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Abstract. The amount and phase of cold season precipitation accumulating in the upper Saint John River basin are critical factors in determining spring runoff, ice-jams, and flooding in downstream communities. To study the impact of winter and spring storms on the snowpack in the upper Saint John River (SJR) basin, the Saint John River Experiment on Cold Season Storms (SAJESS) utilized meteorological instrumentation, upper air soundings, human observations, and hydrometeor macrophotography during winter/spring 2020–21. Here, we provide an overview of the SAJESS study area, field campaign, and existing data networks surrounding the upper SJR basin. Initially, meteorological instrumentation was co-located with an Environment and Climate Change Canada station near Edmundston, New Brunswick, in early December 2020. This was followed by an intensive observation period that involved manual observations, upper-air soundings, a multi-angle snowflake camera, macrophotography of solid hydrometeors, and advanced automated instrumentation throughout March and April 2021. The resulting datasets include optical disdrometer size and velocity distributions of hydrometeors, micro rain radar output, near-surface meteorological observations, and wind speed, temperature, pressure and precipitation amounts from a K63 Hotplate precipitation gauge, the first one operating in Canada. These data are publicly available from the Federated Research Data Repository at https://doi.org/10.20383/103.0591 (Thompson et al., 2022). We also include a synopsis of the data management plan and data processing, and a brief assessment of the rewards and challenges of utilizing community volunteers for hydro-meteorological citizen science.

For the past few decades, remote sensing has been a valuable tool for deriving global information on snow water equivalent (SWE), where products derived from space-borne passive microwave radiometers are favoured as they respond to snow depth, an important component of SWE. GlobSnow, a novel SWE product, has increased the accuracy of global-scale SWE estimates by combining remotely sensed radiometric data with other physiographic characteristics, such as snow depth, as quantified by climatic stations. However, research has demonstrated that passive microwaves algorithms tend to underestimate SWE for deep snowpack. Approaches were proposed to correct for such underestimation; however, they are computer intensive and complex to implement at the watershed scale. In this study, SWEmax information from the near real time 5-km GlobSnow product, provided by Copernicus and the European Space Agency (ESA) and GlobSnow product at 25 km resolution were corrected using a simple bias correction approach for watershed scale applications. This method, referred to as the Watershed Scale Correction (WSC) approach, estimates the bias based on the direct runoff that occurs during the spring melt season. Direct runoff is estimated on the one hand from SWEmax information as main input. Infiltration is also considered in computing direct runoff. An independent estimation of direct runoff from gauged stations is also performed. Discrepancy between these estimates allows for estimating the bias correction factor. This approach is advantageous as it exploits data that commonly exists i.e., flow at gauged stations and remotely sensed/reanalysis data such as snow cover and precipitation. The WSC approach was applied to watersheds located in Eastern Canada. It was found that the average bias moved from 33.5% with existing GlobSnow product to 18% with the corrected product, using the recommended recursive filter coefficient β of 0.925 for baseflow separation. Results show the usefulness of integrating direct runoff for bias correction of existing GlobSnow product at the watershed scale. In addition, potential benefits are offered using the recursive filter approach for baseflow separation of watersheds with limited in situ SWE measurements, to further reduce overall uncertainties and bias. The WSC approach should be appealing for poorly monitored watersheds where SWE measurements are critical for hydropower production and where snowmelt can pose serious flood-related damages.

