| 25 Aug 2025
OzRiCa: An Australian riverine carbon database of concentrations, gas fluxes and isotopes
Abstract. Understanding carbon (C) cycling in riverine ecosystems is crucial for accurate estimates of regional and global C budgets. However, the slow progress in identifying spatial and temporal patterns and drivers of riverine C has been largely driven by limited data availability. This lack of information is particularly acute in Australia. To address this issue, we compile the first comprehensive database (OzRiCa) of concentrations, stable isotopes, and fluxes of particulate organic C (POC), dissolved organic (DOC) and inorganic C (DIC), carbon dioxide (CO2) and methane (CH4) for streams and rivers across Australia, along with concurrent physical and chemical data. The OzRiCa database contains 54,843 observations from 2,879 unique sites derived from state agencies, scientific publications, and newly collected, previously unpublished data. The measurements span the period between 1966 to 2024. The database reveals that most observations and sites are located in the Mediterranean, temperate and subtropical regions of the country, with fewer data in semi-arid and tropical regions. We also highlight patterns in temporal data coverage, with half of the sites limited to a single measurement. The scarcity of long-term and high-resolution temporal data for CO2 and CH4, along with the limited number of direct measurements for DIC and CO2, emphasises the need for more direct, frequent and long-term monitoring efforts to capture the extreme temporal variability of Australian climatic regions. We also identify limited stable isotopic data and concurrent measurement of multiple C species, both limiting our ability to better understand C sources and sinks, as well as in-stream C processes. Future research should prioritise these critical gaps to improve our understanding of riverine C dynamics in Australia. OzRiCa provides a baseline for future research, paving the way for studies of fluvial C fluxes at regional, national, and global scales. This database will also be a valuable resource to studying ecosystem health, water quality, and other biogeochemical processes.
This well-written manuscript presents a large, compiled dataset of carbon concentrations, fluxes, and isotopes for rivers in Australia. The dataset comprises DOC, DIC, POC, CO2, and CH4, while the manuscript describes DOC, DIC, CO2, and CH4. This dataset has the strong potential to support an analysis of riverine carbon fluxes for the Australian continent and includes new, unpublished data. Overall, I find the manuscript and dataset comprehensive and well described, and the dataset will likely support an important analysis of continental-scale carbon fluxes from a relatively understudied region. However, I have a few comments that should be addressed.
First, it might be worth re-considering not describing the POC data in the manuscript. I understand that the authors choose not to describe the POC data because of the relatively small number of samples compared to the other parameters (text says n=51, I’m seeing n=77 in the dataset). However, POC is a part of the dataset, exhibits a large range of values (4 orders of magnitude), and is a relatively unconstrained riverine carbon flux, so the data is valuable. If the authors don’t want to make direct comparisons between POC and DOC/DIC/CO2/CH4 given the very different number of samples, they could include a description of the POC data in a separate sub-section. That being said, it might also be interesting to see POC in Table 2 and Figure 5.
Second, the prevalence of river types could be considered in the discussion of spatial coverage and representativeness of the carbon dataset. Figure 1, Table 1, and the associated text very clearly point out that arid rivers aren’t well represented in the dataset. This point is important in understanding the dataset. However, I also wonder what proportion of streams and rivers in Australia are found in the arid interior? How different is the proportion of samples from arid rivers (~5%) versus from non-arid rivers compared to the proportion of arid to non-arid rivers in Australia?
Other comments:
Ln 134-135: This sentence should be revised. As far as I understand, the authors did not calculate CO2 from literature values of pH and alkalinity, but rather only used literature values of calculated CO2.
Ln 182-183: Headspace equilibration samples for CO2 should be corrected for carbonate equilibria (Koschorreck et al., 2021). At the pH range of the dataset (1st-3rd Quartile: 7-7.8), bicarbonate would be the dominant form of inorganic carbon, and not CO2, so this correction might be important. The authors have DIC and pH so they could apply this correction to the pCO2 values or show that it doesn’t have much of an effect.
Ln 246: Needs to be reworded to make clear that this comparison refers to observations, not concentrations. E.g., “DIC and DOC measurements were the most prevalent measurements in the dataset, across all climatic regions”
Ln 268-9: combine this stand-alone sentence into the next paragraph.
Figure 2 – It’s difficult to see the colors in the legend – consider making the lines in the legend thicker.
Figure 4a – It’s difficult to read the words in the pie chart.
Ln 306 – “The large difference for CO2 estimates *may arise* because…”
Ln 848-861 – How were the study-reach lengths choosen? What were the range of reach lengths? Were there any groundwater inputs or lateral flows along the study reaches? If so, were dilution corrections applied?
Ln 866-876 – Generally direct measurements of CO2 are much better than calculations, given all the errors associated with pH measurements and non-carbonate alkalinity. Why were the headspace replicates so frequently (36% of samples) off by > 20%?
Ln 877-881 – Aren’t estimates of velocity derived from a pair of breakthrough curves/EC sensors? How can only one break through curve be used?
Figure C1 – difficult to read the text on this figure.
Ln 890 – extra “40” before oxygen.
Dataset: There are two extremely high [O2] values in the dataset (37.4 and 98.6 mg/l)
Dataset: More than 6,000 entries don’t have a catchment area. It should be possible to delineate a catchment using a DEM and estimate catchment area.
References:
Koschorreck, M., Prairie, Y. T., Kim, J., & Marcé, R. (2021). Technical note: CO2 is not like CH4 – limits of and corrections to the headspace method to analyse pCO2 in fresh water. Biogeosciences, 18(5), 1619–1627. https://doi.org/10.5194/bg-18-1619-2021