ET = P – R – ΔS

1. DETECTING THE EVAPORATION OF INTERCEPTED WATER OVER AN OLD-GROWTH RAIN FOREST IN THE EASTERN AMAZON USING EDDY FLUX MEASUREMENTS 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

2. Surface water and energy balances ET = P – R – ΔS A = - (Q* - G) = H + LE + St + Adv Evapotranspiration Precipitation Runoff Storage Available energy Net radiation Ground heat flux Sensible heat flux Latent heat flux Canopy heat storage Advection

3. Conventional interception estimates in tropical rain forests 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)

4. 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. Horizontal forest canopy transects, Tapajos National Forest, Brazil (LBA Km67 site) -Where to the put throughfall gauges to get a representative interception estimate? G. Parker, personal comm.

5. Fluxnet sites Is there any further information that can be obtained from the growing number and coverage of flux-tower sites? http://www.fluxnet.ornl.gov

6. 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.

7. Methods 3. How can we quantify interception (INT) losses using the eddy flux method? INT Loss Event LE LE Rain Base state LE time

8. 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.

9. Rain dials: times in GMT (LT+4) Wet season Convective rainfall Nocturnal squall line precipitation Dry season Rains occur frequently at the same times of day  helps to build up a large ensemble of similar cases. Courtesy of D. Fitzjarrald

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

11. Data/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) Ceilometer 4) Radiation boom at 60 m (Lup, Ldown, Sup, Sdown) 5) Temperature, RH profile

12. 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. Alter t=0 (starting time for flux calculation) based on the individual precip. events! Calculation of eddy flux Alter length of time of flux calculation

13. Identifying rain events Ceilometer rain threshold: 1.3, units of log(10000*srad*km)-1, with levels from the ground to 50% of cloud base averaged. Events with available data (day and night) by season Wet Dry All Tipping Bucket (2001-03) 143 63 206 Ceilometer (2001-02) 80 102 182 rain rain ID threshold

14. 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

15. 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

16. 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.

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

18. 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) -Must determine the baseline LE for each event -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.

19. Individual daytime event LE baseline determination 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: [LE]baseline = -Q*frac * [LE]dryensemble

20. 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*

21. 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

23. Daytime event interception estimates

24. Energy balance for dry and afternoon rain-days Where does the energy to re-evaporate intercepted water come from?

25. Conventional interception estimates in tropical rain forests 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

26. 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.

27. Current / future work -Current work includes: 1. Filling event LE during periods when the eddy-covariance system failed (some heavy rain periods) with Penman-Monteith-estimated ET to obtain interception estimates for heavy rain-rate cases. 2. Determining interception evaporation for the early-morning periods following nighttime rainfalls to get the complete interception estimate for nighttime rainfalls. -Future work includes testing of the method at other flux-tower sites with different land cover types.

29. Penman-Monteith equation to estimate ET where QE : Latent heat flux A : Available energy ε : LV SV /CPδ : Saturation deficit r’s , r’a : Stomatal, aerodynamic resistances LV : Latent heat of vaporization ρ : Air density

30. Summary statistics Structural statistics tabulated below show few differences between the sites. G. Parker, personal comm.

31. 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. G. Parker, personal comm.

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

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

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

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

46. Dec. 2001: Avg. dry-day LE, rain-day LE, rain-day H (W/m^2) Rain period LErain LEdry Hrain Dec. 2001: dry-day -Q*, rain-day -Q* ensemble(W/m^2) -Q*dry -Q*rain Rain period Q* + H + LE + St + Adv = 0 Dec. 2001: Early-mid afternoon rain events (1245 – 315 PM LT; 6 rain-event days included)

48. convective synoptic Rain Dial (UT) Afternoon precipitation from local convective activity

49. Wet season

50. Dry season