Articles | Volume 18, issue 11
https://doi.org/10.5194/tc-18-5323-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-5323-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Nov 2024
Research article |
| 19 Nov 2024
| 19 Nov 2024
Unlocking the potential of melting calorimetry: a field protocol for liquid water content measurement in snow
Riccardo Barella
Institute for Earth Observation, Eurac Research, Viale Druso, 1, 39100 Bolzano, Italy
Mathias Bavay
WSL Institute for Snow and Avalanche Research SLF, Davos, 7260, Switzerland
Francesca Carletti
WSL Institute for Snow and Avalanche Research SLF, Davos, 7260, Switzerland
Nicola Ciapponi
Institute for Earth Observation, Eurac Research, Viale Druso, 1, 39100 Bolzano, Italy
Valentina Premier
Institute for Earth Observation, Eurac Research, Viale Druso, 1, 39100 Bolzano, Italy
Institute for Earth Observation, Eurac Research, Viale Druso, 1, 39100 Bolzano, Italy
Related authors
Riccardo Barella, Mathias Bavay, Francesca Carletti, Nicola Ciapponi, Valentina Premier, and Carlo Marin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2892, https://doi.org/10.5194/egusphere-2023-2892, 2024
Preprint archived
Short summary
Short summary
Unlocking the potential of melting calorimetry, traditionally confined to school labs, this paper demonstrates its application in the field for accurate measurement of liquid water content in snow. Dispelling misconceptions about measurement uncertainty, it provide a robust protocol and quantifies associated uncertainties. The findings endorse the broader adoption of melting calorimetry for quantification of snow liquid water content in operational context.
Valentina Premier, Carlo Marin, Giacomo Bertoldi, Riccardo Barella, Claudia Notarnicola, and Lorenzo Bruzzone
The Cryosphere, 17, 2387–2407, https://doi.org/10.5194/tc-17-2387-2023, https://doi.org/10.5194/tc-17-2387-2023, 2023
Short summary
Short summary
The large amount of information regularly acquired by satellites can provide important information about SWE. We explore the use of multi-source satellite data, in situ observations, and a degree-day model to reconstruct daily SWE at 25 m. The results show spatial patterns that are consistent with the topographical features as well as with a reference product. Being able to also reproduce interannual variability, the method has great potential for hydrological and ecological applications.
Valentina Premier, Francesca Moschini, Jesús Casado-Rodríguez, Davide Bavera, Carlo Marin, and Alberto Pistocchi
EGUsphere, https://doi.org/10.5194/egusphere-2025-2157, https://doi.org/10.5194/egusphere-2025-2157, 2025
Short summary
Short summary
Earth observation-derived snow cover data is valuable for evaluating and improving snow modules in hydrological models. We propose a novel calibration of LISFLOOD’s snowmelt coefficient by minimizing errors between observed and modeled snow cover fraction, enhancing pixel-scale accuracy. While basin-scale performance shows minor discrepancies, a more realistic snow module leads to shifts in the timing and magnitude of snowmelt and total runoff, thus affecting the water balance.
Francesca Carletti, Carlo Marin, Chiara Ghielmini, Mathias Bavay, and Michael Lehning
EGUsphere, https://doi.org/10.5194/egusphere-2025-974, https://doi.org/10.5194/egusphere-2025-974, 2025
Short summary
Short summary
This work presents the first high-resolution dataset of wet snow properties for satellite applications. With it, we validate links between Sentinel-1 backscattering and snowmelt stages, and investigate scattering mechanisms through a radiative transfer model. We disclose the influence of liquid water content and surface roughness at different melting stages and address future challenges, such as capturing large-scale scattering mechanisms and enhancing radiative transfer modules for wet snow.
Gwendolyn Dasser, Valentin T. Bickel, Marius Rüetschi, Mylène Jacquemart, Mathias Bavay, Elisabeth D. Hafner, Alec van Herwijnen, and Andrea Manconi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1510, https://doi.org/10.5194/egusphere-2024-1510, 2024
Short summary
Short summary
Understanding snowpack wetness is crucial for predicting wet snow avalanches, but detailed data is often limited to certain locations. Using satellite radar, we monitor snow wetness spatially continuously. By combining different radar tracks from Sentinel-1, we improved spatial resolution and tracked snow wetness over several seasons. Our results indicate higher snow wetness to correlate with increased wet snow avalanche activity, suggesting our method can help identify potential risk areas.
