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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.

Extreme flood events continue to be one of the most threatening natural disasters around the world due to their pronounced social, environmental and economic impacts. Changes in the magnitude and frequency of floods have been documented during the last years, and it is expected that a changing climate will continue to affect their occurrence. Therefore, understanding the impacts of climate change through hydroclimatic simulations has become essential to prepare adaptation strategies for the future. However, the confidence in flood projections is still low due to the considerable uncertainties associated with their simulations, and the complexity of local features influencing these events. The main objective of this doctoral thesis is thus to improve our understanding of the modelling uncertainties associated with the generation of flood projections as well as evaluating strategies to reduce these uncertainties to increase our confidence in flood simulations. To address the main objective, this project aimed at (1) quantifying the uncertainty contributions of different elements involved in the modelling chain used to produce flood projections and, (2) evaluating the effects of different strategies to reduce the uncertainties associated with climate and hydrological models in regions with diverse hydroclimatic conditions. A total of 96 basins located in Quebec (basins dominated by snow-related processes) and Mexico (basins dominated by rain-related processes), covering a wide range of climatic and hydrological regimes were included in the study. The first stage consisted in decomposing the uncertainty contributions of four main uncertainty sources involved in the generation of flood projections: (1) climate models, (2) post-processing methods, (3) hydrological models, and (4) probability distributions used in flood frequency analyses. A variance decomposition method allowed quantifying and ranking the influence of each uncertainty source on floods over the two regions studied and by seasons. The results showed that the uncertainty contributions of each source vary over the different regions and seasons. Regions and seasons dominated by rain showed climate models as the main uncertainty source, while those dominated by snowmelt showed hydrological models as the main uncertainty contributor. These findings not only show the dangers of relying on single climate and hydrological models, but also underline the importance of regional uncertainty analyses. The second stage of this research project focused in evaluating strategies to reduce the uncertainties arising from hydrological models on flood projections. This stage includes two steps: (1) the analysis of the reliability of hydrological model’s calibration under a changing climate and (2) the evaluation of the effects of weighting hydrological simulations on flood projections. To address the first part, different calibration strategies were tested and evaluated using five conceptual lumped hydrological models under contrasting climate conditions with datasets lengths varying from 2 up to 21 years. The results revealed that the climatic conditions of the calibration data have larger impacts on hydrological model’s performance than the lengths of the climate time series. Moreover, changes on precipitation generally showed greater impacts than changes in temperature across all the different basins. These results suggest that shorter calibration and validation periods that are more representative of possible changes in climatic conditions could be more appropriate for climate change impact studies. Following these findings, the effects of different weighting strategies based on the robustness of hydrological models (in contrasting climatic conditions) were assessed on flood projections of the different studied basins. Weighting the five hydrological models based on their robustness showed some improvements over the traditional equal-weighting approach, particularly over warmer and drier conditions. Moreover, the results showed that the difference between these approaches was more pronounced over flood projections, as contrasting flood magnitudes and climate change signals were observed between both approaches. Additional analyses performed over four selected basins using a semi-distributed and more physically-based hydrological model suggested that this type of models might have an added value when simulating low-flows, and high flows on small basins (of about 500 km2). These results highlight once again the importance of working with ensembles of hydrological models and presents the potential impacts of weighting hydrological models on climate change impact studies. The final stage of this study focused on evaluating the impacts of weighting climate simulations on flood projections. The different weighting strategies tested showed that weighting climate simulations can improve the mean hydrograph representation compared to the traditional model “democracy” approach. This improvement was mainly observed with a weighting approach proposed in this thesis that evaluates the skill of the seasonal simulated streamflow against observations. The results also revealed that weighting climate simulations based on their performance can: (1) impact the floods magnitudes, (2) impact the climate change signals, and (3) reduce the uncertainty spreads of the resulting flood projection. These effects were particularly clear over rain-dominated basins, where climate modelling uncertainty plays a main role. These finding emphasize the need to reconsider the traditional climate model democracy approach, especially when studying processes with higher levels of climatic uncertainty. Finally, the implications of the obtained results were discussed. This section puts the main findings into perspective and identifies different ways forward to keep improving the understanding of climate change impacts in hydrology and increasing our confidence on flood projections that are essential to guide adaptation strategies for the future.

