Optimising the sampling strategy in tall tower eddy covariance flux measurements

Tall tower eddy covariance (EC) measurements can be used to narrow down the gap between the ecosystem and the continental scale observations by capturing greenhouse gas (GHG) fluxes in a landscape scale (>10 km²). Because of the large footprint, tall tower platforms enable monitoring of greenhouse gas net fluxes, integrating over a multitude of diverse GHG sources and sinks within anthropogenic ecosystems. Yet, the temporal variability of atmospheric stability and atmospheric boundary layer affects the size of the flux footprint and the quality of EC flux estimates, respectively, thereby complicating the interpretation of surface flux estimates. The objective of this study is to determine an optimal sampling scheme alternating between different measuring heights (z_m) in order to maximise the number of valid flux measurements as well as mitigating the effect of weather fluctuations on the longitudinal position of the footprint.

We used a six months’ data set of continuous turbulence data measured from a recently deployed prototype flux observation station in a rural area close to the Danish Capital of Copenhagen, Zealand. The system is mounted on a 200 m telecommunication tower equipped with 3D ultrasonic anemometers in three different heights (70m, 90m, 115m) and with a TILDAS GHG analyser capable of switching between three sampling lines corresponding to the specified heights.

We define an optimal sampling strategy based on the peak location of the individual, crosswind-integrated footprints from valid samples. As valid, we characterized those flux measurements, when the z_m was within the constant flux layer, as estimated from ceilometer measurement. For each of the half hours, we selected the z_m with the footprint’s peak location closest to a target position.

In this presentation, we demonstrate the ability to constrain the flux footprint within a target landscape area by establishing a sampling schedule across the three sampling heights. The results showed that designing a sampling strategy that combines multiple heights has the potential to bring the aggregated footprint for the entire period (footprint climatology) closer to the targeted area. A similar outcome can be attained when sampling from a single height and excluding the instances where the footprint significantly deviates from the target area. Nevertheless, this comes with the trade-off of discarding valid data. Moreover, the weather effect on the variability of the crosswind-integrated footprints was reduced by setting an optimal, multi-height strategy in comparison to the aggregated footprints from the individual heights.