Suspended sediment concentration and turbidity during
storms.
Version November 2014, completed for EPA grant SFEP E2100-2, funded by the EPA San Francisco Bay
Water Quality Improvement Fund.
Summary of methods:
Former SEC staffer Rebecca Lawton, who initiated SEC’s sediment monitoring program and ran it for 10
years, trained SEC staff and volunteers from Glen Ellen Rotary in the office and at Station A on
December 4, 2013 to assure compliance with the suspended sediment and turbidity QAPP. The relevant
QAPP is Quality Assurance Project Plan for Volunteering Monitoring of Suspended Sediment
Concentration and Turbidity, Sonoma Creek Watershed, Sonoma County, CA. Staff and volunteers
collected water sample bottles (for suspended sediment concentration measurement in the lab) and
turbidity measurements around the clock at five stations along Sonoma Creek during the storm of
February 7-10, 2014. Turbidity measurements are obtained in the field using a turbidometer, with no
samples brought back from the field. Suspended sediment concentration requires lab processing. Staff
and volunteers processed the over 100 sample bottles in SEC’s lab. Processing entails weighing the
water in the sample bottle, filtering the sediment from the water, drying the sediment, and weighing the
sediment. The weights of the water and the sediment enable calculation of the weight of sediment per
volume of water. Staff and an intern conducted data analysis and produced summary graphs comparing
2014 findings with past findings. The results are not significantly different from past findings: acute
suspended sediment levels during storms are physiologically harmful to steelhead. Data, methods,
findings, and graphs are available on the Sonoma Valley Knowledge Base.
Previous Work:
Collecting, processing, and analyzing suspended sediment concentration and turbidity data for this
project continues Sonoma Ecology Center’s monitoring of these parameters begun in 2002, overseen for
10 years by Rebecca Lawton. Past findings informed foundational Sonoma Ecology Center documents:
the Sediment Source Analysis (Lawton, editor, 2006) and the Limiting Factors Analysis for Steelhead
(Micheli, editor, 2006). Both documents informed the analysis conducted by the San Francisco Bay
Regional Water Quality Control Board for the sediment Total Maximum Daily Load (TMDL) for Sonoma
Creek.
Grab Sampling:
“SEC staff established turbidity and SSC grab-sampling stations at 6 monitoring locations in the Sonoma
Creek watershed. Grab sampling, primarily done during and directly following rainfall, consisted of the
simultaneous filling of one 15-milliliter (mL) HACH cell and one 500-mL SSC (Suspended sediment
concentration) sample bottle with stream water. Turbidity cells were analyzed in the field using a HACH
2100P turbidimeter. SSC sample bottles were delivered under chain-of-custody protocols to the M.U.D.
Laboratory at the Sonoma Valley Watershed Station, Eldridge, California. Methods used for analyzing
the SSC grab samples derive in part from the Redwood Sciences Laboratory Standard Operating
Procedures for SSC Determination and from Standard Methods (2540B-Total Solids Dried at 103 to 105
degrees Celsius [C]).”
Field and lab data were inputted word for word into Excel. Data was organized by station and time and
was cross checked for input and math errors. 10% of all values were checked for accuracy. For samples
that were split, the sediment weights were merged. For each final sediment weight (g) run a conversion
to SSC using the following formula as found in the 2011 QAPP.
𝑚𝑔
𝑆𝑒𝑑𝑖𝑚𝑒𝑛𝑡 𝑊𝑒𝑖𝑔ℎ𝑡
𝑆𝑆𝐶 ( ) =
× 1,000,000
𝑆𝑒𝑑𝑖𝑚𝑒𝑛𝑡
𝑊𝑒𝑖𝑔ℎ𝑡
𝐿
+ (𝑇𝑜𝑡𝑎𝑙 𝐿𝑖𝑞𝑢𝑖𝑑 𝑊𝑒𝑖𝑔ℎ𝑡 − 𝑆𝑒𝑑𝑖𝑚𝑒𝑛𝑡 𝑊𝑒𝑖𝑔ℎ𝑡)
2.65
Using this data, linear regressions were run for each station and for all of the stations together.
Automated Data Collection:
The automated data collection system at Station A (STA) at Sonoma Valley Watershed Station,
Eldridge, California, measures stream depth, turbidity, and rainfall. Readings were taken at 15-minute
intervals. Data was downloaded monthly from STA and uploaded into Excel spreadsheets on computers
at the Sonoma Valley Watershed Station.
There are 2 turbidity sensors at STA, each with a high and low sensitivity reading. These report data in
millivolts, which can be converted to NTUs based on the conversion factors given by the manufacturer.
Since there are three different conversion factors listed for different readings, an overall formula was
derived and a conversion factor was made for each millivolt reading.
