Description: Rivers are significant sources of greenhouse gases (GHGs; e.g., CH〈sub〉4〈/sub〉 and CO〈sub〉2〈/sub〉); 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) CH〈sub〉4〈/sub〉 measurements of surface water during a 584-km-long river cruise along the German Elbe to explore heterogeneities in CH〈sub〉4〈/sub〉 concentration at different spatial scales and identify CH〈sub〉4〈/sub〉 hotspots along the river. The median concentration of dissolved CH〈sub〉4〈/sub〉 in the Elbe was 112 nmol L〈sup〉−1〈/sup〉, ranging from 40 to 1,456 nmol L〈sup〉−1〈/sup〉 The highest CH〈sub〉4〈/sub〉 concentrations were recorded at known potential hotspots, such as weirs and harbors. These hotspots were also notable in terms of atmospheric CH〈sub〉4〈/sub〉 concentrations, indicating that measurements in the atmosphere above the water are useful for hotspot detection. The median atmospheric CH〈sub〉4〈/sub〉 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 CH〈sub〉4〈/sub〉 concentrations in the main river. The median CH〈sub〉4〈/sub〉 emission was 251 μmol m〈sup〉−2〈/sup〉 d〈sup〉−1〈/sup〉, resulting in a total of 28,640 mol d〈sup〉−1〈/sup〉 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 CH〈sub〉4〈/sub〉 concentrations in the river, leading to considerable lateral heterogeneity. Thus, river morphology and structures determine the variability of dissolved CH〈sub〉4〈/sub〉 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.

Description: 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.