Judith Vogt

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.

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.

Measurements of surface-atmosphere carbon dioxide (CO ₂ ) and methane (CH ₄ ) fluxes have been relatively sparse across the Arctic tundra and boreal biomes, causing significant uncertainties in carbon budget estimates from the region. While the availability of Arctic-boreal carbon flux data has increased substantially over the past decade, the data have remained spread across different repositories, scientific articles, and unpublished sources, making it difficult to leverage. Here we present a new dataset of monthly Arctic-boreal carbon fluxes (ABCFlux v2) across terrestrial (wetlands and uplands) and freshwater (lakes and rivers) ecosystems compiled from previous syntheses including the Arctic-boreal CO ₂ flux database (ABCFlux v1), the Boreal-Arctic Wetland and Lake Methane Dataset (BAWLD-CH4), and the Global River Methane Database (GRiMeDB). In addition, we consider data from general-purpose (e.g., Zenodo) and flux network repositories, literature, and site principal investigators. The dataset includes surface-atmosphere CO ₂ fluxes of gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange (NEE), alongside CH ₄ fluxes. For aquatic ecosystems, we split CH ₄ fluxes into diffusive and ebullitive flux pathways, and included potential emissions from transient storage in the water column (“storage fluxes”), alongside CO ₂ and CH ₄ concentrations dissolved in the surface water. Fluxes are measured through a variety of methods including chamber and eddy covariance techniques alongside bubble traps, ice-surveys, and concentration-based turbulence-driven modelling in aquatic ecosystems. The monthly flux data are reported together with supporting methodological and environmental metadata. The resulting ABCFlux v2 has 23 847 flux site-months, 8182 concentration site-months, and 199 seasonal observations from 1024 sites, and includes 56 139 reported fluxes (i.e. sum of GPP, Reco, NEE, and CH ₄ fluxes) from the years 1984 to 2024. The majority of monthly observations occurred after 1999. Wetlands had the highest number of site-month observations (8758), followed by boreal forest (6981), lotic ecosystems (6275), lentic ecosystems (3799) and upland tundra (3308). Measurements of CO ₂ dominated the dataset across most ecosystem types (25 222) except for lentic ecosystems, where CH ₄ flux site-months (3098) were more frequent than CO ₂ flux site-months (2915). Overall, ABCFlux v2 includes 160 % more site-months for terrestrial CO ₂ flux data compared to ABCFlux v1. Integrating and updating BAWLD-CH4 flux data from growing season averages to monthly fluxes resulted in 5671 site-months of chamber CH ₄ data compared to 762 site-years. This collaborative initiative, involving contributions from over 260 researchers, provides a comprehensive overview of the current state of the Arctic-boreal carbon flux network and its data, and serves as an important step in reducing uncertainties in Arctic-boreal carbon budgets and in enhancing our understanding of climate feedbacks. The data can be accessed at ORNL DAAC at https://doi.org/10.3334/ORNLDAAC/2448 (Virkkala et al., 2026).

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.

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.

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.

The Arctic-boreal region’s carbon balance is shifting, with major implications for global climate. Models predict high carbon emissions by 2100, consisting of greenhouse gases, carbon dioxide (CO ₂ ) and methane (CH ₄ ), thus a representative carbon flux monitoring network is crucial to constrain fluxes. Eddy flux towers are increasingly used to inform models, and the area towers represent define model limitations. The Arctic-boreal flux tower network has grown from one to over 100 over past decades, however, towers are not designed to have broad regional representation. We analyzed flux tower network improvements made between 2022 and 2024, when targeted sites were added in part by the Permafrost Pathways project. We assessed the representativeness of active sites for four measurement categories: growing season CO ₂ , growing season CH ₄ , year-round CO ₂ , and year-round CH ₄ . Sixteen new or upgraded sites improved representation over 23–55% of the Arctic-boreal region across categories. Seventy-five percent of the region was well represented given improvements in growing season CO ₂ measurements; however, only 45% of the domain was well represented for year-round CH ₄ . We show targeted improvements can obtain better representativeness with limited sites, but continued upgrades are necessary to close gaps, particularly in Russia where gaps remain large.

The microtopography of the Arctic tundra and the associated soil moisture (SM) gradient influence the net ecosystem‐atmosphere exchange of methane (CH ₄ ) and carbon dioxide (CO ₂ ). To quantify fine‐scale variability in a permafrost ecosystem, we measured growing‐season carbon fluxes with closed chambers at Trail Valley Creek, Canada from 2022 to 2024. A total of six landforms were sampled, spanning a wetness gradient from dry (upland tundra, gully) over intermediate (polygons, degraded wetland centers) to wet (transitional zones, trenches) microsites. All landforms were net sources of CH ₄ ; only trenches had high (0.58 mg CH ₄ m ⁻² h ⁻¹ ) fluxes, while the other landforms had fluxes close to zero. Drier elements (upland tundra, polygons) were net CO ₂ sinks, while wetter depressions (gully, degraded centers, transitional zones) were net sources; trenches were a wet exception that still acted as a sink. All fluxes were strongly influenced by air temperature ( T _air ), peaking during the hot summer of 2023. CH ₄ flux variability was dominated by belowground variables (SM and temperature). For CO ₂ fluxes, aboveground ( T _air , and photosynthetically active radiation (PAR)) and belowground controls contributed equally. For CH ₄ , ecosystem respiration, and gross primary production, fitting separate models per landform reduced absolute prediction error (despite lower R ² ), making it preferable when minimizing error. For net ecosystem exchange, a single model fit to all landforms was sufficient. These results show that field studies focusing on small‐scale variability in carbon fluxes should prioritize detailed soil‐layer measurements, T _air , and PAR, while vegetation metrics are optional.