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

Journal Article · · Biogeosciences Discussions (Online)
DOI:https://doi.org/10.5194/bg-2017-41· OSTI ID:1365814
 [1];  [1];  [1];  [2];  [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)
+ Show Author Affiliations
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.
Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1365814
Report Number(s):
SAND--2017-1510J; 654249
Journal Information:
Biogeosciences Discussions (Online), Journal Name: Biogeosciences Discussions (Online) Journal Issue: 18 Vol. 14; ISSN 1810-6285
Publisher:
European Geosciences UnionCopyright Statement
Country of Publication:
United States
Language:
English

References (11)

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Cited By (5)

Calibration of the E3SM Land Model Using Surrogate-Based Global Optimization journal June 2018
Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations journal March 2019
A semiempirical model for horizontal distribution of surface wind speed leeward windbreaks journal July 2019
Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation journal January 2018
Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques journal January 2019

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Related Subjects

54 ENVIRONMENTAL SCIENCES
97 MATHEMATICS AND COMPUTING
AM algorithm
Bayesian calibration
DALEC model
DREAM algorithm
MCMC sampling
multimodality
terrestrial ecosystem models