2020 CSDMS meeting-040


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Assessing the influence of wood on stochastic sediment transport theory

Miles Clark, University of East Anglia Norwich , United Kingdom. m.clark@uea.ac.uk
Georgie Bennett, University of Exeter, University of East Anglia Exeter, Devon , United Kingdom. G.L.Bennett@exeter.ac.uk
Sandra Ryan-Burkett, US Forest Service Fort Collins Colorado, United States. sandra.e.ryan-burkett@usda.gov
Aldina Franco, University of East Anglia Norwich , United Kingdom. A.Franco@uea.ac.uk
David Sear, University of Southampton SOUTHAMPTON, UNITED KINGDOM , United Kingdom. D.Sear@soton.ac.uk

Fluvial bedload is a fundamental process by which coarse sediment is transferred through landscapes by fluvial action and is characterized by cyclic sequences of particle motion and rest. Bedload transport has many complex physical controls but may be well described stochastically by distributions of grain step length and rest time obtained through tracer studies. However, none of these tracer studies have investigated the influence of large wood on distributions of step length or rest time, limiting the applicability of stochastic sediment transport models in these settings. Large wood is a major component of many forested rivers and is increasing because of disturbances such as wildfire and insect infestations as well as the use of wood in rivers as part of ‘natural flood risk management’ practice in the UK. This study aims to investigate and model the influence of large wood on grain-scale bedload transport.

St Louis Creek, an alpine stream in the Fraser Experimental Forest, Colorado, is experiencing increased wood loading resulting from the infestation of the mountain pine beetle in the past decades. We inserted 957 Passive Integrative Transponders (PIT) tagged cobbles in 2016 upstream of a wood loaded reach and measured and tagged >20 pieces of large wood in the channel. We resurvey the cobbles and wood on an annual basis after snowmelt, building distributions of rock-step lengths and rest time distributions as well as observing any changes and transport of large wood. Additionally, we are developing novel active tracer tags, with integrated accelerometer technology, which will help to constrain these distributions and investigate the influence of woody debris.

We observed increased probabilities of grain deposition around large wood in the first 3 years of resurvey data, and preliminary statistical analysis suggests a significant influence of wood presence, and its relative stream position, on transport likelihood and distance, although additional annual data is required to verify its reproducibility. Over the next two snowmelt seasons, active tags will provide detail on the transport behaviour of cobbles at unprecedented levels, allowing us to refine stochastic bedload transport models in environments where biota is significantly interacting with earth surface processes.