lessons learned about ecosystem evaporation from long-term, global flux networks dennis baldocchi...
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Lessons Learned about Ecosystem Evaporation from Long-term, Global Flux
Networks
Dennis Baldocchi and Youngryel RyuUniversity of California, Berkeley
EPFL LATSIS SymposiumLausanne, Switzerland
October, 2010
Motivation, Part 1
• Most Annual Water Budgets are Indirect, Inferred from Water Budgets (ET ~ Precipitation – Runoff)
• Global Network of Direct, Continuous and Multi-year Carbon and Water Eddy Covariance Flux Measurements Exists that has been Under-Utilized with regards to the Annual Water Budget of Terrestrial Ecosystems
• …it is becoming possible to routinely measure evaporation and soil moisture, based on surface and satellite-mounted observation. We can therefore move away from merely closing a water budget, towards considering all the components and dynamics of the hydrological cycle based on observational evidence of all fluxes and states.– A.J. Dolman and de Jeu, 2010 Nature
Geosciences
Motivation, Part 2
Big Picture Question Regarding Predicting and Quantifying Global Evaporation:
• How can We Be ‘Everywhere All the Time?’
Over Arching Questions
• What is Annual ET, as measured directly by Eddy Covariance?
• How does Annual ET respond to Precipitation and Available Energy, & Drought?
• What is Annual ET at Regional and Global Scales using New Generation of Ecohydrological Information, Flux Networks and Satellite-based Remote Sensing?
F ws w sa ~ ' ' s c
a
( )
Eddy Covariance Technique
Mean
Fluctuation
•Direct•In situ•Quasi-Continuous
Restrictions and Conditions for Producing Annual Water Budgets from Eddy Covariance Flux Measurements
• Steady-State Conditions, dC/dt ~ 0• Extensive Fetch, 100m - 1km• Level Terrain, < 0-10o slope• Gaps-Filled Accurately, with Minimum Bias
FLUXNET: From Sea to Shining Sea500+ Sites, circa 2009
www.fluxdata.org
Global distribution of Flux Towers Covers Climate Space Well
Is There an Energy Balance Closure Problem?:Evidence from FLUXNET
Wilson et al, 2002 AgForMet
Timing/SeasonInstrument/Canopy Roughness
Is the Energy Balance Closure Problem a Red-Herring?Forest Energy Balance is Prone to Close when Storage is Considered
Lindroth et al. 2010, Biogeoscience
-100 0 100 200 300 400 500 600-100
0
100
200
300
400
500
600
slope=1.05r2=0.98
wheat
H+
LE (
W m
-2)
Rn-G (W m-2)
-100 0 100 200 300 400 500 600 700 800-100
0
100
200
300
400
500
600
700
800
r2=0.93slope=0.93
Boreas 1994Hourly averages
Old Jack Pine
LE
+H
+S
+G
(W
m-2
)
Rn (W m-2)
Rnet (W m-2
)
0 200 400 600 800
E +
H +
G +
S +
Ps
(W
m-2
)
0
200
400
600
800
Coefficients:
b[0] 3.474
b[1] 1.005
r ² 0.923
Temperate Deciduous Forest
Evidence for Energy Balance Closure:Other Examples from Crops, Grasslands and Forests with Careful
Attention to Soil and Bole Heat Storage
Rnet (W m-2)
-100 0 100 200 300 400
LE
+H
+G
-100
0
100
200
300
400
coefficients:b[0] 5.55b[1] 0.94r ² 0.926
Vaira Grassland, D296-366, 2000
Year
1970 1975 1980 1985 1990 1995 2000
Eva
pora
tion
(mm
yea
r-1)
0
200
400
600
800
1000
Catchment Eddy Covariance Sap Flow Equilibrium Evaporation
Walker Branch Watershed, TN; Wilson et al. 2001
Reasonable Agreement Observed between Eddy Flux measurements of ET + Catchment Studies
Scott, 2010, AgForMet
Arizona Grassland
Part 1, Reading the Data of Annual Flux Measurements
Day
0 50 100 150 200 250 300 350
ET
(m
m d
-1)
0
1
2
3
4
5
6
Day
0 50 100 150 200 250 300 350
ET
(m
m d
-1)
0
1
2
3
4
5
6
Day
0 50 100 150 200 250 300 350
ET
(m
m d
-1)
0
1
2
3
4
5
6
Tropical Forest, Brazil Temperate Deciduous Forest, Tennessee
Savanna Woodland, California
Day
0 100 200 300
ET
(m
m d
-1)
0
1
2
3
4
5
6
Temperate Conifer Rain Forest, British Columbia and Japan
AB
CD
Flux Measurements Reveal Diverse Information on Seasonal Cycles
