Carbon from land to the atmosphere is routed by global river systems
Analysis of global river CO2 emissions on London’s River DIC, CO2 and CH4 using the radiocarbon database and hydroATLAS56
We started with a database of radiocarbon content of the river DIC, CO2 and CH4 from the literature, then added a subset of unpublished data. Radiocarbon content is presented here as F14C. In conventional 14C dating, F14C = 1.0 represents 1950 CE; however, in relation to carbon cycling and the atmospheric 14CO2 record (Fig. 1c), F14C < 1.0 indicates carbon older than 1955 CE and F14C > 1.0 is carbon younger than 1955 CE (Fig. 1b). The database was dominated by DIC measurements, so we demonstrated that DIC and CO2 are probably in isotopic equilibrium (Supplementary Information section 1), allowing us to explore the F14C content of global river CO2 emissions from the database.
In September of 2021, dissolved CO2 and CH4 samples were collected for 14C analysis from a range of urban rivers and canals in London. CO2 samples were taken from several sites on the River Thames and Regent’s Canal, while CH4 samples were taken from Bow Creek. CO2 samples were collected using the super headspace method57, with samples collected by equilibrating 3 l of water with 1 l of CO2-free headspace for three minutes and the headspace injected into a molecular sieve cartridge for transport to the National Environmental Isotope Facility (NEIF) Radiocarbon Laboratory in East Kilbride, UK. CH4 samples were collected with the coiled membrane method21, in which water was slowly pumped through a hydrophobic, gas-permeable membrane into a headspace containing ambient air. The vessel was left to collect CH4 overnight and recovered after 12–18 h; the headspace was collected into foil gas bags and transported by land to the NEIF Radiocarbon Laboratory. CH4 samples were corrected for the ambient air in the headspace following refs. 21,52.
For each data point in the radiocarbon database, we collected information on the catchment characteristics of the sampled river. Unfortunately, the information was not uniformly reported in the published literature. Therefore, for consistency in our analysis, we extracted catchment and hydrological characteristics from HydroATLAS56. HydroATLAS provides catchment and reach characteristics for rivers across the globe at 15-arcsecond resolution and includes parameters on hydrology, physical catchment settings, climate, land cover and use, soils and geology and anthropogenic influences. We extracted selected parameters at both the reach and catchment scale where possible and added these to our database (Supplementary Tables 1 and 4).
Broadleaf and conifer forests are included in the Hydro ATLAS biomes.
New radiocarbon age and uncertainty 61 from a repeat sample at a large number of times in the year of sample sampling (Extended Data Table 1)
in which F14Catm is the normalized F14C value of the sample (Fmsample in fraction modern) divided by the F14C value of the atmosphere in the year of sampling (Fmatmosphere in fraction modern)35.
The database has some locations that have been sampled more than once. A combination of experimental approaches includes repeat sampling, exploration of temporal variations and method development. When a sample location was repeat sampled more than four times in a calendar year (that is, more than 0.5% of all observations), we took the average of the F14C observations at that location for that year and recalculated a new radiocarbon age and uncertainty61. This removal left n = 1,020 observations (Extended Data Table 1).
Source: Old carbon routed from land to the atmosphere by global river systems
Water DIC Samples from Taiwanese and Mekong Rivers with the HydroATLAS Deep Inelastic Microwave Band (HIPS) Detector
Water DIC samples from Taiwanese rivers were collected using methods outlined in refs. 18,58. In Taiwanese rivers, 1-l sampling bottles were submerged into the middle of the channel using a weighted Teflon sampler. On the Mekong River, near-surface samples were collected using a horizontally mounted Niskin-type sampler. The water was taken directly from the river and then used to make foil bags, which had a single-l weigh of 1L. The foil bag was filled with approximately 200–500 ml of filtered river water (depending on expected DIC concentration) and then gently squeezed before closing to ensure that no air was trapped. The filled bag was reweighed and stored at 4 °C during fieldwork, before shipping to the UK, in which the sample was frozen within about a week of collection58.
The HydroATLAS classes included unconsolidated sedams. Siliciclastic Sedimentary Rocks (SS)’, ‘5. Mixed Sedimentary Rocks (SM)’ and ‘6. The Carbonate Sedimentary Rocks are composed of carbonate rocks.
One data point returned ‘No Data (ND)’ from the HydroATLAS lithology classes and was excluded from the lithology analysis. Data from Antarctica were also excluded from the analysis owing to a lack of lithology data (returning ‘Ice and Glaciers (IG)’ from the HydroATLAS lithology classes).
