Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1709-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-1709-2025
© Author(s) 2025. This work is distributed under
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
Damian N. Mingo
Institute of Computational Engineering, Department of Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue de la Fonte, 4364 Esch-sur-Alzette, Luxembourg
Remko Nijzink
Catchment and Eco-hydrology (CAT), Environmental Sensing and Modelling (ENVISION), Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
Christophe Ley
Department of Mathematics, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue de la Fonte, 4364 Esch-sur-Alzette, Luxembourg
Institute of Computational Engineering, Department of Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue de la Fonte, 4364 Esch-sur-Alzette, Luxembourg
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Remko C. Nijzink and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 6289–6309, https://doi.org/10.5194/hess-26-6289-2022, https://doi.org/10.5194/hess-26-6289-2022, 2022
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Most catchments plot close to the empirical Budyko curve, which allows for estimating the long-term mean annual evaporation and runoff. We found that a model that optimizes vegetation properties in response to changes in precipitation leads it to converge to a single curve. In contrast, models that assume no changes in vegetation start to deviate from a single curve. This implies that vegetation has a stabilizing role, bringing catchments back to equilibrium after changes in climate.
Remko C. Nijzink and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 4575–4585, https://doi.org/10.5194/hess-26-4575-2022, https://doi.org/10.5194/hess-26-4575-2022, 2022
Short summary
Short summary
Most catchments plot close to the empirical Budyko curve, which allows for the estimation of the long-term mean annual evaporation and runoff. The Budyko curve can be defined as a function of a wetness index or a dryness index. We found that differences can occur and that there is an uncertainty due to the different formulations.
Remko C. Nijzink, Jason Beringer, Lindsay B. Hutley, and Stanislaus J. Schymanski
Geosci. Model Dev., 15, 883–900, https://doi.org/10.5194/gmd-15-883-2022, https://doi.org/10.5194/gmd-15-883-2022, 2022
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The Vegetation Optimality Model (VOM) is a coupled water–vegetation model that predicts vegetation properties rather than determines them based on observations. A range of updates to previous applications of the VOM has been made for increased generality and improved comparability with conventional models. This showed that there is a large effect on the simulated water and carbon fluxes caused by the assumption of deep groundwater tables and updated soil profiles in the model.
Remko C. Nijzink, Jason Beringer, Lindsay B. Hutley, and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 525–550, https://doi.org/10.5194/hess-26-525-2022, https://doi.org/10.5194/hess-26-525-2022, 2022
Short summary
Short summary
Most models that simulate water and carbon exchanges with the atmosphere rely on information about vegetation, but optimality models predict vegetation properties based on general principles. Here, we use the Vegetation Optimality Model (VOM) to predict vegetation behaviour at five savanna sites. The VOM overpredicted vegetation cover and carbon uptake during the wet seasons but also performed similarly to conventional models, showing that vegetation optimality is a promising approach.
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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. Bayes’ factor is a tool that can be used to compare models; however, it is very difficult to compute Bayes' factor numerically. In our paper, we explore and develop highly efficient algorithms for computing Bayes’ factor of hydrological systems, which will introduce this useful tool for selecting models into everyday hydrological practice.
Hydrologists are often faced with selecting amongst a set of competing models with different...