Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights...

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Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia Restrepo-Coupe, Alfredo Huete

Transcript of Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights...

Page 1: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing

Kamel Didan, Scott Saleska, Natalia Restrepo-Coupe, Alfredo Huete

Page 2: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing

Kamel Didan, Scott Saleska, Natalia Restrepo-Coupe, Alfredo Huete

Page 3: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

What controls the seasonality of photosynthesis across the Amazon basin?: A cross-site analysis of eddy flux tower measurements from the Brasil Flux networkNatalia Restrepo-Coupe, Scott R. Saleska, Humberto R. da

Rocha, Bart Kruijt, Antonio D. Nobre, and Renata G. Aguiar, Alessandro C. da Araujo, Laura S. Borma, Osvaldo M. R. Cabral, Plinio B. de Camargo, Fernando L. Cardoso, Antonio C. Lola da Costa, David R. Fitzjarrald, Michael L. Goulden, Lucy R. Hutyra, Jair M. F. Maia, Yadvinder S. Malhi, Antonio O. Manzi, Scott D. Miller, Celso von Randow, Leonardo D. da Abreu Sá, Ricardo K. Sakai, Julio Tota, Steven C. Wofsy, Fabricio B. Zanchi

Page 4: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Motivation: What is the seasonality of ecosystem metabolism? – Early results from Tapajos National Forest (K67 site) showed unexpected seasonality

A

B

NE

E (

flux

to a

tmos

pher

e)

k

g C

ha-1

mon

th-1

Rtot ▲GPP ▼

TEM ○ ○Data

IBIS X X

upta

kelo

ss to

at

mos

pher

e

GP

P o

r R

tot,

kg C

ha-1

mon

th-1

Saleska et al. (2003) Science.

Observations Models

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1500

2000

2500

3000

3500

-500

-250

0

250

500

Dry Season

Composite annual cycle, 2001-2003

Page 5: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Motivation: What is the seasonality of ecosystem metabolism? – Early results from Tapajos National Forest showed unexpected seasonality – focus on photosynthesis

Jan Apr Jul Oct500

1000

1

2

3

Models

Data

GP

P (

MgC

ha-1

mo-1

)P

AR

(

mol

m-2 s

-1)

Dry Season

Is this typical of sites across the Amazon? If not, what are the differences?

What mechanisms control seasonal variation in ecosystem GPP?

How do Amazonian ecosystems allocate carbon seasonally?

(= Gross Primary Production, GPP)

Questions:

Page 6: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°BrasilFlux Sites - annual rainfall

Forest: K34 (Manaus) K67, K83 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal)Pasture/Ag: K77 (Santarem) FNS (Ji Parana)Cerrado PDG (Sao Paulo)

Page 7: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°BrasilFlux Sites – Central Amazon

Forest: K34 (Manaus) K67, K83 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal)Pasture/Ag: K77 (Santarem) FNS (Ji Parana)Cerrado PDG (Sao Paulo)

Page 8: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°BrasilFlux Sites – Southern Amazon

Forest: K34 (Manaus) K67, K83 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal)Pasture/Ag: K77 (Santarem) FNS (Ji Parana)Cerrado PDG (Sao Paulo)

Page 9: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°BrasilFlux Sites – Southern Savanna

Forest: K34 (Manaus) K67, K83 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal)Pasture/Ag: K77 (Santarem) FNS (Ji Parana)Cerrado PDG (Sao Paulo)

Page 10: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

0

7.5

15

GE

P

(gC

m-2

d-1

)

PEG

0

300

600

pre

c

(mm

mo

-1)

2001 2002 2003 2004200

400

600

PA

R(

mo

l m-2

s-1

)

BrasilFlux Sites – Raw data series

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67

CAXK83Ecuador

Bolivia

Peru

Brasil

FrenchGuyana

Surinam

GuyanaVenezuela

Colombia

Pe-de-Gigante (PDG)

0

7.5

15

GE

P

(gC

m-2

d-1

)

K34

0

300

600

pre

c

(mm

mo

-1)

2000 2002 2004 2006200

400

600

PA

R(

mo

l m-2

s-1

)

Manaus (K34)

Page 11: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Gross Ecosystem Productivity, GEP

Ecosystem Respiration, Re

NEE = eddy-flux + change in canopy storage

(when missing, canopy storage is filled)

0:00 6:00 12:00 18:00 24:00-20

-15

-10

-5

0

5

10

Local Time (hr)

NE

E ( m

ol m

-2 s

-1)

BrasilFlux Sites and Methods

Page 12: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Gross Ecosystem Productivity, GEP

Ecosystem Respiration, Re

NEE = eddy-flux + change in canopy storage

(when missing, canopy storage is filled)

0:00 6:00 12:00 18:00 24:00-20

-15

-10

-5

0

5

10

Local Time (hr)

NE

E ( m

ol m

-2 s

-1)

BrasilFlux Sites and Methods

0 500 1000 1500 2000-5

0

5

10

15

20

25

30

35

K67: May 09 - May 25, 2002

PAR (mol m-2 s-1)

GE

P ( m

ol m

-2 s

-1)

Pc=19.24

GP

P (

g C

m-2 d

ay-1

)