Globally, the number of people at risk from flooding has been increasing since 2000, with the population from the South being more vulnerable. Millions of households are displaced by disasters every year. In 2009, the city of Ouagadougou in Burkina Faso experienced its most disastrous flood ever recorded. As a response, the government designed a permanent relocation plan in Yagma, a village located outside the city of Ouagadougou. The relocation plan disrupted the livelihoods of the households that were affected by the flood, leading many of them to return and rebuild their houses in flood prone areas. This paper contributes to a body of literature analyzing the heritage of postcolonialism on the flood vulnerability on the poorer communities in Ouagadougou. Using a political ecology frame, the thesis attempts to understand how the government of Burkina Faso and flood victims understand land and belongings, and how that understanding shaped the relocation program. After interviewing flood victims and government officials, an analysis revealed that contrasting views are at work. A perspective based on technical calculations and a neo-colonialist vision of development, on the one hand, and a grounded perspective based on relationships to the land and each other, on the other.

Alors que pour la plupart des rivières, la rupture du couvert de glace est un processus d'amont en aval, la rupture en estuaire est un processus de l'aval vers l'amont. Les ondes de marée qui remontent l'embouchure des estuaires parviennent à fracturer la couverture de glace en morceaux lors de la marée montante et transportent ensuite ces morceaux hors de l'estuaire lors de la marée descendante. Les conditions hydrométéorologiques affectant ce processus restent peu documentées et mal comprises. À cette fin, une campagne de terrain de deux ans a été mise sur pied pour surveiller la débâcle printanière dans quatre estuaires du Québec. Les résultats ont montré que l'affaiblissement thermique de la glace est un précurseur nécessaire à la débâcle de celle-ci. D'autres facteurs comme le marnage et le débit sont aussi des paramètres importants pour prédire le type de débâcle qui sera observé. En parallèle, une simulation numérique (Hec-Ras) d'une section de la rivière Saint-Anne, au Québec, a été menée en y ajoutant un barrage à seuil gonflable (à Chute-Panet). Celui-ci permettrait de reproduire artificiellement les cycles de marées. Les critères hydrauliques montrent que l'ajout de ce type de barrage permettrait de faciliter la destruction et l'évacuation du couvert de glace avant que celui-ci ne provoque des inondations dans la ville de Saint-Raymond. Des critères empiriques et l'application de la théorie des poutres reposant sur des fondations élastiques ont aussi été utilisés pour affiner notre compréhension du phénomène de rupture en rivière gelée. Finalement, le troisième volet de cette étude porte sur la réalisation d'une maquette de rivière en laboratoire d'hydraulique. Son utilisation vise à combler les limites des modèles numériques et à étudier différents scénarios d'opération du barrage.

Les troubles anxieux apparaissent très tôt chez les jeunes. Les études corrélationnelles suggèrent que plusieurs facteurs, tels que le stress prénatal maternel (SPM) et la surimplication maternelle contribueraient à leur développement et à leur maintien. L’objectif de l’étude est de mieux comprendre si la surimplication maternelle est une cause ou une conséquence de l’anxiété chez l’enfant, le tout dans un contexte de stress prénatal maternel causé par un désastre naturel. L’échantillon fait partie d’un projet plus grand dans lequel les stress objectif et subjectif ont été mesurés chez les femmes enceintes lors de l’inondation en 2011 à Queensland en Australie. L’échantillon de l’étude inclut 72 dyades mère-enfant, ayant complété une tâche difficile de casse-tête, d’une durée de 5 minutes, lorsque les enfants étaient âgés de 4 ans. À partir des vidéos, les observateurs ont évalué le comportement maternel (la surimplication) et le comportement de l’enfant (l’anxiété) en continu. Des techniques d’analyse séquentielle ont été utilisées pour déterminer la probabilité qu’un comportement maternel de surimplication se produise avant ou après un comportement anxieux et vice versa. Les corrélations obtenues entre le SPM (objectif et subjectif), le comportement anxieux de l’enfant et le comportement de surimplication maternelle n’ont pas été significatives. Le comportement de surimplication n’est pas un médiateur entre le SPM (objectif et subjectif) et le comportement anxieux de l’enfant. Toutefois, les chances qu’un comportement maternel de surimplication suive un comportement anxieux chez l’enfant étaient plus grandes que les chances qu’un comportement anxieux suive un comportement maternel de surimplication. Nos résultats suggèrent que le stress prénatal maternel n’est pas associé avec le comportement de surimplication maternelle, ni avec le comportement anxieux de l’enfant. La surimplication maternelle est plutôt une réaction au comportement anxieux de l’enfant. Il est essentiel, pour de futures recherches, de se rappeler que l’enfant est un participant actif dans la relation mère-enfant et qu’il est important d’étudier la bidirectionnalité de la relation pour mieux comprendre et pour mieux intervenir.

