Estimating Surface-Atmosphere Exchange at Regional Scales

Peter Isaac1, Ray Leuning2 and Jörg Hacker3

1 School of Geography and Environmental Science, Monash University2 CSIRO Marine and Atmospheric Research

3 Airborne Research Australia, Flinders University of South Australia

What Does The Title Mean?

• Surface-Atmosphere Exchange– fluxes of momentum, heat, H2O and CO2

• Regional Scales– of the order of 100 km– bigger than patch scale

• accessible by micro-meteorological techniques

– smaller than continental scale• accessible by inversion techniques

Why Do We Want Regional Scale Fluxes of H2O and CO2?

• Validation of land-surface models (LSMs)

• Estimation of parameters for LSMs at regional scales– avoids some of the issues in scaling-up

• Investigation of catchment scale hydrology

• Validation of inventory-based carbon budgets

What Was OASIS?

• Observations At Several Interacting Scales– multi-organisation experiment– held near Wagga Wagga, New South Wales– intensive field campaigns in October 1994 and

1995• 1994 was a severe drought year, not used

– 8 flux towers, G109 research aircraft, free-flying and tethered balloon systems, FTIR spectrometer, TDL, flask samples, LandSat imagery

528 530 532

Easting (km)

6118

6120

6122

6124

P T

f) W agga

476 478 4806104

6106

6108

6110

PO

C

W

e) Br owning

0 0.2 0.4 0.6 0.8 1

446 448 450

Easting (km)

6095

6097

6099

6101

No

rth

ing

(km

)

WP

d) Urana

440 460 480 500 520 540

6100

6120

No

rth

ing

(k

m)

Murru mb idg ee R iver

Lak e Cu lliva l (d ry)

W agga

Browning

L ockh art

Urana

W aggaW agga

100 150 200 300 400

He ight (m )

c)

144 146 148 150

Longitude

-38

-36

Lat

itu

de

W aggaUrana C anbe rra

Me lb ourne

NSW

VIC

500 1000 1500

He ight (m )

b)

110 120 130 140 150

Longitude

-45

-35

-25

-15

Lat

itu

de

Melb ou rne

Syd ney

Brisb ane

Ad elaide

P erth

Darwin

Wag gaWag ga

Ho bart

a)

OASIS 1995

• Wagga Wagga to Urana

• 100 km transect• ~1 mm/km rainfall

gradient• paired flux towers at

3 locations• aircraft to provide

link from patch to regional scale

4 74 4 75 4 76

Easting (km)

6104

6105

6106

No

rth

ing

(k

m)

WO M

S

R

Pastu reOats

W heat

W heat

Pastu re

Oats

The W attles

Pastu re

Glidin g C lub

Airstrip Rd

Tin

amb

a L

ane

4 75 4 80

Easting (km)

6105

6110

6115

No

rth

ing

(k

m)

POC

W

Mt G alore

440 450 460 470 480 490 500 510 520 530 540

Eastin g (km )

6100

6110

6120

No

rth

ing

(k

m)

Murru mb idg ee R iver

Lake Cu lliva l (d ry)

Wagga

Browning

L ockh art

Urana

WaggaWagga

100 150 200 300 400

He ig ht (m )

c)

Aircraft Flight PatternsPaddock Grid

Transect

0 100 200 300Ground-based

0

100

200

300

k) FE (W m -2)

0 100 200 300

0

100

200

300

Air

cra

ft

j) FH (W m -2)

0 200 400 600 8000

200

400

600

800

i) FN (W m -2)

0 0 .2 0 .4 0 .6Ground-based

0

0 .2

0 .4

0 .6 h) u * (m s -1)

0 0 .1 0 .2 0 .3 0 .4 0 .50

0 .1

0 .2

0 .3

0 .4

0 .5

Air

cra

ft

g) q (gkg -1)

0 0 .5 1 1 .50

0 .5

1

1 .5

f) (K)

0 0 .5 1 1 .5 2Ground-based

0

0 .5

1

1 .5

2

e) u (m s -1)

0 0 .2 0 .4 0 .6 0 .8 10

0 .2

0 .4

0 .6

0 .8

1

Air

cra

ft

d) w (m s -1)

10 15 20 25 30 3510

15

20

25

30

35

c) Ta (C)

0 2 4 6Ground-based

0

2

4

6 b) U (m s -1)

180 270 360180

270

360

Air

cra

ft

a) W D (o)

Tower-Aircraft Comparison

• Low level allows comparison of means, variances and covariances

• Correction for temperature sensor response time

• Correction for surface heterogeneity reduces mean bias to:– 7 Wm-2 for Fh

– -7 Wm-2 for Fe

Paddock Flights

Observing Regional Scale Fluxes

• No single observation technique available that covers all temporal and spatial scales– flux towers

