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Preprints
https://doi.org/10.5194/egusphere-2023-2865
https://doi.org/10.5194/egusphere-2023-2865
08 Jan 2024
 | 08 Jan 2024

Selecting a conceptual hydrological model using Bayes' factors computed with Replica Exchange Hamiltonian Monte Carlo and Thermodynamic Integration

Damian N. Mingo, Remko Nijzink, Christophe Ley, and Jack S. Hale

Abstract. We develop a method for computing Bayes’ factors of conceptual rainfall-runoff models based on thermodynamic integration, gradient-based replica-exchange Markov Chain Monte Carlo algorithms and modern differentiable programming languages. We apply our approach to the problem of choosing from a set of conceptual bucket-type models with increasing dynamical complexity calibrated against both synthetically generated and real runoff data from Magela Creek, Australia. We show that using the proposed methodology the Bayes factor can be used to select a parsimonious model and can be computed robustly in a few hours on modern computing hardware. We introduce formal posterior predictive checks for the selected model. The prior calibrated posterior predictive p-value, which also tests for prior data conflict, is used for the posterior predictive checks. Prior data conflict is when the prior favours parameter values that are less likely given the data.

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Journal article(s) based on this preprint

12 Mar 2025
Selecting a conceptual hydrological model using Bayes' factors computed with replica-exchange Hamiltonian Monte Carlo and thermodynamic integration
Damian N. Mingo, Remko Nijzink, Christophe Ley, and Jack S. Hale
Geosci. Model Dev., 18, 1709–1736, https://doi.org/10.5194/gmd-18-1709-2025,https://doi.org/10.5194/gmd-18-1709-2025, 2025
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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Hydrologists are often faced with selecting amongst a set of competing models with different...
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