Temporal variability of Chlorophyll-a concentration in floodplain lakes in
response to seasonality of Amazon River discharge
Evlyn Moraes Novo, INPE
Claudio Clemente Barbosa, INPE
José Luiz Stech, INPE
Enner Herenio Alcântara, INPE
Conrado Moraes Rudorff, INPE
Arcilan T Assireu, INPE
Background• Optical remote sensing and over 70 in situ
samples acquired at Lago Grande de Curuai, near Santarém (Barbosa, 2005) during rising, high, receding and low Amazon River level indicated chlorophyll concentration higher than that reported by Carvalho et al. (2001) e Melack e Forsberg (2001).
Dai
ly m
ean
vari
atio
n ra
te o
f w
ater
leve
l(cm
/h)
-0.45
-0.15
-0.30
-0.00
-0.30
-0.15
0
200
400
600
800
1000
1200J
an
Feb
Mar
Apr
May
Jun
Jul
Agu
Sep
Oct
Nov
Dec
Sta
te 1
Sta
te 2
Sta
te 3
Sta
te 4
J une 6th
Daily mean water level Daily mean variation rate
instabilityJ anuary 22nd
stabilityNovember 14th
September 24th
Dai
ly m
ean
of w
ater
leve
l (cm
)
Dai
ly m
ean
vari
atio
n ra
te o
f w
ater
leve
l(cm
/h)
-0.45
-0.15
-0.30
-0.00
-0.30
-0.15
0
200
400
600
800
1000
1200J
an
Feb
Mar
Apr
May
Jun
Jul
Agu
Sep
Oct
Nov
Dec
Sta
te 1
Sta
te 2
Sta
te 3
Sta
te 4
J une 6th
Daily mean water level Daily mean variation rate
instabilityJ anuary 22nd
stabilityNovember 14th
September 24th
Dai
ly m
ean
of w
ater
leve
l (cm
)
Barbosa et al. 2008
03/02 a 12/02/04 03/06 a 19/06/0425/09 a 07/10/03
22/11 a 02/12/03
max=110 x min max= 62 x minmax= 109 x minmax= 125 x min
• There are thousands of lakes in the Amazonas/Solimões floodplain (Melack, 1984) but very few of them has been subjected to sistematic study (Melack e Forsberg, 2001).
• Aspects such as lake shape, size, depth, batimetry, connectivity to main river, origin and geomophological evolution (Latrubesse et al. 2005; Almeida Filho and Miranda, 2008) are frequently overlooked;
• Degree of preservation of original vegetation cover is also not informed.
Questions raised• How chlorophyll patterns are related to
fluctuations in the Amazon River level (sediment + water discharge)?
• Are those patterns recurrent?
• What are other factors affecting phytoplankton abundance?
How to answer those questions?
Proposed solution
• Integration of ground sampling, remote sensing and autonomous moored systems– acquire a minimum set of aquatic and atmospheric
variables at high time frequency,
– transmit the data to a processing center,
– make the information available immediately to the user
Proposed Solution:
SIMA
downlink
BRAZILIAN SATELLITES; NOAA/ARGOS
uplink
Users
Users
INPE
Lago Grande de Curuai
Juruti
Óbidos
Satarem
Juruti
Óbidos
Satarem
Curuai
100 kmTest site
Where to put the system ?
08/08/JUL/JUL/
20022002
24/24/JUL/JUL/
20022002
23/23/SEP/SEP/20012001
28/28/OCT/OCT/19991999
12/12/DEC/DEC/20012001
1- Image time series to define limit of the lake across a maximum water level amplitude
Boolean operations over Amazon plume maps derived from Landsat TMBoolean operations over Amazon plume maps derived from Landsat TMtime series at key distinct stages of the hydrologic cycletime series at key distinct stages of the hydrologic cycle
Meteorological variables:
Wind (Direction and velocity), Atmospheric
Pressure, Air Temperature, Moisture, Radiation
(incident and reflected) Limnological variables:
Water temperature at 4 levels
pH, Conductivity, Turbidity, Chlorophyll a
Dissolved Oxigen
What to measure?
