Development of a 2D high-resolution field method to measure liquid water content in snow

The liquid water content (LWC) of snow is a key parameter controlling snowpack stability, runoff generation, and the timing of meltwater release (Vorkauf et al., 2021). With climate warming, rain-on-snow events and earlier snowmelt are becoming more frequent (Beniston et al., 2016), raising challenges for water management, hydropower production, flood warning, and avalanche forecasting. Despite its importance, accurate measurement of LWC in the field remains difficult. Existing methods, such as calorimetry, centrifugal separation, and dielectric sensors (Denoth et al., 1984), provide useful estimates but are limited by relatively high uncertainties (1-2% LWC) and low spatial resolution (> 3 cm). Hyperspectral imaging can resolve LWC variability at millimetre scale but is costly and impractical for routine fieldwork.In recent years, the Snow Physics group at WSL/SLF has developed the SnowImager, a near-infrared (NIR) imaging instrument capable of capturing snow properties at high spatial resolution (Macfarlane et al., 2023). Using this instrument, we investigated the influence of liquid water on reflectance images by comparing the relative difference between a wet snow surface and its (re)frozen dry reference state. The obtained trend as a function of LWC is consistent with theoretical predictions based on a modified single scattering equation that accounts for both LWC and SSA. Building on this result, we developed a straightforward method to estimate LWC from reflectance images acquired with the SnowImager. Preliminary cold-lab and field tests confirmed the feasibility of this approach and demonstrated its potential to produce quantitative, high-resolution 2D maps of LWC.We anticipate that the resulting 2D LWC field method will provide cryospheric researchers with a long-needed, practical, and precise tool to characterize the spatiotemporal dynamics of wet snow. This advancement will support improving wet snow avalanche forecasting, melt water runoff modelling, and climate impact assessments, while enhancing the SnowImager’s role as a versatile instrument for the international snow science community. REFERENCESBeniston, M., & Stoffel, M. (2016). Rain-on-snow events, floods and climate change in the Alps: Events may increase with warming up to 4 °C and decrease thereafter. Science of the Total Environment, 571, 228–236. https://doi.org/10.1016/j.scitotenv.2016.07.146Denoth, A., Foglar, A., Weiland, P., Mätzler, C., Aebischer, H., Tiuri, M., & Sihvola, A. (1984). A comparative study of instruments for measuring the liquid water content of snow. Journal of Applied Physics, 56(7), 2154–2160. https://doi.org/10.1063/1.334215Macfarlane, A. R., Dadic, R., Smith, M. M., Light, B., Nicolaus, M., Henna-Reetta, H., Webster, M., Linhardt, F., Hämmerle, S., & Schneebeli, M. (2023). Evolution of the microstructure and reflectance of the surface scattering layer on melting, level Arctic sea ice. Elementa: Science of the Anthropocene, 11(1), Article 00103. https://doi.org/10.1525/elementa.2022.00103Vorkauf, M., Marty, C., Kahmen, A., et al. (2021). Past and future snowmelt trends in the Swiss Alps: The role of temperature and snowpack. Climatic Change, 165, Article 44. https://doi.org/10.1007/s10584-021-03027-x