Spatial Snow Depth Assessment Using LiDAR Transect Samples and Public GIS Data Layers in the Elbow River Watershed, Alberta

Hopkinson, Chris; Collins, T.; Anderson, Axel; Pomeroy, John W.; Spooner, Ian

doi:10.4296/cwrj3702893

Cited by 30 publications

(33 citation statements)

References 33 publications

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“…ALS enables area-wide data to be gathered in a very high spatial resolution and accuracy. Hopkinson et al (2004) and Deems et al (2006) showed that ALS was an appropriate and accurate method for gathering snow depth measurements, and since then, a rising number of data sets have become available (Deems et al, 2006;Moreno Banos et al, 2009;Dadic et al, 2010a;DeBeer and Pomeroy, 2010;Grünewald and Lehning, 2011;Schöber et al, 2011;Hopkinson et al, 2012). Detailed descriptions of the ALS measurement principle can be found in Geist et al (2009), Baltsavias (1999 and Wehr and Lohr (1999).…”

Section: Airborne Laser Scanningmentioning

confidence: 99%

“…Unrealistic data such as negative snow depths or extremely high values are rare but still need to be filtered. Such outliers mainly occur in very steep and rough terrain, which is attributed to larger measurement errors in such terrain Grünewald and Lehning, 2011;Bollmann et al, 2011;Hopkinson et al, 2012). In this study, we set negative snow depths to zero and excluded extremely high snow depths from the data.…”

Section: Airborne Laser Scanningmentioning

confidence: 99%

“…Therefore, effects of vegetation, such as interception of snow by trees, were not considered and all parts of the investigation areas which are covered by significant vegetation were masked from the data. Furthermore, it has been shown that ALS snow depth measurements are more accurate over alpine areas than adjacent forest covered areas (Hopkinson et al, 2012). In the following, we give a brief overview of the study sites.…”

Section: Site Descriptionmentioning

confidence: 99%

“…The length scale of these subareas was approximately 500 m. Such an aggregation follows the concept of hydrological response units (HRU), which has successfully been applied for semi-distributed hydrological models (Kite and Kouwen, 1992;Rinaldo et al, 2006;Pomeroy et al, 2007) and is consistent with the concept of stratified snow sampling (Steppuhn and Dyck, 1974) where landscape units were defined to minimise within-unit variance of SWE and maximise difference in SWE between units. The facets applied by Daly et al (1994) for the calculation of precipitation gradients or to the concept of snow accumulation units outlined by Hopkinson et al (2012) are also similar approaches. The second method is a simple automatic approach which divides the domain into quadratic subareas.…”

Section: Aggregation Of Sub-areasmentioning

confidence: 99%

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Statistical modelling of the snow depth distribution in open alpine terrain

Grünewald¹,

Stötter

Pomeroy

et al. 2013

Hydrol. Earth Syst. Sci.

111

142

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Abstract. The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m), that 30 to 91 % of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could only explain 23 % of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.

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Section: Airborne Laser Scanningmentioning

confidence: 99%

Section: Airborne Laser Scanningmentioning

confidence: 99%

Section: Site Descriptionmentioning

confidence: 99%

Section: Aggregation Of Sub-areasmentioning

confidence: 99%

See 2 more Smart Citations

Statistical modelling of the snow depth distribution in open alpine terrain

Grünewald¹,

Stötter

Pomeroy

et al. 2013

Hydrol. Earth Syst. Sci.

