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

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.

Download Full-text

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.

Download Full-text

Landscape monitoring of post-industrial areas using LiDAR and GIS technology

Geodesy and Cartography ◽

10.1515/geocart-2015-0010 ◽

2015 ◽

Vol 64 (1) ◽

pp. 125-137 ◽

Cited By ~ 9

Author(s):

Piotr Wężyk ◽

Marta Szostak ◽

Wojciech Krzaklewski ◽

Marek Pająk ◽

Marcin Pierzchalski ◽

...

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Slope Aspect ◽

Gis Technology ◽

Industrial Areas ◽

Cloud Data ◽

Post Industrial ◽

The Impact ◽

Spoil Heaps

Abstract The quarrying industry is changing the local landscape, forming deep open pits and spoil heaps in close proximity to them, especially lignite mines. The impact can include toxic soil material (low pH, heavy metals, oxidations etc.) which is the basis for further reclamation and afforestation. Forests that stand on spoil heaps have very different growth conditions because of the relief (slope, aspect, wind and rainfall shadows, supply of solar energy, etc.) and type of soil that is deposited. Airborne laser scanning (ALS) technology deliver point clouds (XYZ) and derivatives as raster height models (DTM, DSM, nDSM=CHM) which allow the reception of selected 2D and 3D forest parameters (e.g. height, base of the crown, cover, density, volume, biomass, etc). The automation of ALS point cloud processing and integrating the results into GIS helps forest managers to take appropriate decisions on silvicultural treatments in areas with failed plantations (toxic soil, droughts on south-facing slopes; landslides, etc.) or as regular maintenance. The ISOK country-wide project ongoing in Poland will soon deliver ALS point cloud data which can be successfully used for the monitoring and management of many thousands of hectares of destroyed post-industrial areas which according to the law, have to be afforested and transferred back to the State Forest.

Download Full-text

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.

Download Full-text

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.

Download Full-text

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

Hydrology and Earth System Sciences ◽

10.5194/hess-18-4261-2014 ◽

2014 ◽

Vol 18 (10) ◽

pp. 4261-4275 ◽

Cited By ~ 43

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 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.

Download Full-text

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.

Download Full-text

Assessment of ICESat-2 Level 3A Products for Snow Depth Estimation in Remote, Mountainous Watersheds

10.18122/td.1802.boisestate ◽

2021 ◽

Author(s):

Colten Michael Elkin

Keyword(s):

Snow Depth ◽

Strong Relationship ◽

Depth Estimation ◽

Snow Accumulation ◽

Slope Aspect ◽

Glacier Surface ◽

Digital Terrain ◽

Reynolds Creek ◽

The Difference ◽

Mountainous Watersheds

Seasonal snowpack accounts for ~70% of the water supply in the western United States, and measuring snow accumulation and ablation remotely has long been a stated goal of NASA. The 2018 launch of ICESat-2, a spaceborne Lidar system, has offered unparalleled spatial and temporal coverage of mountainous terrain with the potential for unprecedented vertical accuracy. Data from ICESat-2 are used to measure seasonal snow depths using the level-3A ATL08 (land and canopy elevation) product for the Reynolds Creek Experimental Watershed in southwest Idaho and the ATL06 (land ice elevation) product for Wolverine Creek in the Kenai Mountains of Alaska. The methodology for coregistering ICESat-2 transects to reference digital terrain models then estimating snow depths as the difference between the ICESat-2 and reference elevations is described. Median and MAD snow depths for transects from 2019 and 2020 are 3.1 +/- 6.7m at Reynolds Creek EW and are 5.5 +/- 2.1m at Wolverine glacier. Here we find that measuring snow depths using ICESat-2 is crude in variable, vegetated terrain covered by the ATL08 data product, and that there is not a strong relationship between the residual values reported at Reynolds Creek EW and terrain parameters such as slope, aspect, vegetative coverage, and elevation. We do find that the ATL06 analysis results in reasonable first-order estimates of snow depth but that the evolution of the glacier surface elevations must be more accurately constrained in order to ensure the snow depth estimates are unbiased.

Download Full-text

Inferring Watershed-scale Mean Snow Magnitude and Distribution Using Multidecadal Snow Reanalysis Patterns and Snow Pillow Observations

10.22541/au.163250588.84772578/v1 ◽

2021 ◽

Author(s):

Justin Pflug ◽

Steven Margulis ◽

Jessica Lundquist

Keyword(s):

Spatial Heterogeneity ◽

Sierra Nevada ◽

Snow Depth ◽

Snow Accumulation ◽

Airborne Lidar ◽

Watershed Scale ◽

Mountainous Regions ◽

Snow Covered Area ◽

Covered Area ◽

Snow Deposition

The magnitude and spatial heterogeneity of snow deposition are difficult to model in mountainous terrain. Here, we investigated how snow patterns from a 32-year (1985 – 2016) snow reanalysis in the Tuolumne, Kings, and Sagehen Creek, California Sierra Nevada watersheds could be used to improve simulations of winter snow deposition. Remotely-sensed fractional snow-covered area (fSCA) from dates following peak-snowpack timing were used to identify dates from different years with similar fSCA, which indicated similar snow accumulation and depletion patterns. Historic snow accumulation patterns were then used to 1) relate snow accumulation observed by snow pillows to watershed-scale estimates of mean snowfall, and 2) estimate 90 m snow deposition. Finally, snow deposition fields were used to force snow simulations, the accuracy of which were evaluated versus airborne lidar snow depth observations. Except for water-year 2015, which had the shallowest snow estimated in the Sierra Nevada, normalized snow accumulation and depletion patterns identified from historic dates with spatially correlated fractional snow-covered area agreed on average, with absolute differences of less than 10%. Watershed-scale mean winter snowfall inferred from the relationship between historic snow accumulation patterns and snow pillow observations had a ±13% interquartile range of biases between 1985 and 2016. Finally, simulations using 1) historic snow accumulation patterns, and 2) snow accumulation observed from snow pillows, had snow depth coefficients of correlations and mean absolute errors that improved by 70% and 27%, respectively, as compared to simulations using a more common forcing dataset and downscaling technique. This work demonstrates the real-time benefits of satellite-era snow reanalyses in mountainous regions with uncertain snowfall magnitude and spatial heterogeneity.

Download Full-text