Meteoric beryllium-10 fluxes from soil inventory measurements in the East River watershed, Colorado, USA

Marmolejo-Cossío, José María; Larsen, Isaac James; Hidy, Alan J.

doi:10.5194/egusphere-2025-4254

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https://doi.org/10.5194/egusphere-2025-4254

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05 Sep 2025

| 05 Sep 2025

Meteoric beryllium-10 fluxes from soil inventory measurements in the East River watershed, Colorado, USA

José María Marmolejo-Cossío, Isaac James Larsen, and Alan J. Hidy

Abstract. Meteoric beryllium-10 (¹⁰Be_met) has a wide range of applications as a geochronometer and tracer of geological processes. ¹⁰Be_met is produced in the atmosphere by cosmic rays and delivered to Earth’s surface primarily via precipitation. ¹⁰Be_met is particularly suitable for quantifying surface process rates where use of in situ-produced ¹⁰Be is challenging, such as landscapes with quartz-poor bedrock. However, using ¹⁰Be_met for dating and quantifying surface process rates requires constraining depositional fluxes across space and time. Although empirical and physical models for predicting fluxes exist, the predictions can deviate substantially from measured values. Here we quantify ¹⁰Be_met flux in the East River watershed in Colorado, USA where precipitation is dominated by snowfall. We measured the ¹⁰Be_met inventory in soils on five glacial moraines 13–18 ka in age that span 700 m of elevation and calculated ¹⁰Be_met fluxes by dividing each inventory by moraine age. Inheritance-corrected fluxes range from 1.12 x10⁶–3.79x10⁶ atoms cm^-2 yr^-1, and are well correlated with elevation, mean annual precipitation, mean snow depth, and snow water equivalent (R² = 0.84 to 0.99). Regression models based on elevation, precipitation, snow depth and snow water equivalent predict watershed-averaged fluxes of 1.23x10⁶–3.62x10⁶ atoms cm^-2 yr^-1. Predicted fluxes from a published empirical model that estimates fluxes as a function of precipitation were within a factor of 1.1–1.6 of measured values at each site. Fluxes predicted by physically-based general circulation models (GCM) are generally within a factor of three of our estimated watershed-averaged values, but the GCM predictions are too coarse to capture the intra-watershed spatial variability in fluxes. Our results highlight both the importance of factors that drive variability in ¹⁰Be_met delivery to soils and how local calibration can improve estimates of ¹⁰Be_met flux in mountain watersheds.

Received: 30 Aug 2025 – Discussion started: 05 Sep 2025

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José María Marmolejo-Cossío, Isaac James Larsen, and Alan J. Hidy

Status: final response (author comments only)

RC1:
'Comment on egusphere-2025-4254', Anonymous Referee #1, 02 Oct 2025

It is my pleasure to review “Meteoric beryllium-10 fluxes from soil inventory measurements in the East River watershed, Colorado, USA” by Marmolejo-Cossío et al. Meteoric cosmogenic 10Be has been applied to determine rates and dates of various Earth surface processes in recent decades. A key prerequisite for its accurate application lies in constraining the meteoric 10Be depositional flux. Here, the authors aim to quantify the millennial-scale 10Be deposition in the East River watershed using soil inventory 10Be measurements from five glacial moraines with known ages; this approach will provide important insights into meteoric 10Be applications in this area. In general, this is a valuable 10Be dataset worthy of publication in Geochronology. However, several major concerns need to be properly addressed before further consideration. I provide the major comments first, followed by line-by-line comments.

