Development of Real-Time Surface Water Abstraction Management Tools

Efficient use of available water resources to meet demand, whilst maintaining the quality of the aquatic environment has become increasingly important. Water quality challenges associated with diffuse agricultural pollutions have also become widely recognized problems globally. This thesis presents the development of new approaches to improve surface water abstraction management with a view to mitigate the challenges associated with increasing pressures on availability of water resources for public water supply and diffuse agricultural pollution. The first part of the thesis presents the development of a real-time surface water abstraction management scheme that integrates a conceptual rainfall-runoff model, a Bayesian inference based uncertainty analysis tool and a water resources management model that incorporates various operating rules to represent real-world operational constraints. The developed approach enables efficient utilization of available water resources and thus provides improved capability to deal with emerging issues of increasing demand, climate adaptation planning and associated policy reforms. The second part of the thesis describes the development of a new travel time based physically distributed metaldehyde prediction model, which enables water infrastructure operators to consider informed surface water abstraction decisions. Metaldehyde is a soluble synthetic aldehyde pesticide used globally in agriculture and has caused recent concerns due to high observed levels in surface waters utilized for potable water supply. The model provides new approach to represent spatially and temporally disaggregated runoff generation, routing and build-up/wash-off processes using a grid based structure in a GIS environment. Furthermore, a state-of-the-art Monte Carlo based spatial uncertainty analysis tool is employed to assess uncertainties in the metaldehyde prediction model. The structure of the metaldehyde model combined with the availability of high spatiotemporal resolution data has enabled the application of spatial uncertainty analysis of the catchment scale metaldehyde model, which is currently lacking in water quality modelling studies