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River ecosystems receive and process vast quantities of terrestrial organic carbon, the fate of which depends strongly on microbial activity. Variation in and controls of processing rates, however, are poorly characterized at the global scale. In response, we used a peer-sourced research network and a highly standardized carbon processing assay to conduct a global-scale field experiment in greater than 1000 river and riparian sites. We found that Earth’s biomes have distinct carbon processing signatures. Slow processing is evident across latitudes, whereas rapid rates are restricted to lower latitudes. Both the mean rate and variability decline with latitude, suggesting temperature constraints toward the poles and greater roles for other environmental drivers (e.g., nutrient loading) toward the equator. These results and data set the stage for unprecedented “next-generation biomonitoring” by establishing baselines to help quantify environmental impacts to the functioning of ecosystems at a global scale.
The progress of science is tied to the standardization of measurements, instruments, and data. This is especially true in the Big Data age, where analyzing large data volumes critically hinges on the data being standardized. Accordingly, the lack of community-sanctioned data standards in paleoclimatology has largely precluded the benefits of Big Data advances in the field. Building upon recent efforts to standardize the format and terminology of paleoclimate data, this article describes the Paleoclimate Community reporTing Standard (PaCTS), a crowdsourced reporting standard for such data. PaCTS captures which information should be included when reporting paleoclimate data, with the goal of maximizing the reuse value of paleoclimate data sets, particularly for synthesis work and comparison to climate model simulations. Initiated by the LinkedEarth project, the process to elicit a reporting standard involved an international workshop in 2016, various forms of digital community engagement over the next few years, and grassroots working groups. Participants in this process identified important properties across paleoclimate archives, in addition to the reporting of uncertainties and chronologies; they also identified archive-specific properties and distinguished reporting standards for new versus legacy data sets. This work shows that at least 135 respondents overwhelmingly support a drastic increase in the amount of metadata accompanying paleoclimate data sets. Since such goals are at odds with present practices, we discuss a transparent path toward implementing or revising these recommendations in the near future, using both bottom-up and top-down approaches.
Obtaining representative hydrometric values is essential for characterizing extreme events, hydrological dynamics and detecting possible changes on the long-term hydrology. Reliability of streamflow data requires a temporal continuity and a maintenance of the gauging stations, which data are affected by epistemic and random sources of error. An assessment of discharge meterings' and stage-discharge rating curves' uncertainties were carried out by comparing the accuracy of the measuring instruments of two different hydrometric networks (i.e., one analogical and one digital) established in the same river location at the Mediterranean island of Mallorca. Furthermore, the effects of such uncertainties were assessed on the hydrological dynamics, considering the significant global change impacts beset this island. Evaluation was developed at four representative gauging stations of the hydrographic network with analogic (≈40 years) and digital (≈10 years) data series. The study revealed that the largest source of uncertainty in the analogical (28 to 274%) and in the digital (17–37%) networks were the stage-discharge rating curves. Their impact on the water resources was also evaluated at the event and annual scales, resulting in an average difference of water yields of 183% and 142% respectively. Such improvement on the comprehension of hydrometric networks uncertainties will dramatically benefit the interpretation of the long-term streamflow by providing better insights into the hydrologic and flood hazard planning, management and modelling.