Development of an under-ice river discharge forecasting system in Delft-Flood Early Warning System (Delft-FEWS) for the Chaudière River based on a coupled hydrological-hydrodynamic modelling approach - Usman, Kh Rahat; Montero, Rodolfo Alvarado; Ghobrial, Tadros; Anctil, François; van Loenen, Arnejan (Geoscientific Model Development Discussions, 2024)
Abstract. Year-round river discharge estimation and forecasting is a critical component of sustainable water resource management. However, in cold climate regions such as Canada, this basic task gets intricated due to the challenge of river ice conditions. River ice conditions are dynamic and can change quickly in a short period of time. This dynamic nature makes river ice conditions difficult to forecast. Moreover, the observation of under-ice river discharge also remains a challenge since no reliable method for its estimation has been developed till date. It is therefore an active field of research and development. The integration of river ice hydraulic models in forecasting systems has remained relatively uncommon. The current study has two main objectives: first is to demonstrate the development and capabilities of a river ice forecasting system based on coupled hydrological and hydraulic modelling approach for the Chaudière River in Québec; and second is to assess its functionality over selected winter events. The forecasting system is developed within a well-known operational forecasting platform: the Delft Flood Early Warning System (Delft-FEWS). The current configuration of the systems integrates (i) meteorological products such as the Regional Ensemble Prediction System (REPS); (ii) a hydrological module implemented through the HydrOlOgical Prediction LAboratory (HOOPLA), a multi-model based hydrological modelling framework; and (iii) hydraulic module implemented through a 1D steady and unsteady HEC-RAS river ice models. The system produces ensemble forecasts for discharge and water level and provides flexibility to modify various dynamic parameters within the modelling chain such as discharge timeseries, ice thickness, ice roughness as well as carryout hindcasting experiments in a batch production way. Performance of the coupled modelling approach was assessed using “Perfect forecast” over winter events between 2020 and 2023 winter seasons. The root mean square error (RMSE) and percent bias (Pbias) metrics were calculated. The hydrologic module of the system showed significant deviations from the observations. These deviations could be explained by the inherent uncertainty in the under-ice discharge estimates as well as uncertainty in the modelling chain. The hydraulic module of the system performed better and the Pbias was within ±10 %.

Investigating incomplete mixing models in cross junctions under real-world conditions of water distribution networks - Yousefian, Reza; Duchesne, Sophie; Schwarz, Pierre-Olivier (Water Supply, 2024)
Questions have been raised about the correctness of water quality models with complete mixing assumptions in cross junctions of water distribution systems. Recent developments in the mixing phenomenon within cross junctions of water distribution networks (WDNs) have heightened the need for evaluating the existing incomplete mixing models under real-world conditions. Therefore, in this study, two cross junctions with pipe diameters of 100 Â 100 Â 100 Â 100 mm and 150 Â 150 Â 150 Â 150 mm were employed in laboratory experiments to evaluate six existing incomplete mixing models for 25 ﬂow rate scenarios ranging between 1.5 and 3.0 L/s. It was observed that within the same ﬂow rate scenario, the degree of mixing in a cross junction with a pipe relative roughness of 6.00 Â 10À5 (pipe diameter of 25 mm) was higher than that in a cross junction with a pipe relative roughness of 3.00 Â 10À5 (pipe diameter of 50 mm) and smaller. Considering the real-world size of pipes in evaluating the incomplete mixing models showed that two incomplete mixing models, AZRED and the one by Shao et al., had the best accordance with the results of the laboratory experiments.

Adaptation of Dual Drainage to Control Flooding and Enhance Combined Sewer Systems in Highly Urbanized Areas - Ossa Ossa, Juan Esteban; Duchesne, Sophie; Pelletier, Geneviève (2024)
Combined sewer surcharges in densely urbanized areas have become more frequent due to the expansion of impervious surfaces and intensified precipitation caused by climate change. These surcharges can generate system overflows, causing urban flooding and pollution of urban areas. This paper presents a novel methodology to mitigate sewer system surcharges and control surface water. In this methodology, flow control devices and urban landscape retrofitting are proposed as strategies to reduce water inflow into the sewer network and manage excess water on the surface during extreme rainfall events. For this purpose, a 1D/2D dual drainage model was developed for two case studies located in Montreal, Canada. Applying the proposed methodology to these two sites led to a reduction of the volume of wastewater overflows by 100% and 86%, and a decrease in the number of surface overflows by 100% and 71%, respectively, at the two sites for a 100-year return period 3-h Chicago design rainfall. It also controlled the extent of flooding, reduced the volume of uncontrolled surface floods by 78% and 80% and decreased flooded areas by 68% and 42%, respectively, at the two sites for the same design rainfall.

A data science approach to climate change risk assessment applied to pluvial flood occurrences for the United States and Canada - Bourget, Mathilde; Boudreault, Mathieu; Carozza, David A.; Boudreault, Jérémie; Raymond, Sébastien (ASTIN Bulletin: The Journal of the IAA, 2024)
There is mounting pressure on (re)insurers to quantify the impacts of climate change, notably on the frequency and severity of claims due to weather events such as flooding. This is however a very challenging task for (re)insurers as it requires modeling at the scale of a portfolio and at a high enough spatial resolution to incorporate local climate change effects. In this paper, we introduce a data science approach to climate change risk assessment of pluvial flooding for insurance portfolios over Canada and the United States (US). The underlying flood occurrence model quantifies the financial impacts of short-term (12–48 h) precipitation dynamics over the present (2010–2030) and future climate (2040–2060) by leveraging statistical/machine learning and regional climate models. The flood occurrence model is designed for applications that do not require street-level precision as is often the case for scenario and trend analyses. It is applied at the full scale of Canada and the US over 10–25 km grids. Our analyses show that climate change and urbanization will typically increase losses over Canada and the US, while impacts are strongly heterogeneous from one state or province to another, or even within a territory. Portfolio applications highlight the importance for a (re)insurer to differentiate between future changes in hazard and exposure, as the latter may magnify or attenuate the impacts of climate change on losses.

