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Nancy Mesner - Utah State University, Dept of Watershed Sciences Ginger Paige - University of Wyoming David Stevens, Jeff Horsburgh, Doug Jackson-Smith, Darwin Sorensen, Ron Ryel – USU. DESIGNING MONITORING PROGRAMS TO EVALUATE BMP EFFECTIVENESS. - PowerPoint PPT Presentation
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DESIGNING MONITORING PROGRAMS TO EVALUATE BMP EFFECTIVENESS
Funded by grants from USDA-CSREES, EPA 319, NSF
Nancy Mesner - Utah State University, Dept of Watershed SciencesGinger Paige - University of WyomingDavid Stevens, Jeff Horsburgh, Doug Jackson-Smith, Darwin Sorensen, Ron Ryel – USU
Examples from the Little Bear River CEAP Project
Pre-treatment problems: Bank erosion, manure management, flood irrigation problems
Treatments:
bank stabilization, river reach restoration, off-stream watering, improved manure and water management
Common problems in BMP monitoring Common problems in BMP monitoring programs:programs:
• Failure to design monitoring plan around BMP objectives Failure to design monitoring plan around BMP objectives
• A failure to understand pollutant pathways and A failure to understand pollutant pathways and transformations and sources of variaiblity in these dynamic transformations and sources of variaiblity in these dynamic system.system.
• Tend to draw on a limited set or inappropriate approachesTend to draw on a limited set or inappropriate approaches
• Failure to design monitoring plan around BMP objectives
• A failure to understand pollutant pathways and transformations and sources of variaiblity in these dynamic system.
• Tend to draw on a limited set or inappropriate approaches Designing monitoring to address specific objectives
Little Bear River Watershed, Utah
v
1994 11 13 1995 10 13 1996 10 13 1997 11 4 1998 6 10 1999 7 10 2000 6 5 2001 4 7 2002 2 8 2003 4 8 2004 1 8
Total Observations at Watershed Outlet site
Discharge Total phosphorus
1976 - 2004: 162 2411994 - 2004: 72 99
Number of observations each year
• Failure to design monitoring plan around BMP objectives Failure to design monitoring plan around BMP objectives
• A failure to understand pollutant pathways and A failure to understand pollutant pathways and transformations and sources of variability in these dynamic transformations and sources of variability in these dynamic system.system.
• Tend to draw on a limited set or inappropriate approachesTend to draw on a limited set or inappropriate approaches
0
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1995 2000 2005
10105900 - LITTLE BEAR RIVER AT PARADISE, UT
FLO
W, S
TRE
AM
, ME
AN
DA
ILY
CFS
Date
Understanding natural variability – annual variation
Since 2005, measure flow and turbidity at 30 minute Since 2005, measure flow and turbidity at 30 minute intervalsintervals
Stage recording devices to estimate discharge
Turbidity sensors
Dataloggers and telemetry equipment
http://www.campbellsci.com
http://www.ftsinc.com/
http://www.campbellsci.com
Capturing pollutant movement from source Capturing pollutant movement from source to waterbody.to waterbody.
Little Bear River Near Paradise
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2006
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2006
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2006
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2006
3/11
/200
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Date
Streamflow (cfs) Turbidity (NTU)
StormEvent
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30 20 10 5 1 0.45 0.55 0.64 0.72 0.78 0.84 0.89 0.94 0.98
Co
effi
cien
t o
f va
riat
ion
of
esti
mat
es
Grab samples -- sampling frequency (d) Continuous monitoring -- R2 between TP and turbidity
Sampling frequency
Regressions of TP and turbidity
The relative importance of different sources of variability
• Failure to design monitoring plan around BMP objectives Failure to design monitoring plan around BMP objectives
• A failure to understand pollutant pathways and transformations and A failure to understand pollutant pathways and transformations and sources of variability in these dynamic system.sources of variability in these dynamic system.
• Tend to draw on a limited set or inappropriate approachesTend to draw on a limited set or inappropriate approaches
Problem: excess sedimentAverage flow = 20 cfsBMP = series of in-stream sediment basins
Problems with “one-size-fits-all” monitoring Problems with “one-size-fits-all” monitoring designdesign
Rees Creek TSS load
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5000
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15000
20000
25000
30000
35000
40000
45000
50000
1 2 3 4 5 6 7 8 9
weeks
kg /
day
Above
Below
Problem: excess phosphorusAverage flow = 1000 cfsBMP = fence cattle OUT of riparian area and
revegetate
Bear River phosphorus load
0
50
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1 2 3 4 5 6 7 8 9
weeks
load
(kg
/day
)
Focuses on the considerations and decisions necessary as a project is first being considered.
NOT a “how-to” manual of protocols
Document in reviewTraining workshops underway
What is your objective?
Long term trends?
UPDES compliance?
Educational?
Assessment for impairment?
Track response from an implementation?
How does the pollutant move from the source to the waterbody?
How is the pollutant processed or transformed within a waterbody?
What is the natural variability of the pollutant? Will concentrations change throughout a season? Throughout a day?
What long term changes within your watershed may also affect this pollutant?
What else must be monitored to help interpret your data?
How do pollutants “behave” within your watershed.
Monitor the pollutant(s) of concern?
Monitor a “surrogate” variable?
Monitor a response variables?
Monitor the impacted beneficial use?
Monitor the BMP itself?
Monitor human behavior?
Model the response to a BMP implementation.
Collect other data necessary to interpret monitoring results OR calibrate and validate the model?
What to monitor?
Where and when to monitor?
Sampling points
ControlTreatment “A”
BACI DesignAbove and below treatment design
Below-treatment monitoring
stations
Above-treatment
monitoring stations
Choose appropriate monitoring or modeling
How to monitor?
points in time versus continuous
integrated versus grab samples
consider:
cost
skill and training required
accessibility of sites
The road to more effective monitoring….
Monitoring plans require careful thought before anything is implemented.
Consider how the data will be used to demonstrate change.
Use your understanding of your watershed and how the pollutants of concern behave to target monitoring most effectively
Use different approaches for different BMPs
different sources of variability in estimates of loads
