Evaporation in Vineyards
Dennis Baldocchi and Youngryel Ryu
Dept Environmental Science, Policy and Management
University of California, Berkeley
November 30, 2010
Fruition Sciences Symposium, St. Helena, CA
How Much Water Does a Vineyard Lose to Evaporation?
First Grand Challenges to Vintners and ViticulturalistsRegarding Water Balance of the Vineyards
• Know How Much Water the
Vineyard Uses over the Year and
During the Growing Season
– Give it too much water costs $$$$$
in terms of irrigating
– Give it too little water may cost $$$$
in terms of reduced yield and grape
quality
– Give it Optimal Levels of Water will
Earn you $$$$ in terms of producing
top quality Grapes and Wine
Second Grand Challenges to Vintners and Viticulturalists
Regarding Water Balance of the Vineyards
• Know how to Evaluate Water
Use across the Region as a
function of:
–
–
–
–
Climate
Topography
Soil Type
Viticultural Practice, e.g.
Varietal, Planting Density and
Age
• Water Use of a Vineyard can be
Measured with
Micrometeorological Methods
• Water Use of an Appelation can
be Modeled by combining
Micrometeorological Theory
and Remote Sensing
Third Grand, and Ultimate, Challenge
• What are these Optimal Levels of Water Use
and How Do We Find Them?
• ‘Know Thy Site’
– Simplest and Cheapest
• Regular measurements of soil gravimetric
content
– Requires scale, oven, shovel and much
replication and sampling
– Easy with Moderate Cost
• Regular Measurements of Pre-Dawn Water
Potential
– Measures the moisture the Roots See
– Invest in ‘Pressure Bomb’, few $K
– Requires much sampling and replication
– Most Expensive and Sophisticated
• Regular Measurements or Simulations
with Micrometeorological Techniques
– Gives you continuous and direct evaporation
measurements at the field scale
Potential Evaporation
• “the evaporation from an extended surface of
a short grass that is supplied with water and
the canopy covers the ground completely.”
Potential Evaporation in California Wine Regions is Large:
4-5 mm/day (0.15-0.2 inch/day)
A n n u a l G ra ssla n d , 2 0 0 4
14
Ac tu a l L E
P o te n tia l L E
10
 E (M J m
-2
-1
d )
12
8
6
4
2
0
0
50
100
150
200
250
300
350
Day
Potential and Actual Evaporation are Decoupled in Semi-Arid System
E/Eeq
Fraction of Potential EvapoTranspiration,
ETactual/ETref
The Margin for Error is Tight for Providing Optimal Water to the Vineyard
ET = f(varietal, topography, planting density, soil type, climate, trellising)
Enough or
Too Much Water
?
?

