Estimating patch-level fluxes from UAV measurements over a structured wetland through atmospheric inversion coupled to large-eddy simulations

Many natural ecosystems are composed of heterogeneous patches differentiated by e.g., topography, wetness levels or vegetation composition. The resulting small-scale variability in surface-atmosphere exchange fluxes is hard to quantify, since small-scale observations like flux chambers are usually episodic, while larger-scale observations such as eddy-covariance towers integrate over larger footprints, and therefore produce an effective net flux of the mixed landscape. Approaches to decompose such an effective flux signal into patch-level component fluxes would allow to better understand control factors and mechanisms governing the flux rates, and therefore improve estimates of net fluxes, and potential flux trajectories under future climate. In this study, we combine high-resolution modelling of the atmospheric boundary layer with inverse modelling concepts to constrain land-atmosphere exchange fluxes at local to landscape scales, and explore relationships between different land cover types within heterogeneous landscapes and the net exchange processes between surface and atmosphere. We use EULAG (EUlerian LAGrangian), an established Large-Eddy Simulation model, to simulate high-resolution flow patterns induced by heterogeneous permafrost surfaces, and benchmark the spatial variability of modeled concentrations using data from uncrewed Aerial Vehicles (UAV)-based grid surveys of gas concentrations. We then optimize for surface fluxes associated with each patch by applying atmospheric inversion techniques. We present a case study at Stordalen Mire in subarctic Sweden, where we conducted UAV measurements of carbon dioxide mole fractions, and implement this inversion method to differentiate the flux rate signatures from different patch types. The inferred fluxes were subsequently validated with patch-level chamber measurements of carbon dioxide, showing a good match between modeled and observed concentrations. In addition, this method shows a potential in decomposing eddy-covariance fluxes into landcover fluxes, thus inferring flux budgets variability between patches. Our novel technique shows promising results in estimating patch type flux heterogeneity while facilitating the application of inversion methods to high resolution atmospheric models.