Variability of snow depth at the plot scale: implications for mean depth estimation and sampling strategies

López‐Moreno, Juan I.; Fassnacht, S. R.; Beguería, Santiago; Latron, Jérôme

doi:10.5194/tc-5-617-2011

Cited by 74 publications

(78 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The range of CV that we have found here is lower than those reported by, e.g., López-Moreno et al (2015), but seems in agreement with the results by López-Moreno et al (2011) for a survey performed during January. Snow depth spatial variability increases with time during the year (Mé-nard et al, 2014;López-Moreno et al, 2015), due to local heterogeneity in ablation dynamics.…”

Section: Test 2: the Effect Of Spatial Sampling On Snow Depth Statisticscontrasting

confidence: 47%

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

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

Section: Test 2: the Effect Of Spatial Sampling On Snow Depth Statisticscontrasting

confidence: 47%

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

et al. 2016

View full text Add to dashboard Cite

show abstract

“…On the other hand, for the same reason sampled transects should represent lengths longer than L ex (López-Moreno et al, 2011). However, the RMSD analysis of the sample spacing (and thus the sample size) in transects of 200, 100, and 50 m, showed that in most of the land cover groups investigated, less than five samples were adequate to describe the average snow depth with a RMSD error less than 5 %.…”

Section: Discussionmentioning

confidence: 99%

“…When the average of coefficient of variation for each land cover group, representing the whole campaign period, was calculated (Table 4), the dispersion of snow depth was highest over the LR and the B and remained low in the forested land cover groups and over the FM. Coefficient of variation for the same order of magnitude (from 0.12 to 0.22) for different types of sites in a boreal landscape, have been reported by Neumann et al (2006) and for forested test sites by López-Moreno et al (2011).…”

Section: Land Cover Specific Variation Of Snow Propertiesmentioning

confidence: 97%

See 1 more Smart Citation

Spatial and temporal variation of bulk snow properties in northern boreal and tundra environments based on extensive field measurements

Hannula

Lemmetyinen

Kontu

et al. 2016

Geosci. Instrum. Method. Data Syst.

View full text Add to dashboard Cite

Abstract. An extensive in situ data set of snow depth, snow water equivalent (SWE), and snow density collected in support of the European Space Agency (ESA) SnowSAR-2 airborne campaigns in northern Finland during the winter of 2011-2012 is presented (ESA Earth Observation Campaigns data [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016]. The suitability of the in situ measurement protocol to provide an accurate reference for the simultaneous airborne SAR (synthetic aperture radar) data products over different land cover types was analysed in the context of spatial scale, sample spacing, and uncertainty. The analysis was executed by applying autocorrelation analysis and root mean square difference (RMSD) error estimations. The results showed overall higher variability for all the three bulk snow parameters over tundra, open bogs and lakes (due to wind processes); however, snow depth tended to vary over shorter distances in forests (due to snow-vegetation interactions). Sample spacing/sample size had a statistically significant effect on the mean snow depth over all land cover types. Analysis executed for 50, 100, and 200 m transects revealed that in most cases less than five samples were adequate to describe the snow depth mean with RMSD < 5 %, but for land cover with high overall variability an indication of increased sample size of 1.5-3 times larger was gained depending on the scale and the desired maximum RMSD. Errors for most of the land cover types reached ∼ 10 % if only three measurements were considered.The collected measurements, which are available via the ESA website upon registration, compose an exceptionally large manually collected snow data set in Scandinavian taiga and tundra environments. This information represents a valuable contribution to the snow research community and can be applied to various snow studies.

show abstract

“…In contrast to this, the role of topography and fine-scale variability of snow cover evolution is investigated in a number of local studies by, e.g. Jost et al (2007), López-Moreno et al (2011), Grünewald et al (2010), and Schmidt et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Inferring snowpack ripening and melt-out from distributed measurements of near-surface ground temperatures

Schmid¹,

Gubler²,

Fiddes³

et al. 2012

The Cryosphere

View full text Add to dashboard Cite

Abstract. Seasonal snow cover and its melt regime are heterogeneous both in time and space. Describing and modelling this variability is important because it affects diverse phenomena such as runoff, ground temperatures or slope movements. This study presents the derivation of melting characteristics based on spatial clusters of ground surface temperature (GST) measurements. Results are based on data from Switzerland where ground surface temperatures were measured with miniature loggers (iButtons) at 40 locations referred to as footprints. At each footprint, up to ten iButtons have been distributed randomly over an area of 10 m × 10 m, placed a few cm below the ground surface. Footprints span elevations of 2100-3300 m a.s.l. and slope angles of 0-55 • , as well as diverse slope expositions and types of surface cover and ground material. Based on two years of temperature data, the basal ripening date and the melt-out date are determined for each iButton, aggregated to the footprint level and further analysed. The melt-out date could be derived for nearly all iButtons; the ripening date could be extracted for only approximately half of them because its detection based on GST requires ground freezing below the snowpack. The variability within a footprint is often considerable and one to three weeks difference between melting or ripening of the points in one footprint is not uncommon. The correlation of mean annual ground surface temperatures, ripening date and melt-out date is moderate, suggesting that these metrics are useful for model evaluation.

show abstract

Variability of snow depth at the plot scale: implications for mean depth estimation and sampling strategies

Cited by 74 publications

References 28 publications

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

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

Spatial and temporal variation of bulk snow properties in northern boreal and tundra environments based on extensive field measurements

Inferring snowpack ripening and melt-out from distributed measurements of near-surface ground temperatures

Contact Info

Product

Resources

About