the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Nov 2024
Short communication: Updated CRN Denudation datasets in OCTOPUS v2.3
Abstract. OCTOPUS v2.3 includes updated CRN Denudation datasets, adding 1,311 new river basins to the CRN International and CRN Australia collections. The updates bring the total number of basins with recalculated 10Be denudation rates to 5,611, and those with recalculated 26Al rates to 561. To improve data relevance and usability, redundant data fields have been removed, retaining only those relevant to each collection. Additional updates include the introduction of several new data fields: the latitude of the basin centroid and the effective basin-averaged atmospheric pressure, both of which improve interoperability with online erosion rate calculators. Other new fields record the extent of present-day glaciers and their potential impact on denudation rates, as well as estimates of the percentage of quartz-bearing lithologies in each basin — providing a basis for evaluating data quality. The updated data collections can be accessed at https://octopusdata.org (last access: 01 Dec 2024). The CRN International and CRN Australia data collections can also be accessed via their respective digital object identifiers (DOIs).
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RC1: 'Comment on gchron-2024-28', Richard Ott, 29 Nov 2024
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Codilean et al. present an update on the Octopus database, which now includes more data points and several new fields. They present comparisons with other calculators from Cronus and Riversand as well as comparisons among different production rate parameters calculations.
Personally, I am a big fan of the Octopus database and think it is an extremely useful resource. The new fields of glacier area, and quartz-bearing rocks are great additions, holding important information for the interpretation of the data. Moreover, the approach with an open-source code and data base to facilitate interoperability between calculators and reproducibility is important for the field of cosmogenic nuclides, where calculation parameters constantly get updated. Also the figures are of high quality. Therefore, this manuscript is suitable for Geochronology after revision.
My only major comment is that the authors should consider recalculating erosion rates using a time-variant scaling and removing topographic shielding, since they already acknowledge the problems with these approaches in the manuscript. The authors argue that this would cost too much computational time. However, all the basin-average effective atmospheric pressures have already been calculated already, therefore, the time-consuming pixel-based production rate calculation should be necessary anymore. Aren’t all the necessary parameters for, e.g., Riversand now pre-calculated and the actual denudation rate calculation should be reasonably fast? Below I discuss these points in more detail:
Time-invariant scaling: As has been argued, e.g., by Greg Balco in a blog post
https://cosmognosis.wordpress.com/2020/10/10/version-3-erosion-rate-calculator-benchmarked-finally/
time-invariance can really become a problem for slow erosion rates. The bias arises because the current magnetic field strength is high and was lower in the past, and most calibration data are from the past 20kyr, where field strength was high. I quote from the Balco blog: “Samples with lower erosion rates reflect production during longer-ago periods of weaker magnetic field strength and higher production rates, so an erosion rate computed with time-dependent scaling will be higher than one computed with non-time-dependent scaling. “ Balco shows that this bias can be up to 40% and is therefore quite significant.
As the authors argue, many people download Octopus data for global studies and therefore use a large range of low and high erosion rates in their studies. In such a case, time-invariant production rates is a problem because it introduces a systematic bias. For instance, many studies investigate the non-linear relationship between erosion rate and river steepness (ksn) (Adams et al., 2020). Using time-invariant scaling and having a large range in erosion rates, the Ksn-E relationship would become more non-linear just due to the bias introduced by not accounting for magnetic field variation.
From my perspective, an option would be to switch from CAIRN to RIVERSAND (Stübner et al., 2023), as has been done for calculations in figure 3. I understand that requesting the recalculation of all rates using a time-dependent scaling scheme is a big ask, but I invite the authors to assess whether this is feasible.
If the authors choose to stay with the CAIRN calculation, it would be valuable to show a comparison like in Fig. 3C/D, however, using the Octopus CAIRN-St rates versus the Riversand Lm or LSDn rates. The authors selected high-relief basins for their current approach. However, for a figure comparing the time-invariant and time-variant scaling schemes, the author should select studies that contain a large gradient in erosion rates.
Lithology: More details are warranted for the estimation of quartz percentage in the basins. This is a really useful addition to Octopus. However, the authors do not describe, which lithology classes in GliM are assumed to be quartz bearing. GLiM contains layers such as mixed sediment that can be full of quartz or devoid of it. Please, provide more detail on how this crucial number was estimated.
Topographic shielding: The authors state that topographic shielding likely creates a bias towards too low erosion rates. Given that this bias can be up to 10%, it seems like a good idea to remove shielding from the erosion rate calculations once and for all. The authors argue that this is not feasible given the high computational cost. Is the re-calculation of erosion rates really so computationally expensive? As far as I understand, the computationally-expensive part is the pixel-based averaging performed on DEMs. But that part is already done. Therefore, shouldn’t you be able to recalculate erosion rates fairly quickly without shielding and with time-variant scaling scheme, with the output parameters from CAIRN in a different calculator?
L 54: What is UOW? I don’t see this defined.
L54-48: I’m confused. What is the purpose of CRN Large Basins and DRN Denudation UOW? The article mentions that these include the published denudation rates. If that is the only reason for the existence of these two collections, why aren’t the published rates added as fields to CRN International&Australia? Please, clarify.
L82: Please, state the AMS standard for normalization.
L83: It would be nice to have approximately 2-3 sentences telling the reader about the main characteristics of CAIRN: pixel-based production rate, exponential approximation of production rates, topographic shielding, etc.
Thank you for the opportunity to comment on this awesome data compilation and standardization effort.
Richard Ott
References
Adams, B. A., Whipple, K. X., Forte, A. M., Heimsath, A. M., & Hodges, K. V. (2020). Climate controls on erosion in tectonically active landscapes. Science Advances, 6(42), eaaz3166. https://doi.org/10.1126/sciadv.aaz3166
Stübner, K., Balco, G., & Schmeisser, N. (2023). RIVERSAND: A NEW TOOL FOR EFFICIENT COMPUTATION OF CATCHMENTWIDE EROSION RATES. Radiocarbon, 1–14. https://doi.org/10.1017/RDC.2023.74
Citation: https://doi.org/10.5194/gchron-2024-28-RC1 -
AC1: 'Reply on RC1', Alexandru T. Codilean, 03 Dec 2024
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Please see the attached document with our answers to reviewer comments
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AC1: 'Reply on RC1', Alexandru T. Codilean, 03 Dec 2024
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