NTU conversion= 7E-05(mV) + 0.1111
NTU = NTU conversion * mV
The maximum value that the current sensor can read is 2,000 NTU, therefore the next step was to throw
out any data greater than that. The data was then smoothed – using the grab samples as the standard,
each of the 4 sensor readings were examined to see which was the most accurate. If they were all close,
or more than one of them were close, an average was taken. If none of them were accurate, an average
was taken of the previous and next time stamps. The low sensitivity reading is nominally 1/4 the value
of the high sensitivity readings. As the water becomes cloudier, the high sensitivity reading will saturate
and flat-line. At that point we switched over to using the low-sensitivity channel (Allen, 2014). ). Data
smoothing was completed from the time of the first rainfall to when NTU is less than 49 based on the
previous protocols established (Lawton & Flores, 2006).
The sample time of each grab sample at STA was matched
to the nearest automated sample, accounting for daylight
savings time. Once matched, we created a graph of SSC (in
grab samples from STA) against turbidity (in automated
readings at STA) in a natural log relationship that fits a
power curve.
𝑆𝑆𝐶 = 1.4653 × 𝐴𝑢𝑡𝑜 𝑁𝑇𝑈 0.8896
Assessing Biological Significance:
As in past analyses, we assessed the meaning and
significance of the stream condition data by correlating it
directly to impacts on salmonids, as empirically derived by
Newcombe and Jensen (1996) and communicated using their severity index.
To arrive at a ranking in the severity index, we analyzed automated turbidity data from STA along with
correlated SSC values using methods developed by Newcombe and Jensen (1996). To start, data from
the USGS Kenwood gauge was used to determine when discharge approximated 100 cubic feet per
second and to identify potential first flush storms for past years. Data to match the first flush was then
found in the STA records. Duration of each storm was calculated from first rainfall (using the rain gauge
at STA) to when turbidity approximated 49 NTU.
Each previous year’s storm was smoothed in the process described above. SSC was calculated for each
automated NTU value using the relationship derived in the power curve. The SSC values were sorted by
value and then placed in bins set by Newcombe and Jensen using the frequency function of excel.
Exposure to each SSC bin range was calculated.
From hours exposed and SSC, a suspended sediment dose index for STA was calculated:
Suspended Sediment Dose Index = natural log (SSC x Hours Exposed)
(Examples: Using this equation, exposure to 3.13 mg/L SSC for 24 hours results in a dose index of 4.
Similarly, exposure to 75.19 mg/L SSC for just 1 hour results in a dose index of 4 [Fitzgerald, 2004].)
Next we correlated the calculated SSC dose indices from Sonoma Creek to the Newcombe and Jensen
severity index (1996) based on analyses of salmonid species in the Russian River watershed (Chinook
salmon, steelhead trout, and coho salmon [Oncorhynchus kisutch]). Only coho salmon were studied
sufficiently to make a strong correlation between changes in environment and biological response; for
coho salmon, a dose index of 4.55 correlates to a severity index of 4. However, studies for all three
species show that, as the SSC dose index increases, so does the severity of the symptoms observed and
ranked in the index. For all species studied, the severity index ranking correlates to the SSC dose index
as follows:
Severity Index Ranking = 0.7491(SSC dose index) + 0.7625
Using this relationship, an SSC dose index of 4.55 in the Newcombe and Jensen analysis correlates to a
severity index of 4.17. Duration of exposure at each automated turbidity value incorporates duration in
lower-value bins; therefore, duration times are cumulative as turbidity/SSC values increase.
References
Allen, T., 2014. Personal communication with Alex Young of Sonoma Ecology Center. July 7.
Fitzgerald, R., 2004. Salmonid Freshwater Habitat Targets for Sediment-Related Parameters. Draft.
Prepared for State Water Resources Control Board, North Coast Region. October.
Lawton, R. (editor). 2006. Sediment Source Analysis, Sonoma Creek Watershed, California. Sonoma
Ecology Center, Sonoma, California. http://knowledge.sonomacreek.net/SSA
Lawton, R., R. Hunter, and J. Menze, 2002. Final Report, Volunteer Monitoring of Suspended Sediment
Concentration and Turbidity and Watershed Monitoring of Road Remediation in Annadel State Park,
Sonoma Creek Watershed, Sonoma County, California. Prepared for the Sonoma Ecology Center and
Regional Water Quality Control Board, San Francisco Bay Region. September.
Lawton, R., and V. Flores, 2006. Suspended Sediment Concentration. Appendix D to Limiting Factors
Analysis. Prepared for the Sonoma Ecology Center and Regional Water Quality Control Board, San
Francisco Bay Region. December. http://knowledge.sonomacreek.net/node/38.
Lawton, R., 2014. Personal communication with Alex Young of Sonoma Ecology Center. July 1.
Micheli, E. (editor). 2006. Sonoma Creek Watershed Limiting Factors Analysis. Sonoma Ecology Center,
with Stillwater Sciences and UC Berkeley Dept. of Earth and Planetary Sciences. Sonoma Ecology Center,
Sonoma, CA. http://knowledge.sonomacreek.net/LFA
Newcombe, C.P. and J.O.T. Jensen, 1996. Channel Suspended Sediment and Fisheries: A Synthesis for
Quantitative Assessment of Risk and Impact. North American Journal of Fisheries Management.
16(4):693-727.