NOTICE -- The LBA-ECO Project website is no longer being supported.  This archive is a snapshot, as it existed in 2013, of the LBA-ECO website, maintained by NASA Goddard Space Flight Center, and now archived at the ORNL DAAC.  Links to external websites may be inactive. Final data products from the LBA project can be found at the ORNL DAAC.
banner
banner banner banner banner banner banner
banner banner banner banner banner banner banner
home aboutlibrarynews archivecontacts banner

spacer
banner
Investigations
Overview
Abstracts & Profiles
Publications
Research Sites
Meetings
Synthesis Groups
LBA-HYDROMET
LBA-Air-ECO
Logistics
Overview
Field Support
Travel
Visa
Shipping
Data
  Overview
Find LBA Data
Investigator Checklist
Process & Policy
Documentation & Archive
Training & Education
  Overview
Activities Summary
T&E Goals
Student Opportunities
  Folha Amazônica
 
spacer

Investigation:

CD-03 (Fitzjarrald / Moraes)

LBA Dataset ID:

CD03_PASTURE_FLUX

Originator(s):

1. FITZJARRALD, D.R.
      2. SAKAI, R.K.

Point(s) of Contact:

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (ornldaac@ornl.gov)

Dataset Abstract:

Eddy correlation and micrometeorological measurements began in 2001 and continued through 2005 at the pasture site at km 77 on BR-163 just south of the city of Santarem, Para, Brazil. Measurements included turbulent fluxes (momentum, heat, water vapor, and CO2) using the eddy covariance (EC) approach. Other measurements included the CO2 profile, air temperature, humidity, wind speed profile, downward and upward solar and terrestrial radiation, downward and upward photosynthetically active radiation (PAR), atmospheric pressure, rainfall, soil temperature, soil moisture, and soil heat flux. Data are presented in 5 comma-separated ASCII value (csv) files each corresponding roughly to one calendar year.
At the beginning of the measurements, in September 2000, the field was a pasture. In November 2001, the pasture was burned, plowed, and planted in upland (non-irrigated) rice. Land use practices during the study period were recorded and are included in a table in Section 5 of this guide. The EC system was composed of a 3D sonic anemometer (ATI 3D) and an infrared analyzer (LICOR 6262) installed on a 20m tower in the agricultural field. The methodology to calculate the flux is described in detail in Sakai et al. (2004) and a companion file is included that describes in detail the formulae used to calculate the eddy flux variables (CD03_Pasture_Flux_Calculations.pdf).

Beginning Date:

2000-09-01

Ending Date:

2006-01-01

Metadata Last Updated on:

2010-03-15

Data Status:

Archived

Access Constraints:

PUBLIC

Data Center URL:

http://daac.ornl.gov/

Distribution Contact(s):

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (ornldaac@ornl.gov)

Access Instructions:

PUBLIC

Data Access:

IMPORTANT: The LBA-ECO Project website is no longer being supported. Links to external websites may be inactive. Final data products from the LBA project can be found at the ORNL DAAC. Please follow the fair use guidelines found in the dataset documentation when using or citing LBA data.
Datafile(s):

LBA-ECO CD-03 Flux-Meteorological Data, km 77 Pasture Site, Para, Brazil: 2000-2005:  http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=962

Documentation/Other Supporting Documents:

LBA-ECO CD-03 Flux-Meteorological Data, km 77 Pasture Site, Para, Brazil: 2000-2005:  http://daac.ornl.gov/LBA/guides/CD03_Pasture_Flux.html

Citation Information - Other Details:

Fitzjarrald, D.R. and R.K. Sakai. 2010. LBA-ECO CD-03 Flux-Meteorological Data, km 77 Pasture Site, Para, Brazil: 2000-2005. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. doi:10.3334/ORNLDAAC/962

Keywords - Theme:

Uncontrolled Theme Keyword(s):  AIR PRESSURE, AIR TEMPERATURE, DAILY DATA, EDDY CORRELATION, HOURLY DATA, HUMIDITY, PASTURE SITE, PRECIPITATION, SANTAREM (PA), SOIL TEMPERATURE, SOIL WATER CONTENT, SOLAR RADIATION, TURBULENT FLUX, WIND DIRECTION, WIND SPEED

Keywords - Place (with associated coordinates):

Region
(click to view profile)
Site
(click to view profile)
North South East West
  SANTAREM, PA -3.02020 -3.02020 -54.89420 -54.89420

Related Publication(s):

Acevedo, O.C., O.L.L. Moraes, R. Da Silva, D.R. Fitzjarrald, R.K. Sakai, R.M. Staebler, and M.J. Czikowsky. 2004. Inferring nocturnal surface fluxes from vertical profiles of scalars in an Amazon pasture. Global Change Biology 10(5):886-894.

