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

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 qualityrelated 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.

A systems approach for assessing water conservation potential through demand-based water tariffs

“…optimised water pricing) can lead to a reduced need for supply augmentation (Grafton et al, 2015). This strategy in turn can enhance the resilience against the effects that climate change, extreme events and increased human activity may have on a number of water sources, both in terms of quantity and quality (Bertone et al, 2016a;Bertone et al, 2014;Haddeland et al, 2014;. Numerous studies have recently been undertaken, especially in the Australian context such as Grafton et al (2015), in order to optimise water tariffs, and in turn water demand and water supply augmentation.…”

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

“…Qualitative data (e.g., expert knowledge and stakeholder opinion) are frequently used to calibrate and validate the BNs where empirical data are lacking [15,21]. For example, expert knowledge was used to identify the environmental factors (e.g., land use, rainfall, storage level, agricultural areas) affecting the three key water quality parameters (turbidity, colour and crypto) in the reservoirs of New South Wales, Australia, and then quantify the relationships between environmental factors and these water quality variables through populating the CPTs for the model [47]. In addition, meetings with stakeholders, water managers, water experts and researchers were organized to determine the influential variables (e.g., water for people and animals, industrial water demand, groundwater exaction) and their relationships on a water supply and demand system, and then elicit the CPTs based on average values from all participants for the system in Qira Oasis area, Northwest China [3].…”

Water and energy-based optimisation of a “MiniCity”: A system dynamics approach

“…roof area, affecting rainwater collection; or number of floors, affecting the number of occupants and in turn water demand). The use of SD to address challenges associated with urban water supply is increasing within the international water sector (Sahin et al, 2018;Bertone et al, 2016;Park et al, 2014;Rehan et al, 2014). The generic water demand-supply system developed by Chung et al (2008) has been tailored based on the MiniCity's need.…”