Integrating climate risk to reservoir water quality into water infrastructure planning

About the Project
This is an ideal project for a candidate who wants to become highly skilled in assessing climate risks to infrastructure with decision-relevant techniques that are increasingly popular with both industry and academia, while also honing their transferable skills at a high level.

Recent heatwaves have illustrated the risk hotter summers pose to water reservoirs strategic to our water supply, with harmful algal blooms receivingnational media coverage. Algal blooms make water treatment difficult or even impossible, and prevent recreational use of the water. They are more likely to appear in warm and shallow waters, generally the double result of hot weather and drought-related pressures on supply. This means that reservoirs’ water may not be accessible when it is most needed: during hot, dry summers.

Climate change is expected to increase the severity and frequency of both summer heatwaves and droughts, leading to a choice between algal blooms, or preventing them with a combination of emergent technologies (e.g. noninvasive ultrasonic treatment), and by keeping extra water (and depth) in the reservoir. Either way, large but yet unquantified quantities of water cannot be used for consumption. This knowledge gap has crucial implications for drought-resilient planning.

This project aims to address this gap by integrating water quality modelling into advanced decision-making frameworks for water resource planning. This will enable the quantification of climate risk to reservoir water quality in a context of uncertainty in future water demands (withdrawals from reservoirs) and environmental regulation (abstractions from rivers into reservoirs). The work will also propose operational strategies to minimise this risk. Results will inform water planning and billion-pound investment decisions, enhancing drought resilience. A transferable approach will be developed using data from actual UK reservoirs, with the following steps:

1)   Validation of a reservoir water quality model. This will start from a demonstration prototype of a high-performance water quality model of a reservoir. Model calibration and validation will rely on high-quality existing data to represent not only vertical transfers of heat and pollutant loads within reservoirs, but also interventions to tackle pollution (e.g., ultrasounds)

2)   Water quality risk assessment. To mimic water infrastructure operations (abstractions from rivers into reservoirs, intake by water treatment works) as climatic, socio-economic and regulatory conditions change, results from regional water resources simulations of the future will be input into a reservoir water balance model. The model will determine water levels and be coupled to the validated water quality model. This first evaluation of water quality risk will be completed by computational experiments spanning a wider range of possible future conditions affecting water balance and water quality parameters.

3)   Climate-controlled operating rules for reservoirs. This will use the coupled water balance and water quality model to find strategies that mitigate water quality risks even in warmer climate. These strategies will combine technological intervention with modified rules for water use, and these rules will be formulated in a way that is consistent with regional water resource models, so results can be used for water planning. If time allows, the student will have the opportunity to quantify the implications of water quality risk on a regional network that serves several million people.

Funding Notes
We are currently finalising funding for this PhD, in collaboration with industrial partner Anglian Water Services (AWS). The water company serves 6M people in the East of England, and will contribute to the financing of this studentship and to its supervision. Awards for UK students cover tuition fees and a maintenance allowance at the standard RCUK rate – currently £15,609 per annum.

References
Suitable for candidates holding or anticipating award of an MSc, or 1st/2.1 undergraduate degree in an engineering or numerical/physical sciences discipline (including engineering / mathematics / physics / computer science, but also ecology / environmental science / physical geography if with a strong quantitative bent). Candidates should have an enthusiasm for research and a wish to deploy outputs in a practical environment.
Informal enquiries are very welcome. Please contact Dr Charles Rougé on [email protected] or Dr Isabel Douterelo Soler on [email protected]. Applications are welcome now. The start date is planned to be around September 2022.