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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