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Abstract 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 m along the fluvial network of three watersheds within the province of Quebec, Canada. The RF models use 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 1807 reference points were classified for flooding, 1542 for mobility, and 847 for the type of erosion out of the 11,452 reference points for the 1145 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.
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Abstract River confluences are characterized by a complex mixing zone with three‐dimensional (3D) turbulent structures which have been described as both streamwise‐oriented structures and Kelvin–Helmholtz (KH) vertical‐oriented structures. The latter are visible where there is a turbidity difference between the two tributaries, whereas the former are usually derived from mean velocity measurements or numerical simulations. Few field studies recorded turbulent velocity fluctuations at high frequency to investigate these structures, particularly at medium‐sized confluences where logistical constraints make it difficult to use devices such as acoustic doppler velocimeter (ADV). This study uses the ice cover present at the confluence of the Mitis and Neigette Rivers in Quebec (Canada) to obtain long‐duration, fixed measurements along the mixing zone. The confluence is also characterized by a marked turbidity difference which allows to investigate the mixing zone dynamics from drone imagery during ice‐free conditions. The aim of the study is to characterize and compare the flow structure in the mixing zone at a medium‐sized (~40 m) river confluence with and without an ice cover. Detailed 3D turbulent velocity measurements were taken under the ice along the mixing plane with an ADV through eight holes at around 20 positions on the vertical. For ice‐free conditions, drone imagery results indicate that large (KH) coherent structures are present, occupying up to 50% of the width of the parent channel. During winter, the ice cover affects velocity profiles by moving the highest velocities towards the centre of the profiles. Large turbulent structures are visible in both the streamwise and lateral velocity components. The strong correlation between these velocity components indicates that KH vortices are the dominating coherent structures in the mixing zone. A spatio‐temporal conceptual model is presented to illustrate the main differences on the 3D flow structure at the river confluence with and without the ice cover. © 2019 John Wiley & Sons, Ltd.
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Abstract The consensus around the need for a shift in river management approaches to include more natural processes is steadily growing amongst scientists, practitioners, and governmental agencies. The freedom space for rivers concept promotes the delineation of a single space that integrates multiple fluvial dynamics such as floods, lateral migration, channel avulsions, and riparian wetlands connectivity. The objective of this research is to assess the validity of the hydrogeomorphological approach to delineate the freedom space for an extensive sampling of river reaches, covering 167 km, in contrasting watersheds in Quebec (Canada). Comparative analysis was conducted on the relative importance of erosion and flood processes on the freedom space delineation for various fluvial types. Semiautomated tools based on light detection and ranging (LiDAR) digital elevation models were also tested on an additional 274 km of watercourses to facilitate freedom space mapping over extensive zones and for highly dynamics environments such as alluvial fans. In the studied reaches, flood and erosion processes occur respectively, on average, in a space equivalent to 2.6 and 20.6 channel widths. In unconfined landscapes, flood processes represent an area up to almost four times the area of erosion processes expected in a 50‐year period. In partly confined and confined environments, erosion processes are more likely to exceed flooding zone, and therefore need to be integrated in the mapping. This study helps better determine the conditions for which the full methodology of freedom space mapping is required or where semiautomated methods can be used. It provides useful guidelines for the implementation of the freedom space approach.
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Abstract Large‐scale flood modelling approaches designed for regional to continental scales usually rely on relatively simple assumptions to represent the potentially highly complex river bathymetry at the watershed scale based on digital elevation models (DEMs) with a resolution in the range of 25–30 m. Here, high‐resolution (1 m) LiDAR DEMs are employed to present a novel large‐scale methodology using a more realistic estimation of bathymetry based on hydrogeomorphological GIS tools to extract water surface slope. The large‐scale 1D/2D flood model LISFLOOD‐FP is applied to validate the simulated flood levels using detailed water level data in four different watersheds in Quebec (Canada), including continuous profiles over extensive distances measured with the HydroBall technology. A GIS‐automated procedure allows to obtain the average width required to run LISFLOOD‐FP. The GIS‐automated procedure to estimate bathymetry from LiDAR water surface data uses a hydraulic inverse problem based on discharge at the time of acquisition of LiDAR data. A tiling approach, allowing several small independent hydraulic simulations to cover an entire watershed, greatly improves processing time to simulate large watersheds with a 10‐m resampled LiDAR DEM. Results show significant improvements to large‐scale flood modelling at the watershed scale with standard deviation in the range of 0.30 m and an average fit of around 90%. The main advantage of the proposed approach is to avoid the need to collect expensive bathymetry data to efficiently and accurately simulate flood levels over extensive areas.