Bibliographic record
Abstract
This study concerns the application of the cosmic-ray neutron method to monitor snow water equivalent.The authors performed neutron count measurements over two winters (2013/2014 and 2014/2015) in an agricultural field in Saskatoon (Canada).Based on this data, they developed an empirical equation to provide estimates of average SWE which were compared with continuous snow depth measurements.This paper is an interesting presentation of a snow application of the cosmic-ray neutron method.It is also well written and fits well to the scope of Cryosphere Journal.However, some methodological improvements need to be undertaken as outlined in my C1 TCD Interactive commentPrinter-friendly version Discussion paper specific comments.In addition, I am not convinced that the presented method is able to provide quantitative estimates of SWE that are more accurate than the traditional snow depth measurements.Thus, the study should be more critical and should better discuss the potential drawbacks of the method.Author response: Thank you for the excellent comments.As shown in our measured SWE data using snow tube, the point measurements are highly spatially variable.It is impossible to obtain accurate areal SWE without a large number of point measurements.Therefore, a point measurement of continuous snow depth can cause accuracy issues if wanting to upscale the measurement to represent a larger area.For example, melting can occur below the depth sensor or snow could preferentially accumulate around the depth sensor from wind redistribution.Thus, the CRP method should provide a better estimate of average SWE in the area since it does integrate over a larger area.As Anonymous Referee 1 mentioned, it is not very practical to compare the CRP accuracy to an array of continuous measurements since it is not common to have an intensive set up of continuous SWE measurement instruments in the field.Printer-friendly version Discussion paper L184: The value of 4.53 cm suggests that the soil porosity must be at least 0.453.This is extremely high, e.g.sandy soils have typically porosities in the range of 0.30-0.35(Nimmo, 2004).Thus, this value is may be overestimated (see comment above).Author reply: The soil at our site has a fine texture (silt loam) and the top 10 cm of the soil profile had crop residue from previous years incorporated into the soil surface.The fine texture and crop residue caused the bulk density for 0 -10 cm to be 1.01 g cm-3 and the total porosity to be 0.61 cm3.This porosity would allow the value of 4.53 cm to be relevant.L225-229: Such scaling is unnecessary in the case of this study.Scaling would be necessary in case absolute neutron count rates would be important, e.g. in case neutron count measurements from different locations would be compared among each other.However, in this study the neutron counts are converted to snow water equivalents, which is inherently a sort of scaling.Author response: We will remove this scaling from the final corrected neutron counts.Changed in manuscript: Line 259 -263 "The corrected moderated neutron counts were then averaged over 13 hours.A 13-hour running average was used for the moderated neutron intensity counts in order to reduce the inherent noise of the hourly moderated neutron data and reduce measurement uncertainty, yet still allow responses to precipitation events to be observed (Zreda et al., 2008)."L240: This spacing is not appropriate (see comment L114).Add a discussion on the consequences.Author reply: We developed this study before the Köhli et al. (2015) paper came out so we used the spacing of 25, 75, and 200 m based on the original soil sampling schemes for the CRP when a footprint of 300 m was assumed.Changed in manuscript: Line 270 "This sampling scheme is based on a CRP footprint of ∼300 m radius.According to Köhli et al. (2015), the CRP footprint might be smaller C6 TCD Interactive comment Printer-friendly version Discussion paper(∼200 m radius).This study was performed prior to the new estimations of the CRP footprint so a radius of ∼300 m was still assumed and samples along the 200 m radial were included in the snow surveys."L256-259: How did your snow height and SWE data compare with predictions of this equation?Author reply: Our measurements of snow depth and SWE closely matched predictions with the equation proposed by Shook and Gray (1994).We did not include figures showing the comparison between our sampled SWE and predictions based on snow depth because our CRP predicted SWE matched closely to our sampled SWE.Thus it would be as though we were displaying the same info twice on the figures where we compare our CRP-predicted SWE and snow depth estimated SWE.L296: You should also present scatter-plots of the correlations (without the soil water storage adjustment).Author reply: We included the correlation of neutrons and SWE without the soil water storage offset in Figure 3. L321-324: This is very unlikely, since modelling of neutron transport of nonhomogenous environmental conditions have shown that only extreme cases, e.g.discrete objects like tree trunks, may have an influence on neutron intensity (e.g.Franz et al., 2015).In any case, such assumptions would need to be substantiated by a dedicated neutron transport modelling study.Author reply: We do not have neutron transport simulations to back up our statement regarding the penetration of neutrons in snow so we will remove our claim.Changed in manuscript: Line 376 -377 "However, we observed a CRP response to SWE values of greater than 70 mm, when including antecedent soil water in the upper soil profile, during the 2014/15 winter.It is not completely clear why distinct CRP responses occurred at SWE values greater than 70 mm."C7 TCD
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.142 | 0.075 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".