Sakai, R.K., D.R. Fitzjarrald, O.L.L. Moraes, R.M. Staebler, O.C. Acevedo, M.J. Czikowsky, R. Da Silva, E. Brait, and V. Miranda. 2004. Land-use change effects on local energy, water, and carbon balances in an Amazonian agricultural field. Global Change Biology 10(5):895-907.

Data Characteristics (Entity and Attribute Overview):

Data Characteristics:

Data were collected at the pasture site located at km 77 on BR-163 just south of the city of Santarem Para. Measurements began in 2001 and continued through 2005. Data are presented in 5 comma-separated ASCII value (csv) files each corresponding roughly to one calendar year. All files have the same format.



File naming convention:

Filename: CD-03_Pasture_CO2_Fluxes_Km77_Para_YYYY.csv

where YYYY = 2001, 2002, 2003, 2004, 2005

Missing data are represented as -9999

Column Descriptions:

Column Column Heading Units Description

1 year YYYY

2 Julian_day decimal day Julian day or fractional day (e.g., 1.22917) corresponding to the middle of the 30 minute averaging period based on GMT. Local time is GMT - 4.



3 hh decimal hour Decimal hour (e.g., 4.25) corresponding to the middle of the 30 minute averaging period based on GMT. Local time is GMT - 4.



4 wT K*m/s Sensible heat flux. (Kinematic form. To convert to W/m2, multiply by the air density and the specific heat constant at constant pressure.)



5 wT_status 0= data; 1=gap filling; 2= no data no filling; 3= nighttime+ low ustr (ustr.min = 0.2 m/s)



6 wq Kg/Kg*m/s Latent Heat Flux. (Kinematic form. The unit Kg/Kg is the water vapor mixing ratio, that is (Kg of water vapor)/(Kg of air). To convert to W/m2, multiply by the air density and by the latent heat constant for condensation.)



7 < wq_status 0= data; 1=gap filling; 2= no data no filling; 3= nighttime+ low ustr (ustr.min = 0.2 m/s)



8

wco2 mg CO2/m2/s CO2 flux. Rate of vertical transfer of CO2 calculated from measurements above the canopy. Negative values denote movement of CO2 into the canopy.



9 wco2_status 0= data; 2= no data no filling



10 ustr m/s friction velocity; ustr.min=0.2m/s



11

ustr_status 0= data; 2= no data no filling



12 nee mg CO2/m2/s Net Ecosystem Exchange. Net ecosystem exchange, including subcanopy C02 storage. Subcanopy storage includes the entire column from the ground to the height of the eddy covariance system.



13 nee_status 0= data; 1=gap filling; 2= no data no filling; 3= nighttime+ low ustr (ustr.min = 0.2 m/s)



14

S_dw

W/m2

Downward solar radiation flux measured using a Kipp & Zonen net radiometer (model CG2, or CNR1) at 18 m height



15 S_up W/m2 Upward solar radiation flux measured using a Kipp & Zonen net radiometer (model CG2, or CNR1) at 18 m height



16 L_dw W/m2 Downward terrestrial radiation flux measured using a Kipp & Zonen net radiometer (model CG2, or CNR1) at 18 m height



17 L_up W/m2 Upward terrestrial radiation flux measured using a Kipp & Zonen net radiometer (model CG2, or CNR1) at 18 m height



18 PAR_dw micromol/m2/s Downward photosynthetically active radiation flux measured using quantum sensors (Licor, model LI - 190) at 18 m height



19 PAR_up micromol/m2/s Upward photosynthetically active radiation flux measured using quantum sensors (Licor, model LI - 190) at 18 m height



20 Ta_1 degrees C Air temperature at 11.31 m height: Measured using CS500 or HMP45C probes. All probes were enclosed in ventilated radiation shields (MET-ONE, model 076B).



21 Ta_2 degrees C Air temperature at 4.79 m height



22 Ta_3 degrees C Air temperature at 2.20 m height



23 RH_1 % Relative humidity at 11.31 m height: Measured using Vaisala CS500 or HMP45C probes. All probes were enclosed in ventilated radiation shields (MET-ONE, model 076B)



24 RH_2 % Relative humidity at 4.79 m height



25 RH_3 % Relative humidity at 2.20 m height



26 q_1 g/Kg Specific Humidity at 11.31 m height



27 q_2 g/Kg Specific Humidity at 4.79 m height



28 q_3 g/Kg Specific Humidity at 2.20 m height



29 press millibars Air pressure measured using a Vaisala pressure sensor

(model PTB101B) located in the meterological shed.