PAR (umol m-2 sec-1)

Define:Photosynthetic Capacity (Pc)= GPP at PAR: 700 -900

Page 13: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.
Page 14: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°

Results: Seasonal GPP and Pc

0

0.5

1

GE

P G

EP

ma

x-1

K67

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

Central Amazon Forests

Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal)Pasture/Ag: K77 (Santarem) FNS (Ji Parana)Cerrado PDG (Sao Paulo)

Page 15: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°

Results: Seasonal GPP and Pc

0

0.5

1

GE

P G

EP

ma

x-1

CAX

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

0

0.5

1

GE

P G

EP

ma

x-1

K34

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

0

0.5

1

GE

P G

EP

ma

x-1

K67

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

Central Amazon Forests

Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal)Pasture/Ag: K77 (Santarem) FNS (Ji Parana)Cerrado PDG (Sao Paulo)

Page 16: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°

Results: Seasonal GPP and Pc

0

0.5

1

GE

P G

EP

ma

x-1

CAX

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

0

0.5

1

GE

P G

EP

ma

x-1

K34

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

0

0.5

1

GE

P G

EP

ma

x-1

K67

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

Central Amazon Forests

Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal)Pasture/Ag: K77 (Santarem) FNS (Ji Parana)Cerrado PDG (Sao Paulo)

All sites maintain high or increasing dry season GPP No evidence of seasonal water limitation

Page 17: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°

0

0.5

1

GE

P G

EP

ma

x-1

K67

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

0

0.5

1

GE

P G

EP

ma

x-1

K77

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400p

rec

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

Results: Seasonal GPP and Pc Central Amazon: Forest vs. Agriculture

Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal)Pasture/Ag: K77 (Santarem) FNS (Ji Parana)Cerrado PDG (Sao Paulo)

Page 18: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°

0

0.5

1

GE

P G

EP

ma

x-1

RJA

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

Results: Seasonal GPP and Pc Southern Amazon Forests

Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal)Pasture/Ag: K77 (Santarem) FNS (Ji Parana)Cerrado PDG (Sao Paulo)

0

0.5

1

GE

P G

EP

ma

x-1

JAV

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

Page 19: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°

0

0.5

1

GE

P G

EP

ma

x-1

RJA

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

0

0.5

1

GE

P G

EP

ma

x-1

FNS

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

Results: Seasonal GPP and Pc Southern Amazon Forests and Pasture

Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal)Pasture/Ag: K77 (Santarem) FNS (Ji Parana)Cerrado PDG (Sao Paulo)

0

0.5

1

GE

P G

EP

ma

x-1

JAV

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

Pasture Forest Forest

Page 20: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°

0

0.5

1

GE

P G

EP

ma

x-1

K67

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

Results: Seasonal GPP and Pc

0

0.5

1

GE

P G

EP

ma

x-1

RJA

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal)Pasture/Ag: K77 (Santarem) FNS (Ji Parana)Cerrado PDG (Sao Paulo)

Central versus Southern Amazon Forests

Page 21: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°

0

0.5

1

GE

P G

EP

ma

x-1

PDG

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600P

AR

(m

ol m

-2 s

-1)

Results: Seasonal GPP and Pc Southern Savanna

Page 22: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

0

0.5

1

GE

P G

EP

ma

x-1

JAV

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

0

0.5

1

GE

P G

EP

ma

x-1

CAX

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67

CAXK83Ecuador

Bolivia

Peru

Brasil

FrenchGuyana

Surinam

GuyanaVenezuela

Colombia0

0.5

1

GE

P G

EP

ma

x-1

RJA

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

0

0.5

1

GE

P G

EP

ma

x-1

K34

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

0

0.5

1

GE

P G

EP

ma

x-1

FNS

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

0

0.5

1

GE

P G

EP

ma

x-1

K67

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

0

0.5

1

GE

P G

EP

ma

x-1

K77

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

0

0.5

1

GE

P G

EP

ma

x-1

PDG

0

0.5

1

Pc

Pc m

ax

-1

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

200

400

600

PA

R(

mo

l m-2

s-1

)

Results: Seasonal GPP and Pc

Page 23: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Results: GPP, Pc Seasonality, relative to start-date

30 60 90 120 150 180 210 240 270 300 330 3600

0.2

0.4

0.6

0.8

1

Days since dry season onset

Pc

Pc m

ax-1

30 60 90 120 150 180 210 240 270 300 330 3600

0.2

0.4

0.6

0.8

1

Days since dry season onset

GE

P G

EP

max

-1

Page 24: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.
Page 25: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°

Results: Seasonal Climate, Central Amazon

200

400

600

PA

R m

ol m

-2 s

-1 K67

200

350

500

TO

Air

rad

ian

ce

(W m

-2)

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

0

200

400

ET

(mm

mo

-1)

200

400

600

PA

R m

ol m

-2 s

-1 CAX

200

350

500

TO

Air

rad

ian

ce

(W m

-2)

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

0

200

400

ET

(mm

mo

-1)

200

400

600

PA

R m

ol m

-2 s

-1 K77

200

350

500

TO

Air

rad

ian

ce

(W m

-2)

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

0

200

400

ET

(mm

mo

-1)