Major concerns:
1. Consideration of >2 mm fraction in 10Be flux calculation. The authors only measured the <2 mm fraction for 10Be. However, glacial moraines may contain a non-negligible fraction of coarse grains (>2 mm). How did the authors treat this fraction during 10Be flux calculation? As coarse grains may bear very low [10Be], their volume percentage needs to be constrained and accounted for in the 10Be flux calculation.
2. Correction for erosion impact. Since the authors can constrain the erosion rate to some extent (using in-situ 10Be or topographic curvature), I encourage them to correct for its impact on the 10Be flux estimation. Such practice has also been applied in previous 10Be inventory studies (e.g., Clow et al., 2020, Geochron.; Deng et al., 2021, QSR).
Clow, T., et al. (2020). "Calibrating a long-term meteoric 10Be delivery rate into eroding western US glacial deposits by comparing meteoric and in situ produced 10Be depth profiles." Geochronology 2(2): 411-423.
Deng, K., et al. (2021). "Deposition and retention of meteoric 10Be in Holocene Taiwan river terraces." Quaternary Science Reviews 265: 107048.
3. Representation of the Copper Creek profile (Fig. 3a). This profile has 1) a very low pH (~4), 2) poorly constrained 10Be inheritance, and 3) an incomplete inventory (sampled depth is too shallow). It seems unlikely to derive a realistic 10Be flux estimate from this profile. I suggest the authors focus only on the dataset from the other four profiles in the Discussion.
4. Regression between 10Be flux and environmental variables in Fig. 5. All these regression lines are strongly affected by the extremely high 10Be flux data from Copper Creek. If this data point is removed (as demonstrated above), the regression becomes much less obvious. Furthermore, while the precipitation effect is known, it is unclear why the authors plot 10Be flux against other variables—they do not control 10Be flux, and correlation does not imply causation.

Minor comments:
Line 55: If there is no Section 1.2, why is Section 1.1 needed? The text could be considered part of the Introduction.
Line 181: Bulk density should be measured rather than assumed, as it should generally increase with soil depth. If such data are impossible to obtain now, the authors should still propagate an uncertainty for bulk density (e.g., based on prior measurements; Quirk et al., 2024) into the 10Be flux data.
Lines 186-189: It must be emphasized that the sampled soil profile is too shallow and does not reach the depth with [10Be] inheritance, and thus the 10Be flux is a lower-limit estimate in the Copper Creek watershed. Otherwise, readers may believe that the data point in Fig. 3a is a very accurate estimate.
Lines 234-235: The assumption of a zero y-intercept in the MAP-10Be flux relationship requires further justification, as only the additive effect can be characterized by this feature (Willenbring and von Blanckenburg, 2010, ESR).
Table 3: Does MAP take snowfall into consideration? In general, is the effect of snowfall on 10Be deposition the same as the effect of precipitation?
Lines 311-315: Why does the MAP-based regression derive an average F_met lower than most measured data, while other regression models derive an average F_met above most measured data?
Lines 345-350: The soil pH in Copper Creek is much lower than 6, and thus I do not think the authors can claim “minimal chemical loss of 10Be_met.”
Line 379: Why should elevation control F_met?
Line 392 and Fig. 7: Should this be “reduce,” not “increase”?
Lines 395-397: Please describe the solar modulation function used by Heikkilä and von Blanckenburg (2015). Additionally, the authors have used incorrect data from Zheng et al. (2024). A higher solar modulation function (500 MeV) should lead to lower 10Be flux, not higher as described here. After briefly checking Zheng et al. (2024), the 500 MeV scenario may actually be a geomagnetic minimum scenario and is not consistent with modern conditions. The authors must re-check the paper to get the correct model values for comparison. In any case, the authors are encouraged to normalize all fluxes (model and measured) to the same solar modulation function for comparison.
Line 459: According to Fig. 7, it is “higher,” not “lower.”

Others:
Fig. 7: The uncertainty of the measured data is too small to be realistic. I assume only the analytical uncertainty of 10Be is considered here. However, given the uncertainties in bulk density, fraction of >2 mm grains, soil erosion, and chemical leaching (pH effect), the real uncertainty should be much higher but is not included here.
Fig. 8: I suggest adding another line—the average measured F_met of all four profiles (excluding Copper Creek). This average value is useful, and may be very close to the MAP-based estimate and the model from Heikkilä and von Blanckenburg (2015).
7 Data availability: Data must be made available upon publication.

