Abstract
Bayesian statistical inference implemented by stochastic algorithms such as Markov chain Monte Carlo (MCMC) provides a flexible probabilistic framework for model calibration that accounts for both model and parameter uncertainties. The effectiveness of such Monte Carlo algorithms depends strongly on the user‐specified proposal or sampling distribution. In this article, a sequential Monte Carlo (SMC) approach is used to obtain posterior parameter estimates of a conceptual hydrologic model using data from selected catchments in eastern Australia. The results are evaluated against the popular adaptive Metropolis MCMC sampling approach. Both methods display robustness and convergence, but the SMC displays greater efficiency in exploring the parameter space in catchments where the optimal solutions lie in the tails of the prescribed prior distribution. The SMC method is also able to identify a different set of parameters with an overall improvement in likelihood and Nash‐Sutcliffe efficiency for selected catchments. As a result of its population‐based sampling mechanism, the SMC method is shown to offer improved efficiency in identifying parameter optimization and to provide sampling robustness, in particular in identifying global posterior modes.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
