Preprints
https://doi.org/10.5194/egusphere-2025-6148
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2025-6148
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
28 Jan 2026
| 28 Jan 2026
Status: this preprint is open for discussion and under review for The Cryosphere (TC).
Brief communication: Network-wide parameterisation for estimating snow water equivalent through cosmic ray neutron sensors in the Italian Alps
Abstract. We present a novel approach based on leveraging a network-wide parameterisation to derive snow water equivalent (SWE) with cosmic ray neutron sensing (CRNS) probes. The network comprises 26 sites (1422–2901 m asl) in the Italian Alps. The parameterisation was defined by fitting neutron counts to 35 SWE measurements taken at 6 sites in the first half of the 2023–2024 snow season and validated with 111 SWE data from 2023–2024 and 2024–2025 at 13 sites. Our analysis shows that this approach retains good representativeness of the snowpack, which can be extended to unmonitored sites if they have monitored counterparts at similar elevation. This finding overcomes the need for year-round accessibility and increases the number of potential sites for continuous SWE retrieval.
How to cite. Gallarate, M., Colombo, N., Gazzola, E., Valt, M., Ronchi, C., Lanteri, L., Dinale, R., Nadalet, R., Ferraris, S., Gentile, A., Gisolo, D., Freppaz, M., and Acquaotta, F.: Brief communication: Network-wide parameterisation for estimating snow water equivalent through cosmic ray neutron sensors in the Italian Alps, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-6148, 2026.
Competing interests: Author Enrico Gazzola is currently empoyed by the company that produces the CRNS probes used for this work. The other authors declare that they have no competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.Download & links
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Earth Sciences Department - DST, University of Torino, Torino, 10125, Italy
Department of Environmental Sciences, Informatics and Statistics - DAIS, Ca’ Foscari University of Venice, Mestre, 30172, Italy
Centro Interdipartimentale sui Rischi Naturali in Ambiente Montano e Collinare – NatRisk, Grugliasco, 10095, Italy
Nicola Colombo
Department of Agricultural, Forest and Food Sciences – DISAFA, University of Torino, Grugliasco, 10095, Italy
Enrico Gazzola
Finapp S.p.A., Montegrotto Terme, 35036, Italy
Mauro Valt
Agency for Environmental Prevention and Protection of Veneto - ARPAV, Belluno, Italy
Christian Ronchi
Department of Natural and Environmental Risks, Regional Agency for Environmental Protection of Piemonte – ARPA Piemonte, Torino, 10135, Italy
Luca Lanteri
Department of Natural and Environmental Risks, Regional Agency for Environmental Protection of Piemonte – ARPA Piemonte, Torino, 10135, Italy
Roberto Dinale
Office for Hydrology and Dams, Autonomous Province of Bolzano, Bolzano, 39100, Italy
Rudi Nadalet
Office for Hydrology and Dams, Autonomous Province of Bolzano, Bolzano, 39100, Italy
Stefano Ferraris
Interuniversity Department of Regional Studies and Planning – DIST, University of Torino and Polytechnic University of Torino, Torino, 10125, Italy
Alessio Gentile
Interuniversity Department of Regional Studies and Planning – DIST, University of Torino and Polytechnic University of Torino, Torino, 10125, Italy
Davide Gisolo
Interuniversity Department of Regional Studies and Planning – DIST, University of Torino and Polytechnic University of Torino, Torino, 10125, Italy
Michele Freppaz
Department of Agricultural, Forest and Food Sciences – DISAFA, University of Torino, Grugliasco, 10095, Italy
Centro Interdipartimentale sui Rischi Naturali in Ambiente Montano e Collinare – NatRisk, Grugliasco, 10095, Italy
Fiorella Acquaotta
Earth Sciences Department - DST, University of Torino, Torino, 10125, Italy
Centro Interdipartimentale sui Rischi Naturali in Ambiente Montano e Collinare – NatRisk, Grugliasco, 10095, Italy
Short summary
We present a network of 26 sensors measuring snow water equivalent through cosmic rays across the Italian Alps. We study the application of a parameterisation shared by all the probes. The parameters were defined and validated with data taken during two seasons at 13 sites. We show that the parameterisation gives a good estimate of the water equivalent. This finding can contribute to expand data availability by installing similar probes in sites at high elevations and in inaccessible locations.
We present a network of 26 sensors measuring snow water equivalent through cosmic rays across...
This manuscript presents a network-wide parameterisation to estimate snow water equivalent (SWE) using cosmic ray neutron sensing (CRNS) across sites in the Italian Alps.
The topic is highly relevant and of significant interest to the cryosphere and hydrological communities, and the authors’ effort to leverage network information to enhance SWE monitoring with CRNS stations represents a highly valuable contribution.
The manuscript is generally well written, and the approach is promising; however, several major issues related to the representation of the network and the methods, the clarity of the validation procedure, and the robustness of the conclusions need to be addressed before publication. I outline these points in the following major and specific comments.