200

400

600

PA

R m

ol m

-2 s

-1 K34

200

350

500

TO

Air

rad

ian

ce

(W m

-2)

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

0

200

400

ET

(mm

mo

-1)

Radiation:Low variability in top-of-atmosphere radiation, +Surface PAR controlled by clouds

Page 26: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°

200

400

600

PA

R m

ol m

-2 s

-1 RJA

200

350

500

TO

Air

rad

ian

ce

(W m

-2)

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

0

200

400

ET

(mm

mo

-1)

200

400

600

PA

R m

ol m

-2 s

-1 JAV

200

350

500

TO

Air

rad

ian

ce

(W m

-2)

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

0

200

400

ET

(mm

mo

-1)

200

400

600

PA

R m

ol m

-2 s

-1 FNS

200

350

500

TO

Air

rad

ian

ce

(W m

-2)

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

0

200

400

ET

(mm

mo

-1)

Results: Seasonal Climate, Southern AmazonRadiation:

Higher variability in top- of-atmosphere Radiation (with latitude), +Shift in timing of dry season:TOA solar minimum corresponds to dry season little seasonal variation in surface radiation

Page 27: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67K83

CAX

Colombia

VenezuelaGuyana

Surinam FrenchGuyana

Brasil

Peru

Bolivia

Ecuador

300 0 300 600 Kilometers

20° 20°

15° 15°

10° 10°

5° 5°

0° 0°

5° 5°

10° 10°

80°

80°

75°

75°

70°

70°

65°

65°

60°

60°

55°

55°

50°

50°

45°

45°

40°

40°

35°

35°

200

400

600

PA

R m

ol m

-2 s

-1 PDG

200

350

500

TO

Air

rad

ian

ce

(W m

-2)

J FMAMJ J A SOND0

200

400

pre

c

(mm

mo

-1)

0

200

400

ET

(mm

mo

-1)

Results: Seasonal Climate, Southern SavannaPAR sunlight:

Even higher variability in top- of-atmosphere Radiation (with latitude):TOA solar minimum corresponds to dry season dry-season dip in surface radiation

Page 28: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-51.50x+0.89R2=0.06 p=0.005K34

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-36.45x+0.96R2=0.06 p=0.025K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-79.97x+1.10R2=0.20 p=0.000K83

0 0.5 10

0.2

0.4

0.6

0.8

1y=-257.15x+1.29R2=0.15 p=0.000K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=139.74x+0.20R2=0.42 p=0.000CAX

PAR (mmol m-2 s-1)

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=70.78x+0.60R2=0.08 p=0.021JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-73.76x+0.95R2=0.12 p=0.009RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=0.64x+0.65R2=0.00 p=0.989FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=210.73x+-0.36R2=0.32 p=0.000PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-51.50x+0.89R2=0.06 p=0.005K34

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-36.45x+0.96R2=0.06 p=0.025K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-79.97x+1.10R2=0.20 p=0.000K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-257.15x+1.29R2=0.15 p=0.000K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=139.74x+0.20R2=0.42 p=0.000CAX

PAR (mmol m-2 s-1)

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=70.78x+0.60R2=0.08 p=0.021JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-73.76x+0.95R2=0.12 p=0.009RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=0.64x+0.65R2=0.00 p=0.989FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=210.73x+-0.36R2=0.32 p=0.000PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-51.50x+0.89R2=0.06 p=0.005K34

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-36.45x+0.96R2=0.06 p=0.025K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-79.97x+1.10R2=0.20 p=0.000K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-257.15x+1.29R2=0.15 p=0.000K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=139.74x+0.20R2=0.42 p=0.000CAX

PAR (mmol m-2 s-1)

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=70.78x+0.60R2=0.08 p=0.021JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-73.76x+0.95R2=0.12 p=0.009RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=0.64x+0.65R2=0.00 p=0.989FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=210.73x+-0.36R2=0.32 p=0.000PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-51.50x+0.89R2=0.06 p=0.005K34

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-36.45x+0.96R2=0.06 p=0.025K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-79.97x+1.10R2=0.20 p=0.000K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-257.15x+1.29R2=0.15 p=0.000K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=139.74x+0.20R2=0.42 p=0.000CAX

PAR (mmol m-2 s-1)

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=70.78x+0.60R2=0.08 p=0.021JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-73.76x+0.95R2=0.12 p=0.009RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=0.64x+0.65R2=0.00 p=0.989FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=210.73x+-0.36R2=0.32 p=0.000PDG

PAR (mmol m-2 s-1)

Results: GPP Mechanisms: PAR (not!)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-51.50x+0.89R2=0.06 p=0.005K34

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-36.45x+0.96R2=0.06 p=0.025K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-79.97x+1.10R2=0.20 p=0.000K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-257.15x+1.29R2=0.15 p=0.000K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=139.74x+0.20R2=0.42 p=0.000CAX

PAR (mmol m-2 s-1)

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=70.78x+0.60R2=0.08 p=0.021JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-73.76x+0.95R2=0.12 p=0.009RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=0.64x+0.65R2=0.00 p=0.989FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=210.73x+-0.36R2=0.32 p=0.000PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-51.50x+0.89R2=0.06 p=0.005K34

