PhD: Using sensors, satellites and artificial intelligence to improve catchment-scale modelling and nowcasting of microbial risks to water quality via FindAPhD

University of Stirling

Stirling, UK 🇬🇧

About the Project

Background. Scotland’s rivers, lakes and public bathing waters provide substantial benefits to local economies and the broader wellbeing of its citizens, but these benefits can be significantly impaired where episodes of poor microbial water quality impose restrictions on recreational use of water bodies. In Scotland, and elsewhere in the UK, significant improvements in water quality have been achieved since the introduction of the EU Water Framework and Bathing Water Directives. Nevertheless, microbial pollution of water bodies is still an issue in many areas due to runoff from urban and agricultural catchments and discharges from sewer outflows during periods of intense rainfall. While these events are generally shortlived, they can pose significant risks to public health and are predicted to occur more often in the future as the frequency of high rainfall events during summer increases under climate change. Hence there is an urgent need to improve our ability not only to monitor but also to forecast the occurrence of pollution events. The Scottish Environment Protection Agency (SEPA), for example, has developed a two-pronged strategy to mitigate rainfall-driven pollution events focusing on: (a) reducing land-water transfer of faecal indicator organisms (FIOs) at the catchment scale; and (b) the provision of daily ‘nowcasts’ for bathing waters to advise the public against bathing during periods of poor water quality. These approaches rely heavily on the use of predictive models, supported by regular water quality sampling and analysis. In regard to (a), models such as the Sensitive Catchment Integrated Mapping Analysis Platform (SCIMAP) provide a framework for risk-based modelling of diffuse pollution at the catchment-scale enabling the identification of critical source areas as a means to inform more efficient and targeted management interventions In regard to (b), as it is infeasible to undertake compliance sampling on a daily basis across a large number of sites, models have been developed to facilitate real-time (RT) predictions of water quality at sites of interest. SEPA currently use these models to provide nowcasts at a number of bathing waters throughout Scotland with the information relayed to the public through on-site electronic signage. In spite of significant advances in the use of models for predicting microbial risks to water quality over recent years, current approaches are often limited by incomplete representations of the key processes influencing the fate of FIOs in the environment and/or by the lack of appropriate data for model parameterisation, calibration and validation. For example, while the RT models currently used in Scotland provide satisfactory predictions of rainfall-driven pollution events in some catchments, they perform poorly in more complex settings where the interacting effects of other factors such as land cover, meteorology (e.g., temperature, ultraviolet radiation), physico-chemical water quality (e.g., turbidity, salinity) and tidal state also influence FIO dynamics. Furthermore, the models are currently calibrated using historical compliance data generated from weekly sampling, but it is now recognised that Supervisory Team Dr Peter Hunter University of Stirling Dr Jethro Browell University of Glasgow Prof Richard Quilliam University of Stirling Dr David Oliver University of Stirling Case partners [unpack_team names=” George Ponton ” orgs=” Scottish Water “] Further Information for informal enquiries, contact: Dr Peter Hunter [email protected] University of Stirling Key Words Water quality microbial pollution sensor networks modelling forecasting Using sensors, satellites and artificial intelligence to improve catchment-scale modelling and nowcasting of microbial risks to water quality Hazards: Processes-Risk-Resilience IAP2-21-356 2/4 FIOs can exhibit pronounced within-day variability sufficient to influence bathing water classifications. In this context, the recent emergence of novel coliform sensors has the potential to transform early-warning monitoring of microbial water quality not only through the direct delivery of RT information but also by improving the provision of data for assimilation in predictive models. In addition, the increased availability of open and free satellite data (e.g. Copernicus), which can be used to derive a suite of catchment-relevant variables, has the potential to improve spatial representations of these parameters within modelling frameworks. These new data streams not only offer opportunities to improve existing model parameterisations, but also to enable the evaluation of more innovative, data-driven, modelling approaches based on artificial intelligence that have very recently been shown to improve short-term predictions of microbial pollution elsewhere.

The application deadline is January 7th at 17:00. By this deadline applicants must have filled in the IAPETUS online application from following instructions here: https://www.iapetus2.ac.uk/how-to-apply/. The application form requires you to write several sections of text about your interest in this PhD and your suitability for PhD research. Serious applicants are strongly advised to make contact with SUPERVISOR NAME HERE by email well before the deadline to discuss their application.After making the application, candidates will be shortlisted for the next stage of the IAPETUS DTP selection process.       


Funding Notes

Applications are open to UK (and EU nationals in the UK settlement scheme) as well as non-UK applicants from the rest of the world (although there is a limit on the number of studentships that can be offered to non-UK applicants).


References

Richardson J., Feuchtmayr H., Miller C., Hunter P.D., Maberly S.C., and Carvalho L.
(2019). Response of cyanobacteria to warming, extreme rainfall events and nutrient
enrichment. Global Change Biology. 25, 3365-3380.
4/4
Quilliam R.S., Taylor J., and Oliver D.M. (2019). The disparity between regulatory
measurements of E. coli in public bathing waters and the public expectation of
bathing water quality. Journal of Environmental Management 232, 868-874
Porter K.D.H., Reaney S.M., Quilliam R.S., Burgess C. and Oliver D.M. (2017).
Predicting diffuse microbial pollution risk across catchments: the performance of
SCIMAP and recommendations for future development, Science of the Total
Environment, 609, 456-465
Oliver D.M., Porter K.D.H., Pachepsky Y.A., Muirhead R.W., Reaney S.M., Coffey
R., Kay D., Milledge D.G., Hong E., Anthony S.G., Page T., Bloodworth J.W.,
Mellander P-E., Carbonneau P.E., McGrane S.J. and Quilliam R.S. (2016).
Predicting microbial water quality with models: over-arching questions for managing
risk in agricultural catchments, Science of the Total Environment, 544, 39-47


POSITION TYPE

ORGANIZATION TYPE

EXPERIENCE-LEVEL