Major comments
From the 26 sites, only 6 sites were used for calibration and 13 sites for validation. In its current form, the narrative risks implying broader validation than is actually demonstrated, e.g. from the title and the abstract. The authors should clearly and consistently distinguish between (i) the full network, (ii) the calibration subset, (iii) the validation subset, and (iv) sites to which the method is extrapolated. In particular, the abstract and conclusions should avoid wording that could be interpreted as network-wide validation. I recommend explicitly stating the number of sites used for calibration and validation wherever network performance is discussed.
The proposed method for converting neutron counts for SWE (Eqs. 1 and 2) introduces five free parameters (Λmax, Λmin, a1, a2, a3), which are calibrated using 35 SWE observations collected across six sites. While 35 measurements may appear sufficient in absolute terms, the effective degrees of freedom are substantially reduced when accounting for site-level clustering, potential temporal autocorrelation, and shared environmental conditions. This raises the question of whether the calibration is over-parameterised relative to the available independent information content. In particular:
Given that the study aims to propose a network-wide parameterisation, parameter stability and transferability are critical. I therefore encourage the authors to justify the choice of five free parameters explicitly, assess potential overfitting (e.g., via cross-validation or information criteria), and discuss parameter sensitivity and identifiability. If the goal is broad applicability across sites, a reduced or partially constrained parameter set (for example, fixing Λmax and Λmin based on physical reasoning or literature values) might improve robustness and interpretability. Addressing this issue would significantly strengthen the methodological foundation of the manuscript.
The manuscript would benefit from a substantially more detailed description of the CRNS data processing workflow. At present, key preprocessing steps are either only briefly mentioned or not described in sufficient detail to ensure reproducibility and allow readers to evaluate the robustness of the SWE retrieval. In particular, the authors should clearly document:
Given that SWE estimates derived from CRNS are highly sensitive to preprocessing choices, a transparent, step-by-step description is essential. I strongly recommend either adding a dedicated subsection in Methods that outlines the complete processing chain, or roviding a reproducible workflow (e.g., in the Supplement).
Chapter 2 would benefit from a clearer and more logical restructuring with subchapters as follows: 1) Manual SWE sampling, 2) station setup and 3) CRNS processing and SWE calculation including atmospheric pressure correction, incident neutron flux correction, detailed description of parameterisation and calculation of SWE from neutron counts. This reorganisation improves the logical flow of the chapter.
The use of local muon flux measurements to correct for incoming neutrons has not yet been demonstrated to be highly accurate. I recommend that the authors provide a comparison with the standard correction approach to assess the validity and performance of this method.
Specific comments
L58: You should consistently use the term “sensor”.
L68: It is unclear what the authors are referring to with the term “main Alpine watershed.”
L88: Please change “barometric factor correction” to “atmospheric pressure correction” to align with standard CRNS nomenclature. In addition, recent work by Davies et al. (2026) demonstrates that the barometric coefficient is strongly dependent on-site elevation. Given that the network in this study spans a wide elevation range (1422–2901 m a.s.l.), it is unclear whether elevation-dependent variations in the barometric coefficient were accounted for. In case a single coefficient values was used, please provide justification for why you are confident that this does not introduce systematic bias across the network. Addressing this issue is critical, as uncorrected elevation-dependent variations in the barometric coefficient could directly affect the accuracy and comparability of the SWE estimates across the network.
L90: Please explain in greater detail how you determined the baseline neutron count rate. In addition, the current use of the variable name N₀ may lead to confusion with the well-established N₀ parameter used in standard CRNS soil moisture calibration. Because the present study defines a conceptually different baseline quantity, I strongly recommend to adopt a distinct symbol (e.g., N_ref, N_base, or similar).
L93: Please explain in greater detail how you determined the normalised count rate (Nr).
L124-130: In this case these stations need to be excluded from the analysis.
L149-152: Please refer to Figure S1.
L155: The current validation procedure includes both calibration and non-calibration stations. While this provides an overall assessment of performance, it can potentially overestimate the method’s predictive skill because the calibration sites have already influenced the parameterisation. Therefore, I recommend separating the validation sites from calibration site explicitly.
L173: Given that you are using a buried CRNS sensor, whose footprint is already very small, the explanation provided does not seem physically sensible.
L193: The manuscript cites Gottardi et al., 2013, but this reference appears to be inaccessible. Ensuring that all references are accessible is essential for reproducibility and for readers to follow the methodology or contextual background.
L197: It is unclear which variable is being correlated (e.g., SWE, neutron count rate), which stations are included in the analysis (all 26 network sites, only calibration sites, or only validation sites), and whether the correlation refers to site-averaged values, individual measurements, or temporal series. The authors should clarify these points explicitly in the text and in the figure caption.
L197-203: In my view, this correlation analysis is only meaningful if each station has been individually calibrated. Otherwise, the uncertainties introduced by transferring the parameterisation across sites may dominate the variability and artificially inflate or suppress correlation values.
Literature
Davies, P., Baatz, R., Schattan, P., Quansah, E., Amekudzi, L. K., and Bogena, H. R. (2026). On the Variability of the Barometric Effect and Its Relation to Cosmic-Ray Neutron Sensing. Sensors, 26(3), 925.