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-36.45x+0.96R2=0.06 p=0.025K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-79.97x+1.10R2=0.20 p=0.000K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-257.15x+1.29R2=0.15 p=0.000K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=139.74x+0.20R2=0.42 p=0.000CAX

PAR (mmol m-2 s-1)

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=70.78x+0.60R2=0.08 p=0.021JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-73.76x+0.95R2=0.12 p=0.009RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=0.64x+0.65R2=0.00 p=0.989FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=210.73x+-0.36R2=0.32 p=0.000PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-51.50x+0.89R2=0.06 p=0.005K34

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-36.45x+0.96R2=0.06 p=0.025K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-79.97x+1.10R2=0.20 p=0.000K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-257.15x+1.29R2=0.15 p=0.000K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=139.74x+0.20R2=0.42 p=0.000CAX

PAR (mmol m-2 s-1)G

EP

GE

Pm

ax

-10 0.5 1

0

0.2

0.4

0.6

0.8

1

y=70.78x+0.60R2=0.08 p=0.021JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-73.76x+0.95R2=0.12 p=0.009RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=0.64x+0.65R2=0.00 p=0.989FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=210.73x+-0.36R2=0.32 p=0.000PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-51.50x+0.89R2=0.06 p=0.005K34

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-36.45x+0.96R2=0.06 p=0.025K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-79.97x+1.10R2=0.20 p=0.000K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-257.15x+1.29R2=0.15 p=0.000K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=139.74x+0.20R2=0.42 p=0.000CAX

PAR (mmol m-2 s-1)

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=70.78x+0.60R2=0.08 p=0.021JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-73.76x+0.95R2=0.12 p=0.009RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=0.64x+0.65R2=0.00 p=0.989FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=210.73x+-0.36R2=0.32 p=0.000PDG

PAR (mmol m-2 s-1)$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67

CAXK83Ecuador

Bolivia

Peru

Brasil

FrenchGuyana

Surinam

GuyanaVenezuela

Colombia

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-51.50x+0.89R2=0.06 p=0.005K34

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-36.45x+0.96R2=0.06 p=0.025K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-79.97x+1.10R2=0.20 p=0.000K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-257.15x+1.29R2=0.15 p=0.000K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=139.74x+0.20R2=0.42 p=0.000CAX

PAR (mmol m-2 s-1)

GE

P G

EP

ma

x-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=70.78x+0.60R2=0.08 p=0.021JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-73.76x+0.95R2=0.12 p=0.009RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=0.64x+0.65R2=0.00 p=0.989FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=210.73x+-0.36R2=0.32 p=0.000PDG

PAR (mmol m-2 s-1)

Page 29: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67

CAXK83Ecuador

Bolivia

Peru

Brasil

FrenchGuyana

Surinam

GuyanaVenezuela

Colombia

Contrast predictability GEP vs. ET

Results: GPP Mechanisms

200 400 6000

5

10

y=-0.01x+9.43R2=0.06p=0.005K34

GE

P (

gC

m-2

d-1

)

200 400 6000

5

10

y=-0.00x+9.76R2=0.06p=0.025K67

200 400 6000

5

10

y=-0.01x+12.36R2=0.06p=0.039RJA

200 400 600100

150

200

250

300y=0.42x+22.62R2=0.49p=0.000 K34

PAR (mol m-2 s-1)

LE

da

ytim

e (

W m

-2)

200 400 600100

150

200

250

300y=0.31x+76.94R2=0.39p=0.000 K67

PAR (mol m-2 s-1)

200 400 600100

150

200

250

300y=0.24x+84.96R2=0.18p=0.000 RJA

PAR (mol m-2 s-1)

200 400 6000

5

10

y=-0.01x+9.43R2=0.06p=0.005K34

GE

P (

gC

m-2

d-1

)

200 400 6000

5

10

y=-0.00x+9.76R2=0.06p=0.025K67

200 400 6000

5

10

y=-0.01x+12.36R2=0.06p=0.039RJA

200 400 600100

150

200

250

300y=0.42x+22.62R2=0.49p=0.000 K34

PAR (mol m-2 s-1)

LE

da

ytim

e (

W m

-2)

200 400 600100

150

200

250

300y=0.31x+76.94R2=0.39p=0.000 K67

PAR (mol m-2 s-1)

200 400 600100

150

200

250

300y=0.24x+84.96R2=0.18p=0.000 RJA

PAR (mol m-2 s-1)

200 400 6000

5

10

y=-0.01x+9.43R2=0.06p=0.005K34

GE

P (

gC

m-2

d-1

)

200 400 6000

5

10

y=-0.00x+9.76R2=0.06p=0.025K67

200 400 6000

5

10

y=-0.01x+12.36R2=0.06p=0.039RJA

200 400 600100

150

200

250

300y=0.42x+22.62R2=0.49p=0.000 K34

PAR (mol m-2 s-1)

LE

da

ytim

e (

W m

-2)

200 400 600100

150

200

250

300y=0.31x+76.94R2=0.39p=0.000 K67

PAR (mol m-2 s-1)

200 400 600100

150

200

250

300y=0.24x+84.96R2=0.18p=0.000 RJA

PAR (mol m-2 s-1)