Saline and hydrocarbon-bearing fluids detected in shallow aquifers of southern New Brunswick, Canada: Natural occurrence, or deep migration along faults and industrial wellbores? - Bordeleau, G.; Lavoie, D.; Rivard, C.; Pinet, N.; Barton, D.; Hinds, S.; Al, Tom (Science of The Total Environment, 2024)

Trajectoires et visées de l'hydrogéomorphologie au Québec - Buffin‐Bélanger, Thomas; Lachapelle, Félix; Biron, Pascale; Boivin, Maxime (Canadian Geographies / Géographies canadiennes, 2024)
Résumé L'hydrogéomorphologie étudie la dynamique des rivières en se concentrant sur les interactions liant la structure des écoulements, la mobilisation et le transport des sédiments et les morphologies qui caractérisent les cours d'eau et leur bassin‐versant. Elle offre un cadre d'analyse et des outils pour une meilleure intégration des connaissances sur la dynamique des rivières pour la gestion des cours d'eau au sens large, et plus spécifiquement, pour leur restauration, leur aménagement et pour l'évaluation et la prévention des risques liés aux aléas fluviaux. Au Québec, l'hydrogéomorphologie émerge comme contribution significative dans les approches de gestion et d'évaluation du risque et se trouve au cœur d'un changement de paradigme dans la gestion des cours d'eau par lequel la restauration des processus vise à augmenter la résilience des systèmes et des sociétés et à améliorer la qualité des environnements fluviaux. Cette contribution expose la trajectoire de l'hydrogéomorphologie au Québec à partir des publications scientifiques de géographes du Québec et discute des visées de la discipline en recherche et en intégration des connaissances pour la gestion des cours d'eau . , Abstract Hydrogeomorphology studies river dynamics, focusing on the interactions between flow structure, sediment transport, and the morphologies that characterize rivers and their watersheds. It provides an analytical framework and tools for better integrating knowledge of river dynamics into river management in the broadest sense, and more specifically, into river restoration as well as into the assessment and prevention of risks associated with fluvial hazards. In Quebec, hydrogeomorphology is emerging as a significant contribution to risk assessment and management approaches, and is at the heart of a paradigm shift in river management whereby process restoration aims to increase the resilience of fluvial systems and societies, and improve the quality of fluvial environments. This contribution outlines the trajectory of hydrogeomorphology in Quebec, based on scientific publications by Quebec geographers, and discusses the discipline's aims in research and knowledge integration for river management . , Messages clés Les géographes du Québec ont contribué fortement au développement des connaissances et outils de l'hydrogéomorphologie. L'hydrogéomorphologie a évolué d'une science fondamentale à une science où les connaissances fondamentales sont au service de la gestion des cours d'eau. L'hydrogéomorphologie et le cortège de connaissances et d'outils qu'elle promeut font de cette discipline une partenaire clé pour une gestion holistique des cours d'eau.

Modeling Heat and Water Exchanges between the Atmosphere and an 85-km2 Dimictic Subarctic Reservoir Using the 1D Canadian Small Lake Model - Kallel, Habiba; Thiboult, Antoine; Mackay, Murray D.; Nadeau, Daniel F.; Anctil, François (Journal of Hydrometeorology, 2024)
Abstract Accurately modeling the interactions between inland water bodies and the atmosphere in meteorological and climate models is crucial, given the marked differences with surrounding landmasses. Modeling surface heat fluxes remains a challenge because direct observations available for validation are rare, especially at high latitudes. This study presents a detailed evaluation of the Canadian Small Lake Model (CSLM), a one-dimensional mixed-layer dynamic lake model, in reproducing the surface energy budget and the thermal stratification of a subarctic reservoir in eastern Canada. The analysis is supported by multiyear direct observations of turbulent heat fluxes collected on and around the 85-km 2 Romaine-2 hydropower reservoir (50.7°N, 63.2°W) by two flux towers: one operating year-round on the shore and one on a raft during ice-free conditions. The CSLM, which simulates the thermal regime of the water body including ice formation and snow physics, is run in offline mode and forced by local weather observations from 25 June 2018 to 8 June 2021. Comparisons between observations and simulations confirm that CSLM can reasonably reproduce the turbulent heat fluxes and the temperature behavior of the reservoir, despite the one-dimensional nature of the model that cannot account for energy inputs and outputs associated with reservoir operations. The best performance is achieved during the first few months after the ice break-up (mean error = −0.3 and −2.7 W m −2 for latent and sensible heat fluxes, respectively). The model overreacts to strong wind events, leading to subsequent poor estimates of water temperature and eventually to an early freeze-up. The model overestimated the measured annual evaporation corrected for the lack of energy balance closure by 5% and 16% in 2019 and 2020. Significance Statement Freshwater bodies impact the regional climate through energy and water exchanges with the atmosphere. It is challenging to model surface energy fluxes over a northern lake due to the succession of stratification and mixing periods over a year. This study focuses on the interactions between the atmosphere of an irregular shaped northern hydropower reservoir. Direct measurements of turbulent fluxes using an eddy covariance system allowed the model assessment. Turbulent fluxes were successfully predicted during the open water period. Comparison between observed and modeled time series showed a good agreement; however, the model overreacted to high wind episodes. Biases mostly occur during freeze-up and breakup, stressing the importance of a good representation of the ice cover processes.

