Abstract
Estimates of fluvial sediment discharge from in situ instruments are an
important component of large-scale sediment budgets that track long-term
geomorphic change. Suspended sediment load can be reliably estimated
using acoustic or physical sampling techniques; however, bedload is
difficult to measure directly and can consequently be one of the largest
sources of uncertainty in estimates of total load. We propose a
physically-informed predictive empirical model for bedload sand flux as
a function of variables that are measured using existing acoustic or
physical sampling techniques. This model depends on the assumption that
concentration and grain size in suspension are in equilibrium with
reach-averaged boundary conditions. Bayesian inference is used to fit
model parameters to data from eight sand-bed rivers and to simulate
bedload flux over the available gage record at one site on the Colorado
River in Grand Canyon National Park. We find that the cumulative bedload
flux during the nine year period from 2008 to 2016 was
5\% of the cumulative suspended sand load; however,
instantaneous bedload flux ranged from as little as 1\%
of instantaneous suspended sand load to as much as 75\%
of instantaneous suspended sand load due to fluctuations in flow
strength and sediment supply. Changes in bedload flux at a constant
discharge are indicative of short-term sediment supply enrichment and
depletion. Long-term average bedload flux cannot be expected to remain
constant in the future as the river adjusts to changes in sediment
runoff and the dam-regulated discharge regime.