Citation: https://doi.org/10.5194/egusphere-2025-4254-RC1
- AC2:
  'Reply on RC1', Isaac Larsen, 15 Dec 2025
  It is my pleasure to review “Meteoric beryllium-10 fluxes from soil inventory measurements in the East River watershed, Colorado, USA” by Marmolejo-Cossío et al. Meteoric cosmogenic 10Be has been applied to determine rates and dates of various Earth surface processes in recent decades. A key prerequisite for its accurate application lies in constraining the meteoric 10Be depositional flux. Here, the authors aim to quantify the millennial-scale 10Be deposition in the East River watershed using soil inventory 10Be measurements from five glacial moraines with known ages; this approach will provide important insights into meteoric 10Be applications in this area. In general, this is a valuable 10Be dataset worthy of publication in Geochronology. However, several major concerns need to be properly addressed before further consideration. I provide the major comments first, followed by line-by-line comments.
  Thank you for taking the time to review our manuscript and provide feedback.
  Major concerns:
  Consideration of >2 mm fraction in 10Be flux calculation. The authors only measured the <2 mm fraction for 10Be. However, glacial moraines may contain a non-negligible fraction of coarse grains (>2 mm). How did the authors treat this fraction during 10Be flux calculation? As coarse grains may bear very low [10Be], their volume percentage needs to be constrained and accounted for in the 10Be flux calculation.
  
  The till contains clasts that are larger than those that can be reliably sampled using standard sampling equipment for soil bulk density, which is why we opted to use a profile averaged density (following Clow et al., 2020). However we do have bulk density data for the till matrix and we can estimate the proportion of large clasts to refine the flux calculations.
  
  Correction for erosion impact. Since the authors can constrain the erosion rate to some extent (using in-situ 10Be or topographic curvature), I encourage them to correct for its impact on the 10Be flux estimation. Such practice has also been applied in previous 10Be inventory studies (e.g., Clow et al., 2020, Geochron.; Deng et al., 2021, QSR).
  
  Clow, T., et al. (2020). "Calibrating a long-term meteoric 10Be delivery rate into eroding western US glacial deposits by comparing meteoric and in situ produced 10Be depth profiles." Geochronology 2(2): 411-423.
  Deng, K., et al. (2021). "Deposition and retention of meteoric 10Be in Holocene Taiwan river terraces." Quaternary Science Reviews 265: 107048.
  We present this analysis in the manuscript (Figure 4 and Lines 269-279). Clow et al. (2020) we able to determine the impact of erosion because they measured meteoric 10Be in two profiles where in situ-produced 10Be had previously been measured. Deng et al. (2021) model (following Clow et al., 2020) the influence of a wide range of erosion rates on meteoric 10Be fluxes for one of their terraces, but have no constraints on the erosion rates from their field sites. We have independent information on erosion rates for only one of the five study sites, which likely has the higher erosion rates than the other four sites. Hence we do not think applying that erosion rate to the other sites is appropriate. However, we do make inferences regarding how the erosion rate may vary across the sites based on topographic curvature and calculate the impact on fluxes (following Clow et al., 2020). We can include the results in Tabular form and in other analyses.
  
  Representation of the Copper Creek profile (Fig. 3a). This profile has 1) a very low pH (~4), 2) poorly constrained 10Be inheritance, and 3) an incomplete inventory (sampled depth is too shallow). It seems unlikely to derive a realistic 10Be flux estimate from this profile. I suggest the authors focus only on the dataset from the other four profiles in the Discussion.
  
  We focus on all of the profiles in the discussion, including the Copper Creek profile and potential limitations of the data from that profile. Because of the presence of the stone line in that profile, it is unclear whether the sample depth is too shallow.
  
  Regression between 10Be flux and environmental variables in Fig. 5. All these regression lines are strongly affected by the extremely high 10Be flux data from Copper Creek. If this data point is removed (as demonstrated above), the regression becomes much less obvious.
  
  Furthermore, while the precipitation effect is known, it is unclear why the authors plot 10Be flux against other variables—they do not control 10Be flux, and correlation does not imply causation.
  The other variable (elevation) is a proxy for precipitation, due to well-known orographic influences on precipitation rates in mountain landscapes. The other variables (besides mean annual precipitation) are snowfall-based data (e.g., they are precipitation data, just from a different data source) and hence are also expected to correlate with meteoric 10Be fluxes.
  
