Model-Data for Joint Estimation of Biogeochemical Model Parameters from Multiple Experiments: A Bayesian Approach Applied to Mercury Methylation

Rathore, Saubhagya; Painter, Scott

doi:10.12769/1805731

Title: Model-Data for Joint Estimation of Biogeochemical Model Parameters from Multiple Experiments: A Bayesian Approach Applied to Mercury Methylation

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Abstract

This modeling archive supports the manuscript submitted for publication in the Environmental Modeling and Software. This study is supported by ORNL-SFA and IDEAS-Watershed. This study aims to improve calibration of complex biogeochemical models using datasets from multiple experiments targeting specific subprocesses. The proposed Bayesian joint-fitting scheme calibrates the entire biogeochemical model in one go using all the available datasets and estimate parameter uncertainties using Markov Chain Monte Carlo (MCMC). This allows for complete propagation of uncertainties and utilization of the information shared between different datasets. Mapping joint distribution of parameters guides model improvement by identifying null spaces in the parameter space. This archive contains files used to perform MCMC, post-process outputs and visualize results.

Authors:

;

Publication Date:: Wed Jul 07 00:00:00 EDT 2021

Other Number(s):: MCI547

DOE Contract Number:: AC05-00OR22725

Research Org.:: ORNLCIFSFA (Critical Interfaces Science Focus Area); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division

Collaborations:: Oak Ridge National Laboratory

Keywords:: ; East Fork Poplar Creek

OSTI Identifier:: 1805731

DOI:: https://doi.org/10.12769/1805731

Citation Formats


                    Rathore, Saubhagya, and Painter, Scott. Model-Data for Joint Estimation of Biogeochemical Model Parameters from Multiple Experiments: A Bayesian Approach Applied to Mercury Methylation.  United States: N. p., 2021. 
        Web.  doi:10.12769/1805731.

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                    Rathore, Saubhagya, & Painter, Scott. Model-Data for Joint Estimation of Biogeochemical Model Parameters from Multiple Experiments: A Bayesian Approach Applied to Mercury Methylation.  United States.  doi:https://doi.org/10.12769/1805731

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                    Rathore, Saubhagya, and Painter, Scott. 2021.  
"Model-Data for Joint Estimation of Biogeochemical Model Parameters from Multiple Experiments: A Bayesian Approach Applied to Mercury Methylation".  United States.  doi:https://doi.org/10.12769/1805731.  https://www.osti.gov/servlets/purl/1805731. Pub date:Wed Jul 07 00:00:00 EDT 2021

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@article{osti_1805731,

  title        = {Model-Data for Joint Estimation of Biogeochemical Model Parameters from Multiple Experiments: A Bayesian Approach Applied to Mercury Methylation},

  author       = {Rathore, Saubhagya and Painter, Scott},

  abstractNote = {This modeling archive supports the manuscript submitted for publication in the Environmental Modeling and Software. This study is supported by ORNL-SFA and IDEAS-Watershed. This study aims to improve calibration of complex biogeochemical models using datasets from multiple experiments targeting specific subprocesses. The proposed Bayesian joint-fitting scheme calibrates the entire biogeochemical model in one go using all the available datasets and estimate parameter uncertainties using Markov Chain Monte Carlo (MCMC). This allows for complete propagation of uncertainties and utilization of the information shared between different datasets. Mapping joint distribution of parameters guides model improvement by identifying null spaces in the parameter space. This archive contains files used to perform MCMC, post-process outputs and visualize results. },

  doi          = {10.12769/1805731},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Wed Jul 07 00:00:00 EDT 2021},

  month        = {Wed Jul 07 00:00:00 EDT 2021}

}

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DOI: https://doi.org/10.12769/1805731

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