Environmental Engineering Reference
In-Depth Information
1400
2,830
1200
283
Streamflow
1000
28.3
800
600
2.83
Fig. 1.6
Comparison of continuous
measurements of streamfl ow and
turbidity, April 12-May 24, 2002, for
USGS streamgage on the Kansas River
at DeSoto, Kansas, USA. Turbidimeter
saturation occurs around April 22 and
May 14.
Adapted from Rasmussen
et al.
(2005).
400
Turbidity
0.283
200
0
0.0283
Month/day/year
response at SSCs less than about 2 g/L for clay and
silt, and 10 g/L for sand (Ludwig & Hanes 1990),
although Kineke & Sternberg (1992) describe the
capability to measure SSCs up to about 320 g/L (in
the nonlinear region of the OBS response curve).
Specifi cations for an OBS instrument marketed by
Campbell Scientifi c, Inc. (2008) lists an applicable
range of 50-500 g/L; however this should be verifi ed
by the user for local sediment characteristics. The
upper SSC limit for transmissometers depends on
optical path length, but may be as low as about
0.05 g/L (D & A Instrument Co. 1991). Thus, trans-
missometers are more sensitive at low SSCs whereas
OBS sensors have superior linearity in highly turbid
water (Downing 1996) and are less prone to signal
saturation.
Because of the relation between turbidity and PSD,
inferences of SSCs from turbidity measurements (like
all single-frequency optical and acoustical instru-
ments) are best suited for application at sites with
relatively stable PSDs. OBS signal gain is inversely
related to grain size (Sutherland
et al.
2000).
Laboratory investigations of Conner & De Visser
(1992) indicate OBS signal gain is minimally affected
by changes in PSD in the range 200-400
pended sediment. They found the smallest OBS sig-
nal-gain response for black sediment and the largest
for white sediment, with responses from other colors
falling between. They suggest that the level of black-
ness of particles acts to absorb the near-infrared
signal of the OBS, thus modifying its output. Hence,
caution should be exercised in deployments under
varying PSD and particle-color conditions, unless the
instrument is recalibrated for ambient conditions.
Turbidity is often proportional to SSC in the water
column within the measuring range of the sensor.
Empirical relations between turbidity and SSC have
been modeled using linear regression analysis
(Walling 1977; Gilvear & Petts 1985; Buchanan &
Schoellhamer 1995; Lewis 1996; Christensen
et al
.
2000; Uhrich & Bragg 2003; Lietz & Debiak 2005;
Rasmussen
et al
. 2005). If continuously monitored
water-discharge and turbidity data are available on
the same time interval for a site, the derived unit-
value SSCs can be multiplied by their paired water-
discharge data to compute continuous SSL without
the need for interpolation or estimation. When the
turbidity-SSC model is considered adequate as
described below, continuous turbidity data cali-
brated with SSC data from samples collected over a
range of fl ows can provide a more reliable and repro-
ducible SSC time series. When the turbidity-SSC
model is considered inadequate, use of water dis-
charge and turbidity may improve model perform-
ance suffi ciently to justify use of the bivariate model
to produce an SSC time series. Upon derivation of
an acceptable SSC time series, SSL can be computed
from these data and their paired water-discharge
m but
greatly affected by changes when particles are smaller
than about 100
μ
m. They caution against using OBS
when changes in the PSD occur and the suspended
material is less than 100
μ
m. Additionally, the OBS
signal can vary as a function of particle color.
Sutherland
et al.
(2000) found a strong correlation
between observed and predicted OBS measurements
of varying SSCs and ratios of black and white sus-
μ