How does a warm and low-snow winter impact the snow cover dynamics in a humid and discontinuous boreal forest? Insights from observations and modeling in eastern Canada - Bouchard, Benjamin; Nadeau, Daniel F.; Domine, Florent; Anctil, François; Jonas, Tobias; Tremblay, Étienne (Hydrology and Earth System Sciences, 2024)
Abstract. In the boreal forest of eastern Canada, winter temperatures are projected to increase substantially by 2100. This region is also expected to receive less solid precipitation, resulting in a reduction in snow cover thickness and duration. These changes are likely to affect hydrological processes such as snowmelt, the soil thermal regime, and snow metamorphism. The exact impact of future changes is difficult to pinpoint in the boreal forest, due to its complex structure and the fact that snow dynamics under the canopy are very different from those in the gaps. In this study, we assess the influence of a low-snow and warm winter on snowmelt dynamics, soil freezing, snowpack properties, and spring streamflow in a humid and discontinuous boreal catchment of eastern Canada (47.29° N, 71.17° W; ≈ 850 m a.m.s.l.) based on observations and SNOWPACK simulations. We monitored the soil and snow thermal regimes and sampled physical properties of the snowpack under the canopy and in two forest gaps during an exceptionally low-snow and warm winter, projected to occur more frequently in the future, and during a winter with conditions close to normal. We observe that snowmelt was earlier but slower, top soil layers were cooler, and gradient metamorphism was enhanced during the low-snow and warm winter. However, we observe that snowmelt duration increased in forest gaps, that soil freezing was enhanced only under the canopy, and that snow permeability increased more strongly under the canopy than in either gap. Our results highlight that snow accumulation and melt dynamics are controlled by meteorological conditions, soil freezing is controlled by forest structure, and snow properties are controlled by both weather forcing and canopy discontinuity. Overall, observations and simulations suggest that the exceptionally low spring streamflow in the winter of 2020–2120 was mainly driven by low snow accumulation, slow snowmelt, and low precipitation in April and May rather than enhanced percolation through the snowpack and soil freezing.

A Mutual Catastrophe Insurance Framework for Horizontal Collaboration in Prepositioning Strategic Reserves - Zbib, Hani; Balcik, Burcu; Rancourt, Marie-Ève; Laporte, Gilbert (Operations Research, 2024)
Improving Disaster Preparedness Through Mutual Catastrophe Insurance In “A Mutual Catastrophe Insurance Framework for Horizontal Collaboration in Prepositioning Strategic Reserves,” H. Zbib, B. Balcik, M.-È. Rancourt, and G. Laporte present an innovative approach to collaborative disaster preparedness. The novel framework considers a risk-averse mutual insurer offering multiyear insurance contracts with coverage deductibles and limits to a portfolio of risk-averse policyholders. It is designed to foster horizontal collaboration among policyholders for joint disaster preparedness by effectively integrating operational and financial functions. The problem is modeled as a large-scale nonlinear multistage stochastic program and solved by using an effective Benders decomposition algorithm. The framework is validated with real data from 18 Caribbean countries focusing on hurricane preparedness. Given the predicted impacts of climate change, the proposed multiyear mutual catastrophe insurance framework promises to reshape global disaster preparedness and make a profound societal impact by providing a transparent disaster financing plan to protect vulnerable regions. The study’s findings stress the importance of long-term cooperation, prenegotiation of indemnification policies, and strategic setting of deductibles and limits by taking into account the correlation between policyholders. , We develop a mutual catastrophe insurance framework for the prepositioning of strategic reserves to foster horizontal collaboration in preparedness against low-probability high-impact natural disasters. The framework consists of a risk-averse insurer pooling the risks of a portfolio of risk-averse policyholders. It encompasses the operational functions of planning the prepositioning network in preparedness for incoming insurance claims, in the form of units of strategic reserves, setting coverage deductibles and limits of policyholders, and providing insurance coverage to the claims in the emergency response phase. It also encompasses the financial functions of ensuring the insurer’s solvency by efficiently managing its capital and allocating yearly premiums among policyholders. We model the framework as a very large-scale nonlinear multistage stochastic program, and solve it through a Benders decomposition algorithm. We study the case of Caribbean countries establishing a horizontal collaboration for hurricane preparedness. Our results show that the collaboration is more effective when established over a longer planning horizon, and is more beneficial when outsourcing becomes expensive. Moreover, the correlation of policyholders affected simultaneously under the extreme realizations and the position of their claims in their global claims distribution directly affects which policyholders get deductibles and limits. This underlines the importance of prenegotiating policyholders’ indemnification policies at the onset of collaboration. Funding: G. Laporte and M.-È. Rancourt were funded by the Canadian Natural Sciences and Engineering Research Council (NSERC) [Grants 2015-06189 and 2022-04846]. Funding was also provided by the Institute for Data Valorisation (IVADO) and the Canada Research Chair in Humanitarian Supply Chain Analytics. B. Balcik was partially supported by a grant from the Scientific and Technological Research Council of Turkey (TUBITAK) 2219 program. This support is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2021.0141 .
Keywords: Infolettre; Infolettre_Avril_2024

