Presented by Jen Bowman at the Ohio Environmental Council's 2009 Clean Water Conference

1. Monitoring Data Management Data
OEC Clean Water Conference
• WHY do you collect data?
– trends, reconnaissance, research,
planning, design, pre and post
implementation monitoring, etc…..
Jen Bowman
Voinovich School for Leadership and Public Affairs
Ohio University, Athens, Ohio
bowmanj2@ohio.edu
Data Data
• WHAT do you do with your data? • HOW do you use your data?
– Storage, archive/send it, share it, use it, – Analysis, communication, fundraising,
map it, etc… reporting, etc…
Demonstration Setting up a spreadsheet
• www.watersheddata.com
• Describe your site location
• www.ohiowaterresearch.com/map/ • Describe your data
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2. Setting up a spreadsheet Setting up a spreadsheet
• describe your site location • describe your data
– Date and time
– Coordinates – Site type (stream, wetland, pipe, pond, well, etc…)
– Site identification number – Type of BMP
– Any historical names – Data purpose
– Data source
– Site description (landmarks, address, culvert,
– Data funding
road, intersection, upst/dst bridge, etc….)
– Field data collector(s)
– River mile – Credibility
– Drainage area – Laboratory identification #
– Section, county, township – Standard method
– HUC unit - subwatershed – Comments (weather, filtered samples, stream condition,
etc…)
– QAQC (duplicate, split, or spike)
Quality Control and Quality
Assurance (QAQC)
Reporting your Data
• Data validation • State your data purpose
– Set validation limits to alert/flag out of
range values
• Know your audience!
• Data verification
• Use graphics to communicate
– Second person perform data checks
• Laboratory and field accuracy • Simplify but don’t oversimplify
– Percent difference in lab data values
– Calibration shifts in field data • BE ACCURATE and HONEST!
Importance of Long Term
Reporting your Data Monitoring and Evaluation
• Long-term monitoring at a single point • Measure historical changes in water
through time – shows trends
quality
• Longitudinal data along stream gradient - • WQ changes due to implementation
shows impact projects
• Understand hydrologic variation
• Pre and post implementation monitoring –
shows effectiveness • Prioritize restoration work
• Evaluate Restoration targets
• Extent of water quality improvement – • Annual reporting
shows large scale effectiveness
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3. Reporting transient data Reporting transient data
– shows trends – – shows trends
West Branch Sunday Creek
site WB003
18
16 al_total_mgl
14
12
Al mg/l
10
8
6
4
2
0
2/20/2001
8/20/2001
2/20/2002
8/20/2002
2/20/2003
8/20/2003
2/20/2004
8/20/2004
2/20/2005
8/20/2005
2/20/2006
8/20/2006
2/20/2007
8/20/2007
2/20/2008
8/20/2008
2/20/2009
date
Reporting longitudinal data Reporting longitudinal and transient data
– shows impact – shows impact and trends
River Miles Affected Exceed water quality criteria
Little Raccoon Creek Total Aluminum Concentrations
HEWETT FORK IBI
2005 - 2008
60
189 cfs; 3/7/2005 65 cfs; 5/9/2005 8 cfs; 10/3/2005
300 cfs; 2/6/2006 27 cfs; 7/17/2006 73 cfs; 4/24/2007
9 cfs; 7/9/2007 31 cfs; 1/28/2008 Al target 0.087 ccc
50 Al target 0.750 cmc
1.2
40 2000
2004
SCORE
Total Aluminum (mg/l)
1
30 2005
0.8
2006
20 2007 0.6
10 0.4
0 0.2
13.4 9.8 8.3 6.3 3.9 0.9 0
RIVER MILE 24.55 24.3 22.3 22.15 19.5 18.5 12.71 1.17
River Mile
Reporting pre and post Reporting pre and post
implementation results implementation results
- shows effectiveness - shows effectiveness
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4. Determining Extent of Water Shows large scale effectiveness
Quality Improvement River Miles Affected
Stream Miles
Affected/Improved/Recovered
• Chemical
• Biological
• Physical/habitat
Aids in prioritization and planning
Monday Creek pH values
Monitoring data
2001 2006
Interpret data accurately:
• Monitor the project outfall through time
– Pre-construction
– Post-construction
• Collect adequate # of samples during various
flow regimes
• Monitor downstream the project outfall in the
receiving stream to determine impact
• Collect a discharge measurement along with the
water quality sample to calculate loadings
Jobs Hollow
Sample
Monday Creek Stations
4

5. Pre-implementation
data
Post-implementation Data
Longitudinal data
organized into pre- and
post- data time periods
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6. QUESTIONS
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