• direct measurement with good temporal but poor spatial coverage

– aircraft• direct measurement with good spatial but poor

temporal coverage

– remote sensing• indirect measurement with good spatial and good

temporal coverage

Modelling Regional Scale Fluxes

• Limitations of modelling only approach– need regional-scale values for model

parameters• often known at leaf or patch scale but not

regional

– need regional-scale values for fluxes to validate model

• often only available at patch scale

– models incomplete or approximations

Best Of Both Worlds

• Combine observational and modelling techniques to use strengths of each– direct measurement (towers or aircraft) of

fluxes used to infer surface properties– interpolation of surface properties over region

using remotely sensed data– use surface properties in a model to estimate

regional scale fluxes

Surface Properties

• Evaporative fraction, E

• Maximum stomatal conductance, gsx

• Bowen ratio,

• Water use efficiency, WUE

E E AF F

s sxG g f S g D

H EF F

UE C EW F F

1a i

E Aa s

G GF F

G G

Assumptions

• Combined approach uses 2 assumptions applicable in well-watered situations at time scales of several days– temporal evolution of fluxes is primarily driven

by diurnal and synoptic trends in meteorology• solar radiation, temperature, humidity, wind speed

– spatial variation in fluxes is primarily driven by heterogeneity in surface properties

• stomatal conductance, soil moisture, roughness

Constraints

• Combined approach is subject to 2 constraints– bulk meteorological quantities show good

spatial (point to point) correlation• meteorology at a single location can be used for a

region (tile approach in GCMs)

– surface properties show little (ideally no) diurnal variation

• measurement of surface property at any time during day is representative of whole day

Spatial Correlation

2 33 53 86D istance (km )

0

0.2

0.4

0.6

0.8

1

Co

rre

lati

on

u *

FH

FE

FC

2 33 53 86D istance (km )

D

S

FA

G a

a) b)

Diurnal Trend

E and gsx show small diurnal variation at most sites

shows large diurnal variation

• WUE mixed

6 9 12 15H our

-15

-10

-5

0

Wue

(m

gC

O2g

H2O

) g) Crop

6 9 12 15 18H our

h) Pasture

0

0.5

1

1.5

2

e) Crop f) Pasture

0

5

10

15

gs

x (m

ms

-1)

c) Crop

0

5

10

15

20

25

30

35

d) Pasture

0

0.5

1

1.5

E

W agga

Browning

W attles

CooindaUrana

a) Crop b) Pasture

Spatial Variability

• Good agreement between aircraft and tower measurements

• Spatial variability consistent with rainfall gradient

E, gsx, and WUE all show some variation with synoptic conditions

1 0 1 5 2 0 2 51 0 1 5 2 0 2 5October 1995

1 0 1 5 2 0 2 5- 1 5

- 1 0

- 5

0

WU

E (

mg

CO

2gH

2O-1

)

0

1

2

0

10

20

30

40

gs

x (m

ms

-1)

Crop Pasture Aircraft

0

0.5

1

1.5

E

20 8

a) W agga Browning Urana

b)

c)

d)

420 440 460 480 500 520 540

Easting (km )

10

20

30

40

50

Tsf

c (

C)

100

200

(m)

-1

-0 .5

0

0 .5

FC (

mg

m-2

s-1

)

100

200

(m)

100

200

300

400

FH (

Wm

-2)

100

200(m

)

0

100

200

300

FE (

Wm

-2)

100

200

(m)

300

400

500

600

FA (

Wm

-2)

100

200

(m)

19951994

800

900

1000

S

(W

m-2

)

100

200

(m)

a)

b)

c)

d)

e)

f)

W aggaMurrum bidgee

River

BrowningUrana

440 460 480 500 520 540

Easting (km )

-10

-5

0

5

WU

E (

mg

CO

2g

H2O

) 0

0.5

1

1.5

2

0

5

10

15

20

gs

x (m

ms

-1)

0

0.5

1

E

140

220

mA

SL

Aircraft Crop Pasturea)

b)

c)

d)

Urana Browning W agga

Murrum bidgeeRiver

BullenbungPlainsLake

Cullival

Variability Along Transect

420 440 460 480 500 520 540

Easting (km)

6080

6090

6100

6110

6120

6130

No

rth

ing

(km

)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Wagga WaggaBrowningUrana

0 0.2 0.4 0.6 0.8 1N D VI

0

2

4

6

8

10

Pe

rce

nt

Oc

cu

ren

ce

W agga Crop

Browning Crop

W agga Pasture

Browning Pasture

Urana CropUrana Pasture

Remote Sensing : NDVI

LandSat 7 ETM

Lack of FC measurements at Urana will bias regional FC based on tower data

0 400 800 1200 1600 2000Upwind Distance (m)

