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  • 2017metcalfephd

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Development of a modelling framework for integrated catchment flood risk management

Research output: ThesisDoctoral Thesis

Publication date2017
Number of pages306
Awarding Institution
Thesis sponsors
  • JBA Trust
  • European Regional Development Fund
Award date28/11/2017
  • Lancaster University
<mark>Original language</mark>English


Flooding is one of the most significant issues facing the UK and Europe. New approaches are being sought to mitigate its impacts, and distributed, catchment-based techniques are becoming increasingly popular. These employ a range of measures, often working with the catchment’s natural processes, in order to improve flood resilience. There remains a lack of conclusive evidence, however, for the impacts of these approaches on the storm runoff, leading to considerable uncertainty in their effectiveness in terms of mitigating flood risk.
A new modelling framework for design, assessment, and uncertainty estimation of such distributed, nature-based schemes is developed. An implementation of a semidistributed runoff model demonstrates robustness to spatio-temporal discretisation. Alongside a new hydraulic routing scheme, the model is used to evaluate the impacts on flood risk of in-channel measures applied within an 29 km2 agricultural catchment. Maximum additional channel storage of 70,000 m3 and a corresponding reduction of 11% in peak flows is seen. This, however, would not have been insufficient to prevent flooding in the event considered.
Further modifications allow simulation of the impacts of wider measures employing natural processes. This is applied within an uncertainty estimation framework across the headwaters of three mixed-use catchments, ranging in size from 57 km2 to 200km2 , across a series of extreme storm events. A novel surface routing algorithm allows simulation of large arrays distributed features that intercept and store fast runoff. The effect of the measures can be seen across even the most extreme events, with a reduction of up to 15% in the largest peak, albeit that this large impact was associated with a low confidence level.
The methodology can reflect the uncertainty in application of natural flood risk management with a poor or incomplete evidence base. The modelling results demonstrate the importance of antecedent conditions and of the timings and magnitudes of a series of storm events. The results shows the benefits of maximizing features’ storage utilisation by allowing a degree of “leakiness” to enable drain-down between storms. An unanticipated result was that some configurations of measures could synchronise previously asynchronous subcatchment flood waves and have a detrimental effect on the flood risk.
The framework shows its utility in both modelling and evaluation of catchment-based flood risk management and in wider applications where computational efficiency and uncertainty estimation are important.