Abstract

Rivers are morphologically dynamic entities, where flooding leads to fluvial erosion and deposition that in turn promotes the lateral or vertical mobility of the river. Although these natural processes are necessary to maintain the integrity of river ecosystems, when communities or infrastructures are exposed to them, their safety and integrity can be compromised. Predicting river mobility at the watershed scale is particularly essential to understand the impact of extreme events on river systems and increase preparedness with the added pressure from climate change. Using two case study rivers: the Sainte-Anne River and the Du Gouffre River in Quebec, this research assesses the accuracy of geographic information systems (GIS) tools to predict 3 types of river mobility: lateral migration, avulsion and widening. Existing GIS tools were tested on the pre- morphological change high-resolution LiDAR digital elevation model (DEM) and compared to the post- morphological change LiDAR DEM to determine their effectiveness in predicting changes in mobility. To assess lateral migration, the short-term (50 year) mobility space (M50) was computed and compared with the actual mobility space from the flood. To determine corridors of potential avulsions, random noise was added to LiDAR DEMs prior to running several flow accumulation routines. Finally, river widening equations were used to assess the hydrogeomorphological variables that allow for the identification of areas susceptible to channel widening. Testing these tools on mobility hotspots establishes a foundation for developing a methodology to determine the mobility space of the most dynamic reaches of rivers.