  Minor comments:
  Line 55: If there is no Section 1.2, why is Section 1.1 needed? The text could be considered part of the Introduction.
  We can eliminate the section break.
  
  Line 181: Bulk density should be measured rather than assumed, as it should generally increase with soil depth. If such data are impossible to obtain now, the authors should still propagate an uncertainty for bulk density (e.g., based on prior measurements; Quirk et al., 2024) into the 10Be flux data.
  
  We agree, but in practice it is very difficult to measure bulk density in sedimentary deposits that contain boulder-sized clasts. We have some bulk density data from the till matrix and can use that to assess the uncertainty and propagate the uncertainty in subsequent analyses.
  
  Lines 186-189: It must be emphasized that the sampled soil profile is too shallow and does not reach the depth with [10Be] inheritance, and thus the 10Be flux is a lower-limit estimate in the Copper Creek watershed. Otherwise, readers may believe that the data point in Fig. 3a is a very accurate estimate.
  Because of the prominent stone line, it is unclear that the Copper Creek depth profile should look like the other profiles. The stone line appears to be a barrier for physical mixing and it may also limit infiltration/aqueous transport of meteoric 10Be. However, we can add text emphasizing that the inferred flux is likely a minimum value.
  
  Lines 234-235: The assumption of a zero y-intercept in the MAP-10Be flux relationship requires further justification, as only the additive effect can be characterized by this feature (Willenbring and von Blanckenburg, 2010, ESR).
  Without forcing the y-intercept to be zero, many of the regression relationships result in negative y-intercept values. So for zero mean annual precipitation, the regression model predicts a negative meteoric 10Be flux, which is physically implausible. We can clarify this reasoning in the text.
  
  Table 3: Does MAP take snowfall into consideration? In general, is the effect of snowfall on 10Be deposition the same as the effect of precipitation?
  Yes, precipitation includes rain and snow. 7Be deposition has been shown to scale linearly with snowfall (Ishikawa, Y., Murakami, H., Sekine, T. and Yoshihara, K., 1995. Precipitation scavenging studies of radionuclides in air using cosmogenic 7Be. Journal of Environmental Radioactivity, 26(1), pp.19-36.) and presumably 10Be behaves in a geochemically similar fashion.
  
  Lines 311-315: Why does the MAP-based regression derive an average F_met lower than most measured data, while other regression models derive an average F_met above most measured data?
  There are simply different relationships between flux and the different variables. Because these are empirical relationships, it is difficult to assign physical meaning to the differences, but it is possible that the MAP data underpredict precipitation.
  
  Lines 345-350: The soil pH in Copper Creek is much lower than 6, and thus I do not think the authors can claim “minimal chemical loss of 10Be_met.”
  The pH values are less than six. However, a pH of six or less is not diagnostic of chemical loss of meteoric 10Be. In lines 346-349 we indicate:
  “Analysis of a compilation of soil 10Bemet and pH data suggest loss is most likely to occur below a pH of 3.9 (Graly et al., 2010). The 0-10 cm interval sample at Copper Creek site had the lowest measured pH value of 4.3, but pH increased with depth to a value of 5.4 at the base of the soil profile, and pH values for the other sites are higher.”
  Based on the findings of Graly et al (2010), and the measured pH values, we think it is acceptable to infer that there is minimal chemical loss of meteoric 10Be from the Copper Creek profile.
  
  Line 379: Why should elevation control F_met?
  The flux of meteoric 10Be is strongly influenced by precipitation rates. Due to orography, precipitation rates increase with elevation. Hence elevation is expected to influence the flux of meteoric 10Be.
  
  Line 392 and Fig. 7: Should this be “reduce,” not “increase”?
  We will make this correction.
  