Source: Old carbon routed from land to the atmosphere by global river systems
Random Forest Model Building for Large River Catchments Using kruskal.test and Conover-Iman Methods in R with R version 4.1.1
The analyses were done in R version 4.1.1. We used non-parametric Kruskal– Wallis tests with the kruskal.test function in R, and post hoc analyses consisting of Conover–Iman tests, and unpaired two- sample Wilcoxon tests. We used the lm function to do the regression analyses. In the main text, extended data and supplementary information is where the analysis is applied.
The random forest analysis was done using the R package. The F14Catm-DIC used all of the variables from all the large catchments to build random forest models. We assessed the performance of the random forest model prediction by calculating the coefficient of determination (Rd2) and determining the importance of each variable through the increase in the mean square error. A tenfold cross-validation was used to enhance the robustness of the results. The dataset was randomly divided into ten equal-sized samples, with 90% of the data used for training the random forest model, whereas the remaining 10% was used to assess model performance. This process was iterated ten times until each 10% sample used, then the final model performance was computed. The random forest models were built using the same approach and all of the variables.
We first eliminated variables that were more correlated with other input variables than Spearman correlation, to prevent the model results from being influenced by correlated input variables. The remaining variables are shown in Supplementary Table 5 and includes the year of sample collection (‘year’).
We split the model runs by catchment size (Extended Data Figs. 3 and 4) using whole catchment characteristics for rivers with catchments greater than 10 km2 and reach characteristics for rivers with catchments ≤10 km2. The model was only applied to DIC data, as it was limited by the number of data points.
Source: Old carbon routed from land to the atmosphere by global river systems
Global analysis of predictive variables and F14 Catm with partial dependence plots based on pdp_67: a global approach for analysing the impact of cation and anion dissolved ions
We assessed the association between predictor variables and F14Catm with partial dependence plots using the pdp R package67. The plots show how F14 Catm changes when a given input variable changes but all others remain the same in the random forest model. We performed the partial dependence analysis ten times (mirroring the ten iterations of random forest models from using tenfold cross-validation) and plotted the mean values from these ten runs, with the variability across the runs indicated by the shaded area (Extended Data Figs. 3 and 4).
In an ideal world, it would be possible to account for petrogenic inputs to DIC and CO2 for each watershed in the database (and potentially for each sampling point). To do this, we would need to use dissolved cation (Na+, Ca2+, Mg2+, K+) and anion (Cl−, SO42+, Re) data to assess the weathering acids and contributions from carbonate and rock organic matter weathering18,38,39. Unfortunately, most of the studies reporting river DIC and CO2 F14C measurements do not report dissolved ion data, or if they do, do not report the necessary range of cation and anion measurements to complete a weathering-source inversion. We take a global view using our mean F14C DIC values and assess the petrogenic inputs using global estimates of carbonate and rock organic carbon weathering rates.
$$\begin{array}{l}{\rm{Total\; river\; DIC\; flux}}\times {F}^{14}{{\rm{C}}}{{\rm{river}}}\ =\,(({\rm{Lateral\; DIC\; export\; to\; ocean}}+{{\rm{vertical\; CO}}}{2}\,{\rm{emissions\; flux}}\ \,-\,{\rm{weathering\; inputs}})\times (a\times {F}^{14}{{\rm{C}}}{{\rm{decadal}}}+b\times {F}^{14}{{\rm{C}}}{{\rm{millennial}}}))\ \,+\,(c\times {F}^{14}{{\rm{C}}}_{{\rm{petro}}}\times {\rm{weathering\; inputs}})\end{array}$$
2.5 is the average of the times.
We can then calculate the non-petrogenic F14C value (F14Cdecadal+millennial), because the petrogenic source is assumed to contain no radiocarbon (that is, F14C = 0.0). This provided an estimate of the F14Cdecadal+millennial = 0.978 to 1.007.
The data that we collected was not consistent, site specific, and was not matched with the F14C data. This means that we were not able to scale the F14C values in the database with local and regional emission fluxes (Supplementary Information section 4).
Analysis of global river carbon emissions on London’s River DIC, CO2 and CH4 using radiocarbon database and hydroATLAS56 has been done. In September 2021 dissolved CO2 and CH4 samples were collected for 14C analysis from a range of urban rivers and canals. For each data point in the radiocarbon database, we collected information on the catchment characteristics of the sampled river.