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Durant les mois de janvier et février 2019, trois embâcles ont forcé l’arrêt de la navigation commerciale vers le Port de Montréal. Ce mémoire présente les conditions météorologiques associées aux embâcles sur le fleuve Saint Laurent de l’hiver 2018-2019. Il explique que les embâcles se développent à la suite d’arrêts de glace dans le bief problématique du lac Saint-Pierre entre la courbe Louiseville et le bassin Yamachiche. Pour ce faire, l’étude considère la production de glace en amont jusqu’au lac Saint-Louis. Il explique pourquoi ce bief est si vulnérable à l’initiation d’embâcles en présentant les neuf concepts de vulnérabilité du lac Saint-Pierre. De plus, il propose quatorze recommandations concrètes pour améliorer la fiabilité de navigation hivernale en réduisant les risques d’embâcles. En considérant ces recommandations, différentes opportunités de télédétection et une interface utilisateur sont présentées. L’opportunité de télédétection introduit la possibilité d’usage d’images de RADARSAT Constellation Mission et de photographies par drone afin d’évaluer des éléments clés comme la progression du couvert de glace, la largeur effective du chenal, la concentration de glace en transit et la vitesse de la glace. L’interface est un prototype d’outil d’aide à la décision de source libre qui permet d’obtenir d’autres informations quantitatives sur les risques d’arrêts de glace et du même fait, d’embâcles de glace.

An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning.

Wetlands and reservoirs are important water flow and storage regulators in a river basin; therefore, they can play a crucial role in mitigating flood and hydrological drought risks. Despite the advancement of river basin theory and modeling, our knowledge is still limited about the extent to which these two regulators could perform such a role, especially under future climate extremes. To improve our understanding, we first coupled wetlands and reservoir operations into a semi-spatially explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. The projection of future floods and hydrological droughts was performed using the hydrological model during different periods (near future: 2026–2050, middle century: 2051–2075, and end century: 2076–2100) under five future climate change scenarios. We found that the risk of future floods and hydrological droughts can vary across different periods – in particular, it will experience relatively large increases and slight decreases. This large river basin will experience flood events of longer duration, with larger peak flows and volume, and of enhanced flashiness compared to the historical period. Simultaneously, the hydrological droughts will be much more frequent, with longer durations and more serious deficits. Therefore, the risk of floods and droughts will, overall, increase further under future climate change even under the combined influence of reservoirs and wetlands. These findings highlight the hydrological regulation function of wetlands and reservoirs and attest that the combining of wetlands with reservoir operation cannot fully eliminate the increasing future flood and drought risks. To improve a river basin's resilience to the risks of future climate change, we argue that the implementation of wetland restoration and the development of accurate forecasting systems for effective reservoir operation are of great importance. Furthermore, this study demonstrated a wetland–reservoir integrated modeling and assessment framework that is conducive to risk assessment of floods and hydrological droughts and that can be used for other river basins in the world.

Abstract Current flood risk mapping, relying on historical observations, fails to account for increasing threat under climate change. Incorporating recent developments in inundation modelling, here we show a 26.4% (24.1–29.1%) increase in US flood risk by 2050 due to climate change alone under RCP4.5. Our national depiction of comprehensive and high-resolution flood risk estimates in the United States indicates current average annual losses of US$32.1 billion (US$30.5–33.8 billion) in 2020’s climate, which are borne disproportionately by poorer communities with a proportionally larger White population. The future increase in risk will disproportionately impact Black communities, while remaining concentrated on the Atlantic and Gulf coasts. Furthermore, projected population change (SSP2) could cause flood risk increases that outweigh the impact of climate change fourfold. These results make clear the need for adaptation to flood and emergent climate risks in the United States, with mitigation required to prevent the acceleration of these risks.

Floods can cause extensive damage proportional to their magnitude, depending on the watershed hydrology and terrain characteristics. Flood studies generally assume bathymetry as steady, while in reality it is constantly changing due to sediment transport. This study seeks to quantify the impact of different lake bathymetry conditions on flood dynamics. The Hydrotel and Telemac2D models are used to simulate floods for a lake with bathymetries from multiple year surveys. The bathymetries differ in bed elevation due to sediment accumulation and/or remobilisation. Results show that bathymetric differences produce a more noticeable effect for moderate flows than for maximum flows. During moderate flows, shallower bathymetries induce higher water levels and larger water extents. For peak flows, differences in water levels and extent are practically negligible for the different bathymetries tested. Higher water levels during moderate flows could produce longer flooding times and affect the community’s perception of flood impacts.

Habitat loss and degradation is a leading cause of the current biodiversity crisis. In the lake Saint-Pierre floodplain, agricultural intensification has led to the loss of substantial spawning and rearing areas for the yellow perch ( Perca flavescens Mitchill). Restoring perennial vegetation cover is key to ensure the persistence of the population, but the floodplain conditions limit our ability to do so. In this study, we tested the impact of companion plants ( Avena sativa L., Lolium multiflorum L.) and sowing rate on the establishment success of reed canarygrass ( Phalaris arundinacea L.; RCG) in year 2. RCG tolerates a wide range of environmental conditions and can provide the plant cover essential to the reproduction of yellow perch. We hypothesized that companion plants would reduce weed pressure and in turn improve RCG establishment, and that increasing the sowing rate would improve the establishment success. Contrary to our expectations, using companion plants generally reduced the cover and biomass of RCG. It also led to increased weed prevalence in most treatments. In addition, sowing at high rates did not impact RCG cover and biomass. Sowing RCG alone appears to be the most effective option to establish perennial vegetation supporting the recovery of the yellow perch population.

The Canada Centre for Mapping and Earth Observation (CCMEO) uses Radarsat Constellation Mission (RCM) data for near-real time flood mapping. One of the many advantages of using SAR sensors, is that they are less affected by the cloud coverage and atmospheric conditions, compared to optical sensors. RCM has been used operationally since 2020 and employs 3 satellites, enabling lower revisit times and increased imagery coverage. The team responsible for the production of flood maps in the context of emergency response are able to produce maps within four hours from the data acquisition. Although the results from their automated system are good, there are some limitations to it, requiring manual intervention to correct the data before publication. Main limitations are located in urban and vegetated areas. Work started in 2021 to make use of deep learning algorithms, namely convolutional neural networks (CNN), to improve the performances of the automated production of flood inundation maps. The training dataset make use of the former maps created by the emergency response team and is comprised of over 80 SAR images and corresponding digital elevation model (DEM) in multiple locations in Canada. The training and test images were split in smaller tiles of 256 x 256 pixels, for a total of 22,469 training tiles and 6,821 test tiles. Current implementation uses a U-Net architecture from NRCan geo-deep-learning pipeline (https://github.com/NRCan/geo-deep-learning). To measure performance of the model, intersection over union (IoU) metric is used. The model can achieve 83% IoU for extracting water and flood from background areas over the test tiles. Next steps include increasing the number of different geographical contexts in the training set, towards the integration of the model into production.