Implementing a Snow Depth Measurement Sensor for the IoT

As part of a new snowpack monitoring framework, this study evaluated the feasibility of using an LED LIDAR (Leddar) time of flight sensor for snowpack depth measurement. The Leddar sensor has two additional features over simple sonic ranging sensors: (i) the return signal is divided into 16 segments across a 48° field of view, each recording individual distance-to-target (DTT) measurements; (ii) an index of reflectance or intensity signal is recorded for each segment. These two features provide information describing snowpack morphology and surface condition. The accuracy of Leddar sensor DTT measurements for snow depth monitoring was found to be < 20 mm, which was better than the 50 mm quoted by the manufacturer, and the precision was < 5 mm. Leddar and independent sonic ranger snow depth measurement showed strong linear agreement (r2 = 0.98). There was also a strong linear relationship (r2 = 0.98) between Leddar and manual field snow depth measurements. The intensity signal response was found to correlate with snow surface albedo and inversely with air temperature (r = 0.77 and −0.77, respectively).

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Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

The Cryosphere ◽

10.5194/tc-10-257-2016 ◽

2016 ◽

Vol 10 (1) ◽

pp. 257-269 ◽

Cited By ~ 28

Author(s):

Z. Zheng ◽

P. B. Kirchner ◽

R. C. Bales

Keyword(s):

Sierra Nevada ◽

Snow Depth ◽

Point Cloud ◽

Accumulation Point ◽

Snow Accumulation ◽

Slope Aspect ◽

Conifer Forest ◽

Vegetation Effects ◽

Cloud Data ◽

Depth Measurement

Abstract. Airborne light detection and ranging (lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak-snow-accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow-accumulation zone, with a total surveyed area of over 106 km2. The percentage of pixels with at least one snow-depth measurement was observed to increase from 65–90 to 99 % as the sampling resolution of the lidar point cloud was increased from 1 to 5 m. However, a coarser resolution risks undersampling the under-canopy snow relative to snow in open areas and was estimated to result in at least a 10 cm overestimate of snow depth over the main snow-accumulation region between 2000 and 3000 m, where 28 % of the area had no measurements. Analysis of the 1 m gridded data showed consistent patterns across the four sites, dominated by orographic effects on precipitation. Elevation explained 43 % of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14 % over the elevation range of 1500–3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under-canopy areas increased with elevation in the rain–snow transition zone (1500–1800 m) and was about 35 &pm; 10 cm above 1800 m. Lidar has the potential to transform estimation of snow depth across mountain basins, and including local canopy effects is both feasible and important for accurate assessments.

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What drives basin scale spatial variability of snow water equivalent during two extreme years?

The Cryosphere Discussions ◽

10.5194/tcd-7-2943-2013 ◽

2013 ◽

Vol 7 (3) ◽

pp. 2943-2977

Author(s):

G. A. Sexstone ◽

S. R. Fassnacht

Keyword(s):

Linear Regression ◽

Spatial Variability ◽

Multiple Linear Regression ◽

Snow Depth ◽

Snow Water Equivalent ◽

Density Model ◽

Snow Density ◽

Depth Measurement ◽

Basin Scale ◽

Snow Water

Abstract. This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow water equivalent (SWE) spatial variability at the basin scale. Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE. Bivariate relations and multiple linear regression models were developed to understand the relation of SWE with terrain and canopy variables (derived using a geographic information system (GIS)). Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance and model validation suggests the model is transferable to independent data within the bounds of the original dataset. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many time consuming measurements of SWE are often not feasible. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and UTM Northing) were the most important model variables, suggesting that orographic precipitation and storm track patterns are likely consistent drivers of basin scale SWE variability. Terrain characteristics, such as slope, aspect, and curvature, were also shown to be important variables, but to a lesser extent at the scale of interest.

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Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs

Atmosphere ◽

10.3390/atmos13010003 ◽

2021 ◽

Vol 13 (1) ◽

pp. 3

Author(s):

Douglas M. Hultstrand ◽

Steven R. Fassnacht ◽

John D. Stednick ◽

Christopher A. Hiemstra

Keyword(s):

Snow Depth ◽

Depth Distribution ◽

Generalized Additive Models ◽

Winter Season ◽

Regression Tree ◽

Distribution Patterns ◽

Time Frame ◽

Additive Models ◽

Depth Measurement ◽

Accumulation Index

A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, yet the interaction of topography and meteorological patterns tends to generate similar inter-annual snow depth distribution patterns. Here, we question whether snow depth patterns at or near peak accumulation are repeatable for a 10-year time frame and whether years with limited snow depth measurement can still be used to accurately represent snow depth and mean snow depth. We used snow depth measurements from the West Glacier Lake watershed, Wyoming, U.S.A., to investigate the distribution of snow depth. West Glacier Lake is a small (0.61 km2) windswept (mean of 8 m/s) watershed that ranges between 3277 m and 3493 m. Three interpolation methods were compared: (1) a binary regression tree, (2) multiple linear regression, and (3) generalized additive models. Generalized additive models using topographic parameters with measured snow depth presented the best estimates of the snow depth distribution and the basin mean amounts. The snow depth patterns near peak accumulation were found to be consistent inter-annually with an average annual correlation coefficient (r2) of 0.83, and scalable based on a winter season accumulation index (r2 = 0.75) based on the correlation between mean snow depth measurements to Brooklyn Lake snow telemetry (SNOTEL) snow depth data.