Forest Evaporation
ET (mm/y)
500 1000 1500 2000
pd
f
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
TakeHome Points:
ET > 200 mm/yMedian = 402 mm/y
Skewed Distribution, Max ~ 2300 mm/y
Budyko Curve, Fluxnet data
Rn/(ppt)
0 1 2 3 4
Ea
/ppt
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Choudhury Model
Classic View, The Budyko Curve with Evaporation Flux Measurements
Evap Demand >>Precipitation
Precipitation >>Evaporation, whichIs energy limited
Defines Bounds, But Many Sources of Variance RemainX and Y are AutoCorrelated, through ppt
FLUXNET Sites
s/(s+)Rnet (MJ m-2 y-1)
0 500 1000 1500 2000 2500
LE
(M
J m
-2 y
-1)
0
500
1000
1500
2000
2500
r2 = 0.89
Annual Sums of Latent Energy Scales with Equilibrium Energy, in a Saturating Fashion
Forests
ppt (mm y-1)
0 1000 2000 3000 4000 5000
ET
(m
m y
-1)
0
500
1000
1500
2000
2500
Coefficients:b[0] 108.8b[1] 0.464r ² 0.756
Annual Precipitation explains 75% of the Variation in Water Lost Via Forest Evaporation, Globally
About 46% of Annual Precipitation to Forests, Globally, is Evaporated to the Atmosphere
A linear additive model has the following statistics: ET = -141 + 116*Rn + 0.378 * ppt, r2 = 0.819.
The color bar refers to annual ET
Statistical Model between Annual Forest ET, Net Radiation and Precipitation
ppt (mm/y)
200 400 600 800 1000
ET
(m
m/y
)
200
400
600
800
1000
grassland: ET +/- 87 mm/y; ppt +/- 170 mm/yoak savanna: ET +/- 61 mm/y
Small Inter-Annual Variability in ET compared to PPT
In Semi-Arid Regions, Most ET is lost as Precipitation during Driest Years
Mediterranean oaks
ppt (mm y-1)
0 200 400 600 800 1000 1200 1400
ET
(m
m y
-1)
0
100
200
300
400
500
600
Evergreen, FranceEvergreen, PortugalEvergreen, ItalyDeciduous, CaliforniaDeciduous, Italy
Maximum ET is Capped (< 500 mm/y) Near Lower Limit of Mediterranean PPT
Baldocchi et al. 2010 Ecological Applications
Oak Savanna
Hydrological Year
2000 2002 2004 2006 2008 2010
Wa
ter
flux
(mm
/y)
0
200
400
600
800
1000
ET, stand: 416 +/- 61 mm/yET, Trees: 173 +/- 37 mm/yppt: 527 +/- 178 mm/y
Tapping Groundwater Increases Ecosystem Resilience,And Reduces Inter-annual Variability in ET
Consistent with Findings of Stoy et al, Later-Succession Ecosystems invest to Reduce Risk
Pre-Dawn Water Potential Represents Mix of Dry Soil and Water Table
Miller et al WRR, 2010
During Summer MidDay Water Potential is Less Negative than Shallow Soil Water Potential
Plynlimon, Wales
Year
1970 1975 1980 1985 1990 1995 2000 2005 2010
Eva
pora
tion
(mm
y-1
)
200
300
400
500
600
700
800
900
grassland conifer forest
Marc and Robinson, 2007 HESS
Don’t Forget EcologyStand Age also affects differences between ET of forest vs grassland
Part 2, Global Integration of ET
‘Space: The final frontier … To boldly go where no man has gone before’
Captain James Kirk, Starship Enterprise
• Motivation– Global Estimates of ET range from 5.8-8.5 1013 m3/y– Current Class of Remote Sensing-based Estimates of Global ET
models rely on• Empirical approach (machine learning technique)• Form of the Penman-Monteith Equation, with poor constaint on surface
Conductance• Form of the Priestley-Taylor Equation, with empirical tuning of alpha, with
soil moisture deficits• Many forcings come from coarse reanalysis data (several tens of km
resolution)• At most, LAI, NDVI, LST are used from satellite
– We need a Biophysically-based Model, Driven with High-Resolution Spatio-Temporal Drivers for Diagnosis and Prediction and No Tuning
Global ET with a Hybrid Remote-Sensing/Flux Measurement Approach
remote sensingof CO2
Tem
pora
l sca
le
Spatial scale [km]
hour
day
week
month
year
decade
century
local 0.1 1 10 100 1000 10 000 global
forestinventory
plot
Countries EUplot/site
talltowerobser-
vatories
Forest/soil inventories
Eddycovariance
towers
Landsurface remote sensing
