Combining Long-term And High Frequency Water Quality Data

To Understand Ecological Processes In Estuaries

Jane Caffrey

Center for Environmental Diagnostics and Bioremediation

University of West Florida

J.M. Caffrey, UWF

Acknowledgements

• Data– Thomas Chapin, USGS and Hans Jannasch,

MBARI– Scott Phipps, Weeks Bay NERR and John

Haskins, Elkhorn Slough NERR

• Funding - CICEET and NOAA NERR

J.M. Caffrey, UWF

Outline of talk

• Calculation of metabolic rates (primary production, respiration and net ecosystem metabolism) from DO data – Data sondes deployed at NERR– Salinity, temperature, dissolved oxygen, turbidity, pH

• Understanding short term variability in estuarine processes– Deployment of in-situ NO3

- analyzers (developed by Ken Johnson, MBARI)

• Linking physical, chemical and biological processes

J.M. Caffrey, UWF

National Estuarine Research Reserve System

J.M. Caffrey, UWF

Background

• Dissolved oxygen data collected every half hour between 1995-2001.

• Uses diurnal changes in water column oxygen concentrations to estimate primary production, respiration and net ecosystem metabolism

• Developed by H.T. Odum in 1950s

• Describes the trophic status of the water body

– Autotrophic: P > R

– Heterotrophic: R > P

J.M. Caffrey, UWF

Dissolved Oxygen

Diurnal changes in DO result from photosynthesis and respirationGross production= NAP + respiration Net Ecosystem Metabolism (NEM) = NAP - respiration

0

3

6

9

12

4/19 4/20 4/21 4/22 4/23

mg/

l

Night respirationNet apparent production

J.M. Caffrey, UWF

Assumptions

• Respiration rate is constant in light and dark

• System is well mixed vertically

• No advection of water masses with different DO concentrations is occurring – or biology dominates over physics

J.M. Caffrey, UWF

Primary ProductionWeeks Bay

0

5

10

15

20

25

30

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Gro

ss p

rod

uct

ion

gO

2/m

2/d

0

5

10

15

20

25

30

35

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Tem

pera

ture

°C

Gro

ss p

rodu

ctio

n m

g O

2/m

2/d

J.M. Caffrey, UWF

Temperature effectsNorth Inlet-Winyah Bay, SC - Oyster

Landing

r = 0.71

0

4

8

12

16

0 5 10 15 20 25 30 35

Temperature °C

Tot

al r

espi

ratio

n gO

2/m

3/d

Temperature versus metabolic rate correlations• Gross production – 23 sites• Total Respiration – 26 sites• Net ecosystem metabolism – 19 sites

J.M. Caffrey, UWF

Salinity effectsElkhorn Slough, CA – Azevedo Pond

r = 0.39

0

10

20

30

40

50

60

0 5 10 15 20 25 30 35 40Salinity

Gro

ss p

rodu

ctio

n,g

O2/

m3/

d

Salinity versus metabolic rate correlations• Gross Production – 16 sites• Total Respiration –12 sites• Net ecosystem metabolism – 13 sites

J.M. Caffrey, UWF

Net ecosystem by habitat

-8

-7

-6

-5

-4

-3

-2

-1

0

1C

BV

Go

od

win

Isl

an

d

PA

D B

ay

Vie

w

WQ

B C

en

tra

l Ba

sin

AP

A E

ast

Ba

y

GR

B G

rea

t B

ay

GR

B S

qu

am

sco

tt R

ive

r

NA

R P

ott

ers

Co

ve

NA

R T

-wh

arf

WK

B F

ish

Riv

er

WK

B W

ee

ks B

ay

JOB

Sta

tion

9

JOB

Sta

tion

10

RK

B B

lack

wa

ter

Riv

er

RK

B U

pp

er

He

nd

ers

on

CB

M J

ug

Ba

y

CB

M P

atu

xen

t P

ark

HU

D T

ivo

li S

ou

th

CB

V T

ask

ina

s C

ree

k

AC

E B

ig B

asi

n

AC

E S

t P

ierr

e

EL

K S

ou

th M

ars

h

NIW

Oys

ter

La

nd

ing

NIW

Th

ou

san

d A

cre

EL

K A

zeve

do

Po

nd

PA

D J

oe

Le

ary

Slo

ug

hH

UD

Sa

wki

ll

g O

2 m

-2 d

-1

SAV open water mangrove marsh creeks upland

J.M. Caffrey, UWF

Conclusions

• Water quality monitoring data is useful for estimating metabolic rates

• within site variability– temperature – salinity – nutrient concentration – chlorophyll concentration