Riccardo Barella, Mathias Bavay, Francesca Carletti, Nicola Ciapponi, Valentina Premier, and Carlo Marin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2892, https://doi.org/10.5194/egusphere-2023-2892, 2024
Preprint archived
Short summary
Short summary
Unlocking the potential of melting calorimetry, traditionally confined to school labs, this paper demonstrates its application in the field for accurate measurement of liquid water content in snow. Dispelling misconceptions about measurement uncertainty, it provide a robust protocol and quantifies associated uncertainties. The findings endorse the broader adoption of melting calorimetry for quantification of snow liquid water content in operational context.
Valentina Premier, Carlo Marin, Giacomo Bertoldi, Riccardo Barella, Claudia Notarnicola, and Lorenzo Bruzzone
The Cryosphere, 17, 2387–2407, https://doi.org/10.5194/tc-17-2387-2023, https://doi.org/10.5194/tc-17-2387-2023, 2023
Short summary
Short summary
The large amount of information regularly acquired by satellites can provide important information about SWE. We explore the use of multi-source satellite data, in situ observations, and a degree-day model to reconstruct daily SWE at 25 m. The results show spatial patterns that are consistent with the topographical features as well as with a reference product. Being able to also reproduce interannual variability, the method has great potential for hydrological and ecological applications.
Francesca Carletti, Adrien Michel, Francesca Casale, Alice Burri, Daniele Bocchiola, Mathias Bavay, and Michael Lehning
Hydrol. Earth Syst. Sci., 26, 3447–3475, https://doi.org/10.5194/hess-26-3447-2022, https://doi.org/10.5194/hess-26-3447-2022, 2022
Short summary
Short summary
High Alpine catchments are dominated by the melting of seasonal snow cover and glaciers, whose amount and seasonality are expected to be modified by climate change. This paper compares the performances of different types of models in reproducing discharge among two catchments under present conditions and climate change. Despite many advantages, the use of simpler models for climate change applications is controversial as they do not fully represent the physics of the involved processes.
Mathias Bavay, Michael Reisecker, Thomas Egger, and Daniela Korhammer
Geosci. Model Dev., 15, 365–378, https://doi.org/10.5194/gmd-15-365-2022, https://doi.org/10.5194/gmd-15-365-2022, 2022
Short summary
Short summary
Most users struggle with the configuration of numerical models. This can be improved by relying on a GUI, but this requires a significant investment and a specific skill set and does not fit with the daily duties of model developers, leading to major maintenance burdens. Inishell generates a GUI on the fly based on an XML description of the required configuration elements, making maintenance very simple. This concept has been shown to work very well in our context.
Xiaodan Wu, Kathrin Naegeli, Valentina Premier, Carlo Marin, Dujuan Ma, Jingping Wang, and Stefan Wunderle
The Cryosphere, 15, 4261–4279, https://doi.org/10.5194/tc-15-4261-2021, https://doi.org/10.5194/tc-15-4261-2021, 2021
Short summary
Short summary
We performed a comprehensive accuracy assessment of an Advanced Very High Resolution Radiometer global area coverage snow-cover extent time series dataset for the Hindu Kush Himalayan (HKH) region. The sensor-to-sensor consistency, the accuracy related to snow depth, elevations, land-cover types, slope, and aspects, and topographical variability were also explored. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006.
Pirmin Philipp Ebner, Franziska Koch, Valentina Premier, Carlo Marin, Florian Hanzer, Carlo Maria Carmagnola, Hugues François, Daniel Günther, Fabiano Monti, Olivier Hargoaa, Ulrich Strasser, Samuel Morin, and Michael Lehning
The Cryosphere, 15, 3949–3973, https://doi.org/10.5194/tc-15-3949-2021, https://doi.org/10.5194/tc-15-3949-2021, 2021
Short summary
Short summary
A service to enable real-time optimization of grooming and snow-making at ski resorts was developed and evaluated using both GNSS-measured snow depth and spaceborne snow maps derived from Copernicus Sentinel-2. The correlation to the ground observation data was high. Potential sources for the overestimation of the snow depth by the simulations are mainly the impact of snow redistribution by skiers, compensation of uneven terrain, or spontaneous local adaptions of the snow management.
