| 05 Sep 2025
Meteoric beryllium-10 fluxes from soil inventory measurements in the East River watershed, Colorado, USA
Abstract. Meteoric beryllium-10 (10Bemet) has a wide range of applications as a geochronometer and tracer of geological processes. 10Bemet is produced in the atmosphere by cosmic rays and delivered to Earth’s surface primarily via precipitation. 10Bemet 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 10Bemet 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 10Bemet flux in the East River watershed in Colorado, USA where precipitation is dominated by snowfall. We measured the 10Bemet inventory in soils on five glacial moraines 13–18 ka in age that span 700 m of elevation and calculated 10Bemet 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 10Bemet delivery to soils and how local calibration can improve estimates of 10Bemet flux in mountain watersheds.
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.