Abstract
A data-driven, uncertainty-bound estimation technique for bedload transport rates is developed based on passive sensing devices. The model converts sediment samples to a mass in transit for each instantaneous discharge according to impacts detected and a Monte Carlo simulation of the load determined at random from the particle size distribution. Using impact count data autogenically produces a supply-limited, location-specific and high-resolution time-series of bedload rates, while the probabilistic approach inherently accommodates the stochastic nature of bedload transport. Application to the River Avon (Devon, U.K.) provides cross-sectional bedload rate estimates within the bounds of experimental data and calibrated to observed field behaviour. This new procedure offers an alternative ‘class’ of bedload estimation to existing approaches and has the potential for wide-ranging applications in river management and restoration, while contributing to the integration of ‘big data’ into a progressive agenda for hydrogeomorphology research.
DOI
10.1016/j.envsoft.2017.01.012
Publication Date
2017-04-01
Publication Title
Environmental Modelling & Software
Volume
90
Publisher
Elsevier BV
ISSN
1364-8152
Embargo Period
2024-11-25
First Page
182
Last Page
200
Recommended Citation
Soar, P., & Downs, P. (2017) 'Estimating bedload transport rates in a gravel-bed river using seismic impact plates: Model development and application', Environmental Modelling & Software, 90, pp. 182-200. Elsevier BV: Available at: https://doi.org/10.1016/j.envsoft.2017.01.012
Comments
publisher: Elsevier articletitle: Estimating bedload transport rates in a gravel-bed river using seismic impact plates: Model development and application journaltitle: Environmental Modelling & Software articlelink: http://dx.doi.org/10.1016/j.envsoft.2017.01.012 content_type: article copyright: © 2017 Elsevier Ltd. All rights reserved.