A NEW METHOD TO DIRECTLY OBSERVE THE EVAPORATION OF INTERCEPTED WATER OVER AN EASTERN AMAZON OLD-GROWTH RAIN FOREST Matthew Czikowsky (1), David Fitzjarrald

A NEW METHOD TO DIRECTLY OBSERVE THE EVAPORATION OF INTERCEPTED WATER OVER AN EASTERN AMAZON OLD-GROWTH RAIN FOREST

Matthew Czikowsky(1), David Fitzjarrald(1), Ricardo Sakai(1), Osvaldo Moraes(2), Otavio Acevedo(2), and Luiz Medeiros(1)

(1) Atmospheric Sciences Research Center, University at Albany, State University of New York

(2) Universidade Federal de Santa Maria, Brazil

ET = P – R – ΔSΔS A = - (Q* - G) = H + LE + St + Adv

Surface water and energy balancesSurface water and energy balances

Evapotranspiration Precipitation Runoff Storage Available energy Net radiation Ground heat flux

Sensible heat flux

Latent heat flux

Canopy heat storage

Advection

References:

1B,2B:Franken et al.(1982a,b) 3B:Schubart et al. (1984) 4B:Leopoldo et al.(1987) 5B:Lloyd and Marques(1988) 6C:Imbach et al.(1989) 7B,8B:Ubarana(1996) 9C:Cavelier et al.(1997) 10B:Arcova et al.(2003) 11B:Ferreira et al.(2005) 12M:Manfroi et al.(2006) 13A:Wallace and McJannet(2006) 14P:Holwerda et al.(2006) 15B:Germer at al.(2006) 16B:Cuartas et al. (2007)

Conventional interception estimates in tropical rain forestsConventional interception estimates in tropical rain forests

-Furthermore, large annual interception differences can be found within plots in the same forest. (Manfroi et al. 2006), interception ranging from 3 to 25 % in 23 subplots over a 4-ha area.


-Where to put the throughfall gauges to get a representative interception estimate?

0 1 2 3 4 5 6 7km67 Harvard transects July 2003

estimated surface area density

500 550 600 650 700 750 800 850 900 950 1000

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40

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500 550 600 650 700 750 800 850 900 950 1000

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he

igh

t, m

0 50 100 150 200 250 300 350 400 450 500

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horizontal distance, m

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40

transect 1 first half

transect 1 second half





Horizontal forest canopy transects, Tapajos National Forest, Brazil (LBA Km67 site)

G. Parker, personal comm.

Fluxnet sitesFluxnet sites

http://www.fluxnet.ornl.gov

Is there any further information that can be obtained from the growing number and coverage of flux-tower sites?

A new method for measuring interception evaporation A new method for measuring interception evaporation using eddy covariance using eddy covariance

-Advantages of eddy covariance method to estimate interception:

a. May be able to get a more representative interception estimate over the flux footprint area.b. Provides a direct measurement of interception evaporation.

-Disadvantages: a. Can fail during calm nighttime, low-turbulence

conditions. b. Can fail during some heavy-rain periods.

MethodsMethods

How can we quantify interception (INT) losses using the eddy flux method?

LE

time

RainRainBase Base state LEstate LE

Event LEEvent LE

INT Loss

INT Loss

ObjectivesObjectives

-Present a methodology by which one can directly observe the amount of interception evaporation using eddy-covariance data that are available at a number of worldwide flux tower sites.

-Demonstrate the method using data from an old-growth forest site in the eastern Amazon region.

Rain dials: times in GMT (LT+4)

Wet season

Dry season

Convective rainfall

Nocturnal squall line precipitation

Rains occur frequently at the same times of day helps to build up a large ensemble of similar cases. Fitzjarrald et al. (2008)

Little variation in day-to-day cloud fraction and cloud base during the dry season

Data/InstrumentationData/Instrumentation

1) Eddy covariance system at ~ 60 m height, including:

Campbell CSAT 3-D Sonic Anemometer

Licor 6262 CO2/H2O analyzer

2) Precipitation gauge at 42 m height (1-minute data)

3) Vaisala CT-25K Ceilometer operating during periods from April 2001 to July 2003 (30-m resolution backscatter profile every 15 seconds)

4) Radiation boom at 60 m (Lup, Ldown, Sup, Sdown) 5) Temperature, RH profile

Ceilometer

MethodsMethods

1. Identify precipitation events from the ceilometer backscatter profile and

rain gauge. Advantages over using the rain gauge alone: a) Ceilometer detects all rainfall events, including light ones when the rain gauge may not catch any rainfall. b) Get exact starting/ending times for precipitation.

