Fluvial hazards of river mobility and flooding are often problematic for road infrastructure and need to be considered in the planning process. The extent of river and road infrastructure networks and their tendency to be close to each other creates a need to be able to identify the most dangerous areas quickly and cost-effectively. In this study we propose a novel methodology using random forest (RF) machine learning methods to provide easily interpretable fine-scale fluvial hazard predictions for large river systems. The tools developed provide predictions for three models: presence of flooding (PFM), presence of mobility (PMM) and type of erosion model (TEM, lateral migration, or incision) at reference points every 100 meters along the fluvial network of three watersheds within the province of Quebec, Canada. The RF models uses variables focused on river conditions and hydrogeomorphological processes such as confinement, sinuosity, and upstream slope. Training/validation data included field observations, results from hydraulic and erosion models, government infrastructure databases, and hydro- geomorphological assessments using 1-m DEM and satellite/historical imagery. A total of 1,807 reference points were classified for flooding, 1,542 for mobility, and 847 for the type of erosion out of the 11,452 reference points for the 1,145 km of rivers included in the study. These were divided into training (75%) and validation (25%) datasets, with the training dataset used to train supervised RF models. The validation dataset indicated the models were capable of accurately predicting the potential for fluvial hazards to occur, with precision results for the three models ranging from 83% to 94% of points accurately predicted. The results of this study suggest that RF models are a cost-effective tool to quickly evaluate the potential for fluvial hazards to occur at the watershed scale.

Integrating hydrogeomorphological principles into the restoration of degraded rivers can achieve sustainable results for a variety of objectives and human benefits that are consistent with the potential functioning of rivers as well as their uses. Despite recent management approaches inspired by these principles, several restoration projects largely involve controlling river processes and target specific one-dimensional objectives often associated with the habitat of a few high-valued fish species or with rivers steadiness and aesthetics. Although there is overall a lack of post-project monitoring, several projects appear to have failed or had mixed success. This research aims to answer the question: What are the real drivers behind river restoration projects? Four restoration projects in Quebec (Canada) were characterized through a qualitative research process of support and interviews with the organizations running them as well as with two representatives of ministries involved in river restoration and management. The results identify two major drivers for the formulation of restoration objectives: project funding and stakeholder expertise. We propose a new analytical framework including these drivers, which appear to act as both conditions and motivations guiding the objectives of the projects and their diversity. Following diverse sociocultural and legislative contexts shaping these drivers, they may facilitate or restrict the integration of hydrogeomorphological principles towards diversified objectives and benefits. This supports regulation that is better informed by scientific knowledge about hydrogeomorphological and sociocultural river dynamics, knowledge sharing between academic researchers and environmental organizations, and collaboration between stakeholders and communities living around rivers.

A series of recent flood events in Canada affecting areas around lakes and reservoirs have highlighted the need to explicitly represent such features in large scale flood models. Water level fluctuations in lakes are traditionally modelled using detailed hydrological models designed – as far as possible – to represent the actual physical processes that take place. This approach, while appropriate for local-scale studies in data-rich areas, is not applicable for large-scale flood modelling where data availability for model calibration and validation is often severely limited. This paper explores two methodologies, one statistical and one physically based, designed to approximately predict the increase in the water level of lakes in Quebec (Canada) using only limited morphological information about the lakes and the estimated discharge entering the water body during a flood event. Of the two methods, the statistical approach proved to be the most applicable to a large-scale modelling framework as it exhibited lower errors whilst being considerably easier to implement in a semi-automated modelling chain.