| In recent years, a strong debate has emerged in the hydrologic literature regarding what
constitutes an appropriate framework for model evaluation and 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. Here, I compare a formal Bayesian
approach using Markov Chain Monte Carlo (MCMC) sampling 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. |