Estimating bedload transport rates in a gravel-bed river using seismic impact plates: Model development and application. (April 2017)

• Record Type: Journal Article
• Authors: Soar, Philip J.; Downs, Peter W.
• Is Part Of: Environmental modelling & software. Volume 90(2017)
• Page Start: 182
• Page End: 200
• Publication Date: 2017-04
• Subjects: Coarse bedload transport -- Fluvial geomorphology -- Monte Carlo simulation -- Sediment monitoring -- Seismic impact plate -- Uncertainty; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.01.012
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: A model is developed for probabilistic bedload transport rates from impact counts. An alternative class of estimation to direct measurement and transport formulae. Data-driven method does not rest on calibration to measured loads. Application to UK gravel bed river exemplifies supply-limited cross-sectional rates. Facilitates integration of high resolution data into river management and research.