AWMA Southern Section 2010 Annual Conference

Scott KirbyAugust 4, 2010

Background CO2 Exchange (Flux) and Climate Change

Ameriflux Tower Network

Spatial Representativeness in Heterogeneous Landscapes (Scaling)

Towers – Temporally Averaged Fluxes Aircraft – Spatially Averaged Fluxes

Eddy Covariance Method

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30 minute average fluxes are reported to capture samples from all atmospheric turbulence scales

In heterogeneous landscapes, the integrity of tower-based measurements is uncertain due to the issue of unknown source areas (i.e. flux footprints)

Upscaling procedures to extend tower measurements to regional estimates may be biased because tower is not spatially representative of surrounding region

Aircraft have potential to reduce this uncertainty due to their ability to cover a large spatial area within a minimum temporal range

Aircraft encounter turbulence and towers await turbulence

Aircraft have been used for over 20 years as a surface exchange measurement platform (Lenschow et al., 1981; Desjardins et al., 1982)

Traditionally, aircraft have required a ~3-5 km contiguous averaging length, which is similar to the 30 minute temporal average of a tower, in order to sample all scales of turbulence and obtain a proper flux sample (Gioli et al., 2004)

Major limitation in heterogeneous landscapes

Left to Right: Jeff French (University of Wyoming), Steve Brooks (NOAA), Charlie Haynes (UA), Derek Williamson (UA), Scott Kirby (UA), Ed Dumas (NOAA)

Members not pictured: Ron Dobosy (NOAA), Tilden Meyers (NOAA), Philip Hall (NOAA), Christopher Neale (Utah State University), Karl Elebash (UA)

Interior of Sky Arrow showing the pilot’s seat, the passenger’s seat, and the integrated MFP system display. Also note the removable rear seat windows, as well.

Pilot’s Seat MFP System Display Rear Windows Passenger’s Seat

High Glide Ratio (~16:1)Higher Rating of SafetySlower Sampling Speeds

Carbon Fiber Composite FrameLess Flex and VibrationMore Accurate Attitude Measurements

“Pusher” PropellerUncontaminated Samples of Gas and Turbulence

Cost EfficiencyUtilizes High Octane Automotive Gasoline

Pressure SphereAccelerometersGPS

Location and Velocity (Novatel)

Aircraft Attitude (Javad and CMIGITS)

Environmental Variables CO2 and Water Vapor Surface Temperature Dew Point PAR and Net Radiation

Atmospheric Winds

Major national ecosystem (carbon budgeting)

Ameriflux towers operational for >10 years

Very well characterized and studied system

Little topographical relief

Surface roughness characteristics relatively minimal

Separate fluxes from individual land uses in small-scale heterogeneous ecosystems using aircraft-based data

Basic Concept: Select fragments of aircraft-based flux data based on the land use influencing that measurement and apply the eddy covariance data to the sum of fragments from each specific land use.

Methodology was originally developed using data obtained from the Bondville Intensive 2005.

Components Aircraft-based measurements [vertical atmospheric wind vector and

atmospheric constituent of concern (CO2)] Remote Sensing Geographic Information Systems (GIS) Footprint Modeling Conditional Sampling

Taken from: Schmid (1997)

Kljun et al. (2004) developed from the three-dimensional Langrangian Stochastic footprint model of Kljun et al. (2002)

Valid over a wide range of atmospheric stabilities

Applicable at higher elevations

Crosswind-Integrated (1-dimensional)

Computationally efficient

Fragments having a 85% chance of originating from maize or soybean were assigned that land use flag

The highest probability that still provides an adequate number of fragments should be the most accurate.

Aid in development of carbon budgeting

Surface TemperaturesRemote Sensing CalibrationsReservoir Control Management

Boundary-Layer ProfilesHumidityTemperature

Unmanned VehiclesUrban EnvironmentsSmall-Scale Heterogeneity

Coastal Plain Pine PlantationsOld Growth versus Managed StandsFluxes of pine stands at differing stages of growth

Cotton Farms

Terrestrial and Aquatic Surface Temperatures

Hydrologic Budgeting

Satellite-Based Model Calibration

Desjardins, R.L., E.J. Brach, P. Alvo, and P.H. Schuepp (1982). Aircraft monitoring of surface carbon dioxide exchange. Science. 216: 733-735.

Gioli, B., F. Miglietta, B. De Martino, R.W.A. Hutjes, A.J. Dolman, A. Lindroth, M. Schumacher, M.J. Sanz, G. Manca, A. Peressotti, and E.J. Dumas (2004). Comparison between tower and aircraft-based eddy covariance fluxes in five European regions. Agricultural and Forest Meteorology. 127: 1-16.

Kljun, N., M.W. Rotach, and H.P. Schmid (2002). A three-dimensional backward lagrangian footprint model for a wide range of boundary-layer stratifications. Boundary-Layer Meteorology. 103(2): 205-226.

Kljun, N., P. Calanca, M.W. Rotach, and H.P. Schmid (2004). A simple parameterisation for flux footprint predictions. Boundary-Layer Meteorology. 112: 503-523.

Lenschow, D.H., B.B. Stankov, and R. Pearson, Jr. (1981). Estimating the ozone budget in the boundary layer by use of aircraft measurements of ozone eddy flux and mean concentration. Journal of Geophysical Research. 86: 7291-7297.

Schmid, H.P. (1997). Experimental design for flux measurements: matching scales of observations and fluxes. Agricultural and Forest Meteorology. 87: 179-200.

Kirby, S.A., R.J. Dobosy, D.G. Williamson, and E. Dumas (2008). An aircraft-based data analysis method for discerning individual fluxes in a heterogeneous agricultural landscape. Agricultural and Forest Meteorology. 148: 481-489.