Technical note: Benefits of Bayesian estimation of model parameters in a large hydrological model ensemble

Abstract. Quantifying and mitigating parametric and structural uncertainties in hydrological models are crucial to accurately understand and predict the rainfall-runoff process. Despite recent advances in Bayesian approaches for quantifying structural uncertainty using very large hydrological model ensembles, the simultaneous quantification of both parametric and structural uncertainties has yet to be implemented since previous works on large model ensembles have relied on deterministic optimization of parameters. Here we present the potential benefits of Bayesian estimation of parametric uncertainty within a large hydrological model ensemble. We find that Bayesian estimation of model parameters (more generally, change in calibration methods) potentially influences the interpretation of model comparisons. Specifically, Bayesian parametric uncertainty quantification greatly benefits complex models with many parameters, thereby affecting discussions of the appropriate level of model complexity. We also find that Bayesian parametric uncertainty quantification does not substantially improve multi-model hydrological predictions. The adverse effects of parameter misspecification in individual models are effectively mitigated by combining models with diverse structures. Thus, the high computational cost of Bayesian parameter estimation is not paid for to improve rainfall-runoff analysis in a large hydrological model ensemble.