Cited articles
Avanzi, F., Petrucci, G., Matzl, M., Schneebeli, M., and De Michele, C.: Early formation of preferential flow in a homogeneous snowpack observed by micro-CT, Water Resour. Res., 53, 3713–3729, https://doi.org/10.1002/2016WR019502, 2017. a
Barella, R.: Melting Calorimeter TC, Github Repository [code], https://github.com/bare92/melting_calorimeter_TC (last access: 14 November 2024), 2024. a
Davis, R. E., Dozier, J., LaChapelle, E. R., and Perla, R.: Field and Laboratory Measurements of Snow Liquid Water by Dilution, Water Resour. Res., 21, 1415–1420, https://doi.org/10.1029/WR021i009p01415, 1985. a
Denoth, A., Foglar, A., Weiland, P., Mätzler, C., Aebischer, H., Tiuri, M., and Sihvola, A.: A comparative study of instruments for measuring the liquid water content of snow, J. Appl. Phys., 56, 2154–2160, https://doi.org/10.1063/1.334215, 1984. a
Donahue, C., Skiles, S. M., and Hammonds, K.: Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements, The Cryosphere, 16, 43–59, https://doi.org/10.5194/tc-16-43-2022, 2022. a
Fasani, D., Cernuschi, F., and Colombo, L.: Calorimetric determination of wet snow liquid water content: The effect of test conditions on the calorimeter constant and its impact on the measurement uncertainty, Cold Reg. Sci. Technol., 214, 103959, https://doi.org/10.1016/j.coldregions.2023.103959, 2023. a, b, c, d, e, f, g, h, i, j
Fierz, C., Armstrong, R., Durand, Y., Etchevers, P., E., G., McClung, D., Nishimura, K., P.K., S., and Sokratov, S.: The International Classification for Seasonal Snow on the Ground, IHP-VII Technical Documents in Hydrology No 83, IACS Contribution No 1, UNESCO/IHP, https://unesdoc.unesco.org/ark:/48223/pf0000186462 (last access: 8 November 2024), 2009. a
Fisk, D.: Method of Measuring Liquid Water Mass Fraction of Snow by Alcohol Solution, J. Glaciol., 32, 538–539, https://doi.org/10.3189/S0022143000012272, 1986. a, b, c
Gagliano, E., Shean, D., Henderson, S., and Vanderwilt, S.: Capturing the Onset of Mountain Snowmelt Runoff Using Satellite Synthetic Aperture Radar, Geophys. Res. Lett., 50, e2023GL105303, https://doi.org/10.1029/2023GL105303, 2023. a
Halliday, I. G.: The Liquid Water Content of Snow Measurement in the Field, J. Glaciol., 1, 357–361, https://doi.org/10.3189/S0022143000012521, 1950. a, b
Hirashima, H., Avanzi, F., and Wever, N.: Wet-Snow Metamorphism Drives the Transition From Preferential to Matrix Flow in Snow, Geophys. Res. Lett., 46, 14548–14557, https://doi.org/10.1029/2019GL084152, 2019. a
Kendra, J., Ulaby, F., and Sarabandi, K.: Snow probe for in situ determination of wetness and density, IEEE T. Geosci. Remote, 32, 1152–1159, https://doi.org/10.1109/36.338363, 1994. a
Kinar, N. J. and Pomeroy, J. W.: Measurement of the physical properties of the snowpack, Rev. Geophys., 53, 481–544, https://doi.org/10.1002/2015RG000481, 2015. a
Leroux, N. R., Marsh, C. B., and Pomeroy, J. W.: Simulation of preferential flow in snow with a 2-D non-equilibrium Richards model and evaluation against laboratory data, Water Resour. Res., 56, e2020WR027466, https://doi.org/10.1029/2020WR027466, 2020. a
Linlor, W. I., Clapp, F. D., Meier, M. F., and Smith, J. L.: Snow wetness measurements for melt forecasting, in: Operational Appl. of Satellite Snowcover Observations, vol. 1, p. 375, https://ntrs.nasa.gov/api/citations/19760009499/downloads/19760009499.pdf (last access: 8 November 2024), 1975. a
Marin, C., Bertoldi, G., Premier, V., Callegari, M., Brida, C., Hürkamp, K., Tschiersch, J., Zebisch, M., and Notarnicola, C.: Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions, The Cryosphere, 14, 935–956, https://doi.org/10.5194/tc-14-935-2020, 2020. a
Mavrovic, A., Madore, J.-B., Langlois, A., Royer, A., and Roy, A.: Snow liquid water content measurement using an open-ended coaxial probe (OECP), Cold Reg. Sci. Technol., 171, 102958, https://doi.org/10.1016/j.coldregions.2019.102958, 2020. a
