Joint estimation of biogeochemical model parameters from multiple experiments: A bayesian approach applied to mercury methylation

Rathore, Saubhagya S.; Schwartz, Grace E.; Brooks, Scott C.; Painter, Scott L.

doi:10.1016/j.envsoft.2022.105453

Joint estimation of biogeochemical model parameters from multiple experiments: A bayesian approach applied to mercury methylation

Journal Article · Thu Jun 23 00:00:00 EDT 2022 · Environmental Modelling and Software

DOI:https://doi.org/10.1016/j.envsoft.2022.105453· OSTI ID:1875374

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Here, to characterize complex biogeochemical systems, results from multiple experiments, where each targets a specific subprocess, are commonly combined. The resulting datasets are interpreted through the calibration of biogeochemical models for process inference and predictions. Commonly used calibration approaches of fitting datasets from individual experiments to subprocess models one at a time is prone to missing information shared between datasets and incomplete uncertainty propagation. We propose a Bayesian joint-fitting scheme addressing the above-mentioned concerns by jointly fitting all the available datasets, thus calibrating the entire biogeochemical model in one go using Markov Chain Monte Carlo (MCMC). The identification of null spaces in the parameter distributions from MCMC guided the simplification of certain subprocess models. For example, fast kinetic sorption was replaced by equilibrium sorption, and Monod demethylation was replaced by first-order demethylation. Joint fitting of datasets resulted in complete uncertainty propagation with parameter estimates informed by all available data.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

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

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1875374

Journal Information:: Environmental Modelling and Software, Journal Name: Environmental Modelling and Software Journal Issue: 1 Vol. 155; ISSN 1364-8152

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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