Une première centrale hydroélectrique au fil de l'eau au Nunavik : impacts à court terme sur les cycles du mercure et du carbone - St-Aubin, Mariane (2024)
Une première centrale au fil de l'eau (FDE) au Nunavik (QC, Canada), construite en zone de pergélisol continu, alimente la communauté d'Inukjuak en énergie renouvelable depuis 2024. De petite taille, ces constructions ont été peu étudiées par le passé, notamment en lien avec la modification du cycle du mercure (Hg) et à la bioaccumulation de méthylmercure (MeHg) dans les réseaux alimentaires adjacents. Le pergélisol est cependant un potentiel réservoir substantiel de Hg, et la mise en eau pourrait favoriser son dégel, remobilisant ainsi du Hg historique, co-transporté par du carbone (C) ancien. Afin de mieux cerner les impacts d’une inondation en contexte septentrional, des sols, de l’eau de surface et des invertébrés benthiques ont été échantillonnés le long de la rivière Innuksuac avant, pendant et trois mois suivants la mise en eau. Afin d’investiguer le Hg dans la colonne d’eau, la qualité du carbone organique dissous (COD) (i.e. âge et composition) a été étudiée, tandis que le transfert trophique du MeHg au sein du réseau alimentaire a été clarifié à l’aide de l’isotopie stable (ẟ13C et ẟ15N), reflétant la diète et le niveau trophique des organismes. Le ratio Hg : C suggère que les concentrations de Hg dans le sol de la zone d’étude étaient moindres que ce qui était attendu, en se basant de précédentes estimations circompolaires, tandis que la majorité du Hg mesuré se trouvait dans la couche active du pergélisol et n’était donc pas immobilisé par le gel. Néanmoins, la mise en eau a généré une hausse de la concentration de MeHg (~ 7x) et du potentiel de méthylation (~ 4x) dans la couche organique superficielle des sols ennoyés. Cette hausse d’activité s’est reflétée dans les eaux de surface de la baie inondée, qui présentait des concentrations de MeHg dix fois plus élevées que dans les autres sites échantillonnés. Tandis que le COD exogène dérivant du milieu terrestre semble important pour l’apport de Hg inorganique dans le système riverain, le COD récemment dégradé par l’activité microbienne s’est avéré être le meilleur indicateur du potentiel de la méthylation. Une augmentation de la concentration tissulaire de MeHg a finalement été observée au bas de la chaîne trophique, chez les consommateurs primaires (~ 4x) ainsi que chez les invertébrés benthiques arborant une diète omnivore (~ 3x), mais pas chez les organismes prédateurs, suggérant l’existence d’un délai de transfert trophique. Chez les consommateurs primaires, cette augmentation était surtout apparente chez les invertébrés intimement associés à l’environnement benthique de la nouvelle baie inondée, où les signatures de ẟ13C étaient également les plus faibles. Ces résultats offrent un premier portrait à court terme du transport et des transformations du Hg lors d’une inondation en région subarctique, et les hausses enregistrées, bien que non négligeables, se limitent pour l’instant à une faible superficie (< 1 km2) et ne semblent pas se répercuter en aval de la petite baie inondée.

Valeur ajoutée de l’information sur la distribution spatiale du couvert de neige pour la prédiction des débits de crues - Tiwari, Dipti (2024)
Abstract: In Canada, the annual runoff is predominantly influenced by snowmelt following the winter season, with a substantial portion (40-80%) occurring during the spring period, leading to flooding in low-lying areas. Accurate prediction of streamflow is essential for hydropower production, effective flood management, necessitating the incorporation of comprehensive spatially distributed snow observations into hydrological models. This draws the attention to the research question " How can we utilize spatially distributed snow information at various spatial and temporal scales to enhance our understanding of snow processes and apply it for enhanced model calibration to improve hydrological model performance?" The first objective of this thesis is to investigate the utilization of spatially distributed snow information (SNODAS- SNOw Data Assimilation System) for the calibration of a hydrological model and to determine its impact on model performance. A distributed hydrological model, HYDROTEL, has been implemented in the Au Saumon River watershed using input data from ERA-5 Land for temperature data and MSWEP for precipitation data. Seven different calibration experiments are conducted, employing three different objective functions: Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and the SPAtial EFficiency metric (SPAEF). These objective functions are utilized individually or in combination as part of multi-objective calibration processes. This study indicates that utilizing SPAEF for spatial calibration of snow parameters improved streamflow prediction compared to the conventional practice of using RMSE for calibration. SPAEF is further implied to be a more effective metric than RMSE for both sequential and multi-objective calibration. During validation, the calibration experiment incorporating multi-objective SPAEF exhibits enhanced performance in terms of NSE and KGE compared to calibration experiment solely based on NSE. The findings of this study hold significant relevance and potential applicability in emerging satellite technology, particularly the future Terrestrial Snow Mass Mission (TSMM). The study then explores the impact of temporal resolution and signal saturation for model calibration by using SNODAS data as proxy SWE observations mimicking the characteristics of the TSMM product to calibrate the HYDROTEL model. Despite the limitations of it's temporal resolution and signal saturation it is noteworthy that TSMM data exhibits significant potential for enhancing model performance thereby highlighting its utility for hydrological modeling. This study then focuses on the spatio-temporal analysis of snow processes influencing the spatial variability and distribution of snow depth in a small-scale experimental watershed. Drone photogrammetry is employed to capture spatially distributed snow information over the watershed during the winter seasons of 2022 and 2023. The photogrammetric data facilitated the generation of high-resolution digital surface models (DSMs). Empirical Orthogonal Function (EOF) analysis is applied to understand the spatial distribution of snow, enabling a detailed examination of various snow processes at the watershed scale. This thesis explores the added value of spatially distributed snow cover information in predicting spring runoff. Each part of the study contributes to a comprehensive understanding of the spatial distribution of snow and its significance in hydrology.