Data base
How to organize the data?
Data Analyses
• Missing data “correction”
• Export data according to previous definition– Data above or bellow a given threshold– Daily average– Montly average and so on.
Data Analyses
• Correlation analyses– What are the variables explaining changes in
chlrophyll concentration
Results• Correlation analyses
Water Level -0,57 Wind -0,26
Air Temp -0,30 Air Moisture 0,26 Water Temp -0,16
Turbidity 0,66
significant at 95%
0
5
10
15
20
25
30
35
40
45
50
55
601/
jan
20/ja
n
8/fe
v
27/f
ev
18/m
ar
6/ab
r
25/a
br
14/m
ai
2/ju
n
21/ju
n
10/ju
l
29/ju
l
17/a
go
5/se
t
24/s
et
13/o
ut
1/n
ov
20/n
ov
9/d
ez
28/d
ez
Ch
loro
ph
yll
con
cen
trat
ion
(u
g/l
)
0
1
2
3
4
5
6
7
8
9
10
11
Wat
er le
vel (
m)
Chlorophyll Water Level
0
10
20
30
40
50
60
70
80
Rising High Receding Low
Cllo
rop
hyll c
on
cen
trati
on
(u
g/l)
Average Chl (mg/l)
Feb/04
Jun/04
Oct/03
Dec/03
0
2
4
6
8
10
12
14
16
18
20
22
24
26
1-jan 1-fev 1-mar 1-abr 1-mai 1-jun 1-jul 1-ago 1-set 1-out 1-nov 1-dez
Mo
nth
ly A
ve
rag
e o
f C
hlo
rop
hy
ll c
on
ce
ntr
ati
on
(m
g/l)
0
1
2
3
4
5
6
7
8
9
10
11
Mo
nth
ly w
ater
lev
el h
eig
ht
(m)
Chlorophyll Water level (m)
0
5
10
15
20
25
30
35
40
45
50
55
60
jan-0
3
jan-0
3
fev-
03
fev-
03
mar
-03
mar
-03
abr-0
3
abr-0
3
mai-
03
mai-
03
jun-0
3
jun-0
3jul
-03jul
-03
ago-
03
ago-
03
set-0
3
set-0
3
out-0
3
nov-
03
nov-
03
dez-
03
dez-
03
Ch
loro
ph
yll (
ug
/l)
0
100
200
300
400
500
600
700
800
900
1000
Tu
rbid
ity
(NT
U)
Chlorophyll (ug/l) Water Level (m) Turbidez (NTU)
August, 2003April, 2003
chlorophyll and water level
chlorophyll and turbidity
Period=10 daysNegativeLowAssimetricAfter 10 days Of rising waterChl concentraion drops
Period=1 and 10 daysPositiveLowAssimetricAfter 1 day ofIncreased chlConcentration,Turbidity increasesAfter 10 days ofIncrease chl, turbidiyAlso increases.
Conclusions• cross-correlation analysis between daily chlorophyll and
water level shows with 95 percent confidence that the highest value (r= - 0,59) corresponded to a negative lag of 10 days.
• for lags larger and smaller than 50 days the correlation drops and loses statistical significance
• at this lake region seasonal changes in Amazon river discharge explains only about 38 % of daily average of chlorophyll concentration.
• Cross-correlation between chlorophyll and turbidity shows with 95 % confidence level that the highest value (r= 0.
Data base
0
2
4
6
8
10
12
14
16
18
20
22
24
26
1-jan 1-fev 1-mar 1-abr 1-mai 1-jun 1-jul 1-ago 1-set 1-out 1-nov 1-dez
Mo
nth
ly A
ve
rag
e o
f C
hlo
rop
hy
ll c
on
ce
ntr
ati
on
(m
g/l)
0
1
2
3
4
5
6
7
8
9
10
11
Mo
nth
ly w
ater
lev
el h
eig
ht
(m)
Chlorophyll Water level (m)
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