1

Vegetation dynamics and soil water Vegetation dynamics and soil water balance in a water-limited Mediterranean balance in a water-limited Mediterranean

ecosystem on Sardinia, Italyecosystem on Sardinia, Italy

Nicola Montaldo1, John D.

Albertson2 and Marco Mancini3

3- Dipartimento di Ingegneria Idraulica, Ambientale, e del Rilevamento, Politecnico di Milano, Italy

2- Department of Civil and Environmental Engineering, Pratt School of Engineering, Duke University, USA

10–14 December 2007, AGU FALL MEETING

1- Dipartimento di Ingegneria del Territorio, Università di Cagliari (nicola.montaldo@unica.it)

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Field monitoring of land surface fluxes, soil moisture and vegetation dynamics Field monitoring of land surface fluxes, soil moisture and vegetation dynamics for years with different hydro-meteorological conditions of a water-limited for years with different hydro-meteorological conditions of a water-limited Mediterranean heterogeneous ecosystem;Mediterranean heterogeneous ecosystem;

Development of a 3-component (bare soil, grass and woody vegetation) coupled Development of a 3-component (bare soil, grass and woody vegetation) coupled VDM-LSM for modeling land surface dynamics;VDM-LSM for modeling land surface dynamics;

Assess the influence of key environmental factors on the vegetation dynamics for Assess the influence of key environmental factors on the vegetation dynamics for the different annual hydrologic conditionsthe different annual hydrologic conditions

GoalsGoals

MethodologyMethodology Experimental field campaign at Orroli (Sardinia) for monitoring land

surface fluxes and vegetation growth… started in May 2003

Development of a coupled VDM-LSM for competing vegetation species (grass, woody vegetation)

test the coupled model for the Orroli site and data analysis

3

The experiment: Orroli site (From April 2003)

Flumendosa dam

Mulargia dam

4

The experiment: Orroli site in the Flumendosa basin

5

The experiment: Eddy correlation tower for monitoring land surface fluxes

a

b

c

d

a- CNR1 Integral radiometerb- H2O/CO2 gas analyzerc- Soil heatd- CSAT3 Sonic anemometer

Energy balance H+LE=Rn-G

3 infrared transducers, IRTS-P (Apogee)

6

The experiment: LAI estimate with the CEPTOMETER LP-80

PAR (photosynthetically active radiation) sensor

(LI-190SB)

Soil moisture probes (CS616 Campbell sci.)

Silt loam

7

Quickbird imageSpring: 4 May 2004

(spatial resolution 2.8 m)

1 km

The tower

Orroli

The experiment: Remote sensing observations

Quickbird imageSummer: 3 August 2003 (spatial resolution 2.8 m)

The tower

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The field The field heterogeneity heterogeneity (Detto (Detto et al., WRR et al., WRR 2006)2006)

The interpretation of eddy-The interpretation of eddy-covariance measurementscovariance measurementsthrough the foot print model (a through the foot print model (a revised 2-D version 2-D version of revised 2-D version 2-D version of the foot print model of the foot print model of Hsieh et al. Hsieh et al. [2000][2000] )

fv,wv (fraction of woody

vegetation)

Estimate the source area of Estimate the source area of the flux at each time stepthe flux at each time step

Normalized difference

vegetation index (NDVI) of woody

vegetation

NDVI/NDVIMAX

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Bare soil

Grass

Woody veg.

Woody vegetation

transpiration

Bare soil evaporati

on

competition for root zone competition for root zone soil water contentsoil water content

Grass transpirati

on

fw

fg

fsfbs

fv,wvfv,g

Patch mosaic Patchs

Decomposition of the Landscape

The Land Surface model (LSM).. 3-components

Infiltration,I

Drainage, Qdr

Soil moisture,

Runoff

(Albertson and Kiely, J. Hydrology, 2001; Montaldo and Albertson, J. Hydrometeorology,2(6), 2001)

Root zone budget:

(fv,g+fv,wv+ fbs=1)

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with k= w (woody vegetation) or g (grass)

Penmann-Monteith

Evapotranspiration ET= Ev,g + Ev,w + Ebs

Canopy resistance

Ebs=fbs() Ep

f1()

wilt lim

1

0

f2(T)

Tmin Topt

1

0 Tmax

0.05 0.1 0.15 0.2 0.25 0.30

0.2

0.4

0.6

0.8

1

f 1()

Summer 2003. Tree-bare soil

0.05 0.1 0.15 0.2 0.25 0.30

0.2

0.4

0.6

0.8

1

Spring 2004. Tree-grass

f 1()

Grass

Woody veg.

Bare soil [()]

From observations, using the foot print modelFrom observations, using the foot print model

Woody veg.

Detto et al. WRR, 2007

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Vegetation dynamic model of the generic vegetation type

Green (leaves) biomass

Root biomass

Dead biomass

ag , as, ar allocation coefficients,

dinamically estimated

Pg : Gross photosynthesis

Maintenance and growth respirations

Senescence

Litter fall

Production Destruction Derived from Montaldo et al., [WRR, 2005]; Nouvellon et al., 2001

Stem biomass

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Gross photosynthesis

LAIkPAR

eef 1 fraction of PAR absorbed by the canopy

P is the leaf photochemical efficiency [g dry mass/ PAR]

PAR (0.38-0.71 PAR (0.38-0.71 m)m)

Substomatal cavity

ca

caPARPg rr

rrPARfPARP

6.137.1

6.137.1 min,

Montaldo et al., [WRR, 2005]

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Allocation coefficientsAllocation coefficients

Derived from Arora and Boer [GCB, 11, 39-59, 2005]