Élaboration d'une méthode de détection et de caractérisation des occlusions présentes dans les nuages de points massifs de zones résidentielles - Albert, William (2024)
La fréquence et l'ampleur des dommages causés par les inondations au Canada augmentent chaque année, nécessitant des solutions préventives. Les étages inférieurs des bâtiments sont particulièrement vulnérables, justifiant la nécessité de détecter avec précision les ouvertures comme les portes et les fenêtres. Les nuages de points obtenus par télémétrie mobile sont bien adaptés pour identifier la présence de ces ouvertures sur les façades de bâtiments. Toutefois, la présence d'occlusions dans les nuages de points causées par des objets obstruant le faisceau LiDAR rend cette tâche complexe. Le présent projet de maîtrise vise à répondre à cette problématique en proposant une approche pour détecter et caractériser les occlusions dans les scènes acquises dans les milieux résidentiels et ruraux. La conception de la méthode est articulée autour de deux volets : la segmentation sémantique pour étiqueter le nuage de points et la détection d'occlusions par lancer de rayons. La méthode a été validée à partir de données simulées car elles permettent un contrôle total sur l'environnement et facilitent la visualisation des résultats. Cette analyse a ensuite été transposée sur les résultats obtenus à partir du nuage de points réel. Les résultats obtenus par la méthode proposée ont démontré une capacité à détecter la présence d'occlusions dans les milieux résidentiels et ruraux. La combinaison de la segmentation sémantique pour l'étiquetage du nuage de points avec la détection d'occlusions par lancers de rayons a permis une identification robuste des occlusions. Ces résultats soulignent l'efficacité de la solution dans des contextes réalistes, renforçant ainsi sa pertinence pour la détection précise des ouvertures sur les façades, une étape cruciale pour évaluer les risques liés aux inondations. La transposition réussie de l'analyse des données simulées aux résultats du nuage de points réel valide la robustesse de l'approche et son applicabilité dans des scénarios du monde réel.

Improving ensemble streamflow forecasts through targeted post-processing and rating curve uncertainty analysis - Askarinejad, Alireza (2024)
Abstract : Flood forecasting plays a pivotal role in effective water resource management and flood risk mitigation. Despite recent advancements, existing ensemble forecasting systems often grapple with issues of unreliability and under-dispersion. The uncertainty in ensemble forecasts can emanate from various sources, including inputs like precipitation, temperature, and streamflow, as well as initial conditions, model structure and parameters, and boundary conditions. Over the past two decades, numerous endeavors have been made to address bias and under-dispersion through the introduction of various statistical post-processing methods in meteorological and hydrological forecasts. However, a significant challenge lies in selecting the appropriate method and strategy in the forecast chain. Limited research has addressed the integration of pre-processing and post-processing in streamflow ensemble forecasting. Moreover, existing studies have generally focused on lumped hydrological models, while the performance of this integration in distributed hydrological models remains scarcely examined. On the other hand, streamflow data, often indirectly measured through rating curves, are particularly susceptible to errors. While various methods have been proposed to estimate rating curve uncertainty (RCU), the impact of RCU on streamflow forecasting system performance remains underexplored. In addition, current post-processing methods utilized in streamflow forecasting systems have disregarded the inherent uncertainty in observational streamflow data. Thus, this thesis delves into the potential and hurdles associated with considering different sources of uncertainty in flood forecasting systems and their impact on system performance. The primary objectives entail 1) assessing and identifying optimal scenarios from pre-processing of both temperature and precipitation and post-processing of streamflow forecasts approaches, or a combination thereof, and 2) evaluating the consideration of rating curve uncertainty on flood forecasting system performance through incorporating into targeted post-processing methodologies. The thesis focuses on a short-range ensemble streamflow forecasting framework spanning lead times of 1 to 5 days for the au Saumon watershed in southern Quebec, Canada. This watershed, being flood-prone, encountered significant challenges during the 2019 and 2017 flood events, including widespread inundation, road closures, and damage to property and infrastructure. Enhancing streamflow ensemble forecasts and upgrading flood forecasting systems holds the potential to greatly benefit decision-makers and the local populace. Chapter 4 (Results part 1), presented as a scientific article, thus delves into assessing and employing various pre- and post-processing strategy scenarios within the flood forecasting framework employing a spatially distributed hydrological model. By applying different statistical processing and bias correction techniques, the performance and quality of ensemble streamflow forecasts were evaluated across different scenarios. The findings highlight biases and under-dispersion as significant factors affecting raw ensemble forecasts. Pre-processing partially improves raw forecasts but doesn't fully address bias in under-dispersed forecasts. Combining pre- and post-processing enhances forecast skill and reliability, albeit with some variations compared to post-processing alone. Integrating flood events into the training dataset and optimizing its length improves the effectiveness of processing methods, underscoring the critical role of data management strategies in enhancing streamflow forecasting systems. Subsequently, Chapter 5 (Results part 2) , evaluates the consideration of rating curve uncertainty, derived from the Voting Point Method (VPM) and Bayesian Rating curve (BaRatin) estimation methods, on the performance of streamflow ensemble forecasts by integrating into a targeted post-processing approach using Weighted Ensemble Dressing (WED) and Cumulative Distribution Function Matching (CDFM) post-processing methods. Post-processing without RCU enhances forecasting system skill and reliability but overlooks the inherent uncertainty in observational data, posing concerns about its effectiveness. Conversely, integrating RCU improves forecast skill, reliability, and effectively addresses observational data uncertainty, offering a notable advantage over traditional post-processing methods. Comparing WED and CDFM post-processing methods highlighted nuanced differences in forecast outcomes, influenced by uncertainty estimation techniques. Both methods showed favorable accuracy metrics, with RCU integration, especially from VPM, notably enhancing forecast quality. However, variations in RCU from different estimation methods may lead to forecast underestimation or overestimation, warranting careful consideration. These findings collectively shed light on the potential and challenges associated with incorporating different sources of uncertainty into flood forecasting systems.

