LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California

Abstract. We present results from snow-on and snow-off airborne-scanning LiDAR measurements over a 53 km2 area in the southern Sierra Nevada. We found that snow depth as a function of elevation increased approximately 15 cm per 100 m, until reaching an elevation of 3300 m, where depth sharply decreased at a rate of 48 cm per 100 m. Departures from the 15 cm per 100 m trend, based on 1 m elevation-band means of regression residuals, showed slightly less steep increases below 2050 m; steeper increases between 2050 and 3300 m; and less steep increases above 3300 m. Although the study area is partly forested, only measurements in open areas were used. Below approximately 2050 m elevation, ablation and rainfall are the primary causes of departure from the orographic trend. From 2050 to 3300 m, greater snow depths than predicted were found on the steeper terrain of the northwest and the less steep northeast-facing slopes, suggesting that ablation, aspect, slope and wind redistribution all play a role in local snow-depth variability. At elevations above 3300 m, orographic processes mask the effect of wind deposition when averaging over large areas. Also, terrain in this basin becomes less steep above 3300 m. This suggests a reduction in precipitation from upslope lifting and/or the exhaustion of precipitable water from ascending air masses. Our results suggest a cumulative precipitation lapse rate for the 2100–3300 m range of about 6 cm per 100 m elevation for the accumulation period of 3 December 2009 to 23 March 2010. This is a higher gradient than the widely used PRISM (Parameter-elevation Relationships on Independent Slopes Model) precipitation products, but similar to that from reconstruction of snowmelt amounts from satellite snow-cover data. Our findings provide a unique characterization of the consistent, steep average increase in precipitation with elevation in snow-dominated terrain, using high-resolution, highly accurate data and highlighs the importance of solar radiation, wind redistribution and mid-winter melt with regard to snow distribution.

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LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-5327-2014 ◽

2014 ◽

Vol 11 (5) ◽

pp. 5327-5365 ◽

Cited By ~ 2

Author(s):

P. B. Kirchner ◽

R. C. Bales ◽

N. P. Molotch ◽

J. Flanagan ◽

Q. Guo

Keyword(s):

Sierra Nevada ◽

Snow Depth ◽

Elevation Gradient ◽

Precipitable Water ◽

Snow Accumulation ◽

Lapse Rate ◽

Lidar Measurement ◽

Regression Residuals ◽

Snow Distribution ◽

Scanning Lidar

Abstract. We present results from snow-on and snow-off airborne-scanning LiDAR measurements over a 53-km2 area in the southern Sierra Nevada. We found that snow depth as a function of elevation increased approximately 15 cm 100 m-1, until reaching an elevation of 3300 m, where depth sharply decreased at a rate of 48 cm 100 m-1. Departures from the 15 cm 100 m-1 trend, based on 1-m elevation-band means of regression residuals, showed slightly less-steep increases below 2050 m; steeper increases between 2050–3300 m; and less-steep increases above 3300 m. Although the study area is partly forested, only measurements in open areas were used. Below approximately 2050 m elevation, ablation and rainfall are the primary causes of departure from the orographic trend. From 2050 to 3300 m, greater snow depths than predicted were found on the steeper terrain of the northwest and the less-steep northeast-facing slopes, suggesting that ablation, aspect, slope and wind redistribution all play a role in local snow-depth variability. At elevations above 3300 m orographic processes mask the effect of wind deposition when averaging over large areas. Also, terrain in this basin becomes less steep above 3300 m. This suggests a reduction in precipitation from upslope lifting, and/or the exhaustion of precipitable water from ascending air masses. Our results suggest a precipitation lapse rate for the 2100–3300 m range of about 6 cm 100 m-1 elevation. This is a higher gradient than the widely used PRISM (Parameter-elevation Relationships on Independent Slopes Model) precipitation products, but similar to that from reconstruction of snowmelt amounts from satellite snowcover data. Our findings provide a unique characterization of the consistent, steep average increase in precipitation with elevation in snow-dominated terrain, using high-resolution, highly-accurate data, as well as the importance of solar radiation, wind redistribution and mid-winter melt with regard to snow distribution.

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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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Influence of Strong Winds on Snow Distribution and Avalanche Activity

Annals of Glaciology ◽

10.3189/s0260305500007886 ◽

1989 ◽

Vol 13 ◽

pp. 195-201 ◽

Cited By ~ 4

Author(s):

R. Meister

Keyword(s):

Snow Depth ◽

Transport Model ◽

Snow Accumulation ◽

Drift Flux ◽

Zone Control ◽

Flux Measurements ◽

Snow Distribution ◽

Strong Winds ◽

Local Range ◽

Good Agreement

In a local range, crest winds were compared with winds at lower stations to make it possible to initiate a drift-transport model which would predict snow accumulation patterns on leeward slopes. Corrections to the model input were made after consideration of detailed drift-flux measurements in the lowest 2 m above snow surface. Good agreement was found between the total length of large avalanches in a path near the crest, the appropriate wind reading and the corrected snow-depth increments in the rupture zone. Control of medium-sized avalanches likely to cause injury to skiers can be improved with the proposed method.

