Response to comment by Keith Beven on “Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?” |
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| Authors: | Jasper A Vrugt Cajo J F ter Braak Hoshin V Gupta Bruce A Robinson |
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| Institution: | (1) Center for NonLinear Studies (CNLS), Mail Stop B258, Los Alamos National Laboratory (LANL), Los Alamos, NM 87545, USA;(2) Institute for Biodiversity and Ecosystems Dynamics, University of Amsterdam, Amsterdam, The Netherlands;(3) Biometris, Wageningen University and Research Centre, 6700 AC Wageningen, The Netherlands;(4) Department of Hydrology and Water Resources, The University of Arizona, Tucson, AZ 85737, USA;(5) Civilian Nuclear Program Office (SPO-CNP), LANL, Los Alamos, NM 87545, USA |
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| Abstract: | In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework
for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots
within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and
implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare
a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE)
for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently
developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers
model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates
of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the
hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt
to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to
improving hydrologic theory and to better understand and predict the flow of water through catchments. |
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