Methane distribution at high spatial resolution in North Sea estuaries

Rivers are suspected to be a main suppliers of greenhouse gases (methane and carbon dioxide) to coastal seas, while the role of the interjacent tidal flats is still ambiguous. In this study we investigated the role of the Elbe and Weser estuaries as source of methane to the North Sea. We used high spatially resolved methane measurements from an underway degassing system and subsequent analysis with cavity ring down spectroscopy. Thus, a high-resolution representation of the methane distribution in surface waters as well as of hydrographic parameters was obtained for several cruises with two ships in 2019. For most areas, riverine methane was simply diluted by seawater, overlain by a strong tidal signal. However, on several occasions unexpectedly high methane concentrations were observed. Further detailed analysis will elucidate the role of riverine versus tidal impact on coastal North Sea methane fluxes

Spatial Variability in and Hotspots of Methane Concentration in a Large Temperate River

Rivers are significant sources of greenhouse gases (GHGs; e.g., CH4 and CO2); however, our understanding of the large-scale longitudinal patterns of GHG emissions from rivers remains incomplete, representing a major challenge in upscaling. Local hotspots and moderate heterogeneities may be overlooked by conventional sampling schemes. In August 2020 and for the first time, we performed continuous (once per minute) CH4 measurements of surface water during a 584-km-long river cruise along the German Elbe to explore heterogeneities in CH4 concentration at different spatial scales and identify CH4 hotspots along the river. The median concentration of dissolved CH4 in the Elbe was 112 nmol L−1, ranging from 40 to 1,456 nmol L−1 The highest CH4 concentrations were recorded at known potential hotspots, such as weirs and harbors. These hotspots were also notable in terms of atmospheric CH4 concentrations, indicating that measurements in the atmosphere above the water are useful for hotspot detection. The median atmospheric CH4 concentration was 2,033 ppb, ranging from 1,821 to 2,796 ppb. We observed only moderate changes and fluctuations in values along the river. Tributaries did not obviously affect CH4 concentrations in the main river. The median CH4 emission was 251 μmol m−2 d−1, resulting in a total of 28,640 mol d−1 from the entire German Elbe. Similar numbers were obtained using a conventional sampling approach, indicating that continuous measurements are not essential for a large-scale budget. However, we observed considerable lateral heterogeneity, with significantly higher concentrations near the shore only in reaches with groins. Sedimentation and organic matter mineralization in groin fields evidently increase CH4 concentrations in the river, leading to considerable lateral heterogeneity. Thus, river morphology and structures determine the variability of dissolved CH4 in large rivers, resulting in smooth concentrations at the beginning of the Elbe versus a strong variability in its lower parts. In conclusion, groin construction is an additional anthropogenic modification following dam building that can significantly increase GHG emissions from rivers

Methane dynamics in three different Siberian water bodies under winter and summer conditions

Arctic regions and their water bodies are affected by a rapidly warming climate. Arctic lakes and small ponds are known to act as an important source of atmospheric methane. However, not much is known about other types of water bodies in permafrost regions, which include major rivers and coastal bays as a transition type between freshwater and marine environments. We monitored dissolved methane concentrations in three different water bodies (Lena River, Tiksi Bay, and Lake Golzovoye, Siberia, Russia) over a period of 2 years. Sampling was carried out under ice cover (April) and in open water (July-August). The methane oxidation (MOX) rate and the fractional turnover rate (k') in water and melted ice samples from the late winter of 2017 was determined with the radiotracer method. In the Lena River winter methane concentrations were a quarter of the summer concentrations (8 nmol L-1 vs. 31 nmol L-1), and mean winter MOX rate was low (0.023 nmol L-1 d(-1)). In contrast, Tiksi Bay winter methane concentrations were 10 times higher than in summer (103 nmol L-1 vs. 13 nmol L-1). Winter MOX rates showed a median of 0.305 nmol L-1 d(-1). In Lake Golzovoye, median methane concentrations in winter were 40 times higher than in summer (1957 nmol L-1 vs. 49 nmol L-1). However, MOX was much higher in the lake (2.95 nmol L-1 d(-1)) than in either the river or bay. The temperature had a strong influence on the MOX (Q(10) = 2.72 +/- 0.69). In summer water temperatures ranged from 7-14 degrees C and in winter from -0.7 to 1.3 degrees C. In the ice cores a median methane concentration of 9 nM was observed, with no gradient between the ice surface and the bottom layer at the ice-water interface. MOX in the (melted) ice cores was mostly below the detection limit. Comparing methane concentrations in the ice with the underlaying water column revealed methane concentration in the water column 100-1000 times higher. The winter situation seemed to favor a methane accumulation under ice, especially in the lake with a stagnant water body. While on the other hand, in the Lena River with its flowing water, no methane accumulation under ice was observed. In a changing, warming Arctic, a shorter ice cover period is predicted. With respect to our study this would imply a shortened time for methane to accumulate below the ice and a shorter time for the less efficient winter MOX. Especially for lakes, an extended time of ice-free conditions could reduce the methane flux from the Arctic water bodies

