Quantifying the likely magnitude of nature-based flood mitigation effects across large catchments (Q-NFM)

The 2007 floods prompted the UK Government's "Pitt review", which came up with the idea that we need to start to deal with the causes of flooding upstream of the affected communities, rather than rely solely on the downstream engineering solutions. This stimulated a range of organisations to introduce "natural" features into the landscape that may have benefits in terms of reducing flooding (so called "Natural Flood Management, NFM"). Having introduced features these organisations, and local stakeholders working with them, are increasingly asking "Are these features working?" This has highlighted to funders, those implementing the features and scientists alike that there are gaps in the evidence of how individual features (e.g. a single farm pond or a small area of tree planting) work and what are potential downstream benefits for communities at risk of flooding. Stakeholders want both questions answered at the same time, making this one of the most important academic challenges for hydrological scientists in recent years. The only way to quantify the effects of many individual features at larger scales is to use computer models. To be credible, these models also need to produce believable results at individual feature scales. Meeting this challenge is the focus of this research project. Consequently, our primary objective is to quantify the likely effectiveness of these NFM features for mitigating flood risk at large catchment scales in the most credible way. In this context, credibility means being transparent and rigorous in the way that we deal with what we do know and what we don't know when addressing this problem using models. In doing this we need to address particular scientific challenges in the following ways: * We need to show that our models are capable of reproducing downstream floods while at the same time matching observed local hydrological phenomena, such as patterns of soil saturation. Integral to our methodology are observations of these local phenomena to further strengthen the credibility of the modelling. * We use the same models to predict NFM effects by changing key model components. These changes to the components are made in a rigorous way, initially based upon the current evidence. * As evidence of change is so critical, our project necessarily includes targeted experimental work to address some of the serious evidence gaps, to significantly improve the confidence in the model results. * This rigorous strategy provides us with a platform for quantifying the magnitude of benefit that can be offered by different spatial extents of NFM implementation across large areas. By addressing these scientific goals we believe that we can deliver a step change in the confidence of our quantification of the likely effectiveness of NFM measure for mitigating flood risk at large catchment scales.