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Snow depth monitoring with GNSS reflectometry: Results from Antarctica and selected geodetic ground stations

10.5194/ems2021-187 ◽

2021 ◽

Author(s):

Tzvetan Simeonov ◽

Markus Ramatschi ◽

Sibylle Vey ◽

Jens Wickert

Keyword(s):

Solar Radiation ◽

Snow Depth ◽

Snow Accumulation ◽

Phase Changes ◽

New Technique ◽

Depth Measurement ◽

Ice Caps ◽

Single Antenna ◽

The Earth ◽

Gnss Reflectometry

The permanent and seasonal snow covers are an important element of the global hydrological cycle and have substantial influence on global climate. Currently around 10% of the Earth&#8217;s land surface is covered by glaciers, ice caps and snow cover. Snow and ice cover play important role in the Earth&#8217;s climate by reflecting solar radiation and thus decreasing the average Earth temperature. Glaciers and ice caps participate in a positive feedback loop in the Earth&#8217;s climate. By contracting due to increasing temperatures, they reflect less solar radiation, further contributing to the global temperatures increase.Using the single antenna ground-based GNSS Reflectometry (GNSS-R) method for snow depth estimation is an emerging application. A new technique for snow depth measurement using the phase changes in the observed SNR data, rather than the height estimates, is validated in a GNSS-R setup in Antarctic station Neumayer III. The new technique shows improved characteristics to the classical single antenna ground-based GNSS-R snow depth determination method. The validation is done in an environment of constant snow accumulation. The results from new technique show high correlation of the de-trended datasets between the GNSS-R and in-situ snow buoy measurements of 0.85. The de-trended classical height estimations of the SNR show lower correlation to the snow buoys of 0.60.A screening of the International GNSS Service (IGS) global network shows, that snow depth observations are possible in only 7 of the 506 available stations. The main limitations on the stations are the local topography and climate. The snow depth observations from these seven stations are compared with the ERA5 snow depth estimations, local measurements and climate normals. The analysis of the data for station Visby, following the new GNSS-R analysis technique, shows very high correlation of 0.91 and low RMSE of 2.26cm, while the classical GNSS-R estimation has RMSE of 2.48cm and ERA5 shows RMSE of 4.2cm when compared to local meteorological observations.

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Uncertainty analysis for evaluating the accuracy of snow depth measurements

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-4157-2015 ◽

2015 ◽

Vol 12 (4) ◽

pp. 4157-4190 ◽

Cited By ~ 1

Author(s):

J.-E. Lee ◽

G. W. Lee ◽

M. Earle ◽

R. Nitu

Keyword(s):

Snow Depth ◽

Error Propagation ◽

Step Test ◽

Bias Error ◽

Depth Sensor ◽

Depth Measurement ◽

Depth Sensors ◽

Range Check ◽

Base Data ◽

Average Uncertainty

Abstract. A methodology for quantifying the accuracy of snow depth measurement are demonstrated in this study by using the equation of error propagation for the same type sensors and by compariong autimatic measurement with manual observation. Snow depth was measured at the Centre for Atmospheric Research Experiments (CARE) site of the Environment Canada (EC) during the 2013–2014 winter experiment. The snow depth measurement system at the CARE site was comprised of three bases. Three ultrasonic and one laser snow depth sensors and twelve snow stakes were placed on each base. Data from snow depth sensors are quality-controlled by range check and step test to eliminate erroneous data such as outliers and discontinuities. In comparison with manual observations, bias errors were calculated to show the spatial distribution of snow depth by considering snow depth measured from four snow stakes located on the easternmost side of the site as reference. The bias error of snow stakes on the west side of the site was largest. The uncertainty of all pairs of stakes and the average uncertainty for each base were 1.81 and 1.52 cm, respectively. The bias error and normalized bias removed root mean square error (NBRRMSE) for each snow depth sensor were calculated to quantify the systematic error and random error in comparison of snow depth sensors with manual observations that share the same snow depth target. The snow depth sensors on base 12A (11A) measured snow depth larger (less) than manual observation up to 10.8 cm (5.21 cm), and the NBRRMSEs ranged from 5.10 to 16.5%. Finally, the instrumental uncertainties of each snow depth sensor were calculated by comparing three sensors of the same type installed at the different bases. The instrumental uncertainties ranged from 0.62 to 3.08 cm.

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What drives basin scale spatial variability of snowpack properties in northern Colorado?

The Cryosphere ◽

10.5194/tc-8-329-2014 ◽

2014 ◽

Vol 8 (2) ◽

pp. 329-344 ◽

Cited By ~ 24

Author(s):

G. A. Sexstone ◽

S. R. Fassnacht

Keyword(s):

Linear Regression ◽

Spatial Variability ◽

Multiple Linear Regression ◽

Snow Depth ◽

Density Model ◽

Snow Density ◽

Data Set ◽

Depth Measurement ◽

Basin Scale ◽

Snowpack Properties

Abstract. This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow density and snow water equivalent (SWE) variability at the basin scale (100s to 1000s km2). Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE near peak snow accumulation. Bivariate relations and multiple linear regression models were developed to understand the relation of snow density and SWE with terrain variables (derived using a geographic information system (GIS)). Snow density variability was best explained by day of year, snow depth, UTM Easting, and elevation. Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance, and model validation suggests the model is transferable to independent data within the bounds of the original data set. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many time-consuming measurements of SWE are often not feasible. A comparison with a previously developed snow density model shows that calibrating a snow density model to a specific basin can provide improvement of SWE estimation at this scale, and should be considered for future basin scale analyses. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and/or UTM Northing) were the most important SWE model variables, suggesting that orographic precipitation and storm track patterns are likely driving basin scale SWE variability. Terrain curvature was also shown to be an important variable, but to a lesser extent at the scale of interest.

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