The magnitudes of dissolved organic carbon (DOC) exports from boreal peatlands to streams through lateral subsurface flow vary during the ice-free season. Peatland water table depth and the alternation of low and high flow in peat-draining streams are thought to drive this DOC export variability. However, calculation of the specific DOC exports from a peatland can be challenging considering the multiple potential DOC sources within the catchment. A calculation approach based on the hydrological connectivity between the peat and the stream could help to solve this issue, which is the approach used in the present research. This study took place from June 2018 to October 2019 in a boreal catchment in northeastern Canada, with 76.7 % of the catchment being covered by ombrotrophic peatland. The objectives were to (1) establish relationships between DOC exports from a headwater stream and the peatland hydrology; (2) quantify, at the catchment scale, the amount of DOC laterally exported to the draining stream; and (3) define the patterns of DOC mobilization during high-river-flow events. At the peatland headwater stream outlet, the DOC concentrations were monitored at a high frequency (hourly) using a fluorescent dissolved organic matter (fDOM) sensor, a proxy for DOC concentration. Hydrological variables, such as stream outlet discharge and peatland water table depth (WTD), were continuously monitored at hourly intervals for 2 years. Our results highlight the direct and delayed control of subsurface flow from peat to the stream and associated DOC exports. Rain events raised the peatland WTD, which increased hydrological connectivity between the peatland and the stream. This led to increased stream discharge (Q) and a delayed DOC concentration increase, typical of lateral subsurface flow. The magnitude of the WTD increase played a crucial role in influencing the quantity of DOC exported. Based on the observations that the peatland is the most important contributor to DOC exports at the catchment scale and that other DOC sources were negligible during high-flow periods, we propose a new approach to estimate the specific DOC exports attributable to the peatland by distinguishing between the surfaces used for calculation during high-flow and low-flow periods. In 2018–2019, 92.6 % of DOC was exported during flood events despite the fact that these flood events accounted for 59.1 % of the period. In 2019–2020, 93.8 % of DOC was exported during flood events, which represented 44.1 % of the period. Our analysis of individual flood events revealed three types of events and DOC mobilization patterns. The first type is characterized by high rainfall, leading to an important WTD increase that favours the connection between the peatland and the stream and leading to high DOC exports. The second is characterized by a large WTD increase succeeding a previous event that had depleted DOC available to be transferred to the stream, leading to low DOC exports. The third type corresponds to low rainfall events with an insufficient WTD increase to reconnect the peatland and the stream, leading to low DOC exports. Our results suggest that DOC exports are sensitive to hydroclimatic conditions; moreover, flood events, changes in rainfall regime, ice-free season duration, and porewater temperature may affect the exported DOC and, consequently, partially offset the net carbon sequestration potential of peatlands.

In cold regions, ice jams frequently result in severe flooding due to a rapid rise in water levels upstream of the jam. Sudden floods resulting from ice jams threaten human safety and cause damage to properties and infrastructure. Hence, ice-jam prediction tools can give an early warning to increase response time and minimize the possible damages. However, ice-jam prediction has always been a challenge as there is no analytical method available for this purpose. Nonetheless, ice jams form when some hydro-meteorological conditions happen, a few hours to a few days before the event. Ice-jam prediction can be addressed as a binary multivariate time-series classification. Deep learning techniques have been widely used for time-series classification in many fields such as finance, engineering, weather forecasting, and medicine. In this research, we successfully applied convolutional neural networks (CNN), long short-term memory (LSTM), and combined convolutional–long short-term memory (CNN-LSTM) networks to predict the formation of ice jams in 150 rivers in the province of Quebec (Canada). We also employed machine learning methods including support vector machine (SVM), k-nearest neighbors classifier (KNN), decision tree, and multilayer perceptron (MLP) for this purpose. The hydro-meteorological variables (e.g., temperature, precipitation, and snow depth) along with the corresponding jam or no-jam events are used as model inputs. Ten percent of the data were excluded from the model and set aside for testing, and 100 reshuffling and splitting iterations were applied to 80 % of the remaining data for training and 20 % for validation. The developed deep learning models achieved improvements in performance in comparison to the developed machine learning models. The results show that the CNN-LSTM model yields the best results in the validation and testing with F1 scores of 0.82 and 0.92, respectively. This demonstrates that CNN and LSTM models are complementary, and a combination of both further improves classification.

Abstract An intensity–duration–frequency (IDF) curve describes the relationship between rainfall intensity and duration for a given return period and location. Such curves are obtained through frequency analysis of rainfall data and commonly used in infrastructure design, flood protection, water management, and urban drainage systems. However, they are typically available only in sparse locations. Data for other sites must be interpolated as the need arises. This paper describes how extreme precipitation of several durations can be interpolated to compute IDF curves on a large, sparse domain. In the absence of local data, a reconstruction of the historical meteorology is used as a covariate for interpolating extreme precipitation characteristics. This covariate is included in a hierarchical Bayesian spatial model for extreme precipitations. This model is especially well suited for a covariate gridded structure, thereby enabling fast and precise computations. As an illustration, the methodology is used to construct IDF curves over Eastern Canada. An extensive cross-validation study shows that at locations where data are available, the proposed method generally improves on the current practice of Environment and Climate Change Canada which relies on a moment-based fit of the Gumbel extreme-value distribution.