121 fa a

a

121

1

fa s

s

1

1

21

1

f

fa r

r

Woody vegetation

1 rsa

LAIkee

111 fa a

a

1

1

11

1

f

fa r

r

1 ra Grass

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VDM+LSM (3-components) at the Orroli site: Soil Moisture…VDM+LSM (3-components) at the Orroli site: Soil Moisture…

150 250 350 50 150 250 350 50 150 250 350 50 150

0

20

40

60

80

100

120

140

160

Pre

cipi

tatio

n [m

m/d

]

150 250 350 50 150 250 350 50 150 250 350 50 1500

0.1

0.2

0.3

0.4

0.5

0.6

2003 2004 2005 2006

Day of the year

mod

obs

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VDM+LSM at the Orroli site: Surface temperatureVDM+LSM at the Orroli site: Surface temperature

150 250 350 50 150 250 350 50 150 250 350 50 1500

10

20

30

40

50

2003 2004 2005 2006

a)

Sur

face

tem

pera

ture

[°C

]

obs-WVmod-WV

150 250 350 50 150 250 350 50 150 250 350 50 1500

10

20

30

40

50

2003 2004 2005 2006

b)

Day of year

Sur

face

tem

pera

ture

[°C

]

obs-NWVmod-bare soilmod-grass

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VDM+LSM at the Orroli site: Energy balance componentsVDM+LSM at the Orroli site: Energy balance components

150 250 350 50 150 250 350 50 150 250 350 50 1500

2

4

6 2003 2004 2005 2006b)

H [m

m/d

]

150 250 350 50 150 250 350 50 150 250 350 50 1500

2

4

6 2003 2004 2005 2006a)

Rn

[mm

/d]

150 250 350 50 150 250 350 50 150 250 350 50 1500

2

4

6 2003 2004 2005 2006c)

G [m

m/d

]

Day of year

observedmodel

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VDM+LSM (3-components) at the Orroli site:ETVDM+LSM (3-components) at the Orroli site:ET

150 250 350 50 150 250 350 50 150 250 350 50 1500

1

2

3

4

5

2003 2004 2005 2006

a)

E [m

m/d

]observedmodel

150 250 350 50 150 250 350 50 150 250 350 50 1500

200

400

600

800

1000

1200

2003 2004 2005 2006

b)

Day of year

Cum

ulat

ive

Eva

potr

ansp

iratio

n [m

m]

observedmodel

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VDM+LSM (3-components) at the Orroli site:LAIVDM+LSM (3-components) at the Orroli site:LAI

150 250 350 50 150 250 350 50 150 250 350 50 1500

0.5

1

1.5

2

2.5

3GRASS

2003 2004 2005 2006LA

I modelobserved

150 250 350 50 150 250 350 50 150 250 350 50 1500

1

2

3

4

5

6WOODY VEGETATION

2003 2004 2005 2006

LAI

Days of the year

19

150 250 350 50 150 250 350 50 150 250 350 50 1500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.82003 2004 2005 2006

a)

allo

catio

n co

effic

ient

s

ag

ar

as

150 250 350 50 150 250 350 50 150 250 350 50 1500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

2003 2004 2005 2006b)

Day of year

allo

catio

n co

effic

ient

sAllocation coefficients in VDMAllocation coefficients in VDM

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

50

100

150

200

250

300

Pre

cipi

tatio

n [m

m/m

onth

]

2003200420052006Mean 1922-92

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

5

10

15

20

25

30

Tem

pera

ture

[°C

]

Month

Comparison of observed and hystorical mean monthly Precipitation and temperature

21

40 60 80 100 120 140 160 1800

0.5

1

1.5

2

LA

I

40 60 80 100 120 140 160 1800

0.1

0.2

0.3

0.4

0.5

2003200420052006

40 60 80 100 120 140 160 1800

10

20

30

Ta

Influence of soil moisture and temperature on grass dynamics during the observed years

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Correlation between grass Correlation between grass LAILAI and precipitation and precipitation (April and May(April and May))

mean 15-day values of grass LAI versus the aggregated 15-day precipitation values time lagged by 15 days

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ConclusionsConclusions The yearly variability of hydro-meteorological conditions offered a

wide range of conditions for testing the developed 3-component (bare soil, grass and woody vegetation) coupled VDM-LSM model. The model performed well for the whole period of observation and was able to accurately predict vegetation dynamics, soil water balance and land surface fluxes.

Interannual variability of hydromet-conditions can significantly affect vegetation growth in these water limited ecosystems: importance to include VDMs in LSM

The correlation was found to be high when the values of precipitation and LAI are aggregated at 15-day time intervals, and there is a sufficient time lag (15-days) between the forcing (precipitation) and the answer (LAI)

Nicola Montaldo (nicola.montaldo@unica.it)

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Throughfall

Soil water

balance

Drainage

Evapotranspiration

Balance of intercepted water by vegetation

LAI grass

Rainfall

Atmospheric forcings (Ri, RH, u,

T, PAR)

Biomass budget

Photosynthesis

Respiration

Translocation

Senescence

Land Surface Model

Grass VDM

Energy balance

Soil heat dynamic

LSM+VDM coupled model

LAI woody veg.

Biomass budget

Senescence

Respiration

Translocation

Photosynthesis

Woody veg. VDM

Competition for water

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Allocation coefficient model of Allocation coefficient model of Arora and Boer (GCB, 11, 39-59, 2005)Arora and Boer (GCB, 11, 39-59, 2005)

rsAB

aa aa

WABLa

1

21

Woody vegetation

ABABAB

ABABss WL

La

21

)1(

ABABAB

ABABrr WL

Wa

21

)1(

LAIkAB

eeL

1fWAB

Grass

rABABAB

ABABaa a

WL

La

111

ABABAB

ABABrr WL

Wa

11

1