the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Selecting a conceptual hydrological model using Bayes' factors computed with Replica Exchange Hamiltonian Monte Carlo and Thermodynamic Integration
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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RC1: 'Comment on egusphere-2023-2865', Anonymous Referee #1, 03 Apr 2024
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The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2865/egusphere-2023-2865-RC1-supplement.pdf
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AC1: 'Reply on RC1', Damian Mingo Ndiwago, 08 Apr 2024
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We would like to thank the first anonymous reviewer for their thorough and insightful feedback on our manuscript. The reply is attached as a supplement.
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AC1: 'Reply on RC1', Damian Mingo Ndiwago, 08 Apr 2024
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Data sets
Magela Creek data (precipitation, discharge, potential evapotranspiration, temperature) D. N. Mingo and Jack S. Hale https://doi.org/10.5281/zenodo.10202093
Model code and software
Selecting a conceptual hydrological model using Bayes' factors Damian N. Mingo and Jack S. Hale https://doi.org/10.5281/zenodo.10202093
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