Earthquakes pose potentially substantial risks to residents in the Western Quebec seismic zone of eastern Canada, where Ottawa and Montreal are located. In eastern Canada, the majority of houses are not constructed to modern seismic standards and most homeowners do not purchase earthquake insurance for their homes. If a devastating earthquake strikes, homeowners would be left unprotected financially. To quantify financial risks to homeowners in the Western Quebec seismic zone, regional earthquake catastrophe models are developed by incorporating up-to-date public information on hazard, exposure and vulnerability. The developed catastrophe models can quantify the expected and upper-tail financial seismic risks by considering a comprehensive list of possible seismic events as well as critical earthquake scenarios based on the latest geological data in the region. The results indicate that regional seismic losses could reach several tens of billions of dollars if a moderate-to-large earthquake occurs near urban centres in the region, such as Montreal and Ottawa. The regional seismic loss estimates produced in this study are useful for informing earthquake risk management strategies, including earthquake insurance and disaster relief policies.

Abstract The Canadian Precipitation Analysis (CaPA) system provides near-real-time precipitation analyses over Canada by combining observations with short-term numerical weather prediction forecasts. CaPA’s snowfall estimates suffer from the lack of accurate solid precipitation measurements to correct the first-guess estimate. Weather radars have the potential to add precipitation measurements to CaPA in all seasons but are not assimilated in winter due to radar snowfall estimate imprecision and lack of precipitation gauges for calibration. The main objective of this study is to assess the impact of assimilating Canadian dual-polarized radar-based snowfall data in CaPA to improve precipitation estimates. Two sets of experiments were conducted to evaluate the impact of including radar snowfall retrievals, one set using the high-resolution CaPA (HRDPA) with the currently operational quality control configuration and another increasing the number of assimilated surface observations by relaxing quality control. Experiments spanned two winter seasons (2021 and 2022) in central Canada, covering part of the entire CaPA domain. The results showed that the assimilation of radar-based snowfall data improved CaPA’s precipitation estimates 81.75% of the time for 0.5-mm precipitation thresholds. An increase in the probability of detection together with a decrease in the false alarm ratio suggested an improvement of the precipitation spatial distribution and estimation accuracy. Additionally, the results showed improvements for both precipitation mass and frequency biases for low precipitation amounts. For larger thresholds, the frequency bias was degraded. The results also indicated that the assimilation of dual-polarization radar data is beneficial for the two CaPA configurations tested in this study.

Abstract. Floods resulting from river ice jams pose a great risk to many riverside municipalities in Canada. The location of an ice jam is mainly influenced by channel morphology. The goal of this work was therefore to develop a simplified geospatial model to estimate the predisposition of a river channel to ice jams. Rather than predicting the timing of river ice breakup, the main question here was to predict where the broken ice is susceptible to jam based on the river's geomorphological characteristics. Thus, six parameters referred to potential causes for ice jams in the literature were initially selected: presence of an island, narrowing of the channel, high sinuosity, presence of a bridge, confluence of rivers, and slope break. A GIS-based tool was used to generate the aforementioned factors over regular-spaced segments along the entire channel using available geospatial data. An ice jam predisposition index (IJPI) was calculated by combining the weighted optimal factors. Three Canadian rivers (province of Québec) were chosen as test sites. The resulting maps were assessed from historical observations and local knowledge. Results show that 77 % of the observed ice jam sites on record occurred in river sections that the model considered as having high or medium predisposition. This leaves 23 % of false negative errors (missed occurrence). Between 7 and 11 % of the highly predisposed river sections did not have an ice jam on record (false-positive cases). Results, limitations, and potential improvements are discussed.

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.

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.

