Bayesian framework of parameter sensitivity, uncertainty, and identifiability analysis in complex water quality models. (June 2018)

• Record Type: Journal Article
• Authors: Jia, Haifeng; Xu, Te; Liang, Shidong; Zhao, Pei; Xu, Changqing
• Is Part Of: Environmental modelling & software. Volume 104(2018)
• Page Start: 13
• Page End: 26
• Publication Date: 2018-06
• Subjects: Water quality model -- Parameter -- High dimension -- Sensitivity -- Uncertainty -- Identifiability; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.03.001
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: An efficient uncertainty analysis (UA) analytical framework was developed. A multi-chain MCMC method with comprehensive GSA guarantees robustness. Difficulties in parameter identification were attributed to cognitive limitations. The framework provided model users with reminders for future forecasts. The framework was helpful for model developers to identify model imperfection.