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 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 »
- Authors:
-
[1];
[1];
[3]
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Harvard Univ., Cambridge, MA (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1365814
- Report Number(s):
- SAND-2017-1510J
Journal ID: ISSN 1810-6285; 654249
- Grant/Contract Number:
- AC04-94AL85000
- Resource 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
- 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
Citation Formats
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., 2017.
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. https://doi.org/10.5194/bg-2017-41
Lu, Dan, Ricciuto, Daniel, Walker, Anthony, Safta, Cosmin, and Munger, William. Wed .
"Bayesian calibration of terrestrial ecosystem models: A study of advanced Markov chain Monte Carlo methods". United States. https://doi.org/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 = {Wed Feb 22 00:00:00 EST 2017},
month = {Wed Feb 22 00:00:00 EST 2017}
}
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