River predisposition to ice jams: a simplified geospatial model

• Published in: Natural hazards and earth system sciences 2017-07, Vol.17 (7), p.1033-1045
• Main Authors: De Munck, Stéphane, Gauthier, Yves, Bernier, Monique, Chokmani, Karem, Légaré, Serge
• Format: Article
• Language: English
• Subjects: Analysis; Breaking; Breakup; Bridges; Channel morphology; Confluence; Environmental aspects; Flood control; Floods; Geographic information systems; Geographical information systems; Geomorphology; Geomorphology of rivers; Geospatial data; Historic buildings & sites; Ice; Ice accretion; Ice breakup; Ice jams; Mathematical models; Morphology; Municipalities; Natural resources; River channels; River ice; Rivers; Segments; Spatial data; Topographic databases; Watersheds
• ISSN: 1684-9981; 1561-8633; 1684-9981
• DOI: 10.5194/nhess-17-1033-2017

Floods resulting from river ice jams pose a great risk to many riverside municipalities in Canada. The location of an ice jam is mainly influenced by channel morphology. The goal of this work was therefore to develop a simplified geospatial model to estimate the predisposition of a river channel to ice jams. Rather than predicting the timing of river ice breakup, the main question here was to predict where the broken ice is susceptible to jam based on the river's geomorphological characteristics. Thus, six parameters referred to potential causes for ice jams in the literature were initially selected: presence of an island, narrowing of the channel, high sinuosity, presence of a bridge, confluence of rivers, and slope break. A GIS-based tool was used to generate the aforementioned factors over regular-spaced segments along the entire channel using available geospatial data. An ice jam predisposition index (IJPI) was calculated by combining the weighted optimal factors. Three Canadian rivers (province of Québec) were chosen as test sites. The resulting maps were assessed from historical observations and local knowledge. Results show that 77 % of the observed ice jam sites on record occurred in river sections that the model considered as having high or medium predisposition. This leaves 23 % of false negative errors (missed occurrence). Between 7 and 11 % of the highly predisposed river sections did not have an ice jam on record (false-positive cases). Results, limitations, and potential improvements are discussed.