svat model introduction
TRANSCRIPT
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Soil-Vegetation-Atmosphere Transfer (SVAT) Models
Dr. Mathew Williams
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What are SVAT models?
• Simulators of energy and matter exchange between land surface and atmosphere
• Based on mechanistic understanding of the component systems
• Used by meteorologists, climatologists, ecologists and biogeochemists.
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Why do we need SVAT models?
• To assist understanding of observations• To allow hypothesis testing• To extend understanding across space and
time• To provide a basis for prediction
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Model Jargon
• State variables• Parameters• Driving variables• Calibration• Corroboration/validation/testing• Sensitivity analysis
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What is the structure of a typical SVAT model?
• Radiative transfer• Energy balance• Turbulent and diffusive transfer• Stomatal function• Photosynthesis and respiration• Liquid phase water flow
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Small Group Task
• For a SVAT component, define the sub-model structure
• What are the driving variables, the parameters and state variables?
• What are the key connections to other SVAT sub-models?
• How would you calibrate your sub-model?
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Radiative Transferreflectance
transmittance
Absorptance
•Direct and diffuse•NIR vs PAR•Solar geometry•Foliar geometry•Sunlit and shaded
Beer’s Law: I=Io exp(-kL)
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Energy BalanceFirst law of thermodynamics: Energy is always conserved
QhQe
Qc
Qs
Qlout
Qs + Qe + Qh + Qlin + Qlout + Qc = 0
Qlin
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Turbulent and Diffusive Transfer
Boundary layer thickness- leaf size- wind speed- temperature
Turbulent zone
Laminar zone
J = g c/zWind withinCrops and forests
Wind speed
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Stomatal Function
Empirical vs. mechanistic approaches
E = gs cw
gs is responsive to:CO2LightLeaf waterHumidity
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Penman-Monteith Equation
E
sR c g e
s g ga l
n a p H
[ ( [ / ])]1
= psychrometer constantacp = volumetric heat
capacity of dry airs = slope of saturation vapour
pressure curve latent heat of
vapourisation
Rn = net radiatione = vapour pressure deficitga = leaf boundary layer conductancegl = leaf stomatal conductancegH = heat conductance
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Photosynthesis and Respirationlight
CO2 + 2H2O CO2 + 4H + O2 (CH2O) + H2O + O2
LIGHT REACTIONS DARK REACTIONS
Metabolic model = Diffusion model Vc(1-*/Cc)–Rd = gt(Ca-Cc)
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Liquid Phase Water Flow
Rs
2
Rp
Rsn
Rs1
C
s1
sn
s2
E
Rr1
Rr2
Rrn
PlantSoil
AtmosphereCO2
gs Leaf
Stem
Roots
l
)()(
ddΨ
prs
lprswsl
RRRCRRREgh
t
What determines:Root resistance (Rr)?
Plant resistance (Rp)?
Soil resistance (Rs)?
Soil water potential (l)?
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The Soil-Plant-Atmosphere Model
• Multi-layer canopy and soils• 30 minute time-step• Fully coupled liquid and vapour phase
water fluxes• Biochemical model of photosynthesis
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A. Canopy Structure
PHYSICAL COMPONENT
10
n
1En (gsn)
CO2H2O
Rsn
BIOLOGICAL COMPONENT
CnRpn
s
PAR NIR
B. RadiationC. Boundary Layer
D. Soil Water Potential & Soil-Root Hydraulic Conductivity
Layer
ln
Windspeed LAI
Sun &shade
[N]
SOIL PLANT ATMOSPHERE MODEL
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No Yes
1. Increment gs
& calculate gt
2. Determine Leaf
Temperature, Tl
3. Calculate metabolic parameters;
Vcmax, Jmax = f(Tl, [N])
4. Determine assimilation by varying Cc until:
Metabolic model = Diffusion model
Vc(1-*/Cc)-Rd = gt(Ca-Cc)
5. Evaporation (Penman-Monteith)
6. Change in LWP, l /t
7. /gs > &
l >
lmin ?
STOP START LEAF LEVEL PROCESSES
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Harvard Forest
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4.120 4.140 4.160 4.180 4.200 4.220 4.240 4.260 4.280 4.3000
2
4
6
8
10
12
14
164.120 4.140 4.160 4.180 4.200 4.220 4.240 4.260 4.280 4.3000
2
4
6
8
10
12
14
Modelled LE (fine-scale) Measured LE
LE (
W m
-2)
Day of year
Harvard Forest
HFsun_6yrs TR.OPJ 26/11/2001 15:59
Modelled GPP (SPA) Measured GPP
GP
P (
gC m
-2 d
-1)
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4.14 4.16 4.18 4.20 4.22 4.24 4.26 4.28 4.300
10
20
30
Harvard Forest, controls on GPP, 1994
tem
pera
ture
(o C)
Time (d)
4.14 4.16 4.18 4.20 4.22 4.24 4.26 4.28 4.30048
1216202428
irrad
ianc
e (M
J m
-2 d
-1)
4.14 4.16 4.18 4.20 4.22 4.24 4.26 4.28 4.300
2
4
LAI
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Tropical rain forest
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Arctic tundra – northern Alaska
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201 202 203 204 205 206 207 208 209 210 211 212-4
0
4
Modelled NEP (mol m-2 s-1)Day of year
6
178 179 180 181 182 183 184 185 186-4
0
4Measured Modelled
171 172 173 174 175 176 177 178 179-4
0
4
CO2 exchange in three tussock tundra sites, northern Alaska
Mea
sure
d N
EP
(m
ol m
-2 s
-1)
4
3
-4 0 4
-4 0 4
-4 0 4
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SPA(30 min,
process based)
ACM(daily, ‘big leaf’)
Eddy flux
Field data:
LAI, N
Satellite data(NDVI)
Weather stations
GIS
PREDICTIONS
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What you should have learned
• Structure of typical SVAT models• Diagnostic uses (working with eddy flux
data)• Prognostic uses (scaling up)• Key research areas in developing SVAT
models (applicability to global change research)