An improved inversion method for recovering CO2 fluxes over complex terrain areas based on atmospheric measurements
Abstract
Relevance. Changes in the concentration of greenhouse gases (GHGs) in the atmosphere are a key factor in global climate change. To correctly understand the contribution of different ecosystems to the carbon balance, it is necessary to quantitatively assess surface GHG fluxes. However, existing methods, particularly the eddy covariance method, are limited in their applicability to areas with complex terrain and varied vegetation. Therefore, new approaches are needed to reliably estimate GHG fluxes in spatially heterogeneous landscapes. The aim of this study is to develop and test an improved inversion algorithm for estimating surface GHG fluxes in hilly terrain. The algorithm will be based on GHG concentration data obtained from the surface layer of the atmosphere using unmanned aerial vehicles (UAVs). Methods. The proposed approach involves first solving the direct problem of GHG concentration and then minimizing the discrepancy between the measured and modeled values at two control heights within the surface layer. Numerical experiments were conducted using a highresolution, three-dimensional hydrodynamic model to verify the algorithm. This model can reproduce velocity fields, turbulent characteristics, and GHG concentrations in real topographic conditions. Results. The model was tested in a forested, hilly area in the foothills of the Greater Caucasus Range in the Chechen Republic of Russia. The numerical experiments showed that the improved algorithm reliably reconstructs the spatial structure of CO2 fluxes at elevation differences of up to 100 meters. Comparing the results with those of the previous model version revealed a significant increase in calculation accuracy and a reduction in systematic errors when accounting for topographic effects. Practical significance. The developed method provides an opportunity to quantitatively assess GHG fluxes in areas where classic micrometeorological approaches are not applicable. The method can be used in remote monitoring systems to assess the carbon balance of ecosystems using UAVs and ground platforms. It can also be adapted to analyze spatially heterogeneous fluxes of other GHGs.
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