Elizabeth Traver University of Wyoming
16
Elizabeth Traver University of Wyoming Quantifying spatial patterns in time of transpiration in aspen, alder, and sugar maple dominated forests
description
Quantifying spatial patterns in time of transpiration in aspen, alder, and sugar maple dominated forests. Elizabeth Traver University of Wyoming. Why spatial transpiration?. - PowerPoint PPT Presentation
Transcript of Elizabeth Traver University of Wyoming
Elizabeth Traver University of Wyoming
Quantifying spatial patterns in time of transpiration in aspen, alder, and sugar maple
dominated forests
Why spatial transpiration?
• There is solid evidence that VPD is a major driver of transpiration in time, but it does not explain the variable spatial patterns within species.
• Stands of trees tend to be more heterogeneous than homogeneous, with significant boundary areas--what causes these spatial patterns?
• Finding the--possibly edaphic—causes of these spatial patterns, we can use that knowledge to scale up to larger, heterogeneous landscapes.
Hypotheses
• VPD explains the temporal variability in spatial autocorrelation of transpiration.
• At high VPD, edaphic conditions will explain the spatial autocorrelation of variability in transpiration
total N impacts on root properties
soil texture impacts on root cavitation
Sites
• Aspen—Alder: SE of the WLEF tower – 120 m by 120 m with 144 plots in a 3/7 cyclic design– Runs from wetlands in NW to dry upland in SE
• Sugar Maple—Red Pine: NW of the WLEF tower– 120 m by 120 m with 144 plots in a 3/7 cyclic design– Mostly sugar maple crossed by swath of red pine
Aspen-Alder site
Sugar Maple-Red Pine site
WLEF Tower
3/7 Cyclic sampling design.
Methods
• Measure sap flux spatially-- Granier: constant heat flux sensors
• Measure or acquire environmental factors-- micrometeorology from WLEF tower: μ, Q, rh, temp
• Measure soil characteristics within the 3/7 cyclic plots already established– Soil moisture curves– Soil texture– Total N
• Intra-tree scaling -- Asap
• Spatial sampling/analysis -- cyclic, ‘variograms, geostatistics, GIS
Various measurements
and instruments in
action
June 2005
VP
D (
kPa)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Tra
nsp
ira
tio
n (
g t
ree-1
s-1
)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
VPDAlder wet Aspen transitionAspen upland
0.00
0.25
0.50
0.75 20022003
EL
(mm
da
y-1 )
0.0
0.5
1.0
1.5
2.0
20022003
DZ (kPa)
0.0 0.5 1.0 1.50.00
0.25
0.50
20002001
A
B
C
SV
WC
HC
ChEAS A. saccharum
R2 = 0.77
R2 = 0.69
R2 = 0.72
R2 = 0.73
R2 = 0.80 ChEAS
GSref (mmol m-2 s-1)
0 20 40 60 80 100 120
m [
mm
ol m
-2 s
-1 ln
(kP
a)-1
]
0
20
40
60
m = 0.6 GSref
HC 2000HC 2001WC 2002WC 2003 SV 2002SV 2003
A. saccharum
Ewers et al. 2006 Ag For Met
EC n
ugge
t (g
tree
-1 s
-1)2
0
20
40
60
80
EC n
ugge
t (g
tree
-1 s
-1)2
0.0000
0.0002
0.0004
0.0006
EC s
ill (
g tr
ee-1
s-1
)2
0.00
0.01
0.02
0.03
0.04
EC s
ill (
g tr
ee-1
s-1
)2
0.000
0.002
0.004
0.006
VPD (kPa)
0.0 0.5 1.0 1.5 2.0 2.5
EC r
ange
(m
)
0
10
20
30
40
50
VPD (kPa)
0.0 0.5 1.0 1.5 2.0 2.5
EC r
ange
(m
)
0
50
100
150
R2 = 0.95
R2 = 0.98
R2 = 0.85 R2 = 0.75
R2 = 0.54
R2 = 0.55
Nugget, sill, and range of transpiration plotted against VPD.
As transpiration variation increases with VPD, spatial autocorrelation decreases.
(Data from the aspen-alder stand.)
Distance (m)
0 20 40 60 80 100 120 140
Vol
umet
ric S
oil M
oist
ure
()
(%
2 )
0
200
400
600
800
1000
= 26% = 32%
Future work
Using what is already known about the temporal variability of transpiration, look at edaphic conditions to explain the spatial variability.
