Forecasting Persistent Deep Slab Avalanches
| atmire.migration.oldid | 3021 | |
| dc.contributor.advisor | Jamieson, Bruce | |
| dc.contributor.author | Conlan, Michael Joseph William | |
| dc.date.accessioned | 2015-03-25T21:47:43Z | |
| dc.date.available | 2015-06-23T07:00:46Z | |
| dc.date.issued | 2015-03-25 | |
| dc.date.submitted | 2015 | en |
| dc.description.abstract | Persistent deep slab avalanches are often difficult to forecast. For such avalanches to release, fractures are generally initiated in weak layers buried deep in the snowpack, often at depths where induced stresses and strain rates are dampened. The release of these avalanches can occur naturally from influences of the weather or they can be triggered artificially from localized dynamic loads, such as skiers, snowmobiles, and explosives. Improved forecasting for persistent deep slab avalanches requires an understanding of avalanche terrain, snowpack conditions, and weather that causes or influences their release. Natural and artificially triggered avalanches were accessed to examine such factors. Profiles and snowpack tests were conducted near the released start zones. Profiles indicated that persistent weak layers were generally softer than surrounding layers and they exhibited high propagation propensity. Natural avalanches triggered by snowpack warming or artificially triggered avalanches from skiers and snowboarders were often initiated in terrain where the snowpack was relatively thin. Next, a database of naturally triggered persistent deep slab avalanches was obtained to better understand weather conditions that preceded their release. Weather data were obtained from both weather stations and weather model forecasts. Classification trees were created for both data sources and correctly classified 87 and 83 % of the avalanches by their primary cause-of-release, respectively. An expert opinion survey was conducted to compare results of the field and database studies with what avalanche professionals observe in the field. All results of the studies were compiled to create a decision support tool to aid avalanche professionals in forecasting the likelihood of persistent deep slab avalanches. The tool correctly predicted 89 % of natural avalanches and 74 % of non-avalanche days. Lastly, cold laboratory experiments were conducted to determine the strength change of faceted grains above a melt-freeze crust, which is a common weak layer scenario for persistent deep slab avalanches. Shear frame tests indicated that shear strength increased with time, generally following a power law relationship when applying constant overburden load. The results of this dissertation may aid in the decision making process for avalanche professionals when forecasting the likelihood of persistent deep slab avalanches. | en_US |
| dc.identifier.citation | Conlan, M. J. (2015). Forecasting Persistent Deep Slab Avalanches (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26209 | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/26209 | |
| dc.identifier.uri | http://hdl.handle.net/11023/2122 | |
| dc.language.iso | eng | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.institution | University of Calgary | en |
| dc.publisher.place | Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject | Atmospheric Sciences | |
| dc.subject | Applied Mechanics | |
| dc.subject | Engineering--Civil | |
| dc.subject.classification | snow avalanche | en_US |
| dc.subject.classification | Persistent weak layer | en_US |
| dc.subject.classification | avalanche mechanics | en_US |
| dc.subject.classification | snowpack tests | en_US |
| dc.subject.classification | weather | en_US |
| dc.subject.classification | forecasting | en_US |
| dc.subject.classification | decision support | en_US |
| dc.title | Forecasting Persistent Deep Slab Avalanches | |
| dc.type | doctoral thesis | |
| thesis.degree.discipline | Civil Engineering | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Doctor of Philosophy (PhD) | |
| ucalgary.item.requestcopy | true |