the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GRiMeDB: The global river database of methane concentrations and fluxes
Luke C. Loken
Nora J. Casson
Samantha K. Oliver
Ryan A. Sponseller
Marcus B. Wallin
Liwei Zhang
Gerard Rocher-Ros
Abstract. Despite their small spatial extent, fluvial ecosystems play a significant role in processing and transporting carbon in aquatic networks, which results in substantial emission of methane (CH4) to the atmosphere. For this reason, considerable effort has been put into identifying patterns and drivers of CH4 concentrations in streams and rivers and estimating fluxes to the atmosphere across broad spatial scales. Yet progress toward these ends has been slow because of pronounced spatial and temporal variability of lotic CH4 concentrations and fluxes and by limited data availability across diverse habitats and physicochemical conditions. To address these challenges, we present the first comprehensive database of CH4 concentrations and fluxes for fluvial ecosystems along with broadly relevant and concurrent physical and chemical data. The Global River Methane database (GriMeDB; https://doi.org/10.6073/pasta/b7d1fba4f9a3e365c9861ac3b58b4a90) includes 24,024 records of CH4 concentration and 8,205 flux measurements from 5,037 unique sites that were extracted from publications, reports, data repositories, and other outlets published between 1973 and 2021. GriMeDB also includes 17,655 and 8,409 concurrent measurements of concentrations and 4,444 and 1,521 of fluxes for CO2 and nitrous oxide (N2O) respectively. Most observations are date-specific (i.e., not site averages) and many are supported by data for 12 physicochemical variables and 6 site variables. Site variables include codes to characterize marginal channel types (e.g., springs, ditches) and/or presence of human disturbance (e.g., point source inputs, upstream dams). Overall, observations in GRiMeDB encompass a broad range of the climatic, biological, and physical conditions that occur among world river basins, although some geographic gaps remain (e.g., arid regions, tropical regions, high latitudes and altitude systems). The global median CH4 concentration (0.20 μmol L-1) and diffusive flux (0.44 mmol m-2 d-1) in GRiMeDB are lower than estimates from past, site-averaged compilations, although ranges and standard deviations are greater from this larger and more temporally-resolved database. Available flux data are dominated by diffusive measurements despite the recognized importance of ebullitive and plant-mediated CH4 fluxes. Despite these limitations, GriMeDB provides a comprehensive and cohesive resource for examining relationships between CH4 and environmental drivers, estimating the contribution of fluvial ecosystems to CH4 emissions, and to contextualize site-based investigations.
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Emily H. Stanley et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2022-346', Yuanzhi Yao, 13 Nov 2022
The manuscript entitled ” GRiMeDB: The global river database of methane concentrations and fluxes” proposes an a comprehensive database for riverine CH4 and the associate drivers. The proposed databased is based on the earlier work by Stanley et al. (2016). The authors present the flow chart for generating the database, and also the data analysis.
The topic of the manuscript is interesting and relevant to the earth system science community, as methane emission is a potent source of greenhouse gas. Overall, this is a well written manuscript without any apparent flaws. I can recommend the publication of this manuscript with minor revisions.
I also have a minor remark about the ‘first database’ stated in the abstract. I must confess, though, that I did not quite understand the difference between this database and the previous one(MethDB). I think MethDB is the first comprehensive database for river CH4. This work is an extension with significant efforts.Figs. 5 and 6: Can you differentiate the sites for ebullitive and diffusive flux, respectively. It is very important for modelers.
The figures are very nice and useful. Not all of them need to be in color, though. I also struggled a little bit with the legends: I think Figures 11, 12 and 13 should have legends to show the meaning of the colors.Citation: https://doi.org/10.5194/essd-2022-346-RC1 - RC2: 'Comment on essd-2022-346', Karel Castro-Morales, 01 Feb 2023
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RC3: 'Comment on essd-2022-346', Bridget Deemer, 02 Mar 2023
This data paper substantially updates and expands upon a previous fluvial methane database (MethDB) from a similar group of authors that was published in 2016 (Stanley et al. 2016). It will be an important resource for the aquatic biogeochemistry community in general. The paper identifies key gaps in the existing spatial representativeness of stream and river methane emission data (missing data from arid, high altitude, and arctic regions) and also highlights the lack of long time series methane flux data in fluvial ecosystems. The dataset is structured in a unique way that allows users to explore spatio-temporal variation. Instead of reporting mean emissions for a given system, the database provides within-system site and date specific emissions (and summary statistics across the day when relevant). The dataset will be a key resource for those interested in upscaling aquatic greenhouse gas emissions.
