94 GHz radar mapping of terrestrial snow cover
William D. HarcourtDuncan A. RobertsonDavid G. MacfarlaneBrice R. ReaM. R. JamesBlair FyffeMark Diggins
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<p>Terrestrial snow cover is a perennial feature throughout the global cryosphere, taking the form of individual snow patches during summer and becoming more spatially continuous in winter. The characteristics and conditions of these snowpacks can be altered by rapid changes in temperature and precipitation, significantly impacting local ecosystems, upland hydrology and snow avalanche risks. In Scotland, for example, monitoring the hazards associated with snowpack alterations is a central focus of the Scottish Avalanche Information Service (SAIS) and is essential to ensuring the safety of local communities, hill walkers and mountaineers. In this context, the development of new remote sensing techniques for snow monitoring will help the SAIS develop avalanche forecasts and potentially without the need to undertake arduous and dangerous fieldwork. Here, we aim to develop the utility of millimetre-wave radar at 94 GHz as a new remote sensing tool for monitoring snowpacks. We use a ground-based 94 GHz, real-aperture system called AVTIS2 which mechanically scans across a scene of interest to generate radar backscatter images and 3D Digital Elevation Models (DEMs). AVTIS2 uses a narrow beamwidth of 0.35&#176; (i.e. a spot size of 6 m per km) and has a maximum range of ~6 km, enabling kilometre-scale mapping at high angular resolution. This radar system has previously been successful in monitoring the topographic changes of volcanic lava domes, measuring the dynamics of active lava flows and quantifying 94 GHz radar backscatter from glacier ice. We aim to deploy the AVTIS2 millimetre-wave radar in the Cairngorms National Park, Scotland, in January/February 2021 and validate our measurements with a co-located Terrestrial Laser Scanner (TLS). Additionally, we will acquire in situ observations of snow properties to gain a better understanding of how 94 GHz radar signals interact with the snowpack. Overall, we will report on the following: (1) the radar backscatter characteristics from a variety of snow surface conditions at millimetre wavelengths; (2) point cloud and DEM differences between AVTIS2 and TLS measurements over snow-covered terrain; and (3) the effect of snowpack properties on radar backscatter and how this can be used to understand snow-associated hazards.</p>Keywords:
Snowpack
Elevation (ballistics)
Outflow
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Abstract Snowpack dynamics through October 2014–June 2017 were described for a forested, sub‐alpine field site in southeastern Wyoming. Point measurements of wetness and density were combined with numerical modeling and continuous time series of snow depth, snow temperature, and snowpack outflow to identify 5 major classes of distinct snowpack conditions. Class (i) is characterized by no snowpack outflow and variable average snowpack temperature and density. Class (ii) is characterized by short durations of liquid water in the upper snowpack, snowpack outflow values of 0.0008–0.005 cm hr −1 , an increase in snowpack temperature, and average snow density between 0.25–0.35 g cm −3 . Class (iii) is characterized by a partially saturated wetness profile, snowpack outflow values of 0.005–0.25 cm hr −1 , snowpack temperature near 0 °C, and average snow density between 0.25–0.40 g cm −3 . Class (iv) is characterized by strong diurnal snowpack outflow pattern with values as high as 0.75 cm hr −1 , stable snowpack temperature near 0 °C, and stable average snow density between 0.35–0.45 g cm −3 . Class (v) occurs intermittently between Classes (ii)–(iv) and displays low snowpack outflow values between 0.0008–0.04 cm hr −1 , a slight decrease in temperature relative to the preceding class, and similar densities to the preceding class. Numerical modeling of snowpack properties with SNOWPACK using both the Storage Threshold scheme and Richards' equation was used to quantify the effect of snowpack capillarity on predictions of snowpack outflow and other snowpack properties. Results indicate that both simulations are able to predict snow depth, snow temperature, and snow density reasonably well with little difference between the 2 water transport schemes. Richards' equation more accurately simulates the timing of snowpack outflow over the Storage Threshold scheme, especially early in the melt season and at diurnal timescales.