L’estimation du débit en rivières est un paramètre clé pour la gestion des ressources hydriques, la prévention des risques liés aux inondations et la planification des équipements hydroélectriques. Lorsque le débit d’eau est très élevé lors d'évènements extrêmes, les méthodes de jaugeage traditionnelles ne peuvent pas être utilisées. De plus, les stations du réseau hydrométrique sont généralement éparses et leur répartition spatiale n’est pas optimale. Par conséquent, de nombreuses sections de rivières ne peuvent être suivies par des mesures et observations du débit. Pour ces raisons, pendant la dernière décennie, les capteurs satellitaires ont été considérés comme une source d’observation complémentaire aux observations traditionnelles du niveau d’eau et du débit en rivières. L’utilisation d’une telle approche a fourni un moyen de maintenir et d’étendre le réseau d'observation hydrométrique. L’approche avec télédétection permet d’estimer le débit à partir des courbes de tarage qui met en relation le débit instantané (Q) et la géométrie d’une section transversale du chenal (la largeur ou la profondeur effective de la surface d’eau). En revanche, cette méthode est associée à des limitations, notamment, sa dépendance aux courbes de tarage. En effet, en raison de leurs natures empiriques, les courbes de tarage sont limitées à des sections spécifiques et ne peuvent être appliquées dans d’autres rivières. Récemment, des techniques d’apprentissage profond ont été appliquées avec succès dans de nombreux domaines, y compris en hydrologie. Dans le présent travail, l’approche d’apprentissage profond a été choisie, en particulier les réseaux de neurones convolutifs (CNN), pour estimer le débit en rivière. L’objectif principal de ce travail est de développer une approche d’estimation du débit en rivières à partir de l’imagerie RADARSAT 1&amp;2 à l’aide de l’apprentissage profond. La zone d’étude se trouve dans l’ecozone du bouclier boréal à l’Est du Canada. Au total, 39 sites hydrographiques ont fait l’objet de cette étude. Dans le présent travail, une nouvelle architecture de CNN a été a été proposée, elle s'adapte aux données utilisées et permet d’estimer le débit en rivière instantané. Ce modèle donne un résultat du coefficient de détermination (R²) et de Nash-Sutcliffe égale à 0.91, le résultat d’erreur quadratique moyenne égale à 33 m³ /s. Cela démontre que le modèle CNN donne une solution appropriée aux problèmes d’estimation du débit avec des capteurs satellites sans intervention humaine. <br /><br />Estimating river flow is a key parameter for effective water resources management, flood risk prevention and hydroelectric facilities planning. In cases of very high flow of water or extreme events, traditional gauging methods cannot be reliable. In addition, hydrometric network stations are often sparse and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. For these reasons, satellite sensors are considered as a complementary observation source to traditional water level and flow observations in the last decades. The use of this kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate the instantaneous flow (Q) to the geometry of a channel cross-section (the effective width or depth of the water surface). On the other hand, remote sensing is also associated with limitations, notably its dependence on the rating curves. Indeed, due to their empirical nature, rating curves are limited to specific sections and cannot be applied in other rivers. Recently, deep learning techniques have been successfully applied in many fields, including hydrology. In the present work, the deep learning approach has been chosen, in particular convolutional neural networks (CNN), to estimate river flow. The main objective of this work is to develop an approach to estimate river flow from RADARSAT 1&amp;2 imagery using deep learning. In this study, 39 hydrographic sites of the Boreal Shield ecozone in Eastern Canada were considered. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. The achieved results demonstrated a coefficient of determination (R²) and Nash-Sutcliffe values of 0.91, and a root mean square error of 33m³ /s. This indicates the effectiveness of CNN in automatic flow estimation with satellite sensors.

