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MPhil in Engineering for Sustainable Development

global challenges, engineering solutions

Studying at Cambridge

 

James Wark

Evaluating rural water management schemes: Optimising Natural Flood Management (NFM) benefits using AHP

NFM are gaining traction in UK land management practice and flood management policy for their ability to reduce flooding while providing additional social and environmental benefits. However, difficulties arise when choosing the most suitable NFM to implement; their outcomes are not yet clearly established and vary based on catchment, causing the most optimal benefits in different catchments to be uncertain. This report has analysed the relevant dominant benefits—those benefits most preferential and likely to have the greatest effect due to catchment characteristics—for the Swindale Valley. AHP has been used to assess eight NFM measures identified as most useful to the locale. Each measure was assessed across seven benefit categories, the relevant dominant benefits for the site, which were identified as key to stakeholders including: land tenants, recreational visitors, wider public, farmers, and conservation groups. The results indicate that four measures are particularly preferential to the context of Swindale Valley and other upland pastoral catchments with similar attributes: embankment removal, floodplain woodland, riparian woodland, and wider catchment woodland. Sensitivity analysis has been used to account for uncertainties in the AHP and shows that the results are relatively stable to both changes in NFM data and stakeholder preferences. The results differ from those NFM actually implemented in Swindale Valley, likely because NFM projects typically allow for several NFM to be implemented. Given the four highest ranking NFM measures all scored relatively similar in the AHP, further NFM implementation in upland pastoral catchments could benefit from exploring the harmonies between prioritised NFM measures. Future application of AHP should address stakeholder preferences and incorporate more contextualised data to increase the quality of decisions and aid implementation of chosen measures.