Preprints
https://doi.org/10.5194/tc-2021-247
https://doi.org/10.5194/tc-2021-247

  12 Aug 2021

12 Aug 2021

Review status: this preprint is currently under review for the journal TC.

Mapping liquid water content in snow: An intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements

Christopher Donahue1, S. McKenzie Skiles2, and Kevin Hammonds1 Christopher Donahue et al.
  • 1Department of Civil Engineering, Montana State University, Bozeman, MT, 59717, USA
  • 2Department of Geography, University of Utah, Salt Lake City, UT, 84112, USA

Abstract. It is well understood that the distribution and quantity of liquid water in snow is relevant for snow hydrology and avalanche forecasting, yet detecting and quantifying liquid water in snow remains a challenge from the micro- to the macro-scale. Using near-infrared (NIR) spectral reflectance measurements, previous case studies have demonstrated the capability to retrieve surface liquid water content (LWC) of wet snow by leveraging shifts in the complex refractive index between ice and water. However, different models to represent mixed-phase optical properties have been proposed, including (1) internally mixed ice and water spheres, (2) internally mixed water coated ice spheres, and (3) externally mixed interstitial ice and water spheres. Here, from within a controlled laboratory environment, we determine the optimal mixed-phase optical property model for simulating wet snow reflectance using a combination of NIR hyperspectral imaging, radiative transfer simulations (DISORT), and an independent dielectric LWC measurement (SLF Snow Sensor). Maps of LWC were produced by finding the least residual between measured reflectance and simulated reflectance in spectral libraries, generated for each model with varying LWC and grain size, and assessed against the in situ LWC sensor. Our results show that the externally mixed model performed the best, retrieving LWC with an uncertainty of ~1 %, while the simultaneously retrieved grain size better represented wet snow relative to the established scaled band area method. Furthermore, the LWC retrieval method was demonstrated in the field, imaging a snowpit sidewall during melt conditions, mapping pooling water, flow features, and LWC distribution in unprecedented detail.

Christopher Donahue et al.

Status: open (until 07 Oct 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-247', Ryan Webb, 26 Aug 2021 reply
  • RC2: 'Comment on tc-2021-247', Chander Shekhar, 15 Sep 2021 reply

Christopher Donahue et al.

Christopher Donahue et al.

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Short summary
The amount of water within a snowpack is important information for predicting snow melt and wet snow avalanches. From within a controlled laboratory, the optimal method for measuring liquid water content (LWC) at the snow surface or along a snowpit profile using near-infrared imagery was determined. As snow samples melted, multiple models to represent wet snow reflectance were assessed against a more established LWC instrument. The best model represents snow as separate spheres of ice and water.