Spatial heterogeneity in CO₂, CH₄, and energy fluxes: insights from airborne eddy covariance measurements over the Mid-Atlantic region

Abstract

The exchange of carbon between the Earth's atmosphere and biosphere influences the atmospheric abundances of carbon dioxide (CO 2 ) and methane (CH 4 ). Airborne eddy covariance (EC) can quantify surface-atmosphere exchange from landscape-to-regional scales, offering a unique perspective on carbon cycle dynamics. We use extensive airborne measurements to quantify fluxes of sensible heat, latent heat, CO 2 , and CH 4 across multiple ecosystems in the Mid-Atlantic region during September 2016 and May 2017. In conjunction with footprint analysis and land cover information, we use the airborne dataset to explore the effects of landscape heterogeneity on measured fluxes. Our results demonstrate large variability in CO 2 uptake over mixed agricultural and forested sites, with fluxes ranging from −3.4±0.7 to −11.5±1.6 μmol m −2 s −1 for croplands and −9.1±1.5 to −22.7±3.2 μmol m −2 s −1 for forests. We also report substantial CH 4 emissions of 32.3±17.0 to 76.1±29.4 nmol m −2 s −1 from a brackish herbaceous wetland and 58.4±12.0 to 181.2± 36.8 nmol m −2 s −1 from a freshwater forested wetland. Comparison of ecosystem-specific aircraft observations with measurements from EC flux towers along the flight path demonstrate that towers capture ∼30%-75% of the regional variability in ecosystem fluxes. Diel patterns measured at the tower sites suggest that peak, midday flux measurements from aircraft accurately predict net daily CO 2 exchange. We discuss next steps in applying airborne observations to evaluate bottom-up flux models and improve understanding of the biophysical processes that drive carbon exchange from landscapeto-regional scales.

A UAV-Based Eddy Covariance System for Measurement of Mass and Energy Exchange of the Ecosystem: Preliminary Results

“…In this context, airborne flux measurements based on the EC method can be used to directly measure the surface fluxes on the regional scale, showing potential to close the existing observational scale gap [ 13 ]. The airborne EC method is an established flux measurement technique that has been used extensively in recent decades and has been validated against tower-based EC and large aperture scintillometer flux measurement methods [ 13 , 14 , 15 , 16 ]. The major advantage of airborne EC is the ability to measure turbulent fluxes that are more spatially representative than ground-based EC measurements and that have higher temporal resolution and accuracy than those estimated from models [ 17 ].…”

Large Methane Emission Fluxes Observed From Tropical Wetlands in Zambia

“…Airborne eddy-covariance (AEC) gives direct quantification of vertical eddy-fluxes from aircraft (Hannun et al, 2020;Metzger et al, 2013;Vaughan et al, 2016Vaughan et al, , 2021. A vertical eddy-flux is defined as the product of the fluctuating vertical wind speed and the fluctuating concentration (of CH 4 ), averaged over a defined period.…”

“…The airborne eddy-covariance approach is an emerging technique which provides spatially resolved flux measurements that are especially useful for heterogeneous sources. However, eddy-covariance measurements require expensive, high time-resolution instrumentation with parallel sampling of three-dimensional winds, and is highly dependent on meteorology (e.g., Hannun et al, 2020;Metzger et al, 2013;Vaughan et al, 2016Vaughan et al, , 2021. Finally, atmospheric inversion modeling is driven by numerical weather prediction models and thus can account for spatial and temporal variability in meteorological conditions (Ganesan et al, 2014(Ganesan et al, , 2017Rigby et al, 2019).…”

Multi-scale observations of mangrove blue carbon fluxes; the NASA Carbon Monitoring System BlueFlux field campaign

“…When combined with wavelet transforms (Wolfe et al 2018), AEC can characterize spatial gradients in fluxes at model-relevant scales (1-100 km). Flux footprint modeling allows for evaluation of fluxes within the context of surface properties and modeled fluxes (Hannun et al 2020; Vaughan et al 2021). Such data is complementary to ground-based observations, which integrate over a relatively small area but may better constrain site-specific processes and temporal variability.…”