A Bayesian approach to model the trends and variability in urban stormwater quality associated with catchment and hydrologic

WATER RESEARCH

卷:197

Article

Perera, Thamali^1,2,3  McGree, James⁴  Egodawatta, Prasanna^1,5  Jinadasa, K. B. S. N.⁶  Goonetilleke, Ashantha^1,5 

[1] Queensland Univ Technol QUT, Fac Engn, GPO Box 2434, Brisbane, Qld 4001, Australia

[2] Univ SriJayewardenepura, Dept Math, Nugegoda 10250, Sri Lanka

[3] Univ Peradeniya, Postgrad Inst Agr, Peradeniya 20400, Sri Lanka

[4] Queensland Univ Technol QUT, Fac Sci, GPO Box 2434, Brisbane, Qld 4001, Australia

[5] Queensland Univ Technol QUT, Ctr Environm, GPO Box 2434, Brisbane, Qld 4001, Australia

[6] Univ Peradeniya, Dept Civil Engn, Peradeniya 20400, Sri Lanka

关键词: stormwater runoff;    Bayesian hierarchical modelling;    uncertainty analysis;    stormwater quality;    stormwater pollutant processes;   

DOI  :  10.1016/j.watres.2021.117076

来源: Elsevier

【 摘 要 】

Stormwater runoff pollution has become a key environmental issue in urban areas. Reliable estimation of stormwater pollutant discharge is important for implementing robust water quality management strategies. Even though significant attempts have been undertaken to develop water quality models, deterministic approaches have proven inappropriate as they do not address the variability in stormwater quality. Due to the random nature of rainfall characteristics and the differences in catchment characteristics, it is difficult to generate the runoff pollutographs to a desired level of certainty. Bayesian hierarchical modelling is an effective tool for developing complex models with a large number of sources of variability. A Bayesian model does not look for a single value of the model parameters, but rather determines a distribution of the model parameters from which all inference is drawn. This study introduces a Bayesian hierarchical linear regression model to describe a catchment specific runoff pollutograph incorporating the associated uncertainties in the model parameters. The model incorporates catchment and rainfall characteristics including the effective impervious area, time of concentration, rain duration, average rainfall intensity and the antecedent dry period as the contributors to random effects. (c) 2021 Elsevier Ltd. All rights reserved.

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