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Elevation dependency of mountain snow depth

The Cryosphere ◽

10.5194/tc-8-2381-2014 ◽

2014 ◽

Vol 8 (6) ◽

pp. 2381-2394 ◽

Cited By ~ 53

Author(s):

T. Grünewald ◽

Y. Bühler ◽

M. Lehning

Keyword(s):

Snow Depth ◽

Elevation Gradient ◽

Distribution Patterns ◽

Snow Accumulation ◽

Systematic Evaluation ◽

Data Sets ◽

Elevation Gradients ◽

Depth Data ◽

Typical Shape ◽

Depth Relationship

Abstract. Elevation strongly affects quantity and distribution patterns of precipitation and snow. Positive elevation gradients were identified by many studies, usually based on data from sparse precipitation stations or snow depth measurements. We present a systematic evaluation of the elevation–snow depth relationship. We analyse areal snow depth data obtained by remote sensing for seven mountain sites near to the time of the maximum seasonal snow accumulation. Snow depths were averaged to 100 m elevation bands and then related to their respective elevation level. The assessment was performed at three scales: (i) the complete data sets (10 km scale), (ii) sub-catchments (km scale) and (iii) slope transects (100 m scale). We show that most elevation–snow depth curves at all scales are characterised through a single shape. Mean snow depths increase with elevation up to a certain level where they have a distinct peak followed by a decrease at the highest elevations. We explain this typical shape with a generally positive elevation gradient of snow fall that is modified by the interaction of snow cover and topography. These processes are preferential deposition of precipitation and redistribution of snow by wind, sloughing and avalanching. Furthermore, we show that the elevation level of the peak of mean snow depth correlates with the dominant elevation level of rocks (if present).

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Multiscale spatial variability of lidar-derived and modeled snow depth on Hardangervidda, Norway

Annals of Glaciology ◽

10.3189/2013aog62a161 ◽

2013 ◽

Vol 54 (62) ◽

pp. 273-281 ◽

Cited By ~ 29

Author(s):

Kjetil Melvold ◽

Thomas Skaugen

Keyword(s):

Spatial Variability ◽

Snow Depth ◽

Laser Scanning ◽

Regional Scale ◽

Local Scale ◽

Snow Accumulation ◽

Large Variability ◽

Airborne Laser ◽

Snow Distribution ◽

Snow Model

AbstractThis study presents results from an Airborne Laser Scanning (ALS) mapping survey of snow depth on the mountain plateau Hardangervidda, Norway, in 2008 and 2009 at the approximate time of maximum snow accumulation during the winter. The spatial extent of the survey area is >240 km2. Large variability is found for snow depth at a local scale (2 m2), and similar spatial patterns in accumulation are found between 2008 and 2009. The local snow-depth measurements were aggregated by averaging to produce new datasets at 10, 50, 100, 250 and 500 m2 and 1 km2 resolution. The measured values at 1 km2 were compared with simulated snow depth from the seNorge snow model (www.senorge.no), which is run on a 1 km2 grid resolution. Results show that the spatial variability decreases as the scale increases. At a scale of about 500 m2 to 1 km2 the variability of snow depth is somewhat larger than that modeled by seNorge. This analysis shows that (1) the regional-scale spatial pattern of snow distribution is well captured by the seNorge model and (2) relatively large differences in snow depth between the measured and modeled values are present.

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Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra- and inter-annual persistence

The Cryosphere Discussions ◽

10.5194/tcd-8-1937-2014 ◽

2014 ◽

Vol 8 (2) ◽

pp. 1937-1972 ◽

Cited By ~ 3

Author(s):

J. Revuelto ◽

J. I. López-Moreno ◽

C. Azorin-Molina ◽

S. M. Vicente-Serrano

Keyword(s):