From point to globe via integration with remote sensing (and gridded metorology)
From: Markus Reichstein, MPI
Challenge for Landscape to Global Upscaling
Converting Virtual ‘Cubism’ back to Virtual ‘Reality’
Realistic Spatialization of Flux DataRequires the Merging Numerous Data Layers with
varying Time Stamps (hourly, daily, weekly), Spatial Resolution (1 km to 0.5 degree) and Data Sources
(Satellites, Flux Networks, Climate Stations) and Using these Data to Force Mechanistic Biophysical Model
Lessons Learned from the CanOak Model
25+ years of Developing and Testing a Hierarchy of Scaling Models with Flux Measurements at Contrasting Oak Woodland
Sites in Tennessee and California
We Must:• Couple Carbon and Water Fluxes• Assess Non-Linear Biophysical Functions with Leaf-Level
Microclimate Conditions• Consider Sun and Shade fractions separately• Consider effects of Clumped Vegetation on Light Transfer• Consider Seasonal Variations in Physiological Capacity of
Leaves and Structure of the Canopy
Necessary Attributes of Global Biophysical ET Model: Applying Lessons from the Berkeley Biomet Class and CANOAK
• Treat Canopy as Dual Source (Sun/Shade), Two-Layer (Vegetation/Soil) system– Treat Non-Linear Processes with Statistical Rigor (Norman, 1980s)
• Requires Information on Direct and Diffuse Portions of Sunlight– Monte Carlo Atmospheric Radiative Transfer model (Kobayashi + Iwabuchi,, 2008)
• Light transfer through canopies MUST consider Leaf Clumping– Apply New Global Clumping Maps of Chen et al./Pisek et al.
• Couple Carbon-Water Fluxes for Constrained Stomatal Conductance Simulations– Photosynthesis and Transpiration on Sun/Shade Leaf Fractions (dePury and Farquhar,
1996)– Compute Leaf Energy Balance to compute Leaf Saturation Vapor Pressure, IR emission
and Respiration Correctly– Photosynthesis of C3 and C4 vegetation Must be considered Separately
• Use Emerging Ecosystem Scaling Rules to parameterize models, based on remote sensing spatio-temporal inputs
– Vcmax=f(N)=f(albedo) (Ollinger et al; Hollinger et al;Schulze et al.; Wright et al.)– Seasonality in Vcmax is considered (Wang et al.)
Atmosphericradiativetransfer
Canopy photosynthesis,Evaporation, Radiative transfer
Soil evaporation
Beam PAR NIR
Diffuse PAR NIR
Albdeo->Nitrogen -> Vcmax, Jmax
LAI, Clumping-> canopy radiative transfer
dePury & Farquhar two leaf Photosynthesis model
Rnet
Surface conductance
Penman-Monteithevaporation model
Radiation at understory
Soil evaporation
shade sunlit
BESS, Berkeley Evaporation Science Simulator
Help from ModisAzure -Azure Service for Remote Sensing Geoscience
Scientific Results Download
Reduction #1 Queue
Source Metadata
AzureMODIS Service Web Role Portal
Request Queue
Analysis Reduction Stage
Data Collection Stage
Source Imagery Download Sites
. .
.
Reprojection Queue
Derivation Reduction Stage Reprojection Stage
Reduction #2 Queue
DownloadQueue
Scientists
Science results
Puts the Small Biomet Lab into the Global Ecology, Computationally-Intensive Ball Park
• Automation– Downloads thousands of files of MODIS data from NASA ftp
• Reprojection– Converts one geo-spatial representation to another. – Example: latitude-longitude swaths converted to sinusoidal
cells to merge MODIS Land and Atmosphere Products • Spatial resampling
– Converts one spatial resolution to another. – Example is converting from 1 km to 5 km pixels.
• Temporal resampling – Converts one temporal resolution to another.– Converts daily observation to 8 day averages.
• Gap filling – Assigns values to pixels without data either due to inherent
data issues such as clouds or missing pixels.• Masking
– Eliminates uninteresting or unneeded pixels.– Examples are eliminating pixels over the ocean when
computing a land product or outside a spatial feature such as a watershed.
