Insights into the physical processes controlling correlations between snow distribution and terrain properties

Anderson, B.; McNamara, J. P.; Marshall, Hans‐Peter; Flores, Alejandro N.

doi:10.1002/2013wr013714

Cited by 51 publications

(64 citation statements)

References 36 publications

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“…1 and 8). Elder et al (1998), Anderton et al (2004) and Anderson et al (2014) found the variability of spring snow density to be insignificantly correlated with elevation in their studies, while Zhong et al (2014) found negative correlations with elevation in a meta-study of densities in the former USSR. A range of results has also been reported for the snow-density correlation with depth showing both positive and negative correlations depending on the age of the snow and season (Arons and Colbeck, 1995;McCreight and Small, 2014).…”

Section: Snow Densitymentioning

confidence: 97%

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

Kirchner

Bales

Molotch

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. We present results from snow-on and snow-off airborne-scanning LiDAR measurements over a 53 km 2 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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Section: Snow Densitymentioning

confidence: 97%

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

Kirchner

Bales

Molotch

et al. 2014

Hydrol. Earth Syst. Sci.

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“…Removing the large-scale topography has been done in the previous studies in order to capture or quantify the effect of microtopography on carbon fluxes (Wainwright et al, 2015) or soil properties (Gillin et al, 2015). Correlations between the topographic metrics and snow depth are identified using the Pearson product-moment correlation coefficient (Anderson et al, 2014). At each spatial scale, we can compute micro-and macrotopographic metrics such as slope and curvature as well as their correlations with corresponding probe-measured snow depth.…”

Section: Spatial Variability Analysis Of Topography and Snow Depthmentioning

confidence: 99%

“…A geostatistical approach has been used to investigate the spatial variability of snow depth as well as the scales of variability (Anderson et al, 2014). The standard geostatistical analysis starts with creating an empirical variogram, followed by estimating the spatial correlation parameters (Diggle and Ribeiro Jr., 2007).…”

Section: Spatial Variability Analysis Of Topography and Snow Depthmentioning

confidence: 99%

“…Bayesian methods also permit systematic incorporation of expert knowledge or process-specific information, such as the relationships between datasets and parameters. In particular, snow depth is known to be affected by topography and wind direction (e.g., Benson and Sturm, 1993;Anderson et al, 2014;Dvornikov et al, 2015). To our knowledge, such Bayesian data integration methods have never been applied to estimate end-ofwinter snow variability using multiple types of datasets.…”

Section: Introductionmentioning

confidence: 99%

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Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

et al. 2017

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Abstract. This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using groundpenetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided highprecision estimates of snow depth (RMSE = 2.9 cm), with a spatial sampling of 10 cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE = 6.0 cm) and a fine spatial sampling (4 cm × 4 cm). We then investigated the spatial variability of snow depth and its correlation to micro-and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE = 6.0 cm), at 0.5 m resolution and over the lidar domain (750 m × 700 m).

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“…With biweekly temporal resolution, Anderson et al (2014) gained substantial insights into physical controls on seasonal snow processes, albeit with a dependence on statistical scaling to relate transect scale observations to basin scale processes.…”

Section: Introductionmentioning

confidence: 99%

Mapping snow depth at very high spatial resolution with RPAS photogrammetry

Redpath

Sirguey

Cullen

2018

Preprint

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Abstract. Dynamic in time and space, seasonal snow represents a difficult target for ongoing in situ measurement and characterisation. Improved understanding and modelling of the seasonal snowpack requires mapping snow depth at fine spatial resolution. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial variability of snow depth is evaluated within an alpine catchment of the Pisa Range, New Zealand. Digital surface models (DSM) at 0.15 m spatial 10 resolution in autumn (snow-free reference) winter (02/08/2016) and spring (10/09/2016) allowed mapping of snow depth via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by the propagation of check point residuals from the aero-triangulation of constituent DSMs, and via comparison of snow-free regions of the spring and autumn DSMs. The accuracy of RPAS-derived snow depth was validated with in situ snow probe measurements. Results for snow free areas between DSMs acquired in autumn and spring demonstrate repeatability, yet also reveal that elevation errors follow a 15 distribution substantially departing from a normal distribution, symptomatic of the influence of DSM co-registration and terrain characteristics on vertical uncertainty. Error propagation saw snow depth mapped with an accuracy of ±0.08 m (90% c.l.). This is lower than the characterization of uncertainties on snow-free areas (±0.13 m). Comparisons between RPAS and in situ snow depth measurements confirm this level of performance of RPAS photogrammetry, while also highlighting the influence of vegetation on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in 20 situ measurements in resolving fine scale spatial variability. Despite limitations accompanying RPAS photogrammetry, which are relevant to similar applications of surface and volume change analysis, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological basin (~0.4 km 2 ), at very high resolution.Resolving snowpack features associated with re-distribution and preferential accumulation and ablation, snow depth maps provide geostatistically robust insights into seasonal snow processes, with unprecedented detail. Such data will enhance 25 understanding of physical processes controlling spatial distribution of seasonal snow, and their relative importance at varying spatial and temporal scales.The Cryosphere Discuss., https://doi

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Insights into the physical processes controlling correlations between snow distribution and terrain properties

Cited by 51 publications

References 36 publications

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

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

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

Mapping snow depth at very high spatial resolution with RPAS photogrammetry

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