Snow Depth ◽

Depth Distribution ◽

Laser Scanner ◽

Climatic Conditions ◽

Snow Accumulation ◽

Small Catchment ◽

Explanatory Variables ◽

Spanish Pyrenees ◽

Snow Distribution ◽

Variance Explained

Abstract. In this study we analyzed the relations between terrain characteristics and snow depth distribution in a small alpine catchment located in the central Spanish Pyrenees. Twelve field campaigns were conducted during 2012 and 2013, which were years characterized by very different climatic conditions. Snow depth was measured using a long range terrestrial laser scanner and analyses were performed at a spatial resolution of 5 m. Pearson's r correlation, multiple linear regressions and binary regression trees were used to analyze the influence of topography on the snow depth distribution. The analyses were used to identify the topographic variables that better explain the snow distribution in this catchment, and to assess whether their contributions were variable over intra- and inter-annual time scales. The topographic position index, which has rarely been used in these types of studies, most accurately explained the distribution of snow accumulation. Other variables affecting the snow depth distribution included the maximum upwind slope, elevation, and northing (or potential incoming solar radiation). The models developed to predict snow distribution in the basin for each of the 12 survey days were similar in terms of the most explanatory variables. However, the variance explained by the overall model and by each topographic variable, especially those making a lesser contribution, differed markedly between a year in which snow was abundant (2013) and a~year when snow was scarce (2012), and also differed between surveys in which snow accumulation or melting conditions dominated in the preceding days. The total variance explained by the models clearly decreased for those days on which the snow pack was thinner and more patchily distributed. Despite the differences in climatic conditions in the 2012 and 2013 snow seasons, some similarities in snow accumulation patterns were observed.

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

The Cryosphere Discussions ◽

10.5194/tcd-9-4377-2015 ◽

2015 ◽

Vol 9 (4) ◽

pp. 4377-4405

Author(s):

Z. Zheng ◽

P. B. Kirchner ◽

R. C. Bales

Keyword(s):

Sierra Nevada ◽

Snow Depth ◽

Canopy Gaps ◽

Snow Accumulation ◽

Absolute Difference ◽

Lidar Data ◽

Vegetation Effects ◽

Topographic Features ◽

Canopy Coverage ◽

Combining Data

Abstract. Airborne light detection and ranging (LiDAR) snow-on and snow-off measurements collected in the southern Sierra Nevada in the 2010 water year were analyzed for orographic and vegetation effects on snow accumulation during the winter season. Combining data from four sites separated by 10 to 64 km and together covering over 106 km2 area, the 1 m elevation-band-averaged snow depth in canopy gaps as a function of elevation increased at a rate of 15 cm per 100 m until reaching the elevation of 3300 m. The averaged snow depth of the same elevation band from different sites matched up with minor deviation, which could be partially attributed to the variation in other topographic features, such as slope and aspect. As vegetation plays a role in the snow accumulation, the distribution of the vegetation was also studied and shows that the canopy coverage consistently decreased along the elevation gradient from 80 % at 1500 m to near 0 % at above 3300 m. Also, the absolute difference of the averaged snow depth between snow found in canopy gaps and under the canopy increased with elevation, and decreased with canopy coverage disregarding the variation of other topographic features. The influence from the forest density on snow accumulation was quantified based on the snow-depth residuals from 1 m elevation-band-averaged snow depth and the attribute penetration fraction, which is the ratio of the number of ground points to the number of total points per pixel of LiDAR data. The residual increases from −25 to 25 cm at the penetration fraction range of 0 to 80 %; and the relationship could be modeled by exponential functions, with minor fluctuations along the gradient fraction of canopy and small deviation between sites.

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Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

10.5194/essd-2017-106 ◽

2017 ◽

Author(s):

Esteban Alonso-González ◽

J.¬Ignacio López-Moreno ◽

Simon Gascoin ◽

Matilde García-Valdecasas Ojeda ◽

Alba Sanmiguel-Vallelado ◽

...

Keyword(s):

Iberian Peninsula ◽

Snow Depth ◽

Snow Water Equivalent ◽

Meteorological Data ◽

Snow Accumulation ◽

Lapse Rate ◽

Rate Coefficients ◽

Balance Model ◽

Winter Tourism ◽

Snow Water

Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfalls occur in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse-rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism and risk management. The data presented here are available for free download from Zenodo (DOI: https://doi.org/10.5281/zenodo.854618).This paper fully describes the work flow, data validation, uncertainty assessment and possible applications and limitations of the database.

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Canopy Effects on Snow Accumulation: Observations from Lidar, Canonical-View Photos, and Continuous Ground Measurements from Sensor Networks

Remote Sensing ◽

10.3390/rs10111769 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1769 ◽

Cited By ~ 3

Author(s):

Zeshi Zheng ◽

Qin Ma ◽

Kun Qian ◽

Roger Bales

Keyword(s):

Sierra Nevada ◽

Snow Depth ◽

Canopy Cover ◽

Snow Accumulation ◽

Light Detection And Ranging ◽

Regularized Regression ◽

Independent Variables ◽

Depth Sensors ◽

The Third ◽

Lidar Measurements

A variety of canopy metrics were extracted from the snow-off airborne light detection and ranging (lidar) measurements over three study areas in the central and southern Sierra Nevada. Two of the sites, Providence and Wolverton, had wireless snow-depth sensors since 2008, with the third site, Pinecrest having sensors since 2014. At Wolverton and Pinecrest, images were captured and the sky-view factors were derived from hemispherical-view photos. We found the variation of snow accumulation across the landscape to be significantly related to canopy-cover conditions. Using a regularized regression model Elastic Net to model the normalized snow accumulation with canopy metrics as independent variables, we found that about 50 % of snow accumulation variability at each site can be explained by the canopy metrics from lidar.

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