30 Ts_1 degrees C Soil temperature at 0.14 m depth. Soil temperature measured with Campbell Sci. model CS107 probe



31 Ts_2 degrees C Soil temperature at 0.24 m depth



32 Ts_3 degrees C Soil temperature at 0.50m depth



33 Ts_4 degrees C Soil temperature at 1.5 m depth



34 Ts_5 degrees C Soil temperature at 2.0 m depth



35 G_1 W/m2 Soil heat flux at 0.19 m depth: Soil heat flux plate (Campbell Sci. model HFT-3)



36 G_2 W/m2 Soil heat flux at 1.0 m depth



37 Fsoil m3/m3 Soil moisture content at 0.29 m depth: Soil moisture content measured with a Campbell Scientific (model CS615). Soil moisture content calibration performed by Pedro Moura/ Humberto da Costa (IAG-USP).



38 precip mm Precipitation data using a tipping bucket model TE525m installed close to the ground.



39 u2D_1 m/s u component (W->E) from 2D (ATI, model CATI) sonic at 12 m



40 u3D m/s u component (W->E) from 3D sonic (ATI, SATI/3K) model at 8.3 m



41 u2D_2 m/s u component (W->E) from 2D (ATI, model CATI) sonic at 7.2 m



42 u2D_3 m/s u component (W->E) from 2D (ATI, model CATI) sonic at 2.7 m



43 v2D_1 m/s v component (S->N) from 2D (ATI, model CATI) sonic at 12 m



44 v3D m/s v component (S->N) from 3D sonic (ATI, SATI/3K) model at 8.3 m



45 v3D_2 m/s v component (S->N) from 2D (ATI, model CATI) sonic at 7.2 m



46 v3D_3 m/s v component (S->N) from 2D (ATI, model CATI) sonic at 2.7 m



Example data records: CD-03_Pasture_CO2_Fluxes_Km77_Para_2001.csv





Header records



year,Julian_day,hh,wT,wT_status,wq,wq_status,wco2,wco2_status,ustr,ustr_status,nee,nee_status,S_dw,S_up,L_dw,L_up,

PAR_dw,PAR_up,Ta_1,Ta_2,Ta_3,RH_1,RH_2,RH_3,q_1,q_2,q_3,press,Ts_1,Ts_2,Ts_3,Ts_4,Ts_5,G_1,G_2,

Fsoil,precip,u2D_1,u3D,u2D_2,u2D_3,v2D_1,v3D,v3D_2,v3D_3



2001,1.15625,3.75,-3.300E-03,1,-2.9800E-06,1,-9.999E+03,2,-9999,2,-9.999E+03,2,0,0.2,0,0.2,

0.1,0.5,22,22.6,22.1,100,100,100,16.7,17.4,17.1,1000.17,26.2,27.2,27.2,27.6,27.7,-3.9,-0.4,

0.314,0,-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999

2001,1.17708,4.25,-3.700E-03,1,-3.3800E-06,1,-9.999E+03,2,-9999,2,-9.999E+03,2,0,0.5,0,0.5,

0,0.5,22,22.7,22.2,100,100,100,16.7,17.2,17.1,999.706,26.1,27.1,27.2,27.6,27.7,-4,-0.4,

0.314,0,-0.37,-0.2,-0.42,-0.12,-0.89,-0.13,-0.67,-0.77

2001,1.19792,4.75,-5.200E-03,1,-4.6700E-06,1,-9.999E+03,2,-9999,2,-9.999E+03,2,0,0.5,0,0.5,

-0.1,0.4,22.1,23,22.2,100,97.7,100,16.7,17.1,17.1,999.479,26,27.1,27.2,27.6,27.7,-4.1,-0.4,

0.313,0,0.19,-0.36,0.04,-0.12,-1.21,-0.48,-0.97,-0.74

...