<p>Les études portant sur les évènements hydrologiques extrêmes sont d’une grande importance vu leurs nombreux impacts socio-économiques. Ces études reposent dans une large mesure sur la capacité à estimer adéquatement les risques associés à ces évènements. Dans ce cadre, l’analyse fréquentielle (AF) est une des approches les plus utilisées pour la modélisation et l’estimation des risques associés aux évènements extrêmes. Généralement, ces évènements sont caractérisés par plusieurs variables dépendantes, comme le volume, la pointe et la durée pour les crues. Par conséquent, l'analyse de chacune de ces variables séparément ne peut pas fournir une évaluation complète des risques et peut engendrer des pertes de vies humaines ou de biens associés à une sous estimation, ou une augmentation des coûts des ouvrages hydrauliques associés à une surestimation. L’AF multivariée (AFM) permet de pallier ce problème en considérant simultanément ces variables. L’AF classique est basée sur trois hypothèses à savoir l’homogénéité, la stationnarité et l’indépendance. Par ailleurs, différentes conditions non standards telles que la complexité topographique, les perturbations par les aménagements urbains et les changements climatiques peuvent influencer la réponse hydrologique. De telles conditions rendent la prédétermination des crues, par les méthodes classiques d’AFM, un exercice non efficace et mal adapté à de tels contextes. L’objectif de cette thèse consiste à proposer de nouvelles méthodes plus prometteuses pour l'analyse et la modélisation des variables hydrologiques à la fois dans un cadre multivarié et en l’absence de l’hypothèse d’homogénéité. Ces méthodes visent à contourner les limites de celles utilisées dans la littérature. Ces nouvelles méthodes, basées sur les copules, permettent une meilleure estimation des risques des extrêmes hydrologiques en tenant compte des interactions et es dépendances entre les différentes variables. Par conséquent, les gestionnaires des ressources hydriques peuvent prendre des décisions éclairées et mieux adaptées au contexte actuel. Précisément, nous nous intéressons dans une première partie à tester l’homogénéité des séries hydrologiques multivariées. Pour ce faire, nous avons proposé un nouveau test, basé sur les L moments multivariés, capable de détecter la rupture dans la structure de dépendance des variables hydrologiques. Les résultats obtenus montrent la bonne performance du test proposé sous différents scénarios. Par la suite, dans le but de modéliser l’hétérogénéité des séries hydrologiques multivariées, un modèle de mélange de copules a été mis au point. Dans ce cadre, nous avons proposé une nouvelle méthode pour l’estimation des différents paramètres du modèle. La méthode proposée est basée sur l’utilisation des algorithmes génétiques. Sa performance est illustrée sur des séries simulées. Les résultats obtenus montrent que la méthode proposée est un outil efficace capable de fournir de bonnes estimations, en particulier dans un contexte hydrologique. Finalement, deux nouveaux tests d’adéquation pour les copules multiparamètres ont été développés. Le but de ces tests est de vérifier la qualité de l’ajustement de la copule aux données et améliorer ainsi l’estimation des risques associés aux évènements extrêmes. Les résultats révèlent que les tests proposés performent bien dans le contexte hydrologique.<br /><br /> Studies of hydrological extreme events are of great importance given their many socio-economic impacts. These studies rely on the ability to adequately estimate the associated risk. In this context, frequency analysis (FA) is one of the most widely used approaches for modeling and estimating the risks associated with extreme events. Generally, these events are characterized by several correlated random variables. Therefore, analyzing each of these variables separately cannot provide a complete risk assessment and may result in loss of life or property associated with an underestimation, or an increase in the costs of hydraulic structure associated with an overestimation. Multivariate FA (MFA) overcomes this problem by simultaneously considering these variables. Classical FA is based on three assumptions namely homogeneity, stationarity and independence. However, different non-standard conditions such as topographic complexity, disturbances by urban development and climate change can influence the hydrological response. Such conditions make flood predetermination, by conventional MFA methods, an ineffective exercise and unsuitable to such contexts. The main objective of this thesis is to propose new and more promising methods for the analysis and modeling of hydrological variables both in a multivariate framework and in the lack of the homogeneity assumption. These methods aim to overcome the limitations of classical methods used in the literature. These new proposed methods, based on copulas, allow a better estimation of the risks associated to hydrological extremes by taking into account the dependence between the different variables. As a result, water resource managers can make informed decisions that are better suited to the current context. In the first part, we propose a novel statistical test for multivariate heterogeneity detection, based on copula and multivariate L-moments. A simulation study is conducted to evaluate the performance of the proposed test and to compare it with those of existing tests. Results show the ability of the proposed test to discriminate homogeneous and inhomogeneous series. In the second part, we propose a new model based on mixtures of copulas that take into account the heterogeneity of multivariate hydrological series. To estimate the components of this model, we propose a new parameter estimation approach, based on the maximum pseudo-likelihood using genetic algorithms. Results indicate that the proposed method estimates more accurately the parameters even with small sample sizes compared to the existing classical EM method. In the last part, we introduce new goodness of fit (GOF) tests specifically for multiparameter copulas and adapted to hydrometeorological context. More precisely, the proposed GOF tests are based on multivariate L moments. A simulation study is conducted to evaluate and compare the performances of the proposed tests. The results confirm the usefulness of the new GOF tests in comparison with some well-established ones.</p>

