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

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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