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.

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.

When rivers collide, complex three-dimensional coherent flow structures are generated along the confluence’s mixing interface. These structures play important roles in mixing streamborne pollutants and suspended sediment and have considerable bearing on the morphology and habitat quality of the postconfluent reach. A particular structure of interest - streamwise orientated vortices (SOVs) - were first detected in numerical simulations to form in pairs, one on each side of the mixing interface rotating in the opposite sense of the other. Since, it has proven difficult to detect SOVs in situ with conventional pointwise velocimetry instrumentation. Despite the lack of clear evidence to confirm their existence, SOVs are nevertheless considered important drivers of mixing and sediment transport processes at confluences. Additionally, their causal mechanisms are also not fully known which hinders a complete conceptual understanding of these processes. To address these gaps, we analyze observations of strongly coherent SOVs filmed in aerial drone video of a mesoscale confluence with a stark turbidity contrast between its tributaries. Eddy-resolved modeling demonstrates the SOVs’ dynamics could only be accurately reproduced when a density difference (Δρ) was imposed between the tributaries (Δρ = 0.5 kg/m$^{3}$) – providing compelling evidence the observed SOVs are indeed a density-driven class of SOV. This work confirms that SOVs exist, expands understanding of their generative processes and highlights the important role of small density gradients (e.g., less than 0.5 kg/m3) on river confluence hydrodynamics.

Large rivers can retain a substantial amount of nitrogen (N), particularly in submerged aquatic vegetation (SAV) meadows that may act as disproportionate control points for N retention in rivers. However, the temporal variation of N retention remains unknown since past measurements were snapshots in time. Using high frequency measurements over the summers 2012-2017, we investigated how climate variation influenced N retention in a SAV meadow at the confluence zone of two agricultural tributaries entering the St. Lawrence River. Distinctive combinations of water temperature and level were recorded between years, ranging from extreme hot-low (2012) and cold-high (2017) summers (2 ˚C and 1.4 m interannual range). Using an indicator of SAV biomass, we found that these extreme hot-low and cold-high years had reduced biomass compared to hot summers with intermediate levels. In addition, change in main stem water levels were asynchronous with the tributary discharges that controlled NO3- inputs at the confluence. We estimated daily N uptake rates from a moored NO3- sensor, and partitioned these into assimilatory and dissimilatory pathways. Measured rates were variable but among the highest reported in rivers (median 576 mg N m-2 d-1;, range 60 – 3893 mg N m-2 d-1) and SAV biomass promoted greater proportional retention and permanent N loss through denitrification. We estimated that the SAV meadow could retain up to 0.8 kt N per year and 87% of N inputs, but this valuable ecosystem service is contingent on how climate variations modulate both N loads and SAV biomass.

A small gradient in the densities (Δρ) of two rivers was recently shown to develop coherent streamwise orientated vortices (SOVs) in the mixing interface of their confluence. We further investigate this phenomenon at the Coaticook and Massawippi confluence (Quebec, Canada) using eddy-resolved numerical modelling to examine how the magnitude and direction of Δρ; affect this secondary flow feature. Results show that a front from the denser channel always slides underneath the lighter channel independent of the direction of Δρ. When the fast tributary (Coaticook) is denser, coherent clockwise rotating density SOVs tend to form on the slow (Massawippi) side. However, when the slow Massawippi is denser by the same magnitude, anticlockwise secondary flow caused principally by shear induced interfacial instabilities develop on the fast Coaticook side. This shows the inertia of the tributary opposing the lateral propagation of the dense front shapes the secondary flow characteristics of the mixing interface. Moreover, in the absence of a density difference, anticlockwise SOVs are predicted by the model which correspond well to new aerial observations of anticlockwise SOVs at the site. A densimetric Froude number (Fd) convention accounting for the direction of Δρ is proposed to accurately convey the local inertial forces that oppose the lateral propagation of the dense front. Finally, a conceptual model of the mixing interface’s secondary flow structure over a spectrum of plausible Fd values is proposed. The Fd convention provides a flexible and consistent metric for use in future studies examining the effects of Δρ on river confluence hydrodynamics.

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.