Les inondations présentent une grande menace à la sécurité humaine et matérielle. Les effets associés à ces phénomènes naturels risquent d'augmenter encore plus avec les tendances liées aux changements climatiques. Il est donc important de disposer d'outils de prévision et de prévention des crues fiables afin de mitiger les conséquences dévastatrices reliées. La mise en œuvre de ces outils implique des processus physiques assez complexes et nécessite beaucoup de données avec toute l'incertitude associée. Dans cette thèse, on explore les différentes sources d'incertitudes liée à la détermination des niveaux d'eau en rivières principalement dans un contexte de prévision où l'incertitude liée aux données de forçage est très importante. Les analyses conduites sont appliquées à la rivière Chaudière au Québec. En premier lieu, nous avons exploré les différentes sources paramétriques d'incertitude associées à la modélisation hydraulique dans un contexte de simulation avec un accent sur l'amélioration de la calibration du modèle hydraulique. Par la suite, dans un contexte de prévision opérationnel, on a évalué la propagation des sources d'incertitude de la prévision atmosphérique au modèle de rivière en passant par les prévisions hydrologiques avec des techniques probabilistes d'ensemble. La quantification de l'incertitude a montré que les données de forçage sont celles qui contribuent le plus à la description de l'incertitude dans la détermination des niveaux d'eau. L'incertitude paramétrique, dans un contexte de prévision, est quant à elle négligeable. Le recours à des prévisions d'ensemble a permis de produire une prévision de niveau d'eau assez fiable et a montré que celle-ci est fortement liée à la qualité des données qui proviennent de la chaine de prévision hydrométéorologique à l'amont du système de prévision proposé.

