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| Titel |
Theoretical analysis of errors when estimating snow distribution through point measurements |
| VerfasserIn |
E. Trujillo, M. Lehning |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1994-0416
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| Digitales Dokument |
URL |
| Erschienen |
In: The Cryosphere ; 9, no. 3 ; Nr. 9, no. 3 (2015-06-19), S.1249-1264 |
| Datensatznummer |
250116814
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| Publikation (Nr.) |
 copernicus.org/tc-9-1249-2015.pdf |
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| Zusammenfassung |
| In recent years, marked improvements in our knowledge of the statistical
properties of the spatial distribution of snow properties have been achieved
thanks to improvements in measuring technologies (e.g., LIDAR, terrestrial
laser scanning (TLS), and ground-penetrating radar (GPR)). Despite this,
objective and quantitative frameworks for the evaluation of errors in snow
measurements have been lacking. Here, we present a theoretical framework for
quantitative evaluations of the uncertainty in average snow depth derived
from point measurements over a profile section or an area. The error is
defined as the expected value of the squared difference between the real mean
of the profile/field and the sample mean from a limited number of
measurements. The model is tested for one- and two-dimensional survey designs
that range from a single measurement to an increasing number of regularly
spaced measurements. Using high-resolution (~ 1 m) LIDAR snow depths
at two locations in Colorado, we show that the sample errors follow the
theoretical behavior. Furthermore, we show how the determination of the
spatial location of the measurements can be reduced to an optimization
problem for the case of the predefined number of measurements, or to the
designation of an acceptable uncertainty level to determine the total number
of regularly spaced measurements required to achieve such an error. On this
basis, a series of figures are presented as an aid for snow survey design
under the conditions described, and under the assumption of prior knowledge
of the spatial covariance/correlation properties. With this methodology,
better objective survey designs can be accomplished that are tailored to the
specific applications for which the measurements are going to be used. The
theoretical framework can be extended to other spatially distributed snow
variables (e.g., SWE – snow water equivalent) whose statistical properties
are comparable to those of snow depth. |
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