200 400 6000

5

10

y=-0.01x+9.43R2=0.06p=0.005K34

GE

P (

gC

m-2

d-1

)

200 400 6000

5

10

y=-0.00x+9.76R2=0.06p=0.025K67

200 400 6000

5

10

y=-0.01x+12.36R2=0.06p=0.039RJA

200 400 600100

150

200

250

300y=0.42x+22.62R2=0.49p=0.000 K34

PAR (mol m-2 s-1)

LE

da

ytim

e (

W m

-2)

200 400 600100

150

200

250

300y=0.31x+76.94R2=0.39p=0.000 K67

PAR (mol m-2 s-1)

200 400 600100

150

200

250

300y=0.24x+84.96R2=0.18p=0.000 RJA

PAR (mol m-2 s-1)

200 400 6000

5

10

y=-0.01x+9.43R2=0.06p=0.005K34

GE

P (

gC

m-2

d-1

)

200 400 6000

5

10

y=-0.00x+9.76R2=0.06p=0.025K67

200 400 6000

5

10

y=-0.01x+12.36R2=0.06p=0.039RJA

200 400 600100

150

200

250

300y=0.42x+22.62R2=0.49p=0.000 K34

PAR (mol m-2 s-1)

LE

da

ytim

e (

W m

-2)

200 400 600100

150

200

250

300y=0.31x+76.94R2=0.39p=0.000 K67

PAR (mol m-2 s-1)

200 400 600100

150

200

250

300y=0.24x+84.96R2=0.18p=0.000 RJA

PAR (mol m-2 s-1)

Page 30: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67

CAXK83Ecuador

Bolivia

Peru

Brasil

FrenchGuyana

Surinam

GuyanaVenezuela

Colombia

How much capacity is ‘spent’ on growth (GEP= PAR Pc)

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

y=39.40x+0.09

R2=0.44 p=0.000

K34

GE

P P

c-1

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

y=40.42x+0.11

R2=0.66 p=0.000

K67

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

y=45.03x+0.06

R2=0.36 p=0.000

K83

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

y=-7.15x+0.33

R2=0.00 p=0.692

K77

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

y=34.65x+0.15

R2=0.31 p=0.000

CAX

PARdaytime

(mmol m-2 s-1)

GE

P P

c-1

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1y=56.57x+0.03

R2=0.29 p=0.000

JAV

PARdaytime

(mmol m-2 s-1)

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

y=45.56x+0.01

R2=0.25 p=0.000

RJA

PARdaytime

(mmol m-2 s-1)

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1y=26.28x+0.19

R2=0.13 p=0.003

FNS

PARdaytime

(mmol m-2 s-1)

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1y=41.78x+0.12

R2=0.21 p=0.002

PDG

PARdaytime

(mmol m-2 s-1)

GPP Mechanisms: fraction of Photosyn-thetic capacity used is predicted by PAR

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

Page 31: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

$

$ $$$

$$

$

$

JAV

K34

K77

RJA

FNS

PDG

K67

CAXK83Ecuador

Bolivia

Peru

Brasil

FrenchGuyana

Surinam

GuyanaVenezuela

Colombia

How much capacity is ‘spent’ on growth (GEP= PAR Pc)

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

y=39.40x+0.09

R2=0.44 p=0.000

K34

GE

P P

c-1

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

y=40.42x+0.11

R2=0.66 p=0.000

K67

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

y=45.03x+0.06

R2=0.36 p=0.000

K83

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

y=-7.15x+0.33

R2=0.00 p=0.692

K77

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

y=34.65x+0.15

R2=0.31 p=0.000

CAX

PARdaytime

(mmol m-2 s-1)

GE

P P

c-1

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1y=56.57x+0.03

R2=0.29 p=0.000

JAV

PARdaytime

(mmol m-2 s-1)

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1

y=45.56x+0.01

R2=0.25 p=0.000

RJA

PARdaytime

(mmol m-2 s-1)

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1y=26.28x+0.19

R2=0.13 p=0.003

FNS

PARdaytime

(mmol m-2 s-1)

0.4 0.6 0.8 1 1.20

0.2

0.4

0.6

0.8

1y=41.78x+0.12

R2=0.21 p=0.002

PDG

PARdaytime

(mmol m-2 s-1)

GPP Mechanisms: fraction of Photosyn-thetic capacity used is predicted by PAR

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=59.83x+0.16

R2=0.28 p=0.000

K34

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1

y=87.25x+0.10

R2=0.69 p=0.000

K67

0 0.5 10

0.2

0.4

0.6

0.8

1

y=115.38x+0.00

R2=0.25 p=0.000

K83

0 0.5 10

0.2

0.4

0.6

0.8

1

y=-7.21x+0.30

R2=0.00 p=0.889

K77

0 0.5 10

0.2

0.4

0.6

0.8

1

y=74.26x+0.15

R2=0.39 p=0.000

CAX

PAR (mmol m-2 s-1)

GE

P P

c-1

0 0.5 10

0.2

0.4

0.6

0.8

1y=85.18x+0.17

R2=0.18 p=0.001

JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1

y=96.13x+0.02

R2=0.24 p=0.000

RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=39.89x+0.27

R2=0.06 p=0.054

FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.2

0.4

0.6

0.8

1y=64.37x+0.21

R2=0.19 p=0.003

PDG

PAR (mmol m-2 s-1)

Except pasture/ agriculture!