Cette thèse traite des aspects de la quantification de l'incertitude dans l'évaluation des ressources éoliennes avec les pratiques d'analyses d'incertitude et de sensibilité. Les objectifs de cette thèse sont d'examiner et d'évaluer la qualité des pratiques d'analyse de sensibilité dans l'évaluation des ressources éoliennes, de décourager l'utilisation d'une analyse de sensibilité à la fois, d'encourager l'utilisation d'une analyse de sensibilité globale à la place, d'introduire des méthodes d'autres domaines., et montrer comment les analyses d'incertitude et de sensibilité globale ajoutent de la valeur au processus d'aide à la décision. Cette thèse est organisée en quatre articles : I. Une revue des pratiques d'analyse de sensibilité dans l'évaluation des ressources éoliennes avec une étude de cas de comparaison d'analyses de sensibilité individuelles et globales du coût actualisé de l'énergie éolienne offshore ; II. Technique Quasi-Monte Carlo dans l'analyse de sensibilité globale dans l'évaluation des ressources éoliennes avec une étude de cas sur les Émirats Arabes Unis; III. Utilisation de la famille de distribution Halphen pour l'estimation de la vitesse moyenne du vent avec une étude de cas sur l'Est du Canada; IV. Étude d'évaluation des ressources éoliennes offshore du golfe Persique avec les données satellitaires QuikSCAT.Les articles I à III ont chacun donné lieu à une publication évaluée par des pairs, tandis que l'article IV - à une soumission. L'article I propose des classifications par variable de sortie d'analyse de sensibilité, méthode, application, pays et logiciel. L'article I met en évidence les lacunes de la littérature, fournit des preuves des pièges, conduisant à des résultats d'évaluation erronés et coûteux des ressources éoliennes. L'article II montre comment l'analyse de sensibilité globale offre une amélioration au moyen du quasi-Monte Carlo avec ses plans d'échantillonnage élaborés permettant une convergence plus rapide. L'article III introduit la famille de distribution Halphen pour l'évaluation des ressources éoliennes. Article IV utilise les données satellitaires SeaWinds/QuikSCAT pour l'évaluation des ressources éoliennes offshore du golfe Persique. Les principales contributions à l'état de l'art avec cette thèse suivent. À la connaissance de l'auteur, aucune revue de l'analyse de sensibilité dans l'évaluation des ressources éoliennes n'est actuellement disponible dans la littérature, l'article I en propose une. L'article II relie la modélisation mathématique et l'évaluation des ressources éoliennes en introduisant la technique de quasi-Monte Carlo dans l'évaluation des ressources éoliennes. L'article III présente la famille de distribution de Halphen, de l'analyse de la fréquence des crues à l'évaluation des ressources éoliennes. <br /><br />This dissertation deals with the aspects of quantifying uncertainty in wind resource assessment with the practices of uncertainty and sensitivity analyses. The objectives of this dissertation are to review and assess the quality of sensitivity analysis practices in wind resource assessment, to discourage the use of one-at-a-time sensitivity analysis, encourage the use of global sensitivity analysis instead, introduce methods from other fields, and showcase how uncertainty and global sensitivity analyses adds value to the decision support process. This dissertation is organized in four articles: I. Review article of 102 feasibility studies: a review of sensitivity analysis practices in wind resource assessment with a case study of comparison of one-at-a-time vs. global sensitivity analyses of the levelized cost of offshore wind energy; II. Research article: Quasi-Monte Carlo technique in global sensitivity analysis in wind resource assessment with a case study on United Arab Emirates; III. Research article: Use of the Halphen distribution family for mean wind speed estimation with a case study on Eastern Canada; IV. Application article: Offshore wind resource assessment study of the Persian Gulf with QuikSCAT satellite data. Articles I-III have each resulted in a peer-reviewed publication, while Article IV – in a submission. Article I offers classifications by sensitivity analysis output variable, method, application, country, and software. It reveals the lack of collective agreement on the definition of sensitivity analysis in the literature, the dominance of nonlinear models, the prevalence of one-at a-time sensitivity analysis method, while one-at-a-time method is only valid for linear models. Article I highlights gaps in the literature, provides evidence of the pitfalls, leading to costly erroneous wind resource assessment results. Article II shows how global sensitivity analysis offers improvement by means of the quasi-Monte Carlo with its elaborate sampling designs enabling faster convergence. Article III introduces the Halphen distribution family for the purpose of wind recourse assessment. Article IV uses SeaWinds/QuikSCAT satellite data for offshore wind resource assessment of the Persian Gulf. The main contributions to the state-of-the-art with this dissertation follow. To the best of author’s knowledge, no review of sensitivity analysis in wind resource assessment is currently available in the literature, Article I offers such. Article II bridges mathematical modelling and wind resource assessment by introducing quasi-Monte Carlo technique to wind resource assessment. Article III introduces the Halphen distribution family from flood frequency analysis to wind resource assessment.

