land surface evaporation 1. key research issues 2. what we learnt from oasis 3. land surface...
Post on 31-Mar-2015
215 Views
Preview:
TRANSCRIPT
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….)
top related