Page 32: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

- PAR doesn’t control monthly photosynthesis directly.- PAR influences the fraction of capacity utilized as GPP

What controls the seasonality of photosynthetic capacity?

Page 33: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

where:Pc: Ecosystem photosynthetic capacity (gC m-2 d-1)Litter fall, leaf flush (gC m-2 d-1)

Leaf-level parametersA: Photosynthetic assimilation per area of leaf (gC m-2 d-1)SLA: specific leaf area (m2 gC-1)

BrasilFlux Sites and Methods Seasonality of leaf -growth

leaf flush - leaf litter falldPc

A SLAdt

Page 34: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

BrasilFlux Sites and Methods Seasonality of leaf -growth

leaf flush - leaf litter falldPc

A SLAdt

Based on EC measurements

Field measurementDry and wet seasonfield measurement

where:Pc: Ecosystem photosynthetic capacity (gC m-2 d-1)Litter fall, leaf flush (gC m-2 d-1)

Leaf-level parametersA: Photosynthetic assimilation per area of leaf (gC m-2 d-1)SLA: specific leaf area (m2 gC-1)

Page 35: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Results: GPP Mechanisms – K67(Tapajos)

leaf-flush

leaf-fall

wood increment

(gC

m-2 d

-1 )

J FM AM J J A SO ND0

1

2

3

4

Page 36: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

leaf-flush

leaf-fall

wood increment

(gC

m-2 d

-1 )

J FMAMJ J A SO ND0.3

0.4

0.5

0

1

2

3

4

Results: GPP Mechanisms – K67(Tapajos)

Page 37: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

J F M A M J J A S O N D0

5

10

(gC

m-2

d-1

)K67

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

(gC

m-2

d-1

)

K34

J F M A M J J A S O N D0

5

10

(gC

m-2

d-1

)

K83

prec (<100 mm mo-1)

Leaf-flush NPP

Wood-increment NPPWood+Leaf

GEP

Results: GPP Mechanisms – 3 forests

Seasonality of photosynthetic capacity determined by leaf phenology.Leaves grow in the dry season when the sun shines (in central Amazon)

Page 38: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Central Amazon Forest Sites Photosynthesis shows little evidence of seasonal water limitation GPP is high -- or even increasing -- as the dry season progresses.

Southern forest site (Jarú), the converted sites (Santarém K77, and

Ji-Paraná FNS), and the savanna site (PDG) Seasonal patterns consistent with varying degrees of water stress All show dry-season declines in GEP.

Leaf-flush model indicates dry season forest green up at central Amazon forest sites

Complementary patterns in the timing of allocation in central Amazon (wood grows in wet season, leaves flush in the dry season )

Conclusions

Page 39: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Acknowledgments

Funded by the National Aeronautics and Space Administration (NASA) LBA

Page 40: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.
Page 41: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

0 2 40

1

2

3

4y=-1.46x+2.70

R2 =0.50 p=0.000

K67

leaf

-flu

sh

(gC

m-2

d-1

)

wood-increment

(gC m-2 d-1)

0.2 0.3 0.4 0.50

1

2

3y=7.02x+-1.27

R2 =0.27 p=0.000

K67

leaf

-flu

sh

(gC

m-2

d-1

)

PAR (mmol m-2 s-1)

0.3 0.4 0.50

1

2

3

4y=-9.71x+4.85

R2 =0.78 p=0.000K67

leaf

-flu

sh

(gC

m- 2

d- 1

)

0-40cm (m2 m-2)

Results: GPP Mechanisms – K67(Tapajos)

Soil moisture

Page 42: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

At all Amazonian sites Bi-weekly Pc declines with increasing light, contradicting model

assumptions that assume days with high light as more productive than days with low light.

ET and photosynthesis are often thought of as coupled process. Net radiation (Rn) controls ET (R>.30) GEP respond differently to Rn GEP seems to be controlled by complex patterns of production

and loss of photosynthetic capacity The fraction of capacity utilized as GPP, depends on light levels

(GPP Pc- vs. PAR)

Results: GEP Environmental Controls

Page 43: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

0 0.5 10

0.5

1

y=-143.86x+1.18R2=0.50 p=0.000K34

Pc

Pc m

ax

-1

0 0.5 10

0.5

1

y=-174.69x+1.30R2=0.58 p=0.000K67

0 0.5 10

0.5

1

y=-193.66x+1.35R2=0.42 p=0.000K83

0 0.5 10

0.5

1

y=-248.14x+1.31R2=0.17 p=0.000K77

0 0.5 10

0.5

1

y=-16.91x+0.76R2=0.01 p=0.481CAX

PAR (mmol m-2 s-1)

Pc

Pc m

ax

-1

0 0.5 10

0.5

1

y=-98.24x+1.04R2=0.09 p=0.014JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.5

1

y=-171.40x+1.30R2=0.30 p=0.000RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.5

1

y=-69.56x+0.97R2=0.02 p=0.198FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.5

