Quantifying and targeting the multiple benefits of nature based solutions at the catchment scale

Water quality modelling at the operational management catchment scale suffers from large epistemic uncertainties with reduced monitoring and uncertain processes that are rapidly changing with the climate. Assessing the effectiveness of many distributed nature-based solutions (NbS) that change hydrological and geochemical processes in the landscape to help mitigate diffuse nutrient pollution can be even more beset by uncertainties and lead instead to a focus on asset-only improvements, and a corresponding loss of multiple benefits associated with NbS.

Rather than taking a complex integrated catchment modelling approach, we focus here on using regulatory, data-based model, SIMCAT, to first understand the least change in diffuse load NbS must deliver to improve WFD status. This shift can be monetised and combined with other co-benefits of NbS including water resource, habitat and carbon estimated here from additional models. This helps identify waterbodies where the least effort on behalf of NbS is required to improve the status, and these areas can be refined by combining with waterbodies with the greatest potential for NbS. This potential has been mapped in the UK delineating areas for potential wetland restoration, woodland planting, ponds and floodplain restoration. These have been combined and the intersected area of these for each of the WFD waterbodies can then help prioritise further, and assessed against water company plans for future asset-improvements. The process results in a multi criteria analysis where we explore trade-offs between different benefits and mixed solutions that include pipeline future asset improvements.

Having collaboratively agreed the weightings in the MCA and agreed the target WFD waterbody catchments where NbS will be most effective, we introduce an additional step of using a novel 10m gridded risk map from the Fieldmouse model, which identifies pixels in the landscape with greatest load and connectivity to the classification point.  This is used as a heat map to refine which part of the mapped NbS elements would make the greatest difference and where for instance grant allocation can be focussed. The modelling tool can also quantify the reduction in load, although this can still be quite uncertain, the measures are located where they are likely to make the most difference.