Seasonal Variation Of Wave Attenuation Capacity Of Canadian Saltmarsh Vegetation - Caldera, Ganga; Stolle, Jacob; Bang, Damien Pham Van; Murphy, Enda; Knox, Paul (CoastLab 2024: Physical Modelling in Coastal Engineering and Science, 2024)
Nature-based Solutions (NbS) for coastal protection have been widely recognized as sustainable, economical, and eco-friendly alternatives to conventional grey structures, particularly under the threat of climate change (Temmerman et al., 2013). Living shorelines are a form of NbS, which incorporate natural elements (such as saltmarshes) that provide flood and erosion risk management benefits. Climate change impacts, such as rising sea levels and reducing sea-ice cover (Savard et al., 2016), are increasingly motivating communities in Canada to consider incorporating living shorelines in coastal protection schemes. The efficacy of wave energy dissipation by vegetation depends on both hydrodynamic conditions and plant characteristics. However, plant parameters, such as standing biomass exhibit seasonal fluctuations, leading to corresponding variations in attenuation capacity (Schulze et al., 2019). Hence, the design of NbS utilizing saltmarsh vegetation must account for seasonal variations to ensure sustained efficacy, especially within the context of Canadian regional climates, which are typically characterized by extended, stormy winters and shorter summer seasons. Few studies have quantified wave attenuation by real saltmarsh vegetation in large-scale laboratory facilities (Möller et al., 2014; Maza et al., 2015; Ghodoosipour et al., 2022), particularly for species native to the east coast of Canada. There is a knowledge gap on how seasonality affects wave attenuation by saltmarsh vegetation and how attenuation varies from the lower marsh to the higher marsh depending on species-specific plant traits. Research is needed to bridge this gap and develop technical guidance for the design of performant living shorelines in Canada.
Keywords: PhamVanBang_Damien

Extension of a Monolayer Energy-Budget Degree-Day Model to a Multilayer One - Augas, Julien; Foulon, Etienne; Rousseau, Alain N.; Baraër, Michel (Water, 2024)
This paper presents the extension of the monolayer snow model of a semi-distributed hydrological model (HYDROTEL) to a multilayer model that considers snow to be a combination of ice and air, while accounting for freezing rain. For two stations in Yukon and one station in northern Quebec, Canada, the multilayer model achieves high performances during calibration periods yet similar to the those of the monolayer model, with KGEs of up to 0.9. However, it increases the KGE values by up to 0.2 during the validation periods. The multilayer model provides more accurate estimations of maximum SWE and total spring snowmelt dates. This is due to its increased sensitivity to thermal atmospheric conditions. Although the multilayer model improves the estimation of snow heights overall, it exhibits excessive snow densities during spring snowmelt. Future research should aim to refine the representation of snow densities to enhance the accuracy of the multilayer model. Nevertheless, this model has the potential to improve the simulation of spring snowmelt, addressing a common limitation of the monolayer model.
Keywords: Baraer_Michel; Rousseau_Alain

Cumulative Effects of Watershed Disturbances and Run-of-river Dams on Mercury Cycling: Case Study and Recommendations for Environmental Managers - Amyot, M.; Bilodeau, F.; Tremblay, A.; Planas, D.; Walsh, D.; Ponton, D. E. (Environmental Management, 2024)
Abstract Run-of-river power plants (ROR) represent the majority of hydroelectric plants worldwide. Their environmental impacts are not well documented and are believed to be limited, particularly regarding the contamination of food webs by methylmercury (MeHg), a neurotoxin. RORs are typically installed in small rivers where combined effects of watershed disturbances with dam construction can complicate environmental management. We report a multi-year case study on the Saint-Maurice River (Canada) where an unpredicted temporary increase in MeHg accumulation in predator fish was observed after the construction of two ROR plants. The associated pondages acted as sedimentation basins for mercury (Hg) and organic matter from a watershed disturbed by a forest fire and by logging. This fresh organic carbon likely fueled microbial MeHg production. Hg methylation was more associated with environmental conditions than to the presence of Hg, and main methylating microbial groups were identified. A constructed wetland was a site of significant Hg methylation but was not the main source of the fish Hg increase. Organic carbon degradation was the main driver of MeHg accumulation at the base of the food chain whereas trophic levels explained the variations at the top of the food chain. Overall, carbon cycling was a key driver of Hg dynamics in this system, and ROR plants can cause temporary (ca. 12 years) Hg increase in food webs when developed in disturbed watersheds, although this increase is smaller than for large reservoirs. Recommendations for future ROR construction are to establish a good environmental monitoring plan with initial high temporal resolution and to consider recent and potential watershed disturbances in the plan.

Establishing perennial strips in a cultivated floodplain for habitat restoration: monitoring over 4 years and success drivers - Damar, Hada; Vaillancourt, Mathieu; Bourgeois, Bérenger; Poulin, Monique (Restoration Ecology, 2024)
Floodplains, one of the most biologically diverse and productive ecosystems, are under threat from intensive crop production. Implementing perennial strips alongside agricultural ditches and streams could reduce negative impacts of intensive agriculture and restore wildlife habitats in cultivated floodplains. To successfully set up perennial strips, it is important to understand the parameters that drive their establishment. Here we assessed the establishment success of reed canarygrass (RCG; Phalaris arundinacea ) strips in the lake Saint Pierre (LSP) floodplain, Québec, Canada by monitoring RCG biomass and vegetation height over 4 years and identify the factors driving its establishment. A total of 26 RCG strips across six municipalities of LSP were monitored. Biomass and vegetation height of RCG increased over time to reach an average of 5048 kg/ha in year 4 and 104 cm in year 3 in established strips. The RCG established successfully in 62% of surveyed plots and three environmental parameters explained 61% of this success. Establishment of RCG was most successful when a first rain came right after seeding (<3 days). High clay content and low elevation were associated with establishment failures. Overall, our results highlight the ability of RCG strips to restore dense perennial vegetation cover in cultivated floodplain, thereby providing suitable habitat for fish spawning during spring floods. This study provides significant insight into the drivers of establishment of perennial grass strips in highly constrained cultivated areas such as floodplains.
Keywords: Infolettre; Infolettre_Novembre_2024