Les inondations sont une préoccupation majeure avec un potentiel de risques importants pour la sécurité publique ainsi qu'un impact économique et social négatif. Pour développer un modèle hydrodynamique permettant de cartographier et d'évaluer les risques d'inondation, un modèle d'élévation est un élément essentiel. La grande disponibilité de données de télédétection multisources facilite la création d'un modèle numérique d'élévation topo-bathymétrique (TBDEM). Cependant, il peut être très difficile de créer un modèle d'élévation à haute résolution homogène adapté à la cartographie des inondations en raison des divergences entre les données topographiques et bathymétriques causées par des changements temporels, des systèmes de référence horizontaux et verticaux différents, et des différences significatives en termes de résolution, incertitude et zone de couverture. Cette étude présente une méthodologie qui élargit les études précédentes axées sur la cartographie côtière à basse résolution en résolvant les différences spatiales et temporelles des jeux de données multisources tout en maintenant l'intégrité de la morphologie des berges et de l'environnement proche du rivage. Ceci est réalisé en appliquant une nouvelle méthodologie de fusion qui est mieux adaptée aux sources de données en jeu. Une méthode de moindre coût est appliquée aux données topographiques alors qu'une méthode de feathering est appliquée aux données bathymétriques. Pour ce qui est de la zone intermédiaire à l'interface de la terre et de l'eau, des transects sont utilisés pour interpoler entre les données manquantes afin de garantir l'intégrité du littoral. Enfin, une méthode de krigeage empirique bayésien appliqué à l'ensemble des données permet de produire une surface sans discontinuité accompagnée d'une surface d'erreur pour analyser l'incertitude en chaque point du modèle. Des données LiDAR aéroporté ainsi que des données de bathymétrie multifaisceau de la section supérieure du fleuve Saint-Laurent au Québec, Canada ont été combinées en utilisant la méthodologie proposée. Le TBDEM produit dans cette étude constitue une meilleure représentation que les modèles précédents et minimise l'erreur dans les données. La capacité de ce TBDEM à être plus performant que les modèles précédents dans les simulations hydrodynamiques sera testée dans des études futures en utilisant des événements de crue enregistrés précédemment.

RÉSUMÉ : Pour atténuer les risques d'inondation au Québec mais aussi partout dans le monde, plusieurs organismes gouvernementaux et des organismes privés, qui ont dans leurs attributions la gestion des risques des catastrophes naturelles, continuent d'améliorer ou d'innover en matière d'outils qui peuvent les aider efficacement à la mitigation des risques d'inondation et aider la société à mieux s'adapter aux changements climatiques, ce qui implique des nouvelles technologies pour la conception de ces outils. Après les inondations de 2017, le ministère de l'Environnement et de la Lutte contre les changements climatiques (MELCC) du gouvernement du Québec, en collaboration avec d'autres ministères et organismes et soutenu par Ouranos, a initié le projet INFO-Crue qui vise d'une part, à revoir la cartographie des zones inondables et, d'autre part, à mieux outiller les communautés et les décideurs en leur fournissant une cartographie prévisionnelle des crues de rivières. De ce fait, l'objectif de notre travail de recherche est d'analyser de façon empirique les facteurs qui influencent l'adoption d'un outil prévisionnel des crues. La revue de la littérature couvre les inondations et les prévisions, les théories et les modèles d'acceptation de la technologie de l'information (TI). Pour atteindre l'objectif de recherche, le modèle développé s'est appuyé particulièrement sur le modèle qui combine les concepts de la théorie unifiée de l'acceptation et l'utilisation des technologies (UTAUT) de Venkatesh et al. (2003) avec le concept « risque d'utilisation ». Afin de répondre à notre objectif de recherche, nous avons utilisé une méthodologie de recherche quantitative hypothético-déductive. Une collecte de données à l'aide d'une enquête par questionnaire électronique a été réalisée auprès de 106 citoyens qui habitent dans des zones inondables. L'analyse des résultats concorde avec la littérature. La nouvelle variable « risque d'utilisation » rajoutée au modèle UTAUT a engendré trois variables qui sont : « risque psychologique d'utilisation »; « risque de performance de l'outil » et « perte de confiance ». Pour expliquer l'adoption d'un nouvel outil prévisionnel des crues, notre analyse a révélé que cinq variables à savoir : « l'utilité perçue », « la facilité d'utilisation », « l'influence sociale », « la perte de confiance » et « le risque psychologique » sont des facteurs significatifs pour l'adoption du nouvel outil prévisionnel. -- Mot(s) clé(s) en français : Inondation, Prévision, UTAUT, Adoption de la technologie, Risque perçu d'utilisation, facteurs d'adoption, Projet INFO-Crue. -- ABSTRACT : With the aim of mitigating flood risks in Canada as well as around the world, several government and private organizations that have the responsibility of natural hazard risk management, are working hard to improve or innovate the flood mitigation approaches that can help effectively reducing flood risks and helping people adapt to climate change. After the 2017 floods, the Ministry of the Environment and the Fight against Climate Change (MELCC) of the Government of Quebec, in collaboration with other ministries and organizations and supported by Ouranos, initiated the INFO-Crue project which aims at reviewing the mapping of flood zones and providing communities and decision-makers with a forecast mapping of river floods. In this context, the objective of our research is to analyze the factors that may influence the adoption of a flood forecasting tool. The literature review covers flood and forecasting, as well as technology adoption models. To achieve the goal of our research, a conceptual model that combines the Unified Theory of Acceptance and Use of Technology (UTAUT) of Venkatesh et al. (2003) with perceived use risk was developed. A quantitative research methodology was used, and we administrate an electronic questionnaire survey to 106 citizens who live in flood-plain area. Results analysis show that the new variable "perceived use risk" introduced in the model generates three variables which are: "psychological risk"; "performance risk" and "loss of trust". To explain the adoption of a new forecasting tool, our analysis revealed that the following five variables which are "perceived usefulness", "ease of use­", "social influence", "loss of trust" and "psychological risk" are significant factors for the adoption of the new forecasting tool. -- Mot(s) clé(s) en anglais : Flood, Forecasting, UTAUT, Technology Adoption, perceived Risk of use, adoption factors, INFO-Crue project.