0

0.002

0.004

0.006

0.008

0.01

fy z = 4.5 m

z = 20 m

0

0.2

0.4

0.6

0.8

1

f

y

0 200 400 600 800U pwind D istance (m )

-200

-100

0

100

200

Cro

ss

-win

d (

m)

-200

-100

0

100

200

Cro

ss

-win

d (

m)

a) z = 4.5 m

b) z = 20 m

f y_

(x i,z)

NDVI(x i)

( , ) ( )

( , )

y

i ii

SAW y

ii

f x z NDVI xNDVI

f x z

Source Area Weighted NDVI• L = -30 m

• u* = 0.5 ms-1

• z0 = 0.03 m

WD = 20 deg

80% ~ 18,000 m2

80% ~ 216,000 m2

Horst & Weil, 1992 etc

530 531Easting (km)

6120

6121

No

rth

ing

(km

)

a) W agga Pasture

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

530 531Easting (km)

6120

6121

b) W agga Triticale

478 479Easting (km)

6108

6109

No

rth

ing

(km

)

479 480Easting (km)

6108

6109

c) Browning Pasture d) Browning Oats

448 449Easting (km)

6098

6099

No

rth

ing

(km

)

447 448Easting (km)

6098

6099

e) Urana Pasture f) Urana W heat

Source Areas of Tower and Aircraft

Data

420 440 460 480 500 520 5406080

6100

6120

No

rth

ing

(km

)

W

B

U

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

a)

420 440 460 480 500 520 540Easting (km )

0 . 5

0 . 6

0 . 7

0 . 8

0 . 9

1N

DV

I

Pasture

Crop

Aircraft

W aggaBrowningUrana

220

140

b)

m

Terrain

0 . 5 0 . 6 0 . 7 0 . 8 0 . 9 1NDVI

0

0.2

0.4

0.6

0.8

1

E

Aircraft Ground-based

0 . 5 0 . 6 0 . 7 0 . 8 0 . 9 1N D VI

- 5

0

5

1 0

1 5

2 0

gsx

0 . 5 0 . 6 0 . 7 0 . 8 0 . 9 1N D VI

0

1

2

3

0 . 5 0 . 6 0 . 7 0 . 8 0 . 9 1N D VI

-10

-5

0

5

WU

E

a) b)

c) d)

Surface Properties and NDVI

E = 1.7*NDVI - 0.7 r2=0.73

= -6*NDVI + 6 r2=0.75

gsx = 57*NDVI + 34 r2=0.66

WUE = -36*NDVI + 22 r2=0.85

Push Forward To Fluxes

• Interpolate gsx and WUE across region using linear relationship to NDVI

• Use bulk meteorology (FA, S, D, Ta, u) from central location (Browning) plus interpolated surface properties to estimate regional FE

• FH calculated as FH = FA - FE

• FC calculated as FC = WUE x FE

9 11 13 15 17 19 21 23 25 27October 1995

-0.5

0

-1

-0.5

0

FC (

mg

m-2

s-1

)

Browning

W agga

c)

100200

100200

FH (

Wm

-2) -100

0100200

Urana

Browning

W aggab)

0100200

0100200

FE (

Wm

-2) 100

200300400

O bservations Modelled

Urana

Browning

W aggaa)

W UE = f(NDVI)

Comparison With Observations

• Daily averages of FE, FH and FC

• Modelled values from gsx and Penman-Monteith equation

• FE under-predicted at Wagga Wagga and over-predicted at Urana

• FC under-predicted at Wagga Wagga

8 10 12 14 16 18 20 22 24 26 28October 1995

- 1

- 0 . 8

- 0 . 6

- 0 . 4

- 0 . 2

0

FC (

mg

m-2

s-1

) W agga - Browningd)0

50

100

150

200

FH (

Wm

-2)

c)0

50

100

150

200

FE (

Wm

-2)

b)100

200

300

400

500

FE+

FH (

Wm

-2)

O bs g sx-PM ICBL DARLAM/SCAM

a)

Comparison Of Techniques

• Obs is average of sites

• gsx-PM is combined approach

• ICBL is integral convective boundary layer approach (Cleugh et al 2004)

• DARLAM/SCAM is coupled mesoscale/LSM (Finkele et al, 2003)

Limitations

• Daytime only

• Relationship between surface properties and NDVI is empirical– Site and time specific

– WUE relationship not strong

• Soil moisture not included– Effect of soil moisture on surface conductance

is passed on to estimate of gsx (or E)

s sxG g f S g D h

Consequences

• Variation of gsx (or other surface properties) along transect is an artifact of not including soil moisture

• NDVI is a strong function of Lai and Lai is a strong function of soil moisture

• Relationship between gsx (or other surface properties) and NDVI is likely to be a consequence of neglecting soil moisture