2001,365.94792,22.75,-2.780E-02,3,-5.3300E-06,3,4.300E-06,0,0.0823,0,-1.430E-05,3,0,0,0,0,

-0.6,0.7,31,29.7,29,46.6,56.2,58.2,13,14.7,14.6,994.952,28.4,30.2,28.8,27.6,28.2,-1.4,0.3,

0.2,0,-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999

2001,365.96875,23.25,-2.250E-02,1,-4.3000E-06,1,-7.550E-05,0,-9999,2,-3.040E-05,0,0,0,0,0,

-0.7,0.9,29.7,28.8,28,52.2,59.5,62.2,13.6,14.8,14.7,995.457,28.4,30.1,28.8,27.6,28.2,-2.2,0.3,

0.2,0,-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999

2001,365.98958,23.75,-1.900E-02,1,-3.6400E-06,1,-9.999E+03,2,-9999,2,-9.999E+03,2,0,0,0,0,

-0.7,0.9,28.9,27.8,27.5,55.7,65.8,66.4,13.8,15.3,15.2,995.889,28.3,30,28.8,27.5,28.2,-2.7,0.2,

0.2,0,-9999,-9999,-9999,-9999,-9999,-9999,-9999,-9999



Data Application and Derivation:

Eddy Covariance technique correlates wind, temperature, and other scalar fluctuations to estimate turbulent flux, based on Reynolds averaging. Sonic wind speed and sonic temperature are based on Doppler effect of the speed of sound. Carbon and water vapor fluxes are based on turbulent fluxes and measurement of gas concentrations by absorption of infrared radiation. Turbulent fluxes were calculated from deviations found using 30-minute centered running means. To account for any sonic misalignment or topographically-induced flow, a 3D wind rotation was applied to the wind component. In the daytime, tubing attenuation due to gas diffusion at high frequencies had been observed in the spectra for the variables (CO2 and q) measured by the IRGA. This effect also leads to an underestimate of the turbulent fluxes of CO2 and water vapor. A cospectral correction procedure was used to correct the vertical fluxes (Sakai, 2000). It uses the assumption that scalar cospectra have similar shapes at high frequencies. Basically, the normalized cospectrum of the vertical heat flux was used to determine the shape of, and cospectra at, the affected frequencies. At night during stable periods that nonetheless exhibit turbulence, many reported EC measurements do detect the small scale (high frequency) eddies. However, turbulent mixing is often so strongly suppressed at night, especially in open areas surrounded by higher canopy, that the \'stable-continuous\' mixing regime is not observed. Low values of the friction velocity were normal at this site.



The boundary layer budget method (NBLb), or accumulation method, provides an alternative during these periods. Comparing the tower data with profiles obtained using a tethered balloon during two separate field campaigns validates this method at this site.



The gap-filling strategy for daytime fluxes was to use carbon assimilation light curves. The procedure is divided in two steps. First, we determine an empirical relationship between the carbon flux and the PAR, using the Michaelis-Menten model. Second, we use the residual value of the fitted curve to perform a linear fit using the vapor pressure deficit as the dependent variable.

Quality Assessment (Data Quality Attribute Accuracy Report):

Quality Assessment:

An automatic CO2 calibration cycle was performed twice daily for the infrared gas analyzer. Missing values or data that were flagged as unreliable either by an objective algorithm or subjective inspection have been given a value of -9999.

Process Description:

Data Acquisition Materials and Methods:

Carbon dioxide concentration and standard micrometeorological variables were measured on an instrumented 20 m tower installed in a pasture site that was later converted to rice and soybean cultivation.



CO2 concentrations:

An eddy correlation system composed of a 3D sonic anemometer (SATI/3K Applied Technologies) and on an infrared gas analyzer (IRGA, Licor 6262) was installed at 8.75 m height at 5 Hz. Air samples from 11.8, 5.3, 2.7, and 0.5m heights were pumped to a second IRGA at 5 Hz and used to estimate canopy storage.



Micrometeorological variables:

Wind components were measured with 2D sonic anemometers (Applied Technologies CATI/2) at three heights 12.2, 5.7 and 3.1m and recorded at 1 Hz. Anemometers were pointed due east since winds from the east predominate. Air temperature and humidity sensors (Vaisala Humitter CS500, or HMP45C) were located in aspirated radiation shields at 6.1, 4.1, and 2.2 m. Near the tower top (17.8 m) data on incident and reflected global solar radiation (Kipp and Zonen, pyranometer CM11/14 or net radiometer CNR1), photosynthetically active radiation (quantum sensors; Licor LI-190) as well as downwelling and upwelling global long-wave pyrgeometer CG2 or net radiometer CNR1) radiative fluxes were collected at 0.2Hz.