1

y=125.19x+-0.04R2=0.12 p=0.019PDG

PAR (mmol m-2 s-1)

Page 44: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

0 0.5 10

0.5

1

y=-51.50x+0.89R2=0.06 p=0.005K34G

EP

GE

Pm

ax

-1

0 0.5 10

0.5

1

y=-36.45x+0.96R2=0.06 p=0.025K67

0 0.5 10

0.5

1

y=-79.97x+1.10R2=0.20 p=0.000K83

0 0.5 10

0.5

1

y=-257.15x+1.29R2=0.15 p=0.000K77

0 0.5 10

0.5

1

y=139.74x+0.20R2=0.42 p=0.000CAX

PAR (mmol m-2 s-1)

GE

P G

EP

ma

x-1

0 0.5 10

0.5

1

y=70.78x+0.60R2=0.08 p=0.021JAV

PAR (mmol m-2 s-1)

0 0.5 10

0.5

1

y=-73.76x+0.95R2=0.12 p=0.009RJA

PAR (mmol m-2 s-1)

0 0.5 10

0.5

1

y=0.64x+0.65R2=0.00 p=0.989FNS

PAR (mmol m-2 s-1)

0 0.5 10

0.5

1

y=210.73x+-0.36R2=0.32 p=0.000PDG

PAR (mmol m-2 s-1)

Page 45: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.
Page 46: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Leaf-flush model additional data

leaf flush - leaf litter falldPc

A SLAdt

200

400

600

PA

R

(m

ol m

-2 s

-1)

-1

1.5

4CAX

200

400

600

-1

1.5

4

term

s 1

& 2

LF

M

(gC

m-2

d-1

)

K67

200

400

600

PA

R

(m

ol m

-2 s

-1)

-1

1.5

4K83

200

400

600

-1

1.5

4

term

s 1

& 2

LF

M

(gC

m-2

d-1

)

K34

200

400

600

PA

R

(m

ol m

-2 s

-1)

-1

1.5

4RJA

200

400

600

-1

1.5

4

term

s 1

& 2

LF

M

(gC

m-2

d-1

)

BAN

200

400

600

PA

R

(m

ol m

-2 s

-1)

-1

1.5

4PDG

Page 47: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Leaf-flush model additional data

0.2 0.3 0.4 0.50

1

2

3

4y=5.07x+-0.09R2=0.10 p=0.156K83 prelogg

leaf

-flu

sh

(gC

m-2

d-1

)

PAR (mmol m-2 s-1)

0.3 0.4 0.50

1

2

3

4

y=-17.00x+9.26

R2=0.43 p=0.001

K83 prelogg

leaf

-flu

sh

(gC

m-2

d-1

)

0-40cm

(m2 m-2)

0.2 0.3 0.4 0.50

1

2

3

4y=10.50x+-2.18R2=0.40 p=0.000K83 postlogg

leaf

-flu

sh

(gC

m-2

d-1

)

PAR (mmol m-2 s-1)

0.3 0.4 0.50

1

2

3

4

y=-9.72x+5.24

R2=0.47 p=0.000

K83 postlogg

leaf

-flu

sh

(gC

m-2

d-1

)

0-40cm

(m2 m-2)

Page 48: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Leaf-flush model additional data

J FMAMJ J A SO ND0.3

0.4

0.5

soil

moi

stur

e

0

1

2

3

4

(gC

m-2

d-1

)

leaf-flush

leaf-fallwood increment

0 2 40

1

2

3

4y=-3.03x+2.66

R2=0.40 p=0.000

K83

leaf

-flu

sh

(gC

m-2

d-1

)

wood-increment

(gC m-2 d-1)

0.2 0.3 0.4 0.50

1

2

3

4y=8.41x+-1.37R2=0.26 p=0.000K83

leaf

-flu

sh

(gC

m-2

d-1

)

PAR (mmol m-2 s-1)

0.3 0.4 0.50

1

2

3

4

y=-6.56x+4.26

R2=0.18 p=0.001

K83leaf

-flu

sh

(gC

m-2

d-1

)

0-40cm

(m2 m-2)

Page 49: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

Relation GEP and NEE and air temperature

Page 50: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

No ta controlHigh par high GEP

Page 51: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.
Page 52: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.
Page 53: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

20 30 40-20

20

60y=0.03x2+-2.28x+60.76R2=0.03p=0.000K34

ta (degC)

NE

E ( m

olC

m-2

s-1

)

20 30 40-20

20

60y=-0.33x2+17.59x+-214.31R2=0.06p=0.000K67

ta (degC)20 30 40

-20

20

60y=-0.09x2+3.68x+-14.94R2=0.12p=0.000K83

ta (degC)20 30 40

-20

20

60y=-0.30x2+15.66x+-180.85R2=0.16p=0.000CAX

ta (degC)

20 30 40-20

20

60y=-0.12x2+5.49x+-39.18R2=0.12p=0.000RJA

ta (degC)

NE

E ( m

olC

m-2

s-1

)

20 30 40-20

20

60y=-0.03x2+0.76x+14.47R2=0.11p=0.000K77

ta (degC)20 30 40

-20

20

60y=-0.20x2+10.36x+-117.96R2=0.06p=0.000FNS

ta (degC)

