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

Melvold, Kjetil; Skaugen, Thomas

doi:10.3189/2013aog62a161

Cited by 40 publications

(56 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our hypothesis is motivated by the observation of Liston et al (1999), in that, in contrast to summer convective-precipitation systems, the spatial distribution of winter precipitation is more influenced by topographic distributions. Furthermore, it is motivated by the results of Grünewald et al (2013) and Melvold and Skaugen (2013), which confirmed that the snow depth distribution is dominated by topography at scales of several hundred meters.…”

Section: Introductionmentioning

confidence: 93%

“…Recently, Grünewald et al (2013) and Melvold and Skaugen (2013) therefore investigated the influence of scale on aggregated snow depth data. By analyzing snow depth data in differently sized grid cells up to 800 m for several catchments, Grünewald et al (2013) found a lower limit of 400 m for the grid cell size to explain most of the remaining larger-scale spatial variability.…”

Section: Introductionmentioning

confidence: 99%

“…By analyzing snow depth data in differently sized grid cells up to 800 m for several catchments, Grünewald et al (2013) found a lower limit of 400 m for the grid cell size to explain most of the remaining larger-scale spatial variability. By analyzing snow depth data from a large mountainous area in Norway in grid cell sizes up to 1 km, Melvold and Skaugen (2013), however, determined a larger lower limit of 1 km to eliminate most of the spatial variability such that the mean adequately represents the average grid cell snow depth. A reason why a global parameterization might not be derivable at one certain horizontal resolution is that too many different snow-cover shaping processes are still active, at that scale, making it a challenge to parameterize the subgrid snow distribution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fractional snow-covered area parameterization over complex topography

Helbig¹,

Herwijnen²,

Magnusson³

et al. 2015

Hydrol. Earth Syst. Sci.

118

View full text Add to dashboard Cite

Abstract. Fractional snow-covered area (SCA) is a key parameter in large-scale hydrological, meteorological and regional climate models. Since SCA affects albedos and surface energy balance fluxes, it is especially of interest over mountainous terrain where generally a reduced SCA is observed in large grid cells. Temporal and spatial snow distributions are, however, difficult to measure over complex topography. We therefore present a parameterization of SCA based on a new subgrid parameterization for the standard deviation of snow depth over complex topography. Highly resolved snow depth data at the peak of winter were used from two distinct climatic regions, in eastern Switzerland and in the Spanish Pyrenees. Topographic scaling parameters are derived assuming Gaussian slope characteristics. We use computationally cheap terrain parameters, namely, the correlation length of subgrid topographic features and the mean squared slope. A scale dependent analysis was performed by randomly aggregating the alpine catchments in domain sizes ranging from 50 m to 3 km. For the larger domain sizes, snow depth was predominantly normally distributed. Trends between terrain parameters and standard deviation of snow depth were similar for both climatic regions, allowing one to parameterize the standard deviation of snow depth based on terrain parameters. To make the parameterization widely applicable, we introduced the mean snow depth as a climate indicator. Assuming a normal snow distribution and spatially homogeneous melt, snow-cover depletion (SCD) curves were derived for a broad range of coefficients of variations. The most accurate closed form fit resembled an existing fractional SCA parameterization. By including the subgrid parameterization for the standard deviation of snow depth, we extended the fractional SCA parameterization for topographic influences.For all domain sizes we obtained errors lower than 10 % between measured and parameterized SCA.

show abstract

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fractional snow-covered area parameterization over complex topography

Helbig¹,

Herwijnen²,

Magnusson³

et al. 2015

Hydrol. Earth Syst. Sci.

118

View full text Add to dashboard Cite

show abstract

“…Airborne laser scanning (ALS) from helicopters or airplanes can cover larger areas in a shorter time also under difficult avalanche danger situations. Recent studies demonstrate that the accurate mapping of snow depth is possible (Deems et al, 2013;Melvold and Skaugen, 2013). However, the costs to cover larger areas are still high and overflights are, as with digital photogrammetry, restricted to fair weather conditions.…”

Section: Y Bühler Et Al: Snow Depth Mapping In High-alpine Catchmentsmentioning

confidence: 99%

Snow depth mapping in high-alpine catchments using digital photogrammetry

Bühler¹,

et al. 2015

View full text Add to dashboard Cite

Abstract. Information on snow depth and its spatial distribution is crucial for numerous applications in snow and avalanche research as well as in hydrology and ecology. Today, snow depth distributions are usually estimated using point measurements performed by automated weather stations and observers in the field combined with interpolation algorithms. However, these methodologies are not able to capture the high spatial variability of the snow depth distribution present in alpine terrain. Continuous and accurate snow depth mapping has been successfully performed using laser scanning but this method can only cover limited areas and is expensive. We use the airborne ADS80 optoelectronic scanner, acquiring stereo imagery with 0.25 m spatial resolution to derive digital surface models (DSMs) of winter and summer terrains in the neighborhood of Davos, Switzerland. The DSMs are generated using photogrammetric image correlation techniques based on the multispectral nadir and backward-looking sensor data. In order to assess the accuracy of the photogrammetric products, we compare these products with the following independent data sets acquired simultaneously: (a) manually measured snow depth plots; (b) differential Global Navigation Satellite System (dGNSS) points; (c) terrestrial laser scanning (TLS); and (d) ground-penetrating radar (GPR) data sets. We demonstrate that the method presented can be used to map snow depth at 2 m resolution with a vertical depth accuracy of ±30 cm (root mean square error) in the complex topography of the Alps. The snow depth maps presented have an average accuracy that is better than 15 % compared to the average snow depth of 2.2 m over the entire test site.

show abstract

“…The advantages of lidar for spatially characterizing snow depths over large remote areas are finally being used to assess lower-resolution operational hydro-meteorologic snow models. Melvold and Skaugen (2013) used six parallel 500 m × 80 km lidar surveys, each separated by 10 km, to investigate the Norwegian operational temperature index snow model, seNorge. After upscaling the lidar-derived 2 m resolution snow depths to the spatial resolution of the 1 km 2 gridded model output, the modeled results were found to accurately represent the remote sensing estimates despite the lack of sub-grid spatial information within the model structure.…”

Section: A Hedrick Et Al: Lidar Validation Of Snodasmentioning

confidence: 99%

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

et al. 2015

View full text Add to dashboard Cite

Abstract. Repeated light detection and ranging (lidar) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km 2 lidar-derived data set of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically based energy-and-mass-balance snow model with satellite, airborne and automated groundbased observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the conterminous United States. Independent validation data are scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation data set with substantial geographic coverage.Within 12 distinctive 500 × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 lidar acquisitions. This supplied a data set for constraining the uncertainty of upscaled lidar estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled lidar snow depths were then compared to the SNODAS estimates over the entire study area for the dates of the lidar flights. The remotely sensed snow depths provided a more spatially continuous comparison data set and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between lidar observations and SNODAS estimates were most drastic, providing insight into the causal influences of natural processes on model uncertainty.

show abstract

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

Cited by 40 publications

References 46 publications

Fractional snow-covered area parameterization over complex topography

Fractional snow-covered area parameterization over complex topography

Snow depth mapping in high-alpine catchments using digital photogrammetry

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Contact Info

Product

Resources

About