• Among site variability– habitat (organic matter loading)– nutrient loading – residence time

J.M. Caffrey, UWF

Understanding Temporal Patterns

Continuous measurements give greater temporal resolution than discrete measurements

Salinity

0

5

10

15

20

25

J F M A M J J A S O N D

PS

U

J.M. Caffrey, UWF

Relating Runoff to Estuarine Processes

Rainfall in the Weeks Bay watershed leads to reduced salinity at the head of the estuary

0

5

10

15

20

25

J F M A M J J A S O N D

Sal

init

y P

SU

0

40

80

120

160

Rai

nfa

ll m

m

J.M. Caffrey, UWF

In-situ nutrient analysis

J.M. Caffrey, UWF

Seasonal patterns in rainfall, temperature, salinity and nitrate concentrations in Elkhorn

Slough, CA

J.M. Caffrey, UWF

Winter rains lead to extended periods of high NO3

- concentrations in Elkhorn Slough, CA

15

20

25

30

2/8 2/13 2/18 2/23 2/28 3/5 3/10 3/15

Sa

linity

0

1

2

3

4

rain

, cm

0

40

80

120

2/8 2/13 2/18 2/23 2/28 3/5 3/10 3/15

NO

3-

µM

0

1

rain

, cm

15

20

25

30

2/8 2/13 2/18 2/23 2/28 3/5 3/10 3/15

Sa

linity

0

1

2

3

4

rain

, cm

0

40

80

120

2/8 2/13 2/18 2/23 2/28 3/5 3/10 3/15

NO

3-

µM

0

1

rain

, cm

J.M. Caffrey, UWF

Relating Runoff to Nutrient Loading

High NO3- concentrations associated with runoff events in

Weeks Bay, AL during winter rains

0

20

40

60

80

1/3 1/17 1/31 2/14 2/28

NO

3- con

cent

ratio

n, µ

M

0

10

20

30

Sal

inity

, R

ainf

all,

mm

J.M. Caffrey, UWF

Seasonal differences in NO3-

concentrations following runoff events

0

20

40

60

80

0 5 10 15 20 25

Salinity

NO

3-

µM

Jan

Aug

J.M. Caffrey, UWF

What factors contribute to variability?

• Harmonic regression analysis – choose periods of interest: tidal 12.5h, diurnal 24h, and

lunar 29.5d

– Fit data to linear regression– Run full models with all periods and reduced models to

look at contributions of different components

t

kperiod

bt

kperiod

aaNO t x2

sinx2

cos3 110

J.M. Caffrey, UWF

Elkhorn Slough

•Lunar signal most important during winter, capturing runoff events. •Spring-neap forcing of deep Monterey Bay water into Slough (Chapin et al. 2004)•Diurnal signal dominates during summer when biological processes dominate.

0%

25%

50%

75%

100% Lunar

Diurnal

Tidal

J.M. Caffrey, UWF

Weeks Bay

0%

20%

40%

60%

80%4

Ja

n -

25

Ja

n

25

Ja

n -

20

Fe

b

20

Fe

b -

8 M

ar

28

Ju

n -

19

Ju

l

19

Ju

l -9

Au

g

9 A

ug

-7

Se

p

1 N

ov

-2

7 N

ov

Lunar

Diurnal

Tidal

Lunar and diurnal signals also important in Weeks Bay.Not surprising that tidal signal is weak because tides arediurnal rather than semidiurnal.

J.M. Caffrey, UWF

NO3- inputs enhance gross

production in Weeks Bay

0

10

20

30

40

8/9 8/14 8/19 8/24 8/29

NO

3-

µM

, ra

in m

m

0

4

8

12

16

Gro

ss p

rod

uct

ion

g O

2 m

-2 d

-1

J.M. Caffrey, UWF

And Elkhorn Slough

0

10

20

30

40

4/1 4/8 4/15 4/22 4/29 5/6 5/13 5/20 5/27

NO

3 µ

M

0

10

20

30

40

Gro

ss P

rod

uct

ion

gO

2/m

2/d

J.M. Caffrey, UWF

Conclusions and Challenges

• In situ instruments allow you to examine short term temporal variations, e.g. runoff events

• Water quality monitoring data (DO) can be used to estimate metabolic rates.

• How to link these time series together to examine how events at different time scales affect ecological processes

J.M. Caffrey, UWF

Nitrogen Loading

N

MiI

eE

c

C

B

R2 = 0.30

-7

-6

-5

-4

-3

-2

-1

0

1

0 5 10 15 20 25

Nitrogen loading mmol m-2 d-1

Net

eco

syst

em m

etab

olis

m,

g O

2 m

-2 d

-1