Modelling avalanche danger and understanding snow depth variability [Elektronische Ressource] / Michael Schirmer

Informations

• Publié par: rheinisch-westfalischen_technischen_hochschule_-rwth-_aachen
• Publié le: 01 janvier 2011
• Nombre de lectures: 22
• Langue: Deutsch
• Poids de l'ouvrage: 7 Mo

Extrait

Modelling Avalanche Danger and Understanding
Snow Depth Variability
Von der Fakult¨at fu¨r Georessourcen und Materialtechnik der
Rheinisch-Westf¨alischen Technischen Hochschule Aachen
zur Erlangung des akademischen Grades eines
Doktors der Naturwissenschaften
genehmigte Dissertation
vorgelegt von
Michael Schirmer
aus Freiburg
Berichter: Prof. Dr. Christoph Schneider
Dr. Michael Lehning
Tag der mu¨ndlichen Pru¨fung: 12.07.2010
Diese Dissertation ist auf den Internetseiten der Hochschulbibliothek online verfu¨gbarContents
Summary 1
Zusammenfassung 3
1 Introduction 7
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2.1 Avalanche formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2.2 Spatial variability at a regional scale . . . . . . . . . . . . . . . . . . . . . . . 8
1.2.3 Avalanche danger. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 Problematic issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.1 Stability interpretations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.2 Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Statistical forecasting of regional avalanche danger using simulated snow-cover
data 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.2 Performance measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.3 Variable selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.4 Statistical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.1 Variable selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.2 Statistical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4 Discussion and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3 Statistical evaluation of local to regional snowpack stability using simulated
snow-cover data 27
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
iContents
3.2.3 Rating of variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.4 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.5 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.6 Probability forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.7 Suitability of proposed methods. . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.1 Rating of variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.2 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.3.3 Probability forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.4 Discussion and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4 Persistence in intra-annual snow depth distribution. Part I: measurements and
topographic control 45
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.2.1 Field description and data acquisition . . . . . . . . . . . . . . . . . . . . . . 46
4.2.2 Modelling snow depth with terrain variables . . . . . . . . . . . . . . . . . . . 48
4.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3.1 Overview of observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3.2 Temporal evolution of snow depth and snow depth change . . . . . . . . . . . 55
4.3.3 Description of transects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.3.4 Quantitative analysis of inter- and intra-annual consistency . . . . . . . . . . 56
4.3.5 Modelling snow depth with terrain variables . . . . . . . . . . . . . . . . . . . 59
4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5 Persistence in intra-annual snow depth distribution. Part II: fractal analysis of
snow depth development 67
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.2.1 Field description and data acquisition . . . . . . . . . . . . . . . . . . . . . . 68
5.2.2 Fractal analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5.3.1 Omnidirectional variograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.3.2 Directional variograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6 Conclusions and outlook 85
6.1 Avalanche danger and snowpack stability . . . . . . . . . . . . . . . . . . . . . . . . 85
6.2 Snow depth variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
6.3 Spatial variability and avalanche danger . . . . . . . . . . . . . . . . . . . . . . . . . 88
List of Tables 91
iiContents
List of Figures 93
References 97
Acknowledgements 105
iiiivSummary
Thisthesisaddressesthecausesofavalanchedangerataregionalscale. Modelledsnowstratigraphy
variableswerelinked to [1] forecastedavalanchedangerand [2] observedsnowpackstability. 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.
WeusedtheoutputofthephysicallybasedsnowcovermodelSNOWPACKcombinedwithmeteoro-
logical 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 mostimportantsimulatedsnowvariables. Datamining andstatisticalmethods, includ-
ing classification trees, artificial neural networks, support vector machines, hidden Markov Models
andnearest-neighbourmethodsweretrainedontheforecastedregionalavalanchedanger(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]Snowstability, ortheprobabilityofavalancherelease,isoneofthe keyfactorsdefiningavalanche
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 prob-
lem 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