Individual and environmental correlates of tap water consumption among adolescents in Canada - Vézina-Im, Lydi-Anne; Beaulieu, Dominique; Turcotte, Stéphane; Turcotte, Anne-Frédérique; Lessard, Lily; Delisle-Martel, Joannie; Boucher, Danielle; Labbé, Valérie; Gingras, Mariane (Appetite, 2024)
Keywords: Lessard_Lily

Advancing Spatial Drought Forecasts by Integrating an Improved Outlier Robust Extreme Learning Machine with Gridded Data: A Case Study of the Lower Mainland Basin, British Columbia, Canada - Salimi, Amirhossein; Noori, Amir; Ebtehaj, Isa; Ghobrial, Tadros; Bonakdari, Hossein (Sustainability, 2024)
Droughts have extensive consequences, affecting the natural environment, water quality, public health, and exacerbating economic losses. Precise drought forecasting is essential for promoting sustainable development and mitigating risks, especially given the frequent drought occurrences in recent decades. This study introduces the Improved Outlier Robust Extreme Learning Machine (IORELM) for forecasting drought using the Multivariate Standardized Drought Index (MSDI). For this purpose, four observation stations across British Columbia, Canada, were selected. Precipitation and soil moisture data with one up to six lags are utilized as inputs, resulting in 12 variables for the model. An exhaustive analysis of all potential input combinations is conducted using IORELM to identify the best one. The study outcomes emphasize the importance of incorporating precipitation and soil moisture data for accurate drought prediction. IORELM shows promising results in drought classification, and the best input combination was found for each station based on its results. While high Area Under Curve (AUC) values across stations, a Precision/Recall trade-off indicates variable prediction tendencies. Moreover, the F1-score is moderate, meaning the balance between Precision, Recall, and Classification Accuracy (CA) is notably high at specific stations. The results show that stations near the ocean, like Pitt Meadows, have higher predictability up to 10% in AUC and CA compared to inland stations, such as Langley, which exhibit lower values. These highlight geographic influence on model performance.
Keywords: Infolettre; Infolettre_Mai_2024

Coupling a peatland hydrological model with a snowmelt module in order to model the snowmelt runoff in a boreal ombrotrophic peatland in eastern Québec (Canada) - Riahi, Khawla; St-Hilaire, André; Bourgault, Marc-André; Taillardat, Pierre; Prijac, Antonin; Garneau, Michelle (Canadian Journal of Civil Engineering, 2024)
Peatlands are relatively common in the province of Quebec (Canada) where they occupy about 12% of the surface. The hydrology of peatlands remains insufficiently documented, more specifically during the spring period where data are currently lacking in many regions, including in the Quebec boreal territory. The paucity of spring data are due to snowmelt that causes flooding in peatlands and along rivers, which makes hydrometry complicated during this period of the year. In this paper, the Peatland Hydrological Impact Model (PHIM) was coupled with a snowmelt module (CemaNeige) to simulate spring flows in an ombrotrophic peatland located in the Romaine River watershed (Quebec). Discharge data from two summer seasons (2019 and 2020) were used to calibrate the hydrological model. Despite the relatively short time series, the results show a good performance. The simulated spring flows resulting from the PHIM + CemaNeige combination are of the right order of magnitude.
Keywords: Infolettre; Infolettre_Fevrier_2025

Projected seasonal flooding in Canada under climate change with statistical and machine learning - Grenier, Manuel; Boudreault, Jérémie; Raymond, Sébastien; Boudreault, Mathieu (Journal of Hydrology: Regional Studies, 2024)
Keywords: Infolettre; Infolettre_Mai_2024

Water Level Temporal Variability of Lake Mégantic during the Period 1920–2020 and Its Impacts on the Frequency of Heavy Flooding of the Chaudière River (Quebec, Canada) - Goulet, Samuel; Assani, Ali Arkamose; Roy, Alexandre (Hydrology, 2024)
The objective of this study is to analyze the temporal variability in water levels of Lake Mégantic (27.4 km2) during the period 1920–2020 in relation to anthropogenic and natural factors on the one hand, and its impact on the intensity and frequency of heavy flooding (recurring floods ≥ 10 years) of the Chaudière River of which it is the source, on the other hand. The application of four different Mann–Kendall tests showed a significant decrease in lake water levels during this period. The Lombard test revealed two breaks in the average daily maximum and average water levels, but only one break in the average daily minimum water levels. The first shift, which was smoothed, occurred between 1957 and 1963. It was caused by the demolition in 1956 of the first dam built in 1893 and the significant storage of water in the dams built upstream of the lake between 1956 and 1975. The second shift, which was rather abrupt, occurred between 1990 and 1993. It was caused by the voluntary and controlled lowering of the lake’s water levels in 1993 to increase the surface area of the beaches for recreational purposes. However, despite this influence of anthropogenic factors on this drop in water levels, they are negatively correlated with the global warming climate index. It is therefore a covariation, due to anthropogenic factors whose impacts are exerted at different spatial scales, without a physical causal link. However, the winter daily minimum water levels, whose temporal variability has not been influenced by anthropogenic activities, are positively correlated with the NAO and AO indices, but negatively with PDO. Finally, since the transformation of Lake Mégantic into a reservoir following the construction of the Mégantic dam in 1893 and 1973 to control heavy flooding in the Chaudière River, all recurrent floods ≥ 10 years have completely disappeared in the section of this river located downstream of Lake Mégantic. However, the disappearance of these floods and the drop in water levels of Lake Mégantic have not significantly impacted the stationarity in the flow series of the Chaudière River since 1920.
Keywords: Infolettre; Infolettre_Octobre_2024