Snowpack
Outflow
Liquid water content
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Abstract The common observation that snowpack increases with elevation suggests that a catchment's elevation distribution should be a robust indicator of its potential to store snow and its sensitivity to snowpack loss. To capture a wide range of potential elevation‐based responses, we used Monte Carlo methods to simulate 20,000 watershed elevation distributions. We applied a simple function relating warming, elevation, and snowpack to explore snowpack losses from the simulated elevation distributions. Regression analyses demonstrate that snowpack loss is best described by three parameters that identify the central tendency, variance, and shape of each catchment's elevation distribution. Equal amounts of snowpack loss can occur even when catchments are centered within different elevation zones; this stresses the value of also measuring the variance and shape of elevation distributions. Responses of the simulated elevation distributions to warming are nonlinear and emphasize that the sensitivity of mountain forests to snowpack loss will likely be watershed dependent.
Snowpack
Elevation (ballistics)
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Abstract. Manual snowpack observations are an important component of avalanche hazard assessment for the Swiss avalanche forecasting service. Approximately 900 snow profiles are observed each winter, in flat study plots or on representative slopes. So far, these profiles have been manually classified combining both information on snow stability (e.g. Rutschblock test) and snowpack structure (e.g. layering, hardness). To separate the classification of snowpack stability and structure, and also to reduce inconsistencies in ratings between forecasters, we developed and tested an automatic approach to classify profiles by snowpack structure during two winters. The automatic classification is based on a calculated index, which consists of three components: properties of (1) the slab (thickness), (2) weakest layer interface and (3) the percentage of the snowpack which is soft, coarse-grained and consists of persistent grain types. The latter two indices are strongly based on criteria described in the threshold sum approach. The new snowpack structure index allows a consistent comparison of snowpack structure to detect regional patterns, seasonal or inter-annual differences but may also supplement snow-climate classifications.
Snowpack
Layering
National weather service
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Abstract We employ a regression‐based methodology to study the impact of temperature and precipitation on snowpack variability as a function of elevation in the Central Rocky Mountains. Because of the broad horizontal coverage and thermal heterogeneity of the measurement sites employed, we introduce an elevation correction based on the sites' climatological temperature. For the elevation range investigated (1295–2256 m), and assuming an average atmospheric lapse rate of −6.5°C/km, we find a mostly linear relationship between effective elevation and correlation of temperature or precipitation with snow water equivalent and snowpack duration. We estimate a threshold elevation, 1560 ± 120 m, below (above) which temperature (precipitation) is the main driver of the snowpack. This threshold elevation is robust under a range of assumed atmospheric lapse rates. Locations below this elevation are likely to be affected by projected rising temperatures, with important effects on ecosystems and economic activities dependent on snow.
Snowpack
Elevation (ballistics)
Lapse rate
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Citations (61)
Abstract. Manual snowpack observations are an important component of avalanche hazard assessment for the Swiss avalanche forecasting service. Approximately 900 snow profiles are observed each winter, in flat study plots or on representative slopes. So far, these profiles are manually classified combining both information on snow stability (e.g. Rutschblock test) and snowpack structure (e.g. layering, hardness). To separate the classification of snowpack stability and structure, and also to reduce inconsistencies in ratings between forecasters, we developed and tested an automatic approach to classify profiles by snowpack structure during two winters. The automatic classification is based on a calculated index, which consists of three components: properties of (1) the slab (thickness), (2) weakest layer interface and (3) the percentage of the snowpack which is soft, coarse-grained and consists of persistent grain types. The latter two indices are strongly based on criteria described in the threshold sum approach. The new snowpack structure index allows a consistent comparison of snowpack structure to detect regional patterns, seasonal or inter-annual differences but may also supplement snow-climate classifications.
Snowpack
Layering
National weather service
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