2. Calculate eddy fluxes of latent heat a) Form a “base state” ensemble average of the latent heat flux from the days without precipitation

b) Form an ensemble average of the latent heat flux for the precipitation cases.

Calculation of eddy flux

Alter t=0 (starting time for flux calculation)

Alter length of time of flux calculation

based on the individual precip. events!

Identifying rain eventsIdentifying rain events

Events with available data (day and night) by season

Wet Dry All

Tipping Bucket 143 63 206

Ceilometer 80 102 182

Ceilometer rain threshold: 1.3, units of log(10000*srad*km)-1, with levels from the ground to 50% of cloud base averaged.

rain ID thresholdrain

15-minute flux calculations: 4 ways15-minute flux calculations: 4 ways

-Block average -Smoothed mean removal -Linear trend removal -Running mean removal

-LE used in analysis is the average of the linear trend, smoothed mean, and running mean removals.

-Calibration cycles, spike cutoff

Flux datasets formed and ensemble formationFlux datasets formed and ensemble formation

-Two 15-minute flux datasets formed: a. one with uniform start times for the flux-calculation intervals

b. the other with flux-calculation start times based on individual rainfall event start times (t=0)

-Ensembles of LE, H, -Q*, and storage formed for dry days, rain days, and afternoon rain-days

Nighttime rainfall/interception eventsNighttime rainfall/interception events

Approach: -Simpler, baseline LE=0 at night. Integrate nighttime portion of event LE directly; deal with morning LE separately.

-Form ensemble average of events based on the starting time of each rain event.

All nighttime events: ensemble meansMean interception: 4.72% (std.err 0.93%) Mean precip: 3.32 ±0.59mm (n=54)

Individual daytime event LE baseline determinationIndividual daytime event LE baseline determination

Approach:

-Raw baseline LE must be scaled by the event net radiation to reflect the amount of available energy for the event (the net radiation for a given rainfall event is less than the net radiation that would be observed on a dry day at the same time-of-day)

-The dry-day baseline LE for an individual rainfall event should represent the LE that would occur on a dry day under the same radiative conditions as the day with rain.

-Must determine the baseline LE for each event


[LE]baseline = -Q*frac * [LE]dry ensemble

Method used:

Divide the mean of the event –Q* (-Q*ev) by the mean of the dry-day baseline –Q* ([-Q*]dry ensemble) for the time of day of the precipitation event to get the radiative fraction (-Q*frac) for the corresponding time of day covering the precipitation event.

Q*frac = ( ∑ (-Q*ev) / nev) / ( ∑ ([-Q*]dry ensemble) / ndry ensemble)

Multiply this event radiative fraction by the raw dry-day baseline LE ([LE]dry ensemble) for the same time of day to get the baseline LE:


Precipitation event LE

Raw dry-day LE baseline Corrected dry-

day LE baseline

Precipitation event –Q*

Rain-day ensemble –Q*

Dry-day ensemble –Q*

Interception evaporationInterception evaporationDaytime events for rainfall rates <= 16 mm hr-1

Daytime events for rainfall rates > 16 mm hr-1

Blackout period when eddy-covariance does not work

Fill in event LE when eddy-covariance fails with Penman-Monteith-estimated ET

Penman-Monteith equation to estimate ETPenman-Monteith equation to estimate ET

a

s

a

V

E

rr

rL

AQ

'

'

'

1

where QE : Latent heat flux A : Available energy ε : LV SV / CP δ : Saturation deficit

r’s , r’

a : Stomatal, aerodynamic resistances LV : Latent heat of vaporization ρ : Air density

Penman-Monteith equation to estimate ETPenman-Monteith equation to estimate ET

a

s

a

V

E

rr

rL

AQ

'

'

'

1

Terms: A, δ were determined directly from observations

r’a was determined from wind-speed measurements as:

z: anemometer height, z0: roughness length d: displacement height; u(z): wind speed at height z

k: von Karman constant

r’s was found as a residual during rain events when the eddy-covariance

system was working: -Ensemble r’s on rain-days was approximately 40 s m -1 during rainfall periods, not zero.