  Lines 395-397: Please describe the solar modulation function used by Heikkilä and von Blanckenburg (2015). Additionally, the authors have used incorrect data from Zheng et al. (2024). A higher solar modulation function (500 MeV) should lead to lower 10Be flux, not higher as described here. After briefly checking Zheng et al. (2024), the 500 MeV scenario may actually be a geomagnetic minimum scenario and is not consistent with modern conditions. The authors must re-check the paper to get the correct model values for comparison. In any case, the authors are encouraged to normalize all fluxes (model and measured) to the same solar modulation function for comparison.
  We will make this correction.
  
  Line 459: According to Fig. 7, it is “higher,” not “lower.”
  The predicted values all fall below the 1:1 line, so they are “lower”.
  
  Others:
  Fig. 7: The uncertainty of the measured data is too small to be realistic. I assume only the analytical uncertainty of 10Be is considered here. However, given the uncertainties in bulk density, fraction of >2 mm grains, soil erosion, and chemical leaching (pH effect), the real uncertainty should be much higher but is not included here.
  We can add uncertainty due to factors such as bulk density.
  
  Fig. 8: I suggest adding another line—the average measured F_met of all four profiles (excluding Copper Creek). This average value is useful, and may be very close to the MAP-based estimate and the model from Heikkilä and von Blanckenburg (2015).
  We can add this, but are unsure how useful it would be to do so. The result would show that the model-based flux from Heikkilä and von Blanckenburg (2015) was comparable to measured fluxes for the four lowest elevation sites. Even if the flux inferred for the Copper Creek site is a minimum value (for reasons discussed in the text), we don’t think there is justification for ignoring it, especially given the established links (by prior work) between precipitation rates and meteoric 10Be flux and precipitation rates and elevation.
  7 Data availability: Data must be made available upon publication.
  The data will be made available in the U.S. Department of Energy ESS-DIVE repository upon publication.
  
  Citation: https://doi.org/10.5194/egusphere-2025-4254-AC2
RC2:
'Comment on egusphere-2025-4254', Anonymous Referee #2, 17 Nov 2025

This work quantifies meteoric ¹⁰Be fluxes averaged over the last ~20,000 years within an alpine watershed of Colorado’s (USA) Elk Mountains. The authors determine these fluxes by sampling meteoric ¹⁰Be inventories in glacial moraines of known age (from previously determined in situ ¹⁰Be exposure dates) across an elevation gradient of 714 meters. To successfully capture full 10Be inventories, the authors must be sure that that: soil pH is not too low (i.e., <4), erosion is minimal or quantifiable, inherited ¹⁰Be is minimal or quantifiable and samples are continuous and deep enough to capture full inventories. Sample grain size is also significant and sediment in the <2 mm fraction is ideal since meteroic ¹⁰Be is known to preferentially adsorb to finer grains. I am satisfied that the authors carefully account for these factors, and, to the best of their ability, capture complete ¹⁰Be inventories. Their Copper Creek site offers some complications/challenges, but the authors explain how they account for those challenges.
The results from this manuscript demonstrate a strong elevation-dependent relationship for ¹⁰Be flux in the East River watershed that is closely tied to patterns of orographic precipitation (rain and snow). This flux is vulnerable to local-scale influences, and the authors demonstrate that measured inventories are significantly different than fluxes by much coarser resolution, global models. This work underscores the need for locally calibrated meteoric ¹⁰Be fluxes if the nuclide is to be used as a tracer of surface processes.
This manuscript is well-polished in its current form. I offer some minor comment and points of that require clarification below. Following the minor revisions associated with those comments, I think this manuscript will ready for publication. My recommendation is “Accept with Minor Revisions.”
_

Line 50: Add references to Nicole West’s work in the Shale Hills CZO
Line 148: This does not seem sufficiently deep. See note for Figure 3.
Line 152: This combining of splits is not clear. It sounds like samples from multiple depths were combined, but that is not what I think you are describing. Clarify.
Line 181: Are there not bulk density measurements from your own sites?
Line 209: Report your assumed value of D here?
Figure 3: Despite relatively shallow profiles, it does seem that you sample to a sufficient depth for the majority of moraines. Your deepest samples generally approach concentrations representative of inheritance. Copper Creek (with the stone line) is the obvious exception.
FIgure 5: Compelling regressions. These seem like solid relationships. The relationship with elevation is very closely intertwined with the other three variables due to orographic effects, though.

