Comment on egusphere-2026-2262

Résumé fourni par la source

Abstract. Water-air fluxes of carbon dioxide (CO2) and methane (CH4) in lakes exhibit substantial spatial heterogeneity, often varying across remarkably fine spatial scales. While manual flux chamber measurements offer high spatial resolution and the potential to capture this variability, their application is typically constrained by labor intensity and logistical limitations. In contrast, eddy covariance (EC) measurements integrate fluxes over a larger footprint, effectively averaging over spatial gradients and complicating a process-based interpretation of the data. Bridging this gap requires methods that combine spatial precision with scalable, continuous monitoring – essential for advancing our mechanistic understanding of lake carbon dynamics. To facilitate highly resolved biogeochemical measurements, we developed BlueMinerva, an autonomous platform to monitor surface carbon fluxes with the static chamber method, and simultaneously determine physicochemical, biological, bathymetric, and meteorological variables at pre-defined locations. The platform can be programmed to navigate a user-defined track across a waterbody, and collect flux and ancillary data both in transit and at fixed locations for several hours to days, depending on the sensor configuration and related battery requirements. We deployed the setup at Dagow Lake (Germany) and in a small lake in the Stordalen Mire (Sweden). In total, we obtained 485 chamber-derived flux estimates over 72 measurement hours. We compared CO2 flux estimates between measurements with two different gas analyzers that were simultaneously mounted on the BlueMinerva. The lower-cost sensor (CARBOCAP GMP343, Vaisala) performed equally well as the precise, but costlier sensor (LI-7810, LI-COR) as long as measurements were sufficiently long (around 5 min). Furthermore, we compared measured carbon fluxes with those from an eddy covariance tower at Dagow Lake where CH4 fluxes diverged slightly, possibly linked to the usage of different sensors (closed-path versus open-path), while magnitudes of CO2 fluxes matched well. At both lakes, we identified areas of higher emissions, especially for CH4, and were thus able to resolve the spatial variability of carbon fluxes within the waterbodies. Concurrently, we measured differences in meteorological conditions, and critical limnological variables (water temperature, specific conductivity, pH, dissolved oxygen, chlorophyll, phycocyanin, turbidity, and fluorescent dissolved organic matter) – valuable measurements that enable a comprehensive assessment of environmental drivers behind flux variability. We conclude that platforms like the BlueMinerva have the potential to be adopted widely by scientists and stakeholders to better capture biogeochemical processes in lakes at high spatial and temporal resolution.

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