2

02

ln)(

1'

z

dz

zukr a

Interception evaporationInterception evaporationDaytime events for rainfall rates <= 16 mm hr-1

Daytime events for rainfall rates > 16 mm hr-1 (Penman-Montieth filled)

Mean intercepted water binned by rain intensityMean intercepted water binned by rain intensity

Using observed LE (rainfall rates < 16 mm hr -1) Using Penman-Monteith filled LE

Daytime event interception estimatesDaytime event interception estimates

Rainfall rate Mean interception (standard error) for Number of events Measured events Penman-Monteith

filled events

<= 2 mm hr-1 18.0% (12.2%) 21.5% (12.2%) 46

2-16 mm hr-1 9.9% (2.6%) 14.7% (3.5%) 58

> 16 mm hr-1 7.8% (1.6%) 7.8% (1.6%) 25

Interception evaporationInterception evaporation

Mornings after nighttime rainfall events

Additional mean interception (std error) in the morning: 2.5% (1.1%)

Total mean interception (std error) for nighttime rainfall events: 7.2% (1.0%)

1200 – 1400 GMT

1400 – 1800 GMT

1800 – 2200 GMT

Dry Pre-rain

Dry Rain Dry Rain

[LE] / [-Q*] (%) 47.5 44.4 49.3 51.8 55.5 60.7

[H] / [-Q*] (%) 19.9 18.3 20.3 18.7 14.6 11.3

[Sbc] / [-Q*] (%) 10.2 11.8 4.3 4.3 0.0 -3.5

Energy balance for dry and afternoon rain-daysEnergy balance for dry and afternoon rain-days

Where does the energy to re-evaporate intercepted water come from?

References:

1B,2B:Franken et al.(1982a,b) 3B:Schubart et al. (1984) 4B:Leopoldo et al.(1987) 5B:Lloyd and Marques(1988) 6C:Imbach et al.(1989) 7B,8B:Ubarana(1996) 9C:Cavelier et al.(1997) 10B:Arcova et al.(2003) 11B:Ferreira et al.(2005) 12M:Manfroi et al.(2006) 13A:Wallace and McJannet(2006) 14P:Holwerda et al.(2006) 15B:Germer at al.(2006) 16B:Cuartas et al.(2007) 17B: This study


This study

SummarySummary

-We have introduced a methodology by which one can directly observe the amount of interception evaporation using eddy-covariance data that are available at a number of worldwide flux tower sites.

-Tests of the method over an eastern Amazon old-growth rain forest show the method to be effective using direct LE observations under light-to-moderate rainfall rates (<= 16 mm hr-1).

-Penman-Monteith estimated LE can be used during events with heavy rainfall rates (> 16 mm hr -1) when eddy covariance fails and direct LE observations are unavailable.

-Mean interception for all events in the study was 11.6%. For daytime events, mean interception for light, moderate, and heavy rainfall events were 18.0%, 9.9%, and 7.8% respectively.

SummarySummary

-Energy balance comparisons between dry and afternoon rain-days show an approximately 15% increase of evaporative fraction on the rain days, with the energy being supplied by a nearly equivalent decrease in the canopy heat storage.

-Future work includes testing of the method at other flux-tower sites with different land cover types.

AcknowledgementsAcknowledgements

-Harvard University group (including Lucy Hutyra and Elaine Gottlieb) for providing km67 data access and calibration information.

Characteristic Km 67 – intact forest Km 83 - selectively logged

Number of points 1500 487

Cover, fraction0.98 + 0.0526 0.98 + 0.1201

CAI, m2 m-27.39 + 1.118 6.96 + 1.746

Maximum height, m49.5 46.5

Mean weighted height, m13.36 13.40

Mean outer canopy height, m20.14 17.98

Rugosity, m10.03 8.42

Total porosity, %73.51 73.16

Included porosity, %32.36 + 15.83 26.23 + 16.25


Summary statisticsStructural statistics tabulated below show few differences between the sites.

Penman-Monteith equation to estimate ET

a

s

a

V

E

rr

rL

AQ

'

'

'

1

where QE : Latent heat flux A : Available energy ε : LV SV / CP δ : Saturation deficit

r’s , r’

a : Stomatal, aerodynamic resistances LV : Latent heat of vaporization ρ : Air density


0

5

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50

0 0.2 0.4 0.6 0.80

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30

35

40

45

50

0 0.2 0.4 0.6 0.8

surface area density, m2m-3

0

5

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15

20

25

30

35

40

45

50

0 0.2 0.4 0.6 0.8

drano lines at km 67 transects at km 83fetch lines at km 67

mean canopy height profiles, FLONA Tapajos July 2003

Mean height profiles of canopy surface area density in the intact site (km 67) and DRANO study area and in the selectively logged site (km 83). The error bars are standard errors.

