Land Surface Evaporation

1. Key research issues

2. What we learnt from OASIS

3. Land surface evaporation using remote sensing

4. Data requirements

Helen Cleugh and Ray Leuning

CSIRO Atmospheric Research

1. Key Research Issues(a) Why ET?

Quantify evaporation (ET) at landscape (ecosystems, catchments, regions) scales:

• Water limits productivity in Australian ecosystems– Managing landscapes for food & fibre– Managing landscapes for carbon

• ET is the largest output in the water balance and is the only part that can be “managed” (except for irrigation):

– Managing landscapes for water - soil moisture and runoff are the small difference

• Surface energy balance important for weather & climate

The challenge ….We can’t cover everything all of the time …

• in-situ observations:(lysimeters, flux towers)

• aircraft observations:(fluxes, concentrations)

• modelling:(leaf …. region)

cover almost nothing but most of the time

cover almost everything but hardly ever

only pretends to cover everything all of the time

• satellite observations:(AVHRR, MODIS … )

cover everything all of the time but not what we want!

Modified from Dr HP Schmid, Indiana University

1. Key Research Issues(b) Methods to quantify ET

• Multiple space and time scales:– Local, regional, continental; – Spatially distributed or lumped– Sub-diurnal, daily or seasonal

• Monitoring - combining in situ + remotely sensed observations + land surface model

– Remote sensing algorithms– Data assimilation approaches

• Modelling - prognostic and diagnostic:– Initialisation, parameterisation and testing – especially for

Australian ecosystems

2. What we learnt from OASIS

1 Urana Pasture 19952 Urana W heat3 Urana Pasture 19944 W attles5 Kingsvale 19956 Browning 19957 Bullenbung 19948 W agga Central

Elevation (m)

Relief, Drainage, Road, Urbanfrom AUSLIG Topo-250K

150 250 350 450 550

146.5 147.0 147.5-35.5

-35.0

W aggaW aggaLockhart

Urana

N

The Rock

0 10 20km

Flux variation and coherence along OASIS transect

From Leuning et al (2004)

2. Spatially-averaged evaporation – combining aircraft and flux towers (Isaac et al, 2004)

• Aircraft data rich in space, sparse in time Tower data sparse in space, rich in time

• Combine aircraft and tower measurements– Aircraft: measure spatially varying properties (diurnally invariant):

• <gsmax>

• Surface properties (Lai)

• Evaporative fraction (e)

– Flux tower: measure diurnal variation at fixed points in space:• Near surface meteorology (S, A, D, U, T)

– Spatial and temporal evaporation fields using Penman Monteith equation with appropriate forcing

Evaporation – Penman Monteith with a simple conductance model

2 ( , , , )c sx AIG f g S D L1( , , , )s c AIG f D LG A

1 /a

a s

DGE

A G G

gsx and evaporative fraction (e) constant during daylight hours

Spatial variability at local scale - contours of evaporation ratio, max. surface conductance

472 474 476 478 480 482

Easting (km )

6104

6106

6108

6110

6112

6114

No

rth

ing

(km

)B

W

(b) gsx

472 474 476 478 480 482

Easting (km)

6104

6106

6108

6110

6112

6114

No

rth

ing

(km

)

B

W

(a) E

Evaporation – performance of a simple model combining aircraft and tower data

From Isaac (2004)

-0 .5 -0 .4 -0 .3 -0 .2 -0 .1 0Modelled FC (mgm -2s -1)

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0

Ob

se

rve

d F

C (

mg

m-2

s-1)

6 9 12 15 18H our

-0 .5

-0 .4

-0 .3

-0 .2

-0 .1

0

FC (

mg

m-2

s-1

)

50 100 150 200 250Modelled FH (W m -2)

50

100

150

200

250

Ob

serv

ed F

H (

Wm

-2)

6 9 12 15 18H our

0

100

200

300

FH (

Wm

-2)

50 100 150 200 250Modelled FE (W m -2)

50

100

150

200

250

Ob

se

rve

d F

E (

Wm

-2)

Egsx

6 9 12 15 18H our

0

100

200

300

FE (

Wm

-2)

Obs gsx E

a) b)

c) d)

e) f)

Regional evaporation at OASIS

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)

8 10 12 14 16 18 20 22 24 26 28

October, 1995

with a linear expression for the surface conductance:

and MODIS estimates of LAI

A new approach using Penman-Monteith model

( )

(1 / )p a a

a s

sA c G DE

s G G

mins L ai sG c L G

3. Land surface evaporation using remote sensing

Maximum canopy conductance vs antecedent rainfall and NDVI

(a)

July-Sept rainfall 1995 (mm)80 100 120 140 160 180

Gcm

ax (

mo

l m-2

s-1)

0.0

0.5

1.0

1.5

2.0

(b)

NDVI

0.4 0.5 0.6 0.7 0.8 0.9

Gcm

ax (

mo

l m-2

s-1)

0.0

0.5

1.0

1.5

2.0

PastureCrop

0

100

200

300

1/02/01 31/07/01 27/01/02 26/07/02 22/01/03 21/07/03 17/01/04

E

(W

m-2

)

0

100

200

300

01/02/01 31/07/01 27/01/02 26/07/02 22/01/03 21/07/03 17/01/04

E

(W

m-2

)

Measured

MODIS

Tumbarumba

Virginia Park

4. Data requirements to address research questions

• Aircraft provides spatially resolved:– Surface conditions (diurnally invariant) - soil moisture, LST, NDVI,

LAI, albedo, gsmax (derived)

– Surface fluxes (x, z and t, but not continuous)

– Concentration fields (x, z and t, but not continuous)

• Ground-based, sparse sites to capture time variation:– Surface meteorology and fluxes (CO2 and water)

– Calibration data (soil moisture, LAI, NDVI, LST, albedo)

• Land surface model requirements:– ABL profiles, met. forcing, physiological parameters

– Vegetation description

– Antecedent data (rainfall, soil moisture, fluxes….)