Using a parallelized MCMC algorithm in R to identify appropriate likelihood functions for SWAT

J. F. Joseph*, J. H.A. Guillaume

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    68 Citations (Scopus)

    Abstract

    Markov Chain Monte Carlo (MCMC) algorithms allow the analysis of parameter uncertainty. This analysis can inform the choice of appropriate likelihood functions, thereby advancing hydrologic modeling with improved parameter and quantity estimates and more reliable assessment of uncertainty. For long-running models, the Differential Evolution Adaptive Metropolis (DREAM) algorithm offers spectacular reductions in time required for MCMC analysis. This is partly due to multiple parameter sets being evaluated simultaneously. The ability to use this feature is hindered in models that have a large number of input files, such as SWAT. A conceptually simple, robust method for applying DREAM to SWAT in R is provided. The general approach is transferrable to any executable that reads input files. We provide this approach to reduce barriers to the use of MCMC algorithms and to promote the development of appropriate likelihood functions.

    Original languageEnglish
    Pages (from-to)292-298
    Number of pages7
    JournalEnvironmental Modelling and Software
    Volume46
    DOIs
    Publication statusPublished - Aug 2013

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