Rainfall rate Number of events

Event percentage

<= 2 mm hr-1 147 48.8%

2-16 mm hr-1 86 28.6%

> 16 mm hr-1 68 22.6%

All detected rainfall events All detected rainfall events (tipping bucket and ceilometer)(tipping bucket and ceilometer)

Rainfall rate Number of events

Event percentage

<= 2 mm hr-1 84 41.4%

2-16 mm hr-1 63 31.0%

> 16 mm hr-1 56 27.6%

All detected daytime rainfall events All detected daytime rainfall events (tipping bucket and ceilometer)(tipping bucket and ceilometer)

Nighttime rainfall/interception eventsNighttime rainfall/interception events

Approach:

LE

time

RainRain

Base Base state LEstate LE

Event LEEvent LEINT INT LossLoss

Nighttime low-interception events: ensemble meansMean interception: 2.36 ± 0.28% Mean precip: 3.73 ±0.67mm (n=46)

Dec. 2001: Avg. dry-day LE, rain-day LE, rain-day H (W/m^2)Rain period

Rain period

Hrain

LEdry

LErain

Dec. 2001: dry-day -Q*, rain-day -Q* ensemble (W/m^2)

Q* + H + LE + St + Adv = 0

Dec. 2001: Early-mid afternoon rain events (1245 – 315 PM LT; 6 rain-event days included)

-Q*dry

-Q*rain

convective synoptic

Rain Dial (UT)

Afternoon precipitation from local convective activity

Wet season

Dry season

convectiva Lineas ins

Event timing ~ 11pm to 1am LT

Nighttime high-interception, high-wind events: ensemble meansMean interception: 20.6 ± 5.7% Mean precip: 1.40 ±0.81mm (n=4)

Event timing ~ 6pm LT

Nighttime high-interception, low-wind events: ensemble meansMean interception: 16.1 ± 3.6% Mean precip: 0.57 ±0.24mm (n=4)

Remaining workRemaining work

-Forming the morning, afternoon LE ensembles to arrive at daytime interception estimates

-Model estimation of the LE baselines for interception estimates

-Adding the morning interception portion to the nighttime interception estimates

-Interception model estimates, comparisons (Gash)

Daytime rainfall/interception eventsDaytime rainfall/interception events

Approach: -Must determine baseline LE. Methods:

1.Use average monthly LE for dry days.

Advantage: Dry-day conditions are similar with respect to radiation and cloudiness.

Drawback: Limits rain-event ensembles to one month in length because of seasonal LE differences.

Avg. dry-day LE (solid), -Qstar * 0.4 (light blue dashed) Oct. 2001

Avg. dry-day LE (solid), -Qstar * 0.4 (light blue dashed) Nov. 2001

Avg. dry-day LE (solid), -Qstar * 0.4 (light blue dashed) Dec. 2001

Dec. 2001: Avg. dry-day LE, rain-day LE, rain-day H (W/m^2)Rain period

Rain period

Hrain

LEdry

LErain

Dec. 2001: dry-day -Q*, rain-day -Q* ensemble (W/m^2)

Q* + H + LE + St + Adv = 0

Dec. 2001: Early-mid afternoon rain events (1245 – 315 PM LT; 6 rain-event days included)

-Q*dry

-Q*rain

Avg. Sbc (W/m^2) Dec. 2001 no-rain days (24 days)

Avg. Sbc (W/m^2) Dec. 2001 rain days (6 days)

Sbc term: Biomass and canopy air storage (Moore and Fisch, 1986)

Sbc=16.7Tr + 28.0qr + 12.6Tr*

where Tr: hourly air temperature change (C)

qr: hourly specific humidity change (g/kg)

Tr*: 1-hour lagged hourly air temperature change (C)

Rain period

Avg. wind speed, u* (m/s) Dec. 2001 no-rain days (24 days)

Avg. wind speed, u* (m/s) Dec. 2001 rain days (6 days)

Rain period

Daytime rainfall/interception eventsDaytime rainfall/interception events

Approach: -Must determine baseline LE. Methods:

