Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment

Grünewald, Thomas; Schirmer, Michael; Mott, Rebecca; Lehning, Michael

doi:10.5194/tc-4-215-2010

Cited by 242 publications

(291 citation statements)

References 37 publications

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“…Note that snow height determines most of the spatial variability in SWE since density typically does not have a pronounced spatial variability [6]. In the assessment of slope stability (avalanche danger), small scale spatial variability on the scale of <1 m is influential on avalanche release, as is (if such measurements are possible) layer thickness (mm scale) [30].…”

Section: Required Accuracy Of Measurementsmentioning

confidence: 99%

“…Similarly, SWE, HS, and HNS are vital parameters that need to be measured in areas of complex terrain, where wind-driven snow redistribution can significantly change snow height measurements, such that with a 1.5 m average snow depth, the point based HS measurement can typically vary by up to 5 m on a horizontal spatial variability of meters [6,21]. SWE is driven by precipitation, temperature, wind, and incoming/outgoing radiative fluxes.…”

Section: Related Measurement Challengesmentioning

confidence: 99%

“…For assessing spatial snow distribution on a scale of a few meters, an accuracy of 0.1 m is a useful benchmark [6], which would correspond to a range between 20 to 30 mm SWE for typical mean densities in alpine snow covers. Note that snow height determines most of the spatial variability in SWE since density typically does not have a pronounced spatial variability [6].…”

Section: Required Accuracy Of Measurementsmentioning

confidence: 99%

“…Snow and avalanche research institutes have recently started to intensively use LIDAR and TLS measurements for a variety of purposes from surface deformation in permafrost areas to pre/post storm snow height measurements for analyzing snow distribution patterns (see, e.g., [6,25,39,40]). …”

Section: Terrestrial and Airborne Laser Scanningmentioning

confidence: 99%

“…In the forecast of avalanche danger, particularly for wet snow avalanches, important decisions must be made based on these parameters describing the development of the snowpack and its variability with slope angle, aspect, and altitude [4,5]. In snow hydrology, if the spatial distribution of SWE and HS is measured over entire catchments, we can obtain an estimation of the total stored water resources [6,7]. This information is extremely valuable for hydroelectric companies, water supply companies, and risk assessment [8,9].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Soil Moisture & Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives

et al. 2013

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Moisture content in the soil and snow in the alpine environment is an important factor, not only for environmentally oriented research, but also for decision making in agriculture and hazard management. Current observation techniques quantifying soil moisture or characterizing a snow pack often require dedicated instrumentation that measures either at point scale or at very large (satellite pixel) scale. Given the heterogeneity of both snow cover and soil moisture in alpine terrain, observations of the spatial distribution of moisture and snow-cover are lacking at spatial scales relevant for alpine hydrometeorology. This paper provides an overview of the challenges and status of the determination of soil moisture and snow properties in alpine environments. Current measurement techniques and newly proposed ones, based on the reception of reflected Global Navigation Satellite Signals (i.e., GNSS Reflectometry or GNSS-R), or the use of laser scanning are reviewed, and the perspectives offered by these new techniques to fill the current gap in the instrumentation level are discussed. Some key enabling technologies OPEN ACCESSRemote Sens. 2013, 5 3517 including the availability of modernized GNSS signals and GNSS array beamforming techniques are also considered and discussed.

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Section: Required Accuracy Of Measurementsmentioning

confidence: 99%

Section: Related Measurement Challengesmentioning

confidence: 99%

Section: Required Accuracy Of Measurementsmentioning

confidence: 99%

Section: Terrestrial and Airborne Laser Scanningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Soil Moisture & Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives

et al. 2013

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Long-term snow distribution observations in a mountain catchment: Assessing variability, time stability, and the representativeness of an index site

Winstral

Marks

2014

Water Resour. Res.

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1] This study presents an analysis of snow distribution heterogeneity and the factors affecting this variability. The analysis focuses on manually sampled data from 21 snow surveys covering 11 years at the drift-dominated Reynolds Mountain East catchment (0.36 km 2 ) in southwestern Idaho, USA. Surveys were conducted midwinter and in early spring. Interseason and intraseason trends were examined along with the time stability of distributions, goodness-of-fit to theoretical distributions, and the representativeness of an index site as a measure of basin-wide snow water equivalent. The average snow depth coefficient of variation (CV) over the entire time period was 0.71, which is in accordance with broad regional assessments. Higher wind speeds during snow events and increased melt led to increased heterogeneity and higher CVs. Forested sites produced lower CVs presumably due to moderated winds at these sites. Consistent wind directions produced accumulation patterns that were very stable from year-to-year. Many previous studies have suggested that vital subgrid snow heterogeneity in large-scale models can be approximated with parametric distributions. Gamma distributions were preferred over lognormal distributions in describing the overall distribution while in tree-covered regions with less variability there was little difference between the two. It was also found that an index site, akin to the majority of North American mountain weather observation stations, provided a reasonable approximation of catchment-averaged SWE in most years. However, the reliability of this measure decreased in years that deviated from normal patterns.Citation: Winstral, A., and D. Marks (2014), Long-term snow distribution observations in a mountain catchment: Assessing variability, time stability, and the representativeness of an index site, Water Resour. Res., 50, 293-305,

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Potential of a low‐cost sensor network to understand the spatial and temporal dynamics of a mountain snow cover

Pohl

Garvelmann

Wawerla

et al. 2014

Water Resources Research

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The spatial and temporal dynamics of seasonal snow covers play a critical role for many hydrological, ecological, and climatic processes. This paper presents a new, innovative approach to continuously monitor these dynamics using numerous low-cost, standalone snow monitoring stations (SnoMoS). These stations provide snow and related meteorological data with a high temporal and spatial resolution. Data collected by SnoMoS include: snow depth, surface temperature, air temperature and humidity, total precipitation, global radiation, wind speed, and barometric pressure. A total of 99 sensors were placed over the winters 2010/2011 and 2011/2012 at multiple locations within three 40-180 km 2 basins in the Black Forest region of Southern Germany. The locations were chosen to cover a wide range of slopes, elevations, and expositions in a stratified sampling design. Furthermore, ''paired stations'' located in close proximity to each other, one in the open and one underneath various forest canopies, were set up to investigate the influence of vegetation on snow dynamics. The results showed that considerable differences in snow depth and, therefore, snow water equivalent (SWE) are present within the study area despite its moderate temperatures and medium elevation range (400-1500 m). The relative impact of topographical factors like elevation, aspect, and of different types of forest vegetation were quantified continuously and were found to change considerably over the winter period. The recorded differences in SWE and snow cover duration were large enough that they should be considered in hydrologic and climate models.

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Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment

Cited by 242 publications

References 37 publications

Soil Moisture & Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives

Soil Moisture & Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives

Long-term snow distribution observations in a mountain catchment: Assessing variability, time stability, and the representativeness of an index site

Potential of a low‐cost sensor network to understand the spatial and temporal dynamics of a mountain snow cover

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