Cette thèse vise à améliorer notre compréhension du modèle hédonique et de son application sur les données des biens immobiliers afin d'étudier l'impact d'un événement / externalité / environnementale liée à la présence d'inondation sur la valeur des propriétés résidentielles. Étant donné que les données immobilières sont réparties dans l'espace et dans le temps, des "corrections" temporelles et spatiales sont nécessaires dans le processus de modélisation économétrique. La recherche prend appui sur l’équation de prix hédonique. L’analyse empirique recours également à l’estimateur de type différence de différences spatio-temporelles (STDID) afin d’étudier l’effet d’une inondation survenue en 1998 sur le prix des résidences dans la ville de Laval au Canada entre 1995-2007. Les résultats suggèrent que l’utilisation des informations sur les zones inondables dans le but d’évaluer l’impact des inondations sur les valeurs résidentielles n’est pas une approche nécessairement appropriée. Les conclusions suggèrent que la grande hétérogénéité des résultats notés dans la littérature n’est probablement pas étrangère à la façon de définir les résidences touchées par les inondations. Cela signifie que les recherches empiriques sur les effets des inondations sur la valeur immobilière mesurent en réalité la valeur liée à la perception du risque d'inondation et non l’effet réel de l'inondation. Les résultats suggèrent que les applications futures dans la littérature devront porter une attention particulière à la manière de définir les zones inondables et d’identifier les résidences réellement touchées.

This study aims to evaluate the effects of the Canadian Regional Climate Model’s (CRCM) spatial resolution on summer-fall floods simulation. Seven different climate simulations issued from the fourth and the fifth version of the CRCM are employed. Four different climate simulations issued from the fourth version of the CRCM (CRCM4) are compared. They are composed of two simulations driven by the Canadian General Circulation Model (CGCM) and two driven by the ERA-40c reanalysis using grid meshes of 15 km and 45 km resolutions for each driver. Three climate simulations issued from the fifth version of the CRCM (CRCM5) driven by the ERA-Interim at 0.44° (≈ 48 km), 0.22° (≈ 24 km) and 0.11° (≈ 12 km) spatial resolutions are used. All comparisons are evaluated on a daily time-step for the 1961-1990 period (for CRCM4) and for the 1981-2010 period (for CRCM5). These seven simulations (four from CRCM4 and three from CRCM5) are used as input for two hydrological models of varying complexity (HSAMI and MOHYSE). Each model is calibrated using three different objective functions based on the Kling-Gupta Efficiency criteria (KGE) to target the summer-fall floods. Three seasonal indices are used to evaluate the CRCM outputs: bias (temperature), relative bias (precipitation) and variances ratio (temperature and precipitation). In an attempt to evaluate the effects of the spatial resolution on the hydrological modelling of summer-fall floods, streamflow simulations are generated using the seven climate datasets. The generated climate-driven streamflow simulations are analysed by two performance statistics: the seasonal values of KGE and the seasonal relative biases. Summer-fall floods are evaluated through the use of four flood indicators, the 2-year, 5-year, 10-year and 20-year return periods. The results revealed an impact of spatial resolution on climate model outputs (temperature and precipitation) and on summer-fall floods simulation by the two hydrological models and the three different calibration approaches, although this can be due to other elements such as domain size and climate model driver. The flood indicators demonstrate an increase on the summer-fall floods return periods with increasing resolution from both hydrological models. On the other hand the hydrological models structure and the calibration approaches did not show significant impacts on the summer-fall floods. The results highlight the need for further research to assess the additional uncertainty due to the impacts of the climate simulations spatial resolution on hydrological studies.

Abstract Climate change in the Middle East has intensified with rising temperatures, shifting rainfall patterns, and more frequent extreme events. This study introduces the Stacking-EML framework, which merges five machine learning models three meta-learners to predict maximum temperature, minimum temperature, and precipitation using CMIP6 data under SSP1-2.6, SSP2-4.5, and SSP5-8.5. The results indicate that Stacking-EML not only significantly improves prediction accuracy compared to individual models and traditional CMIP6 outputs but also enhances climate projections by integrating multiple ML models, offering more reliable, regionally refined forecasts. Findings show R² improvements to 0.99 for maximum temperature, 0.98 for minimum temperature, and 0.82 for precipitation. Under SSP5-8.5, summer temperatures in southern regions are expected to exceed 45 °C, exacerbating drought conditions due to reduced rainfall. Spatial analysis reveals that Saudi Arabia, Oman, Yemen, and Iran face the greatest heat and drought impacts, while Turkey and northern Iran may experience increased precipitation and flood risks.

