@article{osti_1365814,
title = {Bayesian calibration of terrestrial ecosystem models: A study of advanced Markov chain Monte Carlo methods},
author = {Lu, Dan and Ricciuto, Daniel and Walker, Anthony 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 study, a Differential Evolution Adaptive Metropolis (DREAM) algorithm was 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 DREAM is a multi-chain method and uses differential evolution technique for chain movement, allowing it to be efficiently applied to high-dimensional problems, and can reliably estimate heavy-tailed and multimodal distributions that are difficult for single-chain schemes using a Gaussian proposal distribution. The results were evaluated against the popular Adaptive Metropolis (AM) scheme. DREAM indicated that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identified one mode. The calibration of DREAM resulted in a better model fit and predictive performance compared to the AM. DREAM provides means for a good exploration of the posterior distributions of model parameters. Lastly, it reduces the risk of false convergence to a local optimum and potentially improves the predictive performance of the calibrated model.},
doi = {10.5194/bg-2017-41},
journal = {Biogeosciences Discussions (Online)},
number = 18,
volume = 14,
place = {United States},
year = {2017},
month = {2}
}