Preprints
https://doi.org/10.5194/egusphere-2023-2865
https://doi.org/10.5194/egusphere-2023-2865
08 Jan 2024
 | 08 Jan 2024
Status: this preprint is open for discussion.

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.

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

Status: open (until 02 Apr 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Damian N. Mingo, Remko Nijzink, Christophe Ley, and Jack S. Hale

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

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

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Short summary
Hydrologists are often faced with selecting amongst a set of competing models with different numbers of parameters and ability to fit available data. The Bayes’ factor is a tool that can be used to compare models, however it is very difficult to compute the Bayes’ factor numerically. In our paper we explore and develop highly efficient algorithms for computing the Bayes’ factor of hydrological systems, which will bring this useful tool for selecting models to everyday hydrological practice.