Methylomonas albis sp. nov. and Methylomonas fluvii sp. nov.: Two cold-adapted methanotrophs from the river Elbe and emended description of the species Methylovulum psychrotolerans

Three strains of methanotrophic bacteria (EbAT, EbBT and Eb1) were isolated from the River Elbe, Germany. These Gram-negative, rod-shaped or coccoid cells contain intracytoplasmic membranes perpendicular to the cell surface. Colonies and liquid cultures appeared bright-pink. The major cellular fatty acids were 12:0 and 14:0, in addition in Eb1 the FA 16:1ω5t was also dominant. Methane and methanol were utilized as sole carbon sources by EbBT and Eb1, while EbAT could not use methanol. All strains oxidize methane using the particulate methane monooxygenase. Both strains contain an additional soluble methane monooxygenase. The strains grew optimally at 15–25 °C and at pH 6 and 8. Based on 16S rRNA gene analysis recovered from the full genome, the phylogenetic position of EbAT is robustly outside any species clade with its closest relatives being Methylomonas sp. MK1 (98.24%) and Methylomonas sp. 11b (98.11%). Its closest type strain is Methylomonas methanica NCIMB11130 (97.91%). The 16S rRNA genes of EbBT are highly similar to Methylomonas methanica strains with Methylomonas methanica R-45371 as the closest relative (99.87% sequence identity). However, average nucleotide identity (ANI) and digital DNA-DNA-hybridization (dDDH) values reveal it as distinct species. The DNA G + C contents were 51.07 mol% and 51.5 mol% for EbAT and EbBT, and 50.7 mol% for Eb1, respectively. Strains EbAT and EbBT are representing two novel species within the genus Methylomonas. For strain EbAT we propose the name Methylomonas albis sp. nov (LMG 29958, JCM 32282) and for EbBT, we propose the name Methylomonas fluvii sp. nov (LMG 29959, JCM 32283). Eco-physiological descriptions for both strains are provided. Strain Eb1 (LMG 30323, JCM 32281) is a member of the species Methylovulum psychrotolerans. This genus is so far only represented by two isolates but Eb1 is the first isolate from a temperate environment; so, an emended description of the species is given

Challenges in the Evaluation of Observational Data Trustworthiness From a Data Producers Viewpoint (FAIR+)

Recent discussions in many scientific disciplines stress the necessity of “FAIR” data. FAIR data, however, does not necessarily include information on data trustworthiness, where trustworthiness comprises reliability, validity and provenience/provenance. This opens up the risk of misinterpreting scientific data, even though all criteria of “FAIR” are fulfilled. Especially applications such as secondary data processing, data blending, and joint interpretation or visualization efforts are affected. This paper intends to start a discussion in the scientific community about how to evaluate, describe, and implement trustworthiness in a standardized data evaluation approach and in its metadata description following the FAIR principles. It discusses exemplarily different assessment tools regarding soil moisture measurements, data processing and visualization and elaborates on which additional (metadata) information is required to increase the trustworthiness of data for secondary usage. Taking into account the perspectives of data collectors, providers and users, the authors identify three aspects of data trustworthiness that promote efficient data sharing: 1) trustworthiness of the measurement 2) trustworthiness of the data processing and 3) trustworthiness of the data integration and visualization. The paper should be seen as the basis for a community discussion on data trustworthiness for a scientifically correct secondary use of the data. We do not have the intention to replace existing procedures and do not claim completeness of reliable tools and approaches described. Our intention is to discuss several important aspects to assess data trustworthiness based on the data life cycle of soil moisture data as an example