Tasked Performed with MODIS-AZURE
h12v04h13v04h11v04h10v04h09v04h08v04
h12v05h11v05h10v05h09v05h08v05
h11v06h10v06h09v06h08v06
Photosynthetic Capacity Leaf Area Index
Solar RadiationHumidity Deficits
Leaf Clumping Map, Chen et al. 2005 C4 Vegetation Map, Still et al. 2003
Validate Model Across FLUXNET
<ET> = 503 mm/y == 7.2 1013 m3/y
Ryu et al. in preparation
Global land evaporation: 503 mm yr-1
Ryu et al. unpublished
MODIS-Driven Product Using Biophysics via Cloud Computing
Ryu et al. unpublished
Down-Scale to Regions for Policy and Management Decisions
Scaling and Window Size
Water Management Issues: How Much Water is Lost from the Delta?
Global ET What is the Right Answer?
<ET> (mm/y)
reference ET (m3/y)
613 Fisher et al
550 Jung et al 2010, Nature 6.5 1013
286 Mu et al. 2007
539 +/- 9 Zhang et al. 2010, WRR
467 Van den Hurk et al 2003
649 Boslilovich 2006
560 Jackson et al 2003
410 Yuan et al 2010
Dirmeyer et al 2006 5.8-8.5 1013
Alton et al., 2009 ~6.5 1013
503 Ryu et al 7.2 1013
Why range?Errors in ET?Differences in Land area?Cartesian vs Area-Weighted Averaging?Grid Resolution?
Conclusions• A new Global Database of Directly Measured values of Annual Evaporation has
Emerged– Many Semi-Arid Ecosystems Tap Ground-Water Resources to Minimize Risk and
Vulnerability to Seasonal Drought– Expand Duration of Database to Study Interannual Variation with Climate Fluctuations
and Trends• Several New Evaporation Systems are producing new estimates of Global,
Continental and Local Evaporation at Weekly to Annual Scales at high spatial Resolution, 1-5 km– Mechanistic Biophysical Models enable us to Predict and Diagnose Cause and Effect
into the Future and Past– Working with Jim Hunt to Tests the BESS system at Catchment scale– Products can be used for policy and management and set Priors for large scale
inversion modelling.– Future Work involves Considering Terrain on Radiation Fields, surface wetness and
soil water budgets
Evapo-transpiration
(mm/yr)
Jun
g e
t al
. 20
10 N
atu
re
Global average: 550 mm/yr ~ 6% 65 Eg/yr (±10-15%)
Up-scaling evapotranspiration
Mean ET 539 mm/y
Fisher et al ET maps, 1995: 580 +/- 400 mm/y; Cartesian665 mm/y; area-weighted
Mean ET mm/yr, 1995
0
500
1000
1500
2000
2500
Global ET 0.5 Deg Resolution; ISLSCP Met Drivers
Using Flux Data to produce Global ET maps, V2
No data
0 - 150
150 - 300
300 - 450
450 - 600
600 - 750
750 - 900
900 - 1,236
ET (mm H2O y-1)
180°
180°
135° E
135° E
90° E
90° E
45° E
45° E
0°
0°
45° W
45° W
90° W
90° W
135° W
135° W
180°
180°
60° N 60° N
30° N 30° N
0° 0°
30° S 30° S
60° S 60° S
Fig.9 Global Evapotranspiration (ET) driven by interpolated MERRA meteorological data and 0.5º×0.6º MODIS data averaged from 2000 to 2003.
Wenping Yuan et al 2010 RSE
417±38 mm year−1
Martin Jung/Markus Reichstein
Using Flux data to produce Global ET maps, v3
Mean Global ET: 613 mm/y
Fisher et al, Remote Sensing Environment, 2008
Global ET, 1989, ISLSCP, V1
[N]ppt/Eeq
1 10 100
LA
I
0.1
1
10
Coefficients:b[0] -0.773b[1] 0.936r ² 0.642
Canopy Conductance scales with LAI, which scales with Water Budget and Nutrition
Explaining Budyko, part I
ESPM 129 Biometeorology
Boreal Forest
Vcmax*LAI
0 20 40 60 80 100 120 140 160 180 200
QE/Q
E,e
q
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
k=8.0
k=10
k=7.0
G f LAI G Nc s v~ ( , , , )max
Optimizing Seasonality of Vcmax improves Prediction of Fluxes
Wang et al, 2007 GCB
Critical Partnership with Microsoft Azure Cloud Computing System:Puts the Small Biomet Lab into the Global Ecology Computationally-Intensive
Ball Park
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