Extreme events, water quality and health: a participatory Bayesian risk assessment tool for managers of reservoirs

Bertone, Edoardo, Sahin, Oz, Richards, Russell and Roiko, Anne (2016) Extreme events, water quality and health: a participatory Bayesian risk assessment tool for managers of reservoirs. Journal of Cleaner Production, 135 657-667. doi:10.1016/j.jclepro.2016.06.158

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Author Bertone, Edoardo
Sahin, Oz
Richards, Russell
Roiko, Anne
Title Extreme events, water quality and health: a participatory Bayesian risk assessment tool for managers of reservoirs
Journal name Journal of Cleaner Production   Check publisher's open access policy
ISSN 0959-6526
Publication date 2016-11-01
Year available 2016
Sub-type Article (original research)
DOI 10.1016/j.jclepro.2016.06.158
Open Access Status File (Author Post-print)
Volume 135
Start page 657
End page 667
Total pages 11
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Subject 2105 Renewable Energy, Sustainability and the Environment
2300 Environmental Science
1408 Strategy and Management
2209 Industrial and Manufacturing Engineering
Abstract Extreme weather events pose major challenges for the delivery of safe drinking water, especially in a country like Australia. As a consequence, a participatory Bayesian Network modelling approach was used to develop a risk assessment tool for estimating, and ranking, water quality-related health risks associated with extreme weather events. The model was developed for a large dam supplying a water treatment plant in New South Wales, Australia. This methodological approach addresses challenges associated with fragmented data (for model parameterisation) and parameter uncertainty by eliciting and integrating quantitative and qualitative data (including expert opinions) into a single framework. Key-stakeholders were engaged in developing and then refining separate conceptual models around the three critical parameters of turbidity, water colour and Cryptosporidium sp. These three conceptual models were then combined into a single conceptual model, which then formed the basis for the Bayesian Network model. The final risk assessment tool was able to quantify the sensitivity of the water treatment plant's efficacy (ability to supply high quality potable water) in response to different extreme event scenarios. Overall, landslip-related events were the most concerning for water quality-related health risks, but an emergent outcome was how the scenarios were ranked quite differently depending on the group, and expertise of the stakeholders’ opinions used to run the model. Such tool can assist stakeholders for an effective long-term water resource management.
Formatted abstract
Extreme weather events pose major challenges for the delivery of safe drinking water, especially in a country like Australia. As a consequence, a participatory Bayesian Network modelling approach was used to develop a risk assessment tool for estimating, and ranking, water quality-related health risks associated with extreme weather events. The model was developed for a large dam supplying a water treatment plant in New South Wales, Australia. This methodological approach addresses challenges associated with fragmented data (for model parameterisation) and parameter uncertainty by eliciting and integrating quantitative and qualitative data (including expert opinions) into a single framework. Key-stakeholders were engaged in developing and then refining separate conceptual models around the three critical parameters of turbidity, water colour and Cryptosporidium sp. These three conceptual models were then combined into a single conceptual model, which then formed the basis for the Bayesian Network model. The final risk assessment tool was able to quantify the sensitivity of the water treatment plant's efficacy (ability to supply high quality potable water) in response to different extreme event scenarios. Overall, landslip-related events were the most concerning for water quality-related health risks, but an emergent outcome was how the scenarios were ranked quite differently depending on the group, and expertise of the stakeholders' opinions used to run the model. Such tool can assist stakeholders for an effective long-term water resource management.
Keyword System thinking
Bayesian Networks
Water treatment management
Water quality
Health
Extreme events
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Agriculture and Food Sciences
 
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Citation counts: TR Web of Science Citation Count  Cited 10 times in Thomson Reuters Web of Science Article | Citations
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Created: Sun, 09 Oct 2016, 10:21:33 EST by System User on behalf of School of Agriculture and Food Sciences