Soil-Vegetation-Atmosphere Transfer (SVAT) Models
Dr. Mathew Williams
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.
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
Model Jargon
• State variables
• Parameters
• Driving variables
• Calibration
• Corroboration/validation/testing
• Sensitivity analysis
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
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?
Radiative Transferreflectance
transmittance
Absorptance
•Direct and diffuse•NIR vs PAR•Solar geometry•Foliar geometry•Sunlit and shaded
Beer’s Law: I=Io exp(-kL)
Energy Balance
First law of thermodynamics: Energy is always conserved
QhQe
Qc
Qs
Qlout
Qs + Qe + Qh + Qlin + Qlout + Qc = 0
Qlin
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
Stomatal Function
Empirical vs. mechanistic approaches
E = gs cw
gs is responsive to:CO2LightLeaf waterHumidity
Penman-Monteith Equation
EsR 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 radiation
e = vapour pressure deficitga = leaf boundary layer conductance
gl = leaf stomatal conductance
gH = heat conductance
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)
Liquid Phase Water Flow
Rs
2
Rp
Rsn
Rs1
C
s1
sn
s2
E
Rr1
Rr2
Rrn
PlantSoil
AtmosphereCO2
gs Leaf
Stem
Roots
l
)(
)(
d
dΨ
prs
lprswsl
RRRC
RRREgh
t
What determines:
Root resistance (Rr)?
Plant resistance (Rp)?
Soil resistance (Rs)?
Soil water potential (l)?
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
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
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
Harvard Forest
4.120 4.140 4.160 4.180 4.200 4.220 4.240 4.260 4.280 4.300
0
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)
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
pe
ratu
re(o C
)
Time (d)
4.14 4.16 4.18 4.20 4.22 4.24 4.26 4.28 4.30048
1216202428
irra
dia
nce
(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
LA
I
Tropical rain forest
Arctic tundra – northern Alaska
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
SPA(30 min,
process based)
ACM(daily, ‘big leaf’)
Eddy flux
Field data:
LAI, N
Satellite data(NDVI)
Weather stations
GIS
PREDICTIONS
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)