Abstract
An efficient Bayesian analytical framework was developed to address the challenges of uncertainty analysis and assess the parameter identification problems of complex water quality models with high-dimensional parameter space. The inclusion of a multi-chain Markov Chain Monte Carlo method and comprehensive global sensitive analysis (GSA) guarantees the results to be robust. A high-frequency synthetic data case study was conducted in the EFDC water quality module including 54 parameters. The comprehensive GSA identified 39 completely or partially sensitive parameters for reducing dimensionality, among which only nine were identifiable without significant bias. The fundamental causes of the parameter identification problem could be traced to the cognitive limitations of the real water quality assessment process instead of data scarcity. The framework is powerful for exploring these limitations, generating reminders for model users to use Bayesian estimates in future forecasts, and providing directions for model developers to perfect a model in future work.
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