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Data from: High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems


Roth, Florian et al. (2022), Data from: High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems, Dryad, Dataset,


Coastal methane (CH4) emissions dominate the global ocean CH4 budget and can offset the “blue carbon” storage capacity of vegetated coastal ecosystems. However, current estimates lack systematic, high-resolution, and long-term data from these intrinsically heterogeneous environments, making coastal budgets sensitive to statistical assumptions and uncertainties. Using continuous CH4 concentrations, δ13C-CH4 values, and CH4 sea-air fluxes across four seasons in three globally pervasive coastal habitats, we show that the CHdistribution is spatially patchy over meter-scales and highly variable in time. Areas with mixed vegetation, macroalgae, and their surrounding sediments exhibited a spatiotemporal variability of surface water CH4 concentrations ranging two orders of magnitude (i.e., 6 – 460 nM CH4) with habitat-specific seasonal and diurnal patterns. We observed (1) δ13C-CH4 signatures that revealed habitat-specific CH4 production and consumption pathways, (2) daily peak concentration events that could change >100% within hours across all habitats, and (3) a high thermal sensitivity of the CH4 distribution signified by apparent activation energies of 1 eV that drove seasonal changes. Bootstrapping simulations show that scaling the CH4 distribution from few samples involves large errors, and that 50 concentration samples per day are needed to resolve the scale and drivers of the natural variability and improve the certainty of flux calculations by up to 70%. Finally, we identify northern temperate coastal habitats with mixed vegetation and macroalgae as understudied but seasonally relevant atmospheric CH4 sources (i.e., releasing ≥100 μmol CH4 m−2 day−1 in summer). Due to the large spatial and temporal heterogeneity of coastal environments, high-resolution measurements will improve the reliability of CH4 estimates and confine the habitat-specific contribution to regional and global CH4 budgets.


We quantified the partial pressures of surface water and atmospheric CH4 and CO2 along with the related C-isotopes (i.e., δ13C-CH4, and δ13C-CO2, respectively) in three coastal habitats during five measurement periods in 2020 and 2021 (i.e., 18 – 29 May; 06 – 17 July; 17 – 29 August; 30 November – 08 December 2020; 01 – 06 March 2021). For the measurements, we used an adapted version of the Water Equilibration Gas Analyzer System (WEGAS) (details in Humborg et al., 2019) coupled to a CRDS. The system consists of four major components: (i) a submersible seawater intake pump at around 0.3m water depth mounted to a movable raft that can be deployed non-invasively over the various habitats; (ii) a water handling system comprised of a showerhead equilibrator (1 L headspace volume) and a thermosalinograph (Seabird TSG 45) fed via a hose by the seawater intake pump; (iii) a gas handling system with circulation pumps for the showerhead and ambient air; and (iv) the CRDS gas analyzer for CH4 and CO2 concentration and related C-isotope measurements (model G2201-i, Picarro Inc.). The use of a large seawater intake pump results in the combined measurement of CH4 from ebullition (bubbles) and the dissolved form in the water. The individual contribution of the two forms can, however, not be resolved using the current system. For CH4 and CO2 analysis, gas in the showerhead of the equilibrator was measured for 35 min, followed by gas measurements of ambient air for 10 min (i.e., one complete cycle was 45 min). These measurement cycles (i.e., 35 min water and 10 min air measurements) ran continuously during the five measurement periods mentioned above. The raft with the water intake pump was moved between the defined habitats every 24h from the shore with ropes. Measurements in March were distinct from the other sampling periods due to the ice cover that had been present for 4 to 6 weeks prior to the time of sampling. Here, holes were drilled into the ice and the pump lowered to measure “under-ice” concentrations. We validated the CRDS analyzer’s performance according to the manufacturer’s instructions with “ALPHAGAZTM Stable Isotope Ratio Gases” for Picarro instruments. Specifically, before each deployment period, we injected three standards with varying CO2 and CH4 bulk concentrations, and varying δ13C-CO2 and δ13C-CH4 isotope values (i.e., low = 1.00 ppm CH4, -24.20‰ δ13C-CH4, 250.00 ppm CO2, -5.00‰ δ13C-CO2; natural = 1.77 ppm CH4, -48.30‰ δ13C-CH4, 399.00 ppm CO2, -8.50‰ δ13C-CO2; and high = 10.00 ppm CH4, -68.60‰ δ13C-CH4, 1000.00 ppm CO2, -20.10‰ δ13C-CO2). Measurements with each standard ran for 10 minutes, and 3-point calibration lines were constructed whose regression coefficients were used to scale the unknown sample data if needed.

Concentration and isotope measurement at 1Hz frequency were averaged and logged every 10 seconds. The recorded data were filtered by removing data from the transition period between ambient air and water measurements due to the response time of CRDS to sharp changes in concentrations of CH4 and CO2. Data was also removed during improper functioning (e.g., low water flow). CH4 concentrations in water (in ppm obtained by the CRDS) were converted to molar concentrations (i.e., CH4 in nM) and CO2 was converted to pressure units (i.e., pCO2 in μatm) (Humborg et al., 2019). Alongside CRDS measurements, several other environmental and meteorological variables were recorded. Water temperature and salinity were also recorded with every CRDS measurement with a thermosalinograph (Seabird TSG 45) that was positioned before the showerhead equilibrator. Wind data observations (wind speed) were obtained from a Metek uSonic-3 heated 3D sonic anemometer mounted on a 1.5 m high meteorological mast. The mast was located at the waterline in a coastal bay, approximately 400 m to the North West of the sampled habitats. Mean winds were adjusted to a 10 m reference height assuming a logarithmic profile with neutral stability (Haugen, 1973).

Usage Notes

Please refer to the ReadMe file for more details on this dataset.