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

ΔS ET = P – R – ΔS A = - (Q* - G) = H + LE + St + Adv Surface water and energy balances EvapotranspirationPrecipitationRunoffStorageAvailable energyNet radiationGround 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 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. Conventional interception estimates in tropical rain forests -Where to put the throughfall gauges to get a representative interception estimate? Horizontal forest canopy transects, Tapajos National Forest, Brazil (LBA Km67 site) G. Parker, personal comm.

Fluxnet sites 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 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.

Methods How can we quantify interception (INT) losses using the eddy flux method? LE time Rain Base state LE Event LE INT Loss

Objectives -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/Instrumentation 1) Eddy covariance system at ~ 60 m height, including: Campbell CSAT 3-D Sonic Anemometer Licor 6262 CO 2 /H 2 O 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

Methods 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 events Events with available data (day and night) by season Wet Dry All Tipping Bucket Ceilometer 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 threshold rain

15-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 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 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 means Mean interception: 4.72% (std.err 0.93%) Mean precip: 3.32 ±0.59mm (n=54)

Individual 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

Individual daytime event LE baseline determination [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 ) / n ev ) / ( ∑ ([-Q*] dry ensemble ) / n dry 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:

Individual daytime event LE baseline determination 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 evaporation Daytime 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 ET where Q E : Latent heat flux A : Available energy ε : L V S V / C P δ : Saturation deficit r ’ s, r ’ a : Stomatal, aerodynamic resistances L V : Latent heat of vaporization ρ : Air density

Penman-Monteith equation to estimate ET Terms: A, δ were determined directly from observations r’ a was determined from wind-speed measurements as: z: anemometer height, z 0 : 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.

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

Daytime event interception estimates Rainfall rateMean interception (standard error) for Number of events Measured eventsPenman-Monteith filled events <= 2 mm hr % (12.2%)21.5% (12.2%) mm hr % (2.6%)14.7% (3.5%)58 > 16 mm hr % (1.6%) 25

Interception 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 DryPre- rain Dry Rain Dry Rain [LE] / [-Q*] (%) [H] / [-Q*] (%) [Sbc] / [-Q*] (%) Energy 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 Conventional interception estimates in tropical rain forests This study

Summary -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.

Summary Summary -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.

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

CharacteristicKm 67 – intact forest Km 83 - selectively logged Number of points Cover, fraction CAI, m 2 m Maximum height, m Mean weighted height, m Mean outer canopy height, m Rugosity, m Total porosity, % Included porosity, % G. Parker, personal comm. Summary statistics Structural statistics tabulated below show few differences between the sites.

Penman-Monteith equation to estimate ET where Q E : Latent heat flux A : Available energy ε : L V S V / C P δ : Saturation deficit r ’ s, r ’ a : Stomatal, aerodynamic resistances L V : Latent heat of vaporization ρ : Air density

G. Parker, personal comm. 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 rateNumber of events Event percentage <= 2 mm hr % 2-16 mm hr % > 16 mm hr % All detected rainfall events (tipping bucket and ceilometer)

Rainfall rateNumber of events Event percentage <= 2 mm hr % 2-16 mm hr % > 16 mm hr % All detected daytime rainfall events (tipping bucket and ceilometer)

Nighttime rainfall/interception events Approach: LE time Rain Base state LE Event LE INT Loss

Nighttime low-interception events: ensemble means Mean 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 H rain LE dry LE rain 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

convectivesynoptic 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 means Mean interception: 20.6 ± 5.7% Mean precip: 1.40 ±0.81mm (n=4)

Event timing ~ 6pm LT Nighttime high-interception, low-wind events: ensemble means Mean interception: 16.1 ± 3.6% Mean precip: 0.57 ±0.24mm (n=4)

Remaining 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 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 Avg. dry-day LE (solid), -Qstar * 0.4 (light blue dashed) Nov 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 H rain LE dry LE rain 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 no-rain days (24 days) Avg. Sbc (W/m^2) Dec rain days (6 days) Sbc term: Biomass and canopy air storage (Moore and Fisch, 1986) Sbc=16.7  Tr  qr  Tr* 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 no-rain days (24 days) Avg. wind speed, u* (m/s) Dec rain days (6 days) Rain period

Daytime 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. EF dry = [LE/Q*] dry ensemble 3. Get corrected dry-day baseline LE LE corrdry =[Q*] rain ensemble * [EF] dry Getting rain-day ensemble: 1. Form LE/Q* time series for each rain day 2. EF rain = [LE/Q*] rain ensemble 3. Get corrected rain-day ensemble LE LE corr rain =[Q*] rain ensemble * [EF] rain

LE corr rain LE corr dry EF dry EF rain rain period -Q* rain - Q* dry rain period Dec. 2001: 6 rain-days included

Individual daytime event LE baseline determination 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 evaporation Daytime events for rainfall rates <= 16 mm hr 1

Interception evaporation Daytime events for rainfall rates > 16 mm hr 1

Fluxnet sites by landcover type

Evapotranspiration (ET) HydrologyMicrometeorology Surface 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 km 2 ) to large watershed size (> 500 km 2 ) ΔS ET = P – R – ΔS A = - (Q* - G) = H + LE + Adv Link between both approaches is on the small watershed scale!

RO I < f ? A Hydrological Model P Channel Y Surface is retention full? N Subsurface STORM FLOW Retention Depression Channel N Y BASE FLOW Detention Ground Water Vegetation E Two types of FLOW or RUNOFF : 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) Soil moisture

Surface water balance:Energy balance: annually  S  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) 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, 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 Wind speed, u*

All nighttime events: ensemble means Mean 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

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

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

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

Methods How can we quantify interception (INT) losses using the eddy flux method? LE time Rain Base state LE Event LE INT Loss

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 Conventional interception estimates in tropical rain forests 17B 16B

Energy balance for dry and afternoon rain-days Q* + H + LE + St + Adv = 0 -Q* LE H 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)

Summary -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 % (12.2%) mm hr % (2.6%) 58 > 16 mm hr -1 NA 25 Daytime event interception estimates