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16 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10
of sediment, relative magnitudes of the tributary flows,
timing of releases of stored water for water management, and
proximity of episodic sediment sources. The uncertainty and
stochasticity of the relations between these factors motivated
the USACE’s interest in the use of surrogate sediment
measurement technology in this study.
The selected regression based on 3-MHz acoustic
backscatter represented 93 percent of the variability in SSC
and resulted in an average RPD between measured and
estimated SSC of +8.6 percent. Standard error for the 3-MHz
model was lower and variance in residuals was lower and
more constant than for all other models indicating best fit.
Best agreement (lowest RPD) was observed when fines were
between about 70 and 85 percent of total SSC. Estimates of
SSC when the sand fraction was high were not substantially
improved by using the model with the 0.5-MHz ADVM, even
when using a model that did not incorporate the continuously
measured noise level. This is likely because most of the sand
fraction is very fine and fine sand (100 mg/L)
(fig. 6). The upper and lower 95-percent confidence level for
the sample with highest concentration, 210 mg/L, plotted
well below the value estimated by the surrogate model. RPD
for individual observations ranged from -43 to +80 percent,
but most of the high RPDs occurred at low SSC, when small
differences between estimated and measured values can result
in high percent differences. At high SSC (>100 mg/L), mean
RPD was -33 percent, meaning that in general, the regression
model underestimated measured SSC when high. A possible
reason for model underestimation at high SSC is that more
sand is transported during these periods. Sand may travel
lower in the water column than finer materials owing to higher
mass and may not be captured within the sampling volume
of the ADVMs, which are installed approximately mid-depth
in the water column. An additional source of error may have
been that 4 of the sample concentrations (5.1, 19, 38, and
104 mg/L) used to develop the surrogate model were averages
of 10 concentrations of discrete samples collected across the
cross section. However, none of these sample concentrations
appear to be highly influential in the regression (fig. 5) so
likely do not contribute to substantial model error. The USGS
evaluated whether the inclusion of additional explanatory
variables in the regression would improve estimates at high
SSC. Some of the evaluated variables included the fraction
Estimated suspended-sediment concentration,
in milligrams per liter
250
200
150
100
50
EXPLANATION
Upper 95-percent confidence level
Observation
Lower 95-percent confidence level
1:1 Line
0
0 50 100 150 200 250
Measured suspended-sediment concentration, in milligrams per liter
Figure 6. Measured and estimated total suspended
sediment concentrations in the Clearwater River at
Spalding, Idaho, based on a surrogate model with
acoustic backscatter.