NE

E ( m

olC

m-2

s-1

)20 30 40

-20

20

60y=-0.04x2+2.23x+-20.65R2=0.01p=0.000PDG

ta (degC)

Page 54: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

2005 Drought

Page 55: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

0

300

600p

rec

(mm

mo

nth

-1)

K67 2005 (red) 2002-2004 (black)

200

500

800

PA

R(

mo

l m-2

s-1

)

0

7.5

15

GE

P

(gC

m-2

d-1

)

JFMA M J JA SOND10

30

50

Pc

(gC

m-2

d-1

)

JFMA M J JA SOND0.02

0.09

0.16

LU

E(g

C

mo

lPA

R)

JFMA M J JA SOND30

75

120

GE

Psa

t

(gC

m-2

d-1

)

J FMA M J J A SOND-1.5

-0.5

0.5

1.5

Le

af-

flush

(gC

m- 2

d-1

)

K67 Leaf-flush 2005 (red) 2002-2006 (black)

J FMA M J J A SOND-1.5

-0.5

0.5

1.5

Le

af-

flush

(gC

m- 2

d-1

)

K67 K67 Leaf-flush 2005 (red) 2002-2004 (black)

Page 56: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

0

300

600

pre

c

(mm

mo

nth

-1)

JAV 2005 (red) 2002-2006 (black)

200

500

800

PA

R(

mo

l m-2

s-1

)

0

7.5

15

GE

P

(gC

m-2

d-1

)

J FMA MJ JA SOND10

30

50

Pc

(gC

m-2

d-1

)

JFMAMJ JA SOND0

0.045

0.09L

UE

(gC

m

olP

AR

)

JFMA MJ JA SOND30

65

100

GE

Psa

t

(gC

m-2

d-1

)

Page 57: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

0

300

600p

rec

(mm

mo

nth

-1)

K34 2005 (red) 2000-2004&2006 (black)

200

500

800

PA

R(

mo

l m-2

s-1

)

0

7.5

15

GE

P

(gC

m-2

d-1

)

JFMA MJ JA SOND10

30

50

Pc

(gC

m-2

d-1

)

JFMA MJ JA SOND0

0.045

0.09

LU

E(g

C

mo

lPA

R)

JFMAMJ JA SOND30

75

120

GE

Psa

t

(gC

m-2

d-1

)

J FMA M J J A SOND-1.5

-0.5

0.5

1.5

Le

af-

flush

(gC

m- 2

d-1

)

K34 2005 (red) 2000-2006 (black)

J FMA M J JA SO ND-1.5

-0.5

0.5

1.5

Le

af-

flush

(gC

m- 2

d-1

)

K34 2005 (red) 2000-2004 & 2006 (black)

Page 58: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

0

300

600p

rec

(mm

mo

nth

-1)

K34 2006 (red) 1999-2006 (black)

200

500

800

PA

R(

mo

l m-2

s-1

)

0

7.5

15

GE

P

(gC

m-2

d-1

)

J FMAMJ JA SOND10

30

50

Pc

(gC

m-2

d-1

)

JFMAMJ JA SOND0

0.045

0.09

LU

E(g

C

mo

lPA

R)

JFMAMJ JA SOND30

75

120

GE

Psa

t

(gC

m-2

d-1

)

Page 59: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.
Page 60: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

2

4

6

8

10

12

GE

P (

gC m

-2d-1

)

K67 2002-2006

GEP

GEPnoSco2

J F M A M J J A S O N D-30

-20

-10

Per

cent

age

Abs

olut

e E

rror

GE

Pno

Sco

2 vs.

GE

P

2

4

6

8

10

12

GE

P (

gC m

-2d-1

)

RJA 1999-2002

J F M A M J J A S O N D-60

-50

-40

Per

cent

age

Abs

olut

e E

rror

GE

Pno

Sco

2 vs.

GE

P Sco

22

4

6

8

10

12

GE

P (

gC m

-2d-1

)

K34 1999-2006

J F M A M J J A S O N D-40

-30

-20

Per

cent

age

Abs

olut

e E

rror

GE

Pno

Sco

2 vs.

GE

P Sco

2

BrasilFlux Tower Sites: GEP calculations

Missing Sco

Page 61: Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.

2

4

6

8

10

12

GE

P (

gC m

-2d-1

)

K67 2002-2006

GEP

GEPnoSco2

J F M A M J J A S O N D-30

-20

-10

Per

cent

age

Abs

olut

e E

rror

GE

Pno

Sco

2 vs.

GE

P

2

4

6

8

10

12

GE

P (

gC m

-2d-1

)

RJA 1999-2002

J F M A M J J A S O N D-60

-50

-40

Per

cent

age

Abs

olut

e E

rror

GE

Pno

Sco

2 vs.

GE

P Sco

22

4

6

8

10

12

GE

P (

gC m

-2d-1

)

K34 1999-2006

J F M A M J J A S O N D-40

-30

-20

Per

cent

age

Abs

olut

e E

rror

GE

Pno

Sco

2 vs.

GE

P Sco

2

BrasilFlux Tower Sites: GEP calculations

Missing Sco2