2. Use Evaporative Fraction (EF) to determine corrected baselines.

Getting dry-day baseline: 1. Form LE/Q* time series for each dry day

2. EFdry= [LE/Q*]dry ensemble

3. Get corrected dry-day baseline LE LEcorrdry=[Q*]rain ensemble * [EF]dry

Getting rain-day ensemble: 1. Form LE/Q* time series for each rain day

2. EFrain= [LE/Q*]rain ensemble

3. Get corrected rain-day ensemble LE LEcorr rain=[Q*]rain ensemble * [EF]rain

LEcorr rain

LEcorr dry

EFdry EFrain

rain period

-Q*rain

-Q*dry

rain period

Dec. 2001: 6 rain-days included


Precipitation event LE

Raw dry-day LE baseline Corrected dry-

day LE baseline

Precipitation event –Q*

Rain-day ensemble –Q*Dry-day ensemble –Q*


Daytime events for rainfall rates <= 16 mm hr1


Daytime events for rainfall rates > 16 mm hr1

http://www.fluxnet.ornl.gov

Fluxnet sites by landcover typeFluxnet sites by landcover type

Evapotranspiration (ET)Evapotranspiration (ET)

Hydrology MicrometeorologySurface water balance: Energy balance:

Advantages: LE is directly measured by eddy-covariance method

Disadvantages: Eddy-covariance method fails during calm nights with low turbulence

Eddy-covariance method often fails during and shortly after rainfall events (interception)

Flux footprint changes with wind speed, direction

Spatial scale: Up to the small watershed size

Advantages: P, R are directly measured, and widely available

Disadvantages: ET is found as a residual, or is estimated by other means (e.g. Penman-Monteith equation)

Difficult to determine storage term ΔS over large areas (however annually ΔS ≈ 0)

Spatial scale: Small watershed (1 – 10 km2) to large watershed size (> 500 km2)

ET = P – R – ΔSΔS A = - (Q* - G) = H + LE + Adv

Link between both approaches is on the small watershed scale!

RO

I < f ?

A Hydrological ModelA Hydrological ModelA Hydrological ModelA Hydrological ModelP

Channel

Y

Surfaceis

retentionfull?

N

Subsurface

STO

RM

FLO

WS

TO

RM

FLO

W

RetentionRetention

DepressionDepression

ChannelChannel

N

Y

BA

SE FLO

WB

AS

E FLO

W

DetentionDetention

Ground WaterGround Water

VegetationVegetation

E

Tw

o t

yp

es o

f FLO

W o

r R

UN

OFF :

Tw

o t

yp

es o

f FLO

W o

r R

UN

OFF :

Six types of storage:Six types of storage:

T

I > f

P. E. Black, 2002

Each storage reservoir has a characteristic time scale (to help assess transient features)

(Interception)(Interception)

SoilSoil moisturemoisture

Surface water balance: Energy balance:AdvLEHGQA )*(

annually S 0

1992-2000

P-R

Upward fluxes are positive

Fitzjarrald et al. (2001)

ET = P – R – ΔS

At HF, long-term annual measured ET (481 mm) is nearly equal to P-R estimated ET (483 mm)

Czikowsky and Fitzjarrald (2004)

Courtesy of G. Parker

Smithsonian Environmental Research Center (SERC) Forest Hydrology

Components of Evapotranspiration (ET)Components of Evapotranspiration (ET)

1. Transpiration

3. Bare-soil evaporation

2. Interception evaporation

Lateral flow to stream

Background

-Climate models: Water balance not closed on regional scales

-Roads et al. (2002): (GEWEX experiment) Water balance errors as large as the associated runoff over tropical regions (Amazon, tropical W. Africa, SE Asia) and in Canada annually

GEWEX experiment sites

GEWEX news (2002)

Mackenzie GEWEX study (MAGS)

GEWEX Americas Prediction Project (GAPP)

Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA)

Coupling of the Tropical Atmosphere and Hydrological Cycle (CATCH)

Baltic Sea Experiment (BALTEX)

GEWEX Asian Monsoon Experiment (GAME)

Murray-Darling Basin Water Budget Experiment (MDB)

Mackenzie Basin, CanadaMackenzie Basin, Canada

-E

P

-R(mod)

RSW

-R(obs)

Surface water residual RSW is largest in late-winter, early-spring season, decreasing in magnitude during the spring season.

Roads et al. (2002)

The model is drying out the soil too quickly!