111

142

View full text Add to dashboard Cite

show abstract

“…Existing techniques include terrestrial or airborne laser scanning (e.g., Hopkinson et al, 2004;Deems et al, 2006Deems et al, , 2013Prokop et al, 2008;Dadic et al, 2010;Grünewald et al, 2010Grünewald et al, , 2013Lehning et al, 2011;Hopkinson et al, 2012;Grünewald and Lehning, 2015;Hedrick et al, 2015), SAR (synthetic aperture radar, Luzi et al, 2009), aerial photography (Blöschl and Kirnbauer, 1992;König and Sturm, 1998;Worby et al, 2008), time-lapse photography (Farinotti et al, 2010), and optical and micro-wave data from satellite platforms (Parajka and Blöschl, 2006;Dietz et al, 2012). The good performance of these methods has been widely discussed, but survey expenses are still a constraint (Hood and Hayashi, 2010).…”

Section: Introductionmentioning

confidence: 99%

Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation

et al. 2016

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Abstract. We investigate snow depth distribution at peak accumulation over a small Alpine area ( ∼ 0.3 km 2 ) using photogrammetry-based surveys with a fixed-wing unmanned aerial system (UAS). These devices are growing in popularity as inexpensive alternatives to existing techniques within the field of remote sensing, but the assessment of their performance in Alpine areas to map snow depth distribution is still an open issue. Moreover, several existing attempts to map snow depth using UASs have used multi-rotor systems, since they guarantee higher stability than fixed-wing systems. We designed two field campaigns: during the first survey, performed at the beginning of the accumulation season, the digital elevation model of the ground was obtained. A second survey, at peak accumulation, enabled us to estimate the snow depth distribution as a difference with respect to the previous aerial survey. Moreover, the spatial integration of UAS snow depth measurements enabled us to estimate the snow volume accumulated over the area. On the same day, we collected 12 probe measurements of snow depth at random positions within the case study to perform a preliminary evaluation of UAS-based snow depth. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to −0.073 m and a RMSE equal to 0.14 m. We have also explored how some basic snow depth statistics (e.g., mean, standard deviation, minima and maxima) change with sampling resolution (from 5 cm up to ∼ 100 m): for this case study, snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m. This provides a possible indication of sampling resolution in similar conditions.

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Postglacial adjustment of steep, low-order drainage basins, Canadian Rocky Mountains

Hoffmann

Müller

Johnson

et al. 2013

J. Geophys. Res. Earth Surf.

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[1] It is generally argued that Pleistocene glaciation results in increased sediment flux in mountain systems. An important, but not well constrained, aspect of Pleistocene glacial erosion is the geomorphic decoupling of cirque basins from main river systems. This study provides a quantitative link between glacier-induced basin morphology, postglacial erosion, and sediment delivery for mountain headwaters (with basin area <10 km 2 ). We analyze the morphology of 57 headwater basins in the Canadian Rockies and establish postglacial sediment budgets for select basins. Notable differences in headwater morphology suggest different degrees of erosion by cirque glaciers, which we classify into headwater basins with either cirque or noncirque morphology. Despite steeper slope gradients in cirque basins, higher-mean postglacial erosion rates in basins with noncirque morphology (0.43-0.6 mm a À1 ) compared to those in cirques (0.19-0.39 mm a À1 ) suggest a more complex relationship between hillslope erosion and slope gradient in calcareous mountain environments than implied by the threshold hillslope concept. Higher values of channel profile concavity and lower channel gradients in cirques imply lower transport capacities and, thus, lower sediment delivery ratios (SDR). These results are supported by (i) postglacial SDR values for cirques and noncirque basins of <15% and >28%, respectively, and (ii) larger fan sizes at outlets of noncirque basins compared to cirques. Although small headwater basins represent the steepest part of mountain environments and erode significant postglacial sediment, the majority of sediment remains in storage under interglacial climatic conditions and does not affect large-scale mountain river systems.

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Spatial Snow Depth Assessment Using LiDAR Transect Samples and Public GIS Data Layers in the Elbow River Watershed, Alberta

Cited by 30 publications

References 33 publications

Statistical modelling of the snow depth distribution in open alpine terrain

Statistical modelling of the snow depth distribution in open alpine terrain

Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation

Postglacial adjustment of steep, low-order drainage basins, Canadian Rocky Mountains

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