What we can measure vs what we want to know. · What we can measure vs. What we want to know....

Post on 17-Aug-2019

214 views 0 download

Transcript of What we can measure vs what we want to know. · 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

SCIENTIFIC AND TECHNICAL ADVISORY COMMITTEE

18 September 2015

9/15/2015 Inland Bays STAC Meeting, September 2015 1

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

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)

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

Autonomous Platforms to get spatial resolution and parameters that

cannot be sensed remotely?

9/15/2015 Inland Bays STAC Meeting, September 2015 5

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

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

17 July 2014 8 Days0 1 2 3 4 5

O2

Satu

ratio

n (%

)

0

100

200

300

Instantaneous Measurements need Context: O2 example

9/15/2015 Inland Bays STAC Meeting, September 2015 9

Diel Hypoxia: Tyler, Brady, and Targett (2009)

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!

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

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

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

).

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

9/15/2015 Inland Bays STAC Meeting, September 2015 15