Quantifying the efficacy of Natural Flood Management in agricultural headwater catchments

  • Tamsin H Lockwood

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)


Natural Flood Management encompasses a range of measures which aim to restore hydrological and morphological catchment features, slow, store, and filter hillslope and channel flows and decrease downstream flood risk. Whilst traditional forms of flood management have an extensive evidence base to inform their design and management, there is currently only a small quantity of datasets that characterise and assess the function, and impact of more natural forms of flood management. In response, this thesis presents multiple chapters that have assessed the function and impacts of a range of NFM measures, including a focus on the role and implications of design, function, and management, though the analysis of generated empirical datasets for agricultural SW-England headwater catchments.

An event definition methodology was developed to separate rainfall events and quantify the impact of channel-based NFM structures for storing and slowing flow. During the largest recorded events, marked peak flow reduction downstream of offline water storage ponds (up to 7%) and leaky woody dams (up to 56%) was shown. An assessment of NFM management tasks highlighted the marked impact of site maintenance to sustain the storage function of an online pond site, where outflow management was demonstrated to have a greater impact on flow storage than the recorded rainfall inputs. Evaluating the impact of subsoiling for NFM through single-ring infiltration and rainfall simulation methods demonstrated the potential for targeted agricultural management to reduce overland flow, despite high observed inter-field variability.

This thesis contributes to the UK NFM empirical evidence base, presenting case study examples and quantified hydrological parameter values that have analysed the impact of NFM measures in response to rainfall. This information is important for use as observational data in future modelling applications and to provide design and management recommendations for future NFM applications.
Date of Award27 Sept 2022
Original languageEnglish
Awarding Institution
  • University of Bristol
SupervisorGemma Coxon (Supervisor), Jim Freer (Supervisor) & Katerina Michaelides (Supervisor)

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