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

Lu, Dan; Ricciuto, Daniel; Walker, Anthony; Safta, Cosmin; Munger, William

doi:10.5194/bg-2017-41

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

Journal Article · Wed Feb 22 00:00:00 EST 2017 · Biogeosciences Discussions (Online)

DOI:https://doi.org/10.5194/bg-2017-41· OSTI ID:1365814

Lu, Dan ^[1];

^[1];

^[1]; Safta, Cosmin ^[2];

^[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)

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.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1365814

Report Number(s):: SAND-2017-1510J; 654249

Journal Information:: Biogeosciences Discussions (Online), Vol. 14, Issue 18; ISSN 1810-6285

Publisher:: European Geosciences UnionCopyright Statement

Country of Publication:: United States

Language:: English

References (8)

Response to comment by Keith Beven on “Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?” Vrugt, Jasper A.; ter Braak, Cajo J. F.; Gupta, Hoshin V. Stochastic Environmental Research and Risk Assessment, Vol. 23, Issue 7 https://doi.org/10.1007/s00477-008-0284-9	journal	October 2008
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Cited By (5)

Calibration of the E3SM Land Model Using Surrogate-Based Global Optimization Lu, Dan; Ricciuto, Daniel; Stoyanov, Miroslav Journal of Advances in Modeling Earth Systems, Vol. 10, Issue 6 https://doi.org/10.1002/2017ms001134	journal	June 2018
Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations Wang, S.; Wang, Y. Climate Dynamics, Vol. 53, Issue 3-4 https://doi.org/10.1007/s00382-019-04702-7	journal	March 2019
A semiempirical model for horizontal distribution of surface wind speed leeward windbreaks Yuan, Fenghui; Wu, Jiabing; Wang, Anzhi Agroforestry Systems, Vol. 94, Issue 2 https://doi.org/10.1007/s10457-019-00417-0	journal	July 2019
Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques Lu, Dan; Ricciuto, Daniel Geoscientific Model Development, Vol. 12, Issue 5 https://doi.org/10.5194/gmd-12-1791-2019	journal	January 2019
Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation Fer, Istem; Kelly, Ryan; Moorcroft, Paul R. Biogeosciences, Vol. 15, Issue 19 https://doi.org/10.5194/bg-15-5801-2018	journal	January 2018

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