what we can measure vs what we want to know. · what we can measure vs. what we want to know....
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![Page 1: What we can measure vs what we want to know. · What we can measure vs. What we want to know. William Ullman School of Marine Science and Policy . University of Delaware, Lewes](https://reader030.vdocuments.mx/reader030/viewer/2022040417/5d56f55888c9936e418b6f24/html5/thumbnails/1.jpg)
What we can measure vs. What we want to know.
William Ullman School of Marine Science and Policy
University of Delaware, Lewes
SCIENTIFIC AND TECHNICAL ADVISORY COMMITTEE
18 September 2015
9/15/2015 Inland Bays STAC Meeting, September 2015 1
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Old Monitoring Paradigm • Measure what we can measure as often as we
can afford to do so! – Useful (maybe) for status and long-term trends – Useful for management? (maybe not) – Easy to measure with sensors
• Temperature, Salinity, Depth, pH, Turbidity (Secchi Depth) – Harder to measure, but useful (sampling, filtering,
laboratory analysis) • Dissolved and Particulate Nutrients • Contaminants • Organisms (plankton, benthos, nekton) • Process Variables (incubations requiring labs)
9/15/2015 Inland Bays STAC Meeting, September 2015 2
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Newer Monitoring Paradigm • “Problem-Based Monitoring.” Determine
what is needed; Don’t determine what is not! • Focus on needs: Target problems/issues that
can ultimately be managed!
– Limited parameters (but improving) – Limited number of instruments (expense) – Limited deployment sites (infrastructure)
9/15/2015 Inland Bays STAC Meeting, September 2015 3
Geospatial Surveys (variable x,y; fixed z,t)
Continuous Monitoring (variable t, maybe z; fixed x,y)
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Spatial Remote Sensing of
Chlorophyll in Delaware Bay
(Note pixel size is too large to
permit sensing of the Inland Bays.)
9/15/2015 Inland Bays STAC Meeting, September 2015 4
http://coastwatch.chesapeakebay.noaa.gov/region_cd.php
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Autonomous Platforms to get spatial resolution and parameters that
cannot be sensed remotely?
9/15/2015 Inland Bays STAC Meeting, September 2015 5
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Parameters that can be Continuously Sensed
• Conductivity, Temperature, Depth • Suspended Particles (turbidity, size, shape,
species of bacteria, phytoplankton, zooplankton)
• Dissolved parameters (inorganic nutrients, some dissolved organic carbon characteristics; carbonate system characteristics: pH, TCO2, PCO2, alkalinity)
9/15/2015 Inland Bays STAC Meeting, September 2015 6
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Continuous Sensing of Relevant Parameters focuses Attention on
Processes (Rate of Change) Rather Than Instantaneous Status
• What processes are important to our ecosystem? • What measurements are needed to quantify the
rates of these processes? • What sensors are available to make these
measurements? • What computational support is needed to get
from the measurements to the needs?
9/15/2015 Inland Bays STAC Meeting, September 2015 7
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17 July 2014 8 Days0 1 2 3 4 5
O2
Satu
ratio
n (%
)
0
100
200
300
Instantaneous Measurements need Context: O2 example
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9/15/2015 Inland Bays STAC Meeting, September 2015 9
Diel Hypoxia: Tyler, Brady, and Targett (2009)
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Murderkill Estuary, Bowers DE
14-15 November 2013 Automated Water Quality 10
Salin
ity (L
OBO
)
0
5
10
15
20
25
30
10-Oct 17-Oct 24-Oct 31-Oct 07-Nov 14-Nov 21-Nov 28-Nov 05-Dec 12-Dec 19-Dec 26-Dec 02-Jan 09-Jan
Nitr
ate
(M
, LO
BO)
0
50
100
150
200 Sampling Interval: 2 weeks
Salin
ity (L
OBO
)
0
5
10
15
20
25
30
10-Oct 17-Oct 24-Oct 31-Oct 07-Nov 14-Nov 21-Nov 28-Nov 05-Dec 12-Dec 19-Dec 26-Dec 02-Jan 09-Jan
Nitr
ate
(M
, LO
BO)
0
50
100
150
200 Sampling Interval: 5 days
Salin
ity (L
OBO
)
0
5
10
15
20
25
30
10-Oct 17-Oct 24-Oct 31-Oct 07-Nov 14-Nov 21-Nov 28-Nov 05-Dec 12-Dec 19-Dec 26-Dec 02-Jan 09-Jan
Nitr
ate
(M
, LO
BO)
0
50
100
150
200 Sampling Interval: 1 day
Salin
ity (L
OBO
)
0
5
10
15
20
25
30
10-Oct 17-Oct 24-Oct 31-Oct 07-Nov 14-Nov 21-Nov 28-Nov 05-Dec 12-Dec 19-Dec 26-Dec 02-Jan 09-Jan
Nitr
ate
(M
, LO
BO)
0
50
100
150
200 Sampling Interval: 1 hr
High Frequency Sampling Allow
s O
bservations of Cause and Effects!
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Continuous Monitoring In Delaware (Delaware is rich in continuous monitoring
resources, but we need to do more than collect the data!)
• USGS (Discharge Monitoring, some basic CTD measurements)
• DGS (Groundwater levels and flow; Water Quality Monitoring at Coursey Pond)
• DEOS (Aggregated weather and other data from various sources)
• DNREC* (some more advanced sensors have been deployed at Millsboro Pond and the Nanticoke River near Bridgeville)
• Kent County (Land Ocean Biogeochemical Observatory, Bowers, Delaware, operated by UD-SMSP)
9/15/2015 Inland Bays STAC Meeting, September 2015 11
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14-15 November 2013 Automated Water Quality 12
WQM: CTD, Chla, Turbidity
STOR-X Data
Logger
ECO-CDS (CDOM)
Power and Data Cables
SUNA (NO3-)
Kent County LOBO
Cycle-PO4
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YSI-EXO w
ith S:CAN
Spectrolyzer at C
oursey Pond (NSF N
EWR
Net)
9/15/2015 Inland Bays STAC Meeting, September 2015 13
Greenspan Aqualab
Gre
ensp
an A
qual
ab D
eplo
yed
at N
antic
oke
Riv
er n
ear B
ridge
ville
(DN
REC
).
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What do we really want to know?
Can automated sensors help us?
Which ones, where, and how?
What do we need to do this well?
9/15/2015 Inland Bays STAC Meeting, September 2015 14
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9/15/2015 Inland Bays STAC Meeting, September 2015 15