Soil environment:

Soil temperatures (Campbell Inc 108) were measured at 0.10, 0.244, 0.50, 1.5, and 2.0 m depth. Soil heat flux and soil moisture were both measured at 0.3 m depth (Campbell Inc HFT3 and CS615, respectively)



Cropping and land use practices:

The table below shows the cropping and land use practice intervals at the km77 site over the study period.



Crop Initial date (Julian days) Initial year End date (Julian days) End Year



pasture

244

2000

318

2001



bare

319

2001

54

2002



rice

55

2002

164

2002



fallow

165

2002

354

2002



bare

355

2002

1

2003



rice

2

2003

125

2003



bare

126

2003

134

2003



soybean

135

2003

254

2003



fallow

255

2003

1

2004



bare

2

2004

13

2004



rice

14

2004

94

2004



bare

95

2004

121

2004



soybean

122

2004

244

2004



fallow

245

2004

82

2005



bare

83

2005

90

2005



soybean

91

2005

240

2005



fallow

241

2005

365

2005







References:

Acevedo O.C., O.L.L. Moraes, R. Da Silva, et al. 2004.Inferring nocturnal surface fluxes from vertical profiles of scalars in an Amazon pasture, Global Change Biology 10: 886-894. doi:10.1111/j.1529-8817.2003.00755.x





Anthoni, PM, Law, BE Unsworth, MH (1999) Carbon and water vapor exchange of an open-canopied ponderosa pine ecosystem: . Agricultural and Forest Meteorology, 95, 151-168. doi:10.1016/S0168-1923(99)00029-5



Aubinet, M.A., A. Ibrom, U. Rannik et al. 2000. Estimates of the annual net carbon and water exchange of forests: the EURO-FLUX methodology. Advances in Ecological Research 30: 13-175.



Hollinger, D.Y., F.M. Kelliher, J.N. Byers et al. 1994. Carbon dioxide exchange between an undisturbed old-growth temperate forest and teh atmosphere. Ecology 75: 134-150. doi:10.2307/1939390





Laubach, J.and K.G. McNaughton. 1998. A spectrum-independent procedure for correcting eddy fluxes with separated sensors. Boundary Layer Meterology 89: 445-467. doi:10.1023/A:1001759903058





Leuning, R. and J. Moncrieff. 1990.Eddy covariance of CO2 measurements using open and closed path CO2 analyzers: correction for analyzer water vapour sensitivity and damping of fluctuations in air sampling tubes. Boundary Layer Meteorology 53: 63-76. doi:10.1007/BF00122463





McMillen,R. 1988. An eddy correlation technique with extended applicability to non-simple terrain. Boundary Layer Meteorology 43: 231-245. doi:10.1007/BF00128405





Sakai, R.K. 2000. Observational study of turbulent exchange between the surface and canopy layer over several forest types. Ph.D. thesis. Department of Earth and Atmospheric Sciences, University at Albany SUNY.



Sakai, R.K., D.R. Fitzjarrald, O.L.L. Moraes, R.M. Staebler, O.C. Acevedo, M.J. Czikowsky, R. Da Silva, E. Brait, and V. Miranda. 2004. Land-use change effects on local energy, water, and carbon balances in an Amazonian agricultural field. Global Change Biology 10(5):895-907. doi:10.1111/j.1529-8817.2003.00773.x



Wyngaard, J.C. 1972. Scalar fluxes in the planetary boundary layer- theory, modeling and measurement. Boundary Layer Meteorology 50: 49-75. doi:10.1007/BF00120518









Related Publications



Sakai, R.K., D.R. Fitzjarrald, O.L.L. Moraes, R.M. Staebler, O.C. Acevedo, M.J. Czikowsky, R. Da Silva, E. Brait, and V. Miranda. 2004. Land-use change effects on local energy, water, and carbon balances in an Amazonian agricultural field. Global Change Biology 10(5):895-907. doi:10.1111/j.1529-8817.2003.00773.x



Acevedo, O.C., O.L.L. Moraes, R. Da Silva, D.R. Fitzjarrald, R.K. Sakai, R.M. Staebler, and M.J. Czikowsky. 2004. Inferring nocturnal surface fluxes from vertical profiles of scalars in an Amazon pasture. Global Change Biology 10(5):886-894. doi:10.1111/j.1529-8817.2003.00755.x

Skip navigation linksHOME | ABOUT | LIBRARY | NEWS ARCHIVE | CONTACTS | INVESTIGATIONS | LOGISTICS | DATA |TRAINING & EDUCATION

NASA logo
ORNL DAAC
Get Acrobat Reader