? Volume of
Water/Volume Soil
ET and Soil Water Deficits:
Water Potential and Evaporation with Native CA Vegetation, Oaks and Grasslands
oak savanna
1 .0
p re d a w n w a te r p o te n tia l
s o il w a te r p o te n tia l
 E / E e q
0 .8
0 .6
0 .4
0 .2
0 .0
-5
-4
-3
-2
-1
so il w a te r p o te n tia l (M P a )
1 .2 5
Baldocchi et al., 2004 AgForMet
1 .0 0
Root-Weighted Soil Moisture Matches
Water Potential
a n n u a l g ra sPre-Dawn
s la n d
0
BESS, Breathing-Earth Science Simulator
Atmospheric
radiative
transfer
Beam PAR
NIR
Canopy
photosynthesis,
Evaporation,
Radiative transfer
Diffuse PAR
NIR
Rnet
LAI, Clumping-> canopy radiative transfer
shade
sunlit
Albdeo->Nitrogen -> Vcmax, Jmax
dePury & Farquhar two leaf
Photosynthesis model
Surface conductance
Penman-Monteith
evaporation model
Radiation at understory
Soil evaporation
Soil evaporation
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Napa County Wine Districts
Annual Evapotranspiration, Napa County
Leaf Area Grand Growth Start
Leaf Area Grand Growth, End + Bloom
Pre_Veraison Stage
Post Veraison Summer Water Deficits
Case Study 1, Sonoma County
ET ~ 20 to 30 inches per year
Compare
Carneros, Valley of the Moon, Dry Creek, Russian River,
Alexander and Knights Valleys
Seasonal Trends in Sonoma County ET
Carneros Greatest in April; Least during the Summer
Conclusions
•Actual EvapoTranspiration is Very Sensitive to Changes in Soil
Moisture and Climate, so a Scientific Approach is Warranted to Manage
Water Application Best.
•Mechanistic Theories and Satellite Information are Producing Maps of
ET at 1 km scale across the Wine Growing Region of California
•New ET Maps can be used for Planning Vineyard Siting, Varietal
Selection, Irrigation Management, Vineyard Design
Effects of Leaf Area and Photosynthetic Capacity on Normalized
Evaporation:
Well-Watered Conditions
Priestley-Taylor
= 1.26
1 .3
1 .2
Q E /Q E ,e q
1 .1
1 .0
0 .9
0 .8
0 .7
0 .6
0
20
40
60
80
100
120
V c m a x *L A I
Canveg Model, Baldocchi and Meyers, 1998 AgForMet
140
160
180
200
Stomatal Conductance Scales with N, via Photosynthesis
Photosynthetic Capacity
Scales with Nitrogen
Stomatal Conductance Scales with
Photosynthesis
Stomatal Conductance scales
with Nitrogen
-1
)
80
m a x im u m s to m a ta l c o n d u c ta n c e (m m s
0 .2 0
0 .1 8
60
-1
0 .1 4
s )
o a k , va ryin g lig h t
Ca: 360 ppm
T a: 25 C
-2
0 .1 2
V c m a x ( m o l m
g s (m m o l m
-2
-1
s )
0 .1 6
0 .1 0
0 .0 8
0 .0 6
40
20
0 .0 4
0 .0 2
0
0 .0 0
0
1
2
3
4
A ( m o l m
5
-2
6
7
a fte r S c h u lz e e t a l (1 9 9 4 )
12
10
8
6
4
2
0
5
8
10
15
20
25
le a f n itro g e n (m g g
-1
s )
0 .0
0 .5
1 .0
1 .5
2 .0
-2
N a (g m )
Wilson et al. 2001, Tree Physiology
Schulze et al 1994. Annual Rev Ecology
14
2 .5
3 .0
30
-`1
)
35
40
Challenge for a Computationally-Challenged Biometeorology Lab:
Extracting Data Drivers from Global Remote Sensing to Run the Model
MOD05
Precipitable water
MOD06
cloud
MOD07
Temperature, ozone
MCD43
albedo
MOD11
Skin temperature
MOD15
LAI
POLDER
Foliage clumping
Net radiation
aerosol
Atmospheric radiative transfer
MOD04
Canopy
radiative
transfer
Youngryel was lonely with 1 PC
Residual Energy Balance
• Net Radiometer, NR-Lite: $1600
• Soil Heat flux, Hukseflux: $500
• Sonic anemometer: RM Young, 81000VRE:
$3200
• Data logger: Campbell CR-1000: $1500
•
as far as topic
This 2010 vintage has been really puzzling for all vineyard managers and winemakers in the sense that
phenological stages were reached either later than usual or in some cases, never (some varietals had berries that
never turned red).
Everyone "feels" that it is climate related, without really understanding what parameters of the climate are driving
those changes. I think that using ET as a way to characterize climate variations is a first step in that direction. Thus,
it would be ideal if a cross vintage comparison could be shown so that people understand to which extend 2010
was different from 2009 and 2008 for instance.
Based on your suggestions, here is what I think would be interesting:
1. From simple to fancy evaporation estimates measurement methods (it would be best if you could keep it very
simple, without complex equations if possible !)
2. Effect of evaporation on soil water balance
3. Surface renewal and eddy covariance : I would not spend too much time on those for fear of losing the
attention; but I think it is good to mention that this is the newest and most promising method with one "punch
slide".
4. Relationship between normalized Evaporation and soil moisture : this is the central piece! This would be really
useful to illustrate how/why different years have different effects on soil moisture. In that part a multi- year
comparison would be really relevant (if available).
5. Spatial variations of normalized Evaporation : Great! I would highlight within a small geographical area (like
Napa valley and Sonoma valley) what is the magnitude of the differences to be expected within a year.
6. I would conclude - if possible- with a comparison of the effect that different years vs. different locations within
Napa can have on evaporative demand...I think this should be a really interesting way to compare the effect of
vintage vs. terroir on plant.
Actual Evaporation
•
•
•
•
•
•
•
Aerodynamic Approach
Energy Balance (Bowen Ratio) Approach
Eddy Covariance
Lysimeter
Evaporation Pan
Soil Water Budget
Combination Method
– Penman Equation
– Penman-Monteith Equation
– Modified Priestly-Taylor Method
• Climatological Methods
– Thornthwaite Equation
Bowen Ratio Method,
measured with temperature and humidity gradients
 
