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 ([email protected])
2
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
8
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
9
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)
10
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
11
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
12
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]
13
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
14
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
15
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
16
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
17
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
18
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
20
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
22
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
23
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 ([email protected])
24
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
25
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