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Reference canopy conductance through space and time: Unifying properties and their conceptual basis. D. Scott Mackay 1 Brent E. Ewers 2 Eric L. Kruger 3 Jonathan Adelman 2 Mike Loranty 1 Sudeep Samanta 3 1 SUNY at Buffalo 2 University of Wyoming 3 UW-Madison. - PowerPoint PPT Presentation
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ChEAS 2005 D.S. Mackay June 1-2, 2005
Reference canopy conductance through space and time:Unifying properties and their conceptual basis
D. Scott Mackay1 Brent E. Ewers2 Eric L. Kruger3
Jonathan Adelman2 Mike Loranty1 Sudeep Samanta3
1SUNY at Buffalo 2University of Wyoming 3UW-Madison
NSF Hydrologic Sciences EAR-0405306EAR-0405381EAR-0405318
ChEAS 2005 D.S. Mackay June 1-2, 2005
Problem
• Prediction of water resources from local to global scales requires an understanding of important hydrologic fluxes, including transpiration
• Current understanding of these fluxes relies on “center-of-stand” observations and “paint-by-numbers” scaling logic
• Spatial gradients are ignored, but this is an unnecessary simplification
• New scaling logic is needed that includes linear or nonlinear effects of spatial gradients on water fluxes
ChEAS 2005 D.S. Mackay June 1-2, 2005
Why is canopy transpiration important to hydrology?W
ate
r F
lux
(Sa
p fl
ux
or
12
2m
WL
EF
) (m
m/d
ay)
0.0
1.0
2.0
3.0
4.0
5.0
2001
VPD (KPa)
0.0 0.5 1.0 1.5 2.00.0
1.0
2.0
3.0
4.0
5.0WLEF 122m eddy covarianceAggregated sap flux
R2=0.88
R2= 0.65
2000
R2 = 0.79
R2 = 0.91
Average annual precipitation:800 mm
Growing season precipitation:300-500 mm
Growing season evapotranspiration:350-450 mm
Canopy transpiration (forest):150-200 mm
Canopy transpiration (aspen):300 mm
Ewers et al., 2002 (WRR)Mackay et al., 2002 (GCB)
ChEAS 2005 D.S. Mackay June 1-2, 2005
Assumptions
• Transpiration is too costly to measure everywhere, and so appropriate sampling strategies are needed
• The need for parameterization (e.g., sub-grid variability) will never go away
• Both forcing on and responses to transpiration are spatially related (or correlated), but this correlation is stronger in some places
• Human activities may increase or decrease this correlation
ChEAS 2005 D.S. Mackay June 1-2, 2005
Transpiration[mm (30-min) –1]
0 .05 .1 .15 .2
What if we increase edge effects?
Center-of-StandBasis
Spatial Gradient Basis
ChEAS 2005 D.S. Mackay June 1-2, 2005
Why is Transpiration a Nonlinear Response?
RelativeResponse
Relative water demand
StomatalConductance (Jarvis, 1980;
Monteith, 1995)
Transpiration (No stomata) “hydraulic failure”
ReferenceConductance
Transpiration (With Stomata) “prevents hydraulic failure”
Prevention of hydraulic failure is a key limiting factor for carbon gain and nutrient use by woody plants.
ChEAS 2005 D.S. Mackay June 1-2, 2005
Conceptual Basis of Spatial Reference Conductance
GS = GSref – mlnD m = 0.6GSref
(Oren et al., 1999)
GSref1 2 3
0
30
60
90
120
150
D
GS
Gsref
m = d
d ln
- G
D
S
m
EnvironmentalGradient
Canopy stomatal controlof leaf water potential
Hydraulic“Universal” line
Mapping from spatial domain into a linear parameter domain
ChEAS 2005 D.S. Mackay June 1-2, 2005
0
0.4
0.8
1.2
0 0.5 1 1.5 2
G Sref (mm s-1)
m [mm s-1
ln(kPa)-1]
red pine
aspen
sugar maple
alder
cedar
slope = 0.6
y = 0.601x - 0.022
R2 = 0.96
0
0.4
0.8
1.2
0 0.5 1 1.5 2
G Sref (mm s-1)m
[m
ms
-1 ln
(kP
a)-1
]
Mackay et al., 2003 (Advances in Water Resources)
ChEAS 2005 D.S. Mackay June 1-2, 2005
Hypothesis 1
• GSref varies in response to spatial gradients within forest stands, but the relationship between GSref and m remains linear
• Note that 1/D 1- 0.6ln(D) for 1 ≤ D ≤ 3 kPa; error is maximum of 16% at 2 kPa
