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 BalanceFirst 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 

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

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

)()(

ddΨ

prs

lprswsl

RRRCRRREgh

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 4.3000

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)

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

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)