Quantifying the efficacy of in-situ Natural Flood Management (NFM) through monitoring and model predictions including uncertainty

Natural Flood Management (NFM) is an approach that seeks to reduce flood risk by protecting, restoring and emulating the natural function of catchments, rivers, floodplains and coast. NFM is one of a range of mitigation measures that is being considered to improve the resilience of downstream communities affected by flooding. It is widely accepted that a better evidence base is required in order to deploy NFM as a standard approach to managing flood risk. This needs to characterise the efficacy of the different approaches available and their deployment in the landscape to maximise cost-effectiveness. Climate predictions suggest the UK will receive an increase in winter rainfall, more extreme events and increased rainfall intensities . New approaches are needed to cope with the anticipated increased flooding associated with the changing climate. Therefore fundamental research is needed to inform the on-going national debate on the efficacy of NFM within an overall strategy of integrated catchment management for alleviating flood risk to vulnerable communities.

This project aims to provide the evidence base for quantifying the effectiveness of NFM measures so provide better cost-benefit analyses and strategic deployment of NFM approaches. The project will achieve this through the implementation of novel field based monitoring of extensive NFM infrastructure in the River Parrett and Tone catchments. This evidence will then underpin the development and characterisation of these features in spatially distributed rainfall-runoff models that can be used to test different configurations and spatial connectivities in the landscape.

The NERC CASE partners Natural England (NE) and FWAG SW have invested substantially in the installation of NFM measures in the Parrett and Tone catchments. However as yet the effectiveness of these combined structures has not been quantified. This research will take advantage of this extensive infrastructure by monitoring their dynamics during storm events using novel sensors of cameras and level gauges.

This evidence will enable the further development of Dynamic TOPMODEL, a catchment rainfall-runoff model that simulates hydrological connectivity using spatially derived Hydrological Response Units (HRU's). The model can resolve landscape features down to a few meters such as NFM controls. These developments will result in a dynamic catchment modelling system that will test different types and configurations of NFM measures in the landscape. For the first time this will ensure the evidence base for NFM is coupled to the modeling and the inherent uncertainties quantified.