H
E
C p ( T 2  T1 )
 

Mv
Ma
P
E 
( e 2  e1 )
Rn  G
1 

( T 2  T1 )
( e 2  e1 )
Energy Balance Method
Rn  H   E  G
Rn  G
K 
http://pages.unibas.ch/geo/mcr/Projects/EBEX/img_profile/profile02.jpg
a(Cp

z


P

e
z
)
Rn, net radiation flux density, W m-2
H, sensible heat flux density
E, latent heat flux density
G, soil heat flux density
Cp, specific heat of air
ESPM 228 Adv Topic Micromet & Biomet
Eddy Covariance,
Flux Density: mol m-2 s-1 or J m-2 s-1
F   aws ~  a  w ' s '
s(
c
a
)
Surface Renewal
H  C p
a
ls
T e m p e ra tu re
l
a
s
Time
Paw U et al., 1989
ESPM 228 Adv Topic Micromet & Biomet
zc
Validation at Vaira Ranch
600
y = 1.00x
R2 = 0.92
-2
H (W m )
400
200
0
-200
-100
0
100
200
 H SR (W m-2)
ESPM 228 Adv Topic Micromet & Biomet
300
400
500
Eddy C ov
D346
V a ira G ra ssla n d
sfc re n e w a l
250
200
Sfc Renewal
Relatively Cheap and Simple
Needs Calibration for your Site or Class
-2
H (W m )
150
100
50
0
-5 0
600
800
1000
1200
1400
1600
1800
T im e (h o u rs)
ESPM 228 Adv Topic Micromet & Biomet
 E / E e q
0 .6
ET and Soil Water Deficits:
Water Potential and Evaporation with Native CA Vegetation, Oaks and Grasslands
0 .4
0 .2
0 .0
-5
-4
-3
-2
-1
0
so il w a te r p o te n tia l (M P a )
oak savanna
1 .0
1 .2 5
p re d a w n w a te r p o te n tia l
a n n u a l g ra s s la n d
s o il w a te r p o te n tia l
0 .8
0 .6
 E / E e q
 E / E e q
1 .0 0
0 .7 5
0 .4
0 .5 0
0 .2
0 .2 5
0 .0 0
0 .0
-5
-4
-3
-2
-1
so il w a te r p o te n tia l (M P a )
1 .2 5
Root-Weighted Soil Moisture Matches
a nPre-Dawn
n u a l g ra s s la n d Water Potential
 E / E e q
1 .0 0
Baldocchi et al., 2004 AgForMet
0 .7 5
0
-3 .0
-2 .5
-2 .0
-1 .5
-1 .0
-0 .5
so il w a te r p o te n tia l (M P a )
ET of Annual Grass responds to water
deficits differently than Trees
0 .0
1 .2 5
1 .0 0
ET and Soil Water Deficits:
Root-Weighted Soil Moisture
 E / E e q
s u m m e r ra in
0 .7 5
0 .5 0
0 .2 5
0 .0 0
0 .0 0
0 .0 5
0 .1 0
0 .1 5
0 .2 0
0 .2 5
3
0 .3 0
0 .3 5
0 .4 0
-3
 w e ig h te d b y ro o ts (cm cm )
G ra ssla n d
1 .0
O a k S a va n n a
1 .2 5
0 .8
1 .0 0
 E / E e q
 E / E e q
s u m m e r ra in
0 .7 5
0 .6
0 .4
0 .5 0
0 .2
0 .2 5
0 .0
0 .0 0
0 .0 0
0 .0 5
0 .1 0
0 .1 5
0 .2 0
0 .2 5
0 .3 0
0 .3 5
0 .4 0
0 .0 0
0 .0 5
0 .1 0
0 .1 5
0 .2 0
3
3
-3
 w e ig h te d b y ro o ts (cm cm )
1 .0
O a k S a va n n a
0 .8
Baldocchi
et al., 2004 AgForMet
0 .2 5
-3
 w e ig h te d b y ro o ts (cm cm )
0 .3 0
Grapes are Much More Sensitive to Soil Water Deficits than Native Oaks and Grasses
Southern France, Syrah
Pellegrino et al 2004, Plant and Soil
ET Thompson Seedless, Fresno
Williams et al. 2003 Irrigation Sci