Moffat, R. J.: Describing the uncertainties in experimental results, Exp. Therm. Fluid Sci., 1, 3–17, 1988. a
Moure, A., Jones, N., Pawlak, J., Meyer, C., and Fu, X.: A Thermodynamic Nonequilibrium Model for Preferential Infiltration and Refreezing of Melt in Snow, Water Resour. Res., 59, e2022WR034035, https://doi.org/10.1029/2022WR034035, 2023. a
Perla, R.: Real permittivity of snow at 1 MHz and 0°C, Cold Reg. Sci. Technol., 19, 215–219, https://doi.org/10.1016/0165-232X(91)90011-5, 1991. a, b, c, d
Perla, R. and Banner, J.: Calibration of capacitive cells for measuring water in snow, Cold Reg. Sci. Technol., 15, 225–231, https://doi.org/10.1016/0165-232X(88)90069-9, 1988. a
Picard, G., Leduc-Leballeur, M., Banwell, A. F., Brucker, L., and Macelloni, G.: The sensitivity of satellite microwave observations to liquid water in the Antarctic snowpack, The Cryosphere, 16, 5061–5083, https://doi.org/10.5194/tc-16-5061-2022, 2022. a, b
Proksch, M., Rutter, N., Fierz, C., and Schneebeli, M.: Intercomparison of snow density measurements: bias, precision, and vertical resolution, The Cryosphere, 10, 371–384, https://doi.org/10.5194/tc-10-371-2016, 2016. a, b
Schlumpf, M., Hendrikx, J., Stormont, J., and Webb, R.: Quantifying short-term changes in snow strength due to increasing liquid water content above hydraulic barriers, Cold Reg. Sci. Technol., 218, 104056, https://doi.org/10.1016/j.coldregions.2023.104056, 2024. a
Stein, J., Laberge, G., and Lévesque, D.: Monitoring the dry density and the liquid water content of snow using time domain reflectometry (TDR), Cold Reg. Sci. Technol., 25, 123–136, https://doi.org/10.1016/S0165-232X(96)00022-5, 1997. a
Techel, F. and Pielmeier, C.: Point observations of liquid water content in wet snow – investigating methodical, spatial and temporal aspects, The Cryosphere, 5, 405–418, https://doi.org/10.5194/tc-5-405-2011, 2011. a, b
Valence, E., Baraer, M., Rosa, E., Barbecot, F., and Monty, C.: Drone-based ground-penetrating radar (GPR) application to snow hydrology, The Cryosphere, 16, 3843–3860, https://doi.org/10.5194/tc-16-3843-2022, 2022. a
Webb, R. W., Marziliano, A., McGrath, D., Bonnell, R., Meehan, T. G., Vuyovich, C., and Marshall, H.-P.: In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications, Remote Sens., 13, 4617, https://doi.org/10.3390/rs13224617, 2021. a, b
Wever, N., Fierz, C., Mitterer, C., Hirashima, H., and Lehning, M.: Solving Richards Equation for snow improves snowpack meltwater runoff estimations in detailed multi-layer snowpack model, The Cryosphere, 8, 257–274, https://doi.org/10.5194/tc-8-257-2014, 2014. a
Wever, N., Vera Valero, C., and Fierz, C.: Assessing wet snow avalanche activity using detailed physics based snowpack simulations, Geophys. Res. Lett., 43, 5732–5740, https://doi.org/10.1002/2016GL068428, 2016. a
Yosida, Z.: Instruments and Methods: A Calorimeter for Measuring the Free Water Content of Wet Snow, J. Glaciol., 3, 574–576, https://doi.org/10.3189/S0022143000023698, 1960. a, b
Yosida, Z.: Free water content of wet snow, Physics of Snow and Ice: proceedings, 1, 773–784, 1967. a
Short summary
This research revisits a classic scientific technique, melting calorimetry, to measure snow liquid water content. This study shows with a novel uncertainty propagation framework that melting calorimetry, traditionally less trusted than freezing calorimetry, can produce accurate results. The study defines optimal experiment parameters and a robust field protocol. Melting calorimetry has the potential to become a valuable tool for validating other liquid water content measuring techniques.
This research revisits a classic scientific technique, melting calorimetry, to measure snow...