The paper contains helpful visualizations and tables that orient the reader to key aspects of the dataset. One useful addition the authors could consider adding is a database schema diagram that shows how unique IDs can be used to link the four.csv files (concentrations, fluxes, sites, and source). This type of diagram could provide a visual guide for a data user describing which connections are “one to one” versus “one to many”. For example, on first look at the data files, I found myself a bit confused as to how I would link the flux data to the concentration data. Is there always a “one to one” link where each row of flux data has a matching row of data in the “concentration” data file?
Another novel aspect of this database is the inclusion of both diffusive and ebullitive methane emissions and the comprehensive annotation of specific methodological approaches (where the previous MethDB database only reported diffusive emissions). I think this aspect of the dataset should be mentioned in the abstract. I also think it would be interesting to visualize and/or further describe differences in the emissions that are generated via different methods. There is already some discussion of the potential importance of ebullition in overall fluvial methane flux (lines 522-523 citing certain papers), but the authors do not report any statistics regarding the fraction of emissions that are ebullitive in their own dataset (for sites with independent estimates of both flux pathway). In my work with reservoir methane emissions, the potential predictors of emission became clearer when we stopped combining diffusive-only emission estimates with those that integrated across both flux pathways (Deemer et al. 2016). I think this division may be a bit harder to make in the river literature (since it may be harder to discern which estimates were truly diffusive-only), but I think some basic summary of the flux data by method would be helpful. In looking at the data, it looks like you only have 8 rows of data with total methane flux recorded. You do mention that 85% of the data is diffusive-only, but I’m surprised there are only 8 rows that have estimates of both diffusive and ebullitive emission. This could be explicitly called out.
Thank you to the authors for this important contribution to the field.
Line by Line Comments
Line 35- If you can fit it, I suggest including the ranges and/or standard deviations here
Lines 88-95- I assume beaver ponds were not included as “marginal” fluvial systems, but you might explicitly mention this here. Also, what about river reaches upstream of weirs?
Line 131-132- Figure 2 doesn’t really make this distinction regarding sites that were used in multiple studies. Consider either adding this or annotating it somehow directly in figure legend.
Line 175- Consider changing the wording in this title (and/or in the text directly below it) to “Concentrations Table and Fluxes Table” to make extra clear that they are two separate tables.
Line 183- Delete duplicate use of the word “both”
Lines 221-228- So, is it true that in some cases the same concentration data might be applied to many rows of flux estimates (one to many)?
Lines 287-289- This pattern is also true for lake and reservoir methane data—65% of the lake/reservoir methane emission estimates in a recent dataset were collected since 2015 (Rosentreter et al. 2021, Deemer and Holgerson 2021)
Lines 292-294- Wow! I can’t believe how short the longest flux record is—much shorter than the lake literature.
Line 335- Do you mean 4% of the global river surface? I don’t think you mean land surface from looking at the map (more than 4% seems to be shaded darker tones of orange, but some of this is in surface-water poor areas like the Sahara).
Line 408- Include the definition for “IMP” like you do for the other site types.
Figure 12- The relationship between flux and total N & P looks stronger than for DOC or dissolved oxygen, but this isn’t called out where you discuss drivers (lines 571-590) . You might consider citing some of the wetland, lake and reservoir literature that has linked methane emission to productivity/chlorophyll a.
Line 480- You could discuss insights on spatial/temporal resolution from the lake literature here. Wik et al. 2016 Geophysical Research Letters showed that spatial and temporal under-representation generally led to underestimates of emission in lakes.
Line 509- Remove either “few” or “several”
Line 522- It isn’t clear if this 30-90% range comes from your entire database, or just from the few papers cited here. Or maybe there are only three papers that quantify both pathways together? In the lake and reservoir literature, the fraction of emissions that are ebullitive can range dramatically (undetectable to almost all of the emission), with ebullitive emission contributing a median of 78% of methane emissions in reservoirs and 54% in lakes- Deemer and Holgerson 2021).
Line 584- Rosentreter et al. 2021 also used latitude to upscale stream and river emissions.
Line 599- I thought Burns et al. 2018 reported rather high methane emissions from glacial systems?
Line 628- Add the word “from” between “data” and “world”
Tables A3 and A4- I suggest explicitly clarifying that “new” units are the relevant units for the data you report.
Citation: https://doi.org/10.5194/essd-2022-346-RC3
Emily H. Stanley et al.
Data sets
GRiMeDB: a comprehensive global database of methane concentrations and fluxes in fluvial ecosystems with supporting physical and chemical information ver 1. Stanley, E. H., L. C. Loken, N. J. Casson, S. K. Oliver, R. A. Sponseller, M. B. Wallin, L. Zhang, and G. Rocher-Ros https://doi.org/10.6073/pasta/b7d1fba4f9a3e365c9861ac3b58b4a90
Emily H. Stanley et al.
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