NCEPR-II Model

High-interception nighttime events: 2 types:

A. High wind events occurring around midnight (squall lines?)

B. Light wind events occurring near the evening transition

Event timing: 11pm to 1am LT

Event timing: ~ 6pm LT

LE

LE

Wind speed, u*

Wind speed, u*

All nighttime events: ensemble meansMean interception: 4.72 ± 0.93% Mean precip: 3.32 ±0.59mm (n=54)

All nighttime events (n=54) Dry season night (n=9)

All nighttime events

0 1 2 3 4 5 6 7km67 Harvard transects July 2003

estimated surface area density

500 550 600 650 700 750 800 850 900 950 1000

20

40

0 50 100 150 200 250 300 350 400 450 500

10

20

30

40

50

500 550 600 650 700 750 800 850 900 950 1000

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0 50 100 150 200 250 300 350 400 450 500

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he

igh

t, m

0 50 100 150 200 250 300 350 400 450 500

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500 550 600 650 700 750 800 850 900 950 1000

horizontal distance, m

20

40







Figure 1. Height sections of canopy surface area density along six 500m m transects at the intact forest site, km 67.

5 10 155 10 15 20 25 30 355 10 15 20 25 30 35 40 45 505 10 15 20

5

10

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30

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5 10 15 20 25 30 35 40 455 10 15 20 25

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5 10 15 20 25 30 35 40 45 50

10 20 30 40 50 60 70 80

vertical slice of canopy surface areaat Drano transects km67 Tapajos

10 20 30 40 50 60

10

20

30

40

"A" "B+C"

"D" "E" "F" "G"

"H" "I" "J"

Figure 2. Height sections of canopy surface area density along short transects in the DRANO study area at the intact forest site, km 67.

km83 transects July 2003

150 200 250 300 350 400 450 500 5500

20

40

200 250 300 350 400 4500

20

40

200 250 300 350 400 450 5000

20

40

LT1 [100,175]->[100,475]

LT3 [150,150]->[150,550]

LT2 [200,175]->[200,525]

Figure 3. Height sections of canopy surface area density along three transects at the selectively logged forest site, km 83

MethodsMethods

How can we quantify interception (INT) losses using the eddy flux method?

LE

time

RainRain

Base Base state LEstate LE

Event LEEvent LE

INT INT LossLoss

References:

1B,2B:Franken et al.(1982a,b) 3B:Schubart et al. (1984) 4B:Leopoldo et al.(1987) 5B:Lloyd and Marques(1988) 6C:Imbach et al.(1989) 7B,8B:Ubarana(1996) 9C:Cavelier et al.(1997) 10B:Arcova et al.(2003) 11B:Ferreira et al.(2005) 12M:Manfroi et al.(2006) 13A:Wallace and McJannet(2006) 14P:Holwerda et al.(2006) 15B:Germer at al.(2006) 16B:Cuartas et al.(2007) 17B: This study


17B

17B16B

Energy balance for dry and afternoon rain-daysEnergy balance for dry and afternoon rain-days

Q* + H + LE + St + Adv = 0

-Q*

LEH

S

Mean energy-balance components: dry-days (solid) and afternoon rain-days (dashed)

LE / -Q*

H / -Q*

S / -Q*

Mean evaporative, sensible heat, and storage fraction: dry-days (solid) and afternoon rain-days (dashed)

SummarySummary

-We have introduced a methodology by which one can directly observe the amount of interception evaporation using eddy-covariance data that are available at a number of worldwide flux tower sites.

-Tests of the method over an eastern Amazon old-growth rain forest show the method to be effective under light-to-moderate rainfall rates (<= 16 mm hr-1).

-Mean interception for moderate daytime rainfall events was about 10%, with light events at 18%.

-Energy balance comparisons between dry and afternoon rain-days show an approximately 15% increase of evaporative fraction on the rain days, with the energy being supplied by a nearly equivalent decrease in the canopy heat storage.

-Future work includes testing of the method at other flux-tower sites with different land cover types.

Rainfall rate Mean interception (standard error)

Number of events

<= 2 mm hr-1 18.0% (12.2%) 46

2-16 mm hr-1 9.9% (2.6%) 58

> 16 mm hr-1 NA 25

Daytime event interception estimatesDaytime event interception estimates

Documents

A NEW METHOD TO DIRECTLY OBSERVE THE EVAPORATION OF INTERCEPTED WATER OVER AN EASTERN AMAZON OLD-GROWTH RAIN FOREST Matthew Czikowsky (1), David Fitzjarrald