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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 <strong>USGS</strong> 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.
Use of Surrogate Models To Estimate Suspended Sediment 17 of unregulated flow passing the site, turbidity, the square of 3-MHz_ABS corr , and ratios of attenuation and backscatter for the 3- and 0.5-MHz ADVMs. The fraction of unregulated flow term is discussed in more detail in Wood (2010). None of the variables substantially improved the regression statistics or SSC estimates in comparison with the base model using 3-MHz_ABS corr alone. At low SSC (
Page 1 and 2: Prepared in cooperation with the U.
Page 3 and 4: Use of Surrogate Technologies to Es
Page 5 and 6: iii Contents Abstract .............
Page 7 and 8: v Conversion Factors and Datums Con
Page 9 and 10: Use of Surrogate Technologies to Es
Page 11 and 12: Introduction 3 The Lower Granite Lo
Page 13 and 14: Site Descriptions and Surrogate Ins
Page 15 and 16: Site Descriptions and Surrogate Ins
Page 17 and 18: Methods 9 Surrogate Instrument Data
Page 19 and 20: Methods 11 Acoustic Absorption by S
Page 21 and 22: Use of Surrogate Models To Estimate
Page 23: Use of Surrogate Models To Estimate
Page 35 and 36: References Cited 27 Summary and Con
Page 37 and 38: References Cited 29 SonTek/Yellow S
Page 39 and 40: Publishing support provided by the

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