Assessing the efficacy of offline water storage ponds for natural flood management

Land use changes, landscape modifications and changing climate have resulted in local to regional flood risk increases in recent decades. As an alternative to traditional engineering approaches, there has been a movement towards catchment-based flood risk management, a subset of which is natural flood management (NFM). NFM aims to enhance flood resilience through the slowing and storing of runoff and flow, based on the restoration and augmentation of hydrological and morphological catchment features. However, despite research highlighting their potential benefits, there is a limited quantity of robust and science-based empirical evidence on how these structures function in the landscape and their efficacy in reducing flood hazards. To address this knowledge gap and contribute to the growing NFM evidence base, this study evaluates the efficacy of offline water storage ponds for flow attenuation. Two contrasting pond sites in the Tone and Parrett catchment headwaters (SW-England) were monitored from April 2018 to December 2020 for channel flow, pond volume, and rainfall. Field-scale, high resolution (1.61 cm spatial support) digital elevation models from Structure-from-Motion and manual surveys were collected to quantify pond dynamics and connectivity. A comprehensive, storm definition methodology was developed for event separation, to enable quantification of the impact of pond behaviour on stream flow, across storm hydrographs. Results show that where offline ponds function as designed, with direct pond filling from the channel, peak flow attenuation can reach 7% (maximum event AEP of 83%). Smaller events, where pond filling occurs directly from rainfall or runoff display only a maximum of 3% peak flow reduction (event AEP from 83% –>99%). The ability of ponds to attenuate flow is heavily reliant on sufficient structural conditions for ponds to fill directly from the channel and to drain slowly following event peaks. This study provides an empirical database for future NFM applications, including key criteria for future design and for use as observational data in modelling applications, by upscaling the efficacy of ponds to reduce flood impacts at larger scales.