Meteorological control on snow depth evolution and snowpack energy exchanges in an agro-forested environment by a measurement-based approach: A case study in Sainte-Marthe, Eastern Canada - Dharmadasa, Vasana; Kinnard, Christophe; Baraër, Michel (Agricultural and Forest Meteorology, 2024)
Keywords: Baraer_Michel; Kinnard_Christophe

Analysis of the impacts of climate change, physiographic factors and land use/cover on the spatiotemporal variability of seasonal daily mean flows in southern Quebec (Canada) - Assani, Ali A. (Applied Water Science, 2024)
Abstract The objective of this study is to compare the spatiotemporal variability of seasonal daily mean flows measured in 17 watersheds, grouped into three homogeneous hydroclimatic regions, during the period 1930–2023 in southern Quebec. With regard to spatial variability, unlike extreme daily flows, seasonal daily mean flows are very poorly correlated with physiographic factors and land use and land cover. In fall, they are not correlated with any physiographic or climatic factor. In winter, they are positively correlated with the rainfall and winter daily mean maximum temperatures. In spring, they are strongly correlated positively with the snowfall but negatively with the spring daily mean maximum temperatures. However, in summer, they are better correlated with forest area and, to a lesser extent, with the rainfall. As for their temporal variability, the application of six different statistical tests revealed a general increase in daily mean flows in winter due to early snowmelt and increased rainfall in fall. In summer, flows decreased significantly in the snowiest hydroclimatic region on the south shore due to the decrease in the snowfall. In spring, no significant change in flows was globally observed in the three hydroclimatic regions despite the decrease in the snowfall due to the increase in the rainfall. In fall, flows increased significantly south of 47°N on both shores due to the increase in the rainfall. This study demonstrates that, unlike extreme flows, the temporal variability of seasonal daily average flows is exclusively influenced by climatic variables in southern Quebec. Due to this influence, seasonal daily mean flows thus appear to be the best indicator for monitoring the impacts of changes in precipitation regimes and seasonal temperatures on river flows in southern Quebec.
Keywords: Infolettre; Infolettre_Mai_2024

Improvement of Soil Water Assessment Tool (SWAT) wetland module for modelling of ephemeral pond hydrology - Bizhanimanzar, Mohammad; Larocque, Marie; Roux, Marjolaine (Hydrological Processes, 2024)
Abstract Ephemeral ponds (EPs) are seasonally flooded isolated wetlands that provide a variety of hydroecological benefits, including the provision of breeding habitat for several amphibian and invertebrate species. However, the lack of their explicit representation in hydrological models limits a comprehensive understanding of their interaction with surrounding landscapes and their vulnerability in the context of human interventions and climate change. The purpose of this research was to improve the isolated wetland module of the Soil Water Assessment Tool (SWAT) to better represent EP hydrology. The changes include (1) representation of groundwater and hypodermic flow as the only inflows from the pond drainage surface, due to the intermittent and negligible presence of inflow from surface runoff in forested ponds, (2) revision of how evapotranspiration within EPs is represented and (3) implementation of distinct volume‐area‐depth relationships for ponds based on their geometrical shape. The accuracy of these improvements was assessed against that of a previous isolated wetland formulation in replicating water depth observations of 10 EPs of a portion of the Kenauk forest (68 km 2 ) in the Canadian Shield of the Outaouais region (Québec, Canada). The comparison results show that the revised SWAT model presented here significantly improves the distinct filling and drying water cycle of EPs (average root mean square error of 0.1 m of the revised model vs. 0.23 m for the original model). Besides, the new module allowed to identify that hypodermic flow, evapotranspiration and seepage to the underlying soil are the main EP source and sinks. The new module also allowed to explicitly quantify the differences in filling/drying pattern of the EPs of the Kenauk forest and unlike the original model structure, the new module was able to closely replicate the interannual variation of spring and annual hydroperiod duration.
Keywords: Infolettre; Infolettre_Avril_2024

Flood occurrence and impact models for socioeconomic applications over Canada and the United States - Grenier, Manuel; Boudreault, Mathieu; Carozza, David A.; Boudreault, Jérémie; Raymond, Sébastien (Natural Hazards and Earth System Sciences, 2024)
Abstract. Large-scale socioeconomic studies of the impacts of floods are difficult and costly for countries such as Canada and the United States due to the large number of rivers and size of watersheds. Such studies are however very important for analyzing spatial patterns and temporal trends to inform large-scale flood risk management decisions and policies. In this paper, we present different flood occurrence and impact models based upon statistical and machine learning methods of over 31 000 watersheds spread across Canada and the US. The models can be quickly calibrated and thereby easily run predictions over thousands of scenarios in a matter of minutes. As applications of the models, we present the geographical distribution of the modelled average annual number of people displaced due to flooding in Canada and the US, as well as various scenario analyses. We find for example that an increase of 10 % in average precipitation yields an increase in the displaced population of 18 % in Canada and 14 % in the US. The model can therefore be used by a broad range of end users ranging from climate scientists to economists who seek to translate climate and socioeconomic scenarios into flood probabilities and impacts measured in terms of the displaced population.
Keywords: Infolettre; Infolettre_Decembre_2024

Stratégie pour les sciences 2024 à 2029 d’Environnement et Changement climatique Canada - Gouvernement du Canada (2024)
Cette stratégie oriente les activités scientifiques d'Environnement et Changement climatique Canada afin de favoriser un avenir plus vert et plus durable. Elle met l'accent sur nos gens, nos valeurs et nos priorités tournées vers l'avenir en tant que ministère fédéral à vocation scientifique.