Abstract. Dissolved organic carbon (DOC) trends, predominantly showing long-term increases in concentration, have been observed across many regions of the Northern Hemisphere. Elevated DOC concentrations are a major concern for drinking water treatment plants, owing to the effects of disinfection byproduct formation, the risk of bacterial regrowth in water distribution systems, and treatment cost increases. Using a unique 30-year data set encompassing both extreme wet and dry conditions in a eutrophic drinking water reservoir in the Great Plains of North America, we investigate the effects of changing source-water and in-lake water chemistry on DOC. We employ novel wavelet coherence analyses to explore the coherence of changes in DOC with other environmental variables and apply a generalized additive model to understand predictor–DOC responses. We found that the DOC concentration was significantly coherent with (and lagging behind) flow from a large upstream mesotrophic reservoir at long (> 18-month) timescales. DOC was also coherent with (lagging behind) sulfate and in phase with total phosphorus, ammonium, and chlorophyll a concentrations at short (≤ 18-month) timescales across the 30-year record. These variables accounted for 56 % of the deviance in DOC from 1990 to 2019, suggesting that water-source and in-lake nutrient and solute chemistry are effective predictors of the DOC concentration. Clearly, climate and changes in water and catchment management will influence source-water quality in this already water-scarce region. Our results highlight the importance of flow management to shallow eutrophic reservoirs; wet periods can exacerbate water quality issues, and these effects can be compounded by reducing inflows from systems with lower DOC. These flow management decisions address water level and flood risk concerns but also have important impacts on drinking water treatability.

Abstract Fluvial biogeomorphology has proven to be efficient in understanding the evolution of rivers in terms of vegetation succession and channel adjustment. The role of floods as the primary disturbance regime factor has been widely studied, and our knowledge of their effects on vegetation and channel adjustment has grown significantly in the last two decades. However, cold rivers experiencing ice dynamics (e.g., ice jams and mechanical breakups) as an additional disturbance regime have not yet been studied within a biogeomorphological scope. This study investigated the long‐term effects of ice dynamics on channel adjustments and vegetation trajectories in two rivers with different geomorphological behaviours, one laterally confined (Matapédia River) and one mobile (Petite‐Cascapédia River), in Quebec, Canada. Using dendrochronological analysis, historical data and aerial photographs from 1963 to 2016, this study reconstructed ice jam chronologies, characterized flood regimes and analysed vegetation and channel changes through a photointerpretation approach. The main findings of this study indicate that geomorphological impacts of mechanical ice breakups are not significant at the decadal and reach scales and that they might not be the primary factors of long‐term geomorphological control. However, results have shown that vegetation was more sensitive to ice dynamics. Reaches presenting frequent ice jams depicted high regression rates and turnovers even during years with very low floods, suggesting that ice dynamics significantly increase shear stress on plant patches. This study also highlights the high resiliency of both rivers to ice jam disturbances, with vegetation communities and channel forms recovering within a decade. With the uncertainties following the reach/corridor and decadal scales, future research should focus on long‐term monitoring and refined spatial scales to better understand the mechanisms behind the complex interactions among ice dynamics, vegetation and hydrogeomorphological processes in cold rivers.

ABSTRACT Flood risk management (FRM) involves planning proactively for flooding in high‐risk areas to reduce its impacts on people and property. A key challenge for governments pursuing FRM is to pinpoint assets that are highly economically exposed and vulnerable to flood hazards in order to prioritize them in policy and planning. This paper presents a novel flood risk assessment, making use of a dataset that identifies the location, dwelling type, property characteristics, and potential economic losses of Canadian residential properties. The findings reveal that the average annual costs are $1.4B, but most of the risks are concentrated in high‐risk areas. Data gaps are uncovered that justify replication through local validation studies. The results provide a novel evidence base for specific reforms in Canada's approach to FRM, with a focus on insurance that improves both implementation and effectiveness.