Citation: https://doi.org/10.5194/egusphere-2025-4254-RC2
- AC1: 'Reply on RC2', Isaac Larsen, 15 Dec 2025
  
  This work quantifies meteoric ¹⁰Be fluxes averaged over the last ~20,000 years within an alpine watershed of Colorado’s (USA) Elk Mountains. The authors determine these fluxes by sampling meteoric ¹⁰Be inventories in glacial moraines of known age (from previously determined in situ ¹⁰Be exposure dates) across an elevation gradient of 714 meters. To successfully capture full 10Be inventories, the authors must be sure that that: soil pH is not too low (i.e., <4), erosion is minimal or quantifiable, inherited ¹⁰Be is minimal or quantifiable and samples are continuous and deep enough to capture full inventories. Sample grain size is also significant and sediment in the <2 mm fraction is ideal since meteroic ¹⁰Be is known to preferentially adsorb to finer grains. I am satisfied that the authors carefully account for these factors, and, to the best of their ability, capture complete ¹⁰Be inventories. Their Copper Creek site offers some complications/challenges, but the authors explain how they account for those challenges.
  The results from this manuscript demonstrate a strong elevation-dependent relationship for ¹⁰Be flux in the East River watershed that is closely tied to patterns of orographic precipitation (rain and snow). This flux is vulnerable to local-scale influences, and the authors demonstrate that measured inventories are significantly different than fluxes by much coarser resolution, global models. This work underscores the need for locally calibrated meteoric ¹⁰Be fluxes if the nuclide is to be used as a tracer of surface processes.
  This manuscript is well-polished in its current form. I offer some minor comment and points of that require clarification below. Following the minor revisions associated with those comments, I think this manuscript will ready for publication. My recommendation is “Accept with Minor Revisions.”
  
  Thank you for taking the time to review our manuscript and provide feedback.
  _
  
  Line 50: Add references to Nicole West’s work in the Shale Hills CZO
  We will make this addition.
  
  Line 148: This does not seem sufficiently deep. See note for Figure 3.
  We could not make the shallowest bit deeper by hand because of the presence of a stone line, and due to the remote location, could not bring in heavy equipment to make the pit deeper.
  
  Line 152: This combining of splits is not clear. It sounds like samples from multiple depths were combined, but that is not what I think you are describing. Clarify.
  Samples from multiple depths were combined. We will clarify the text.
  
  Line 181: Are there not bulk density measurements from your own sites?
  There are bulk density measurements. However, it was exceptionally difficult to collect volume-controlled samples in these materials. The density measurements do not include large particles, which are prevalent in the moraine deposits, but in response to the comments from Reviewer 1 we will incorporate those data in our calculations.
  
  Line 209: Report your assumed value of D here?
  We will do this.
  
  Figure 3: Despite relatively shallow profiles, it does seem that you sample to a sufficient depth for the majority of moraines. Your deepest samples generally approach concentrations representative of inheritance. Copper Creek (with the stone line) is the obvious exception.
  We agree with this interpretation.
  
  FIgure 5: Compelling regressions. These seem like solid relationships. The relationship with elevation is very closely intertwined with the other three variables due to orographic effects, though.
  We agree. We discuss the inter-related nature of these variables in line 365: “Fmet is highly correlated with elevation, MAP, snow depth, and SWE, which is unsurprising because these variables are related to one another via orographic controls on precipitation rates”.
  
  Citation: https://doi.org/10.5194/egusphere-2025-4254-AC1

José María Marmolejo-Cossío, Isaac James Larsen, and Alan J. Hidy

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Short summary

Collision of cosmic rays with the atmosphere produces rare atoms, including beryllium-10, which falls out in precipitation and binds to soils. Measurements on glacial deposits from the last ice age in Colorado, USA show that millions of beryllium-10 atoms fall on each square centimeter of land each year. The rate of delivery increases with elevation and precipitation, which allowed us to make maps of the beryllium-10 flux for an entire watershed that will be used to measure erosion rates.


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