A model for quantifying the effectiveness of leaky barriers as a flood mitigation intervention in an agricultural landscape

Martha Lucia Villamizar Velez, Chris Stoate, Jeremy Biggs, John Szczur, Penny Williams, Colin David Brown

Research output: Contribution to journalArticlepeer-review


Leaky barriers have become an important mitigation option within natural flood management to reduce downstream flood risk. Modelling is a key tool to aid in the design of leaky barrier installations for flood mitigation, but there is limited evidence about the accuracy of model representations of the system. Here, the hydrological model SWAT was combined with a water routing model that simulates multiple leaky barriers as permeable sluice gates. Storage behind individual barriers was quantified using barrier dimensions and LIDAR topography. The model was applied to a series of 27 leaky barriers installed as part of a long-term manipulation experiment into a 11-km2 intensive lowland agricultural catchment in Leicestershire, England. Evaluation of the model against flow data collected before and after leaky barrier installation and time-lapse photography taken across storm events at individual barriers demonstrated robust model performance (Nash-Sutcliffe efficiency and R2 across 19 validation events were 0.84±0.14 and 0.91±0.08, respectively). Empirical and modelling data were then combined to demonstrate that the 17,700 m3 of water storage provided by the 27 leaky barriers reduced peak flows at the catchment outlet by 22±6% and delayed the peak in flow by up to 5 h for 11 storm events recorded after all barriers had been installed. The volume of storage utilised prior to the flood event was a key factor influencing the reduction in peak flow, and a sensitivity analysis indicated that barriers should be permeable to accelerate drain-down of the barrier and help to mitigate risk from multiple storm events occurring in sequence.
Original languageEnglish
Pages (from-to)365-378
JournalRiver Research and Applications
Issue number3
Early online date13 Jan 2024
Publication statusPublished - 5 Mar 2024

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