• Thus many empirical stomatal conductance models are applicable, but discrepancies will occur at moderate mid-day D when it is hydrologically most relevant
ChEAS 2005 D.S. Mackay June 1-2, 2005
ChEAS, HC 2001
Q0
(m
ol m
-2s-1
)
0
500
1000
1500
2000
D (
kPa)
0.0
0.5
1.0
1.5
2.0
2.5Q0
D
Julian Day
188 190 192 194
EC (
mm
30-
min
-1)
0.00
0.05
0.10
0.15
0.20ModelMeasured
A. saccharumA
B
ChEAS 2005 D.S. Mackay June 1-2, 2005
ChEAS, WC 2001
Q0
(m
ol m
-2s-1
)
0
500
1000
1500
2000
D (
kPa)
0.0
0.5
1.0
1.5
2.0
2.5Q0
D
Julian Day
194 196 198 200
EC (
mm
30-
min
-1)
0.00
0.05
0.10
0.15
0.20ModelMeasured
A. saccharumA
B
ChEAS 2005 D.S. Mackay June 1-2, 2005
Julian Day
194 196 198 200
EC (
mm
30-
min
-1)
0.00
0.05
0.10
0.15
0.20ModelMeasured
ChEAS, SV 2001
Q0
(m
ol m
-2s-1
)
0
500
1000
1500
2000
D (
kPa)
0.0
0.5
1.0
1.5
2.0
2.5Q0
D
A. saccharumA
B
ChEAS 2005 D.S. Mackay June 1-2, 2005
S
SS QQ
QDgg 1max
Hydraulic constraint
Light sensitivity
Some model realizations follow hydraulic theory
Best dynamic response
Agricultural and Forest Meteorology (in review)
ChEAS 2005 D.S. Mackay June 1-2, 2005
These models preserve plant hydraulics and represent the regional variability for Sugar maple
Agricultural and Forest Meteorology (in review)
ChEAS 2005 D.S. Mackay June 1-2, 2005
Aspen flux study, northern Wisconsin
Wetland
Transition
Upland
X – sample pointX - Aspen
Funded by NSF Hydrological Sciences
ChEAS 2005 D.S. Mackay June 1-2, 2005
ChEAS, Aspen, August 3, 2004
0
20
40
60
80
100
120
0 20 40 60 80 100 120 140 160 180
Simulated G Sref (mmol m-2 s-1)
Sim
ulat
ed m
(m
mol
m-2
s-1
kP
a-1)
WetlandTransitionalUplandm = 0.6Gsref
Aspen Restricted Simulations
Funded by NSF Hydrological Sciences
ChEAS 2005 D.S. Mackay June 1-2, 2005
Lodgepole pine study, Wyoming
A1, riparian zone
Row 4, lower slope
Row 5, mid-slope
Row 6, mid-slope
Row 7, mid-slope
Row 8, upper slope
X – sample pointX – Lodgepole pine
ChEAS 2005 D.S. Mackay June 1-2, 2005
Wyoming, Lodgepole Pine, August 25, 2004
0
10
20
30
40
50
60
Simulated G Sref (mmol m-2 s-1)
Sim
ula
ted
m (
mm
ol m
-2 s
-1 k
Pa
-1)
A1Row 4Row 5Row 6Row 7Row 8m = 0.6 Gsref
0
10
20
30
0 10 20 30 40 50 60 70 80 90
Simulated G Sref (mmol m-2 s-1)
So
il m
ois
ture
(%
)
A1, riparian zone
Row 4, lower slope
Row 5, mid-slope
Row 6, mid-slope
Row 7, mid-slope
Row 8, upper slope
Basal area crowding
Lodgepole Pine Restricted Simulations
ChEAS 2005 D.S. Mackay June 1-2, 2005
ReferenceCanopyConductance
Water availability Indexlow
high
low
high
xeric mesic
HydraulicConstraintIndex
Summary of Ecohydrologic Constraints
high low
ChEAS 2005 D.S. Mackay June 1-2, 2005
Hypothesis 2
• Variation in leaf gS within and among species and environments is positively related with leaf nitrogen content and leaf-specific hydraulic conductance
• The relative response of gSmax to light intensity (Q) is governed in large part by leaf, and this dependence underlies stomatal sensitivity to D
– Corollary i: gS will increase with increasing Q until it reaches a limit imposed leaf, which for a given leaf is mediated primarily by D
– Corollary ii: The limit imposed on relative stomatal conductance (g/gSmax) by leaf (relative to the threshold linked to runaway cavitation, crit) is consistent within and among species
ChEAS 2005 D.S. Mackay June 1-2, 2005
Hypothesis 3
• The model complexity needed to accurately predict transpiration is greater in areas of steep spatial gradients in species and environmental factors
• Model complexity (e.g. number of functions, non-linearity) should be increased when absolutely necessary, and it should subject to a penalty
• We should gain new knowledge whenever we are forced to increase a model’s complexity
ChEAS 2005 D.S. Mackay June 1-2, 2005
ChEAS 2005 D.S. Mackay June 1-2, 2005