RÉSUMÉ: Les inondations sont considérées comme l'un des risques naturels les plus dangereux au monde. Plusieurs pays souffrent des conséquences néfastes des inondations. Au Canada, plusieurs provinces ont subi des inondations au cours du siècle dernier. Par exemple, la rivière des Outaouais a été confrontée à de nombreuses inondations comme en 2017 et 2019. La population d'Ottawa continue à augmenter d'une année à l'autre. C'est pour cela que nous avons choisi la rivière des Outaouais comme étude de cas pour ce projet dans le but de protéger la société contre les risques causés par les inondations. Les pays adoptent plusieurs solutions basées sur différentes méthodes afin de minimiser les dommages causés par les crues. La plupart des scientifiques s'accordent que la prévision des crues est la meilleure façon de limiter les conséquences des crues. Les systèmes de prévision des crues sont indispensables dans les pays fréquemment confrontés à des crues. Ils visent à fournir un long délai d'exécution et à fournir aux autorités et aux décideurs des informations suffisantes. Par conséquent, ils auront suffisamment de temps pour prendre les mesures adéquates pour sauver la vie de la population et limiter les catastrophes économiques dues aux inondations. ABSTRACT: Floods are one of the most catastrophic natural disasters in Canada and around the world that can cause loss of life and damages to properties and infrastructures. Saguenay flood (1996), southern Alberta flood (2013), and Ottawa floods (2017, 2019), are a few examples of Canadian floods with tremendous socio-economic impacts. Flood forecasting and predicting its characteristics (e.g., its magnitude and extent) has an important role in preventing and mitigating such flood impacts. Particularly, short-term forecasting is crucial for early warning systems and emergency response to floods. This study presents an integrated hydraulic-hydrologic modeling system for flood prediction. In this system, the Delft3D two-dimensional hydrodynamic model is connected with a HEC-HMS hydrologic model and observation data to provide an automatic exchange of data and results. Delft3D and HEC-HMS were chosen for this study because they were widely used and provided good results. In addition, they were applied in several flood forecasting studies. The prediction weather data and watershed characteristics provide input to the hydrological model to predict streamflow conditions, which are then automatically fed into the hydrodynamic model. The hydrodynamic model simulates the flood characteristics such as water level, 2D depth-averaged velocity field, and flood extent.