h1

TY  - THES
AU  - Schirmer, Michael
TI  - Modelling avalanche danger and understanding snow depth variability
CY  - Aachen
PB  - Publikationsserver der RWTH Aachen University
M1  - RWTH-CONV-125935
SP  - IV, 105 S. : Ill., graph. Darst., Kt.
PY  - 2010
N1  - Prüfungsjahr: 2010. - Publikationsjahr: 2011
N1  - Aachen, Techn. Hochsch., Diss., 2010
AB  - 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
KW  - Lawine (SWD)
KW  - Davos / Eidgenössisches Institut für Schnee- und Lawinenforschung (SWD)
KW  - Variabilität (SWD)
KW  - Schnee (SWD)
KW  - Lidar (SWD)
KW  - Modellierung (SWD)
KW  - Statistik (SWD)
KW  - Fraktal (SWD)
LB  - PUB:(DE-HGF)11
UR  - https://publications.rwth-aachen.de/record/64658
ER  -