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@PHDTHESIS{Schirmer:64658,
author = {Schirmer, Michael},
othercontributors = {Schneider, Christoph},
title = {{M}odelling avalanche danger and understanding snow depth
variability},
address = {Aachen},
publisher = {Publikationsserver der RWTH Aachen University},
reportid = {RWTH-CONV-125935},
pages = {IV, 105 S. : Ill., graph. Darst., Kt.},
year = {2010},
note = {Prüfungsjahr: 2010. - Publikationsjahr: 2011; Aachen,
Techn. Hochsch., Diss., 2010},
abstract = {This thesis addresses the causes of avalanche danger at a
regional scale. Modelled snow stratigraphy variables were
linked to [1] forecasted avalanche danger and [2] observed
snowpack stability. Spatial variability of snowpack
parameters in a region is an additional important factor
that influences the avalanche danger. Snow depth and its
change during individual snow fall periods are snowpack
parameters which can be measured at a high spatial
resolution. Hence, the spatial distribution of snow depth
and snow depth change due to individual snow storms were
observed [3]. Furthermore, this spatial dataset was
characterised with a fractal analysis and results were
related to deposition processes [4]. In the following, each
subject is described in more detail: [1] In the past,
numerical prediction of regional avalanche danger using
statistical methods with meteorological input variables has
shown insufficiently accurate results, possibly due to the
lack of snow stratigraphy data. Detailed snow-cover data
were rarely used because they were not readily available
(manual observations). With the development and increasing
use of snow-cover models this deficiency can now be
rectified and model output can be used as input for
forecasting models. We used the output of the physically
based snow cover model SNOWPACK combined with meteorological
variables to investigate and establish a link to regional
avalanche danger. Snow stratigraphy was simulated for the
location of an automatic weather station near Davos
(Switzerland) over nine winters. Only dry-snow situations
were considered. A variety of selection algorithms was used
to identify the most important simulated snow variables.
Data mining and statistical methods, including
classification trees, artificial neural networks, support
vector machines, hidden Markov Models and nearest-neighbour
methods were trained on the forecasted regional avalanche
danger (European avalanche danger scale). The best results
were achieved with a nearest neighbour method which used the
avalanche danger level of the previous day as additional
input. A cross-validated accuracy (hit rate) of $73\%$ was
obtained. This study suggests that modelled
snow-stratigraphy variables, as provided by SNOWPACK, are
able to improve numerical avalanche forecasting.[2] Snow
stability, or the probability of avalanche release, is one
of the key factors defining avalanche danger. Most snow
stability evaluations are based on field observations, which
are time-consuming and sometimes dangerous. Through
numerical modelling of the snow cover stratigraphy, the
problem of having sparsely measured regional stability
information can be overcome. In this study we compared
numerical model output with observed stability. Overall, 775
snow profiles combined with Rutschblock scores and release
types for the area surrounding five weather stations were
rated into three stability classes. Snow stratigraphy data
were then produced for the locations of these five weather
stations using the snow cover model SNOWPACK. We observed
that (i) an existing physically based stability
interpretation implemented in SNOWPACK was applicable for
regional stability evaluation; (ii) modelled variables
equivalent to those manually observed variables found to be
significantly discriminatory with regard to stability, did
not demonstrated equal strength of classification; (iii)
additional modelled variables that cannot be measured in the
field discriminated well between stability categories.
Finally, with objective feature selection, a set of
variables was chosen to establish an optimal link between
the modelled snow stratigraphy data and the stability rating
through the use of classification trees. Cross-validation
was then used to assess the quality of the classification
trees. A true skill statistic of 0.5 and 0.4 was achieved by
two models that detected "rather stable" or "rather
unstable" conditions, respectively. The interpretation
derived could be further developed into a support tool for
avalanche warning services for the prediction of regional
avalanche danger.[3] Terrestrial and Airborne Laser Scanning
(TLS and ALS) techniques have only recently developed to the
point where they allow wide-area measurements of snow
distribution in varying terrain. Multiple TLS measurements
are presented showing the snow depth development for a
series of precipitation events. We observe that the pattern
of maximum accumulation is similar for the two years
presented here (correlation up to r=0.97). Storms arriving
from the Northwest show persistent snow depth distributions
and contribute most to the final accumulation pattern. Snow
depth patterns of maximum accumulation for the two years is
more similar than the distribution created by any two pairs
of individual storms. A decrease in variance of snow depth
change with time was observed, while variance of snow depth
was increasing. Based on the strong link between
accumulation patterns and terrain, we investigated the
ability of a model based on terrain and wind direction to
predict accumulation patterns. This approach of Winstral,
which describes wind exposure and shelter, was able to
predict the general accumulation pattern over scales of
slopes but failed to match observed variance. Furthermore, a
high sensitivity to the local wind direction was
demonstrated. We suggest that Winstral's model could form a
useful tool for application from hydrology and avalanche
risk assessment to glaciology.[4] We present analysis of
high resolution laser scanning data of snow depths in the
Wannengrat catchment (introduced in [3]) using
omni-directional and directional variograms for three
specific terrain features; cross-loaded slopes, lee slopes
and windward slopes. A break in scaling behavior was
observed in all sub-areas, which can be seen as the
roughness scale of summer terrain which is modified by the
snow cover. In the wind-protected lee slope a different
scaling behavior was observed, compared to the two
wind-exposed areas. The wind-exposed areas have a smaller
ordinal intercept, a smaller short range fractal dimension D
and a larger scale break distance L than the wind-protected
lee slope. Snow depth structure inherits characteristics of
dominant NW storms, which results e.g. in a trend towards
larger break distances in the course of the accumulation
season. This can be interpreted as a result of surface
smoothing at increasing scales. Similar scaling
characteristics were obtained for two different years at the
end of the accumulation season. Since snow depth structure
is altered strongly by NW storms, this inter-annual
consistency may strongly depend on their frequency in an
accumulation period. The analysis of directional variograms
suggests that existing anisotropies can be explained by the
orientation of terrain features with respect to the
predominant wind direction.},
keywords = {Lawine (SWD) / Davos / Eidgenössisches Institut für
Schnee- und Lawinenforschung (SWD) / Variabilität (SWD) /
Schnee (SWD) / Lidar (SWD) / Modellierung (SWD) / Statistik
(SWD) / Fraktal (SWD)},
cin = {551520 / 530000},
ddc = {550},
cid = {$I:(DE-82)551520_20140620$ / $I:(DE-82)530000_20140620$},
typ = {PUB:(DE-HGF)11},
urn = {urn:nbn:de:hbz:82-opus-36352},
url = {https://publications.rwth-aachen.de/record/64658},
}
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