Votre recherche

Atmospheric methane (CH4) concentrations have increased to 2.5 times their pre-industrial levels, with a marked acceleration in recent decades. CH4 is responsible for approximately 30% of the global temperature rise since the Industrial Revolution. This growing concentration contributes to environmental degradation, including ocean acidification, accelerated climate change, and a rise in natural disasters. The column-averaged dry-air mole fraction of methane (XCH4) is a crucial indicator for assessing atmospheric CH4 levels. In this study, the Sentinel-5P TROPOMI instrument was employed to monitor, map, and estimate CH4 concentrations on both regional and global scales. However, TROPOMI data exhibits limitations such as spatial gaps and relatively coarse resolution, particularly at regional scales or over small areas. To mitigate these limitations, a novel Convolutional Neural Network Autoencoder (CNN-AE) model was developed. Validation was performed using the Total Carbon Column Observing Network (TCCON), providing a benchmark for evaluating the accuracy of various interpolation and prediction models. The CNN-AE model demonstrated the highest accuracy in regional-scale analysis, achieving a Mean Absolute Error (MAE) of 28.48 ppb and a Root Mean Square Error (RMSE) of 30.07 ppb. This was followed by the Random Forest (RF) regressor (MAE: 29.07 ppb; RMSE: 36.89 ppb), GridData Nearest Neighbor Interpolator (NNI) (MAE: 30.06 ppb; RMSE: 32.14 ppb), and the Radial Basis Function (RBF) Interpolator (MAE: 80.23 ppb; RMSE: 90.54 ppb). On a global scale, the CNN-AE again outperformed other methods, yielding the lowest MAE and RMSE (19.78 and 24.7 ppb, respectively), followed by RF (21.46 and 27.23 ppb), GridData NNI (25.3 and 32.62 ppb), and RBF (43.08 and 54.93 ppb).

ABSTRACT Urbanization is leading to more frequent flooding as cities have more impervious surfaces and runoff exceeds the capacity of combined sewer systems. In heavy rainfall, contaminated excess water is discharged into the natural environment, damaging ecosystems and threatening drinking water sources. To address these challenges aggravated by climate change, urban blue-green water management systems, such as bioretention cells, are increasingly being adopted. Bioretention cells use substrate and plants adapted to the climate to manage rainwater. They form shallow depressions, allowing infiltration, storage, and gradual evacuation of runoff. In 2018, the City of Trois-Rivières (Québec, Canada) installed 54 bioretention cells along a residential street, several of which were equipped with access points to monitor performance. Groundwater quality was monitored through the installation of piezometers to detect potential contamination. This large-scale project aimed to improve stormwater quality and reduce sewer flows. The studied bioretention cells reduced the flow and generally improved water quality entering the sewer system, as well as the quality of stormwater, with some exceptions. Higher outflow concentrations were observed for contaminants such as manganese and nitrate. The results of this initiative provide useful recommendations for similar projects for urban climate change adaptation.

AbstractThe frequency and severity of floods has increased in different regions of the world due to climate change. Although the impact of floods on human health has been extensively studied, the increase in the segments of the population that are likely to be impacted by floods in the future makes it necessary to examine how adaptation measures impact the mental health of individuals affected by these natural disasters. The goal of this scoping review is to document the existing studies on flood adaptation measures and their impact on the mental health of affected populations, in order to identify the best preventive strategies as well as limitations that deserve further exploration. This study employed the methodology of the PRISMA-ScR extension for scoping reviews to systematically search the databases Medline and Web of Science to identify studies that examined the impact of adaptation measures on the mental health of flood victims. The database queries resulted in a total of 857 records from both databases. Following two rounds of screening, 9 studies were included for full-text analysis. Most of the analyzed studies sought to identify the factors that drive resilience in flood victims, particularly in the context of social capital (6 studies), whereas the remaining studies analyzed the impact of external interventions on the mental health of flood victims, either from preventive or post-disaster measures (3 studies). There is a very limited number of studies that analyze the impact of adaptation measures on the mental health of populations and individuals affected by floods, which complicates the generalizability of their findings. There is a need for public health policies and guidelines for the development of flood adaptation measures that adequately consider a social component that can be used to support the mental health of flood victims.

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.

This paper presents a new framework for floodplain inundation modeling in an ungauged basin using unmanned aerial vehicles (UAVs) imagery. This method is based on the integrated analysis of high-resolution ortho-images and elevation data produced by the structure from motion (SfM) technology. To this end, the Flood-Level Marks (FLMs) were created from high-resolution UAV ortho-images and compared to the flood inundated areas simulated using the HEC-RAS hydraulic model. The flood quantiles for 25, 50, 100, and 200 return periods were then estimated by synthetic hydrographs using the Natural Resources Conservation Service (NRCS). The proposed method was applied to UAV image data collected from the Khosban village, in Taleghan County, Iran, in the ungauged sub-basin of the Khosban River. The study area is located along one kilometre of the river in the middle of the village. The results showed that the flood inundation areas modeled by the HEC-RAS were 33%, 19%, and 8% less than those estimated from the UAV’s FLMs for 25, 50, and 100 years return periods, respectively. For return periods of 200 years, this difference was overestimated by more than 6%, compared to the UAV’s FLM. The maximum flood depth in our four proposed scenarios of hydraulic models varied between 2.33 to 2.83 meters. These analyses showed that this method, based on the UAV imagery, is well suited to improve the hydraulic modeling for seasonal inundation in ungauged rivers, thus providing reliable support to flood mitigation strategies

Geohazards associated with the dynamics of the liquid and solid water of the Earth’s hydrosphere, such as floods and glacial processes, may pose significant risks to populations, activities and properties [...]

Floods are the most common natural hazard worldwide. GARI is a flood risk management and analysis tool that is being developed by the Environmental and Nordic Remote Sensing Group (TENOR) of INRS in Quebec City (Canada). Beyond mapping the flooded areas and water levels, GARI allows for the estimation, analysis and visualization of flood risks for individuals, residential buildings, and population. Information can therefore be used during the different phases of flood risk management. In the operational phase, GARI can use satellite radar images to map in near real-time the flooded areas and water levels. It uses an innovative approach that combines Radarsat-2 and hydraulic data, specifically flood return period data. Information from the GARI enable municipalities and individuals to anticipate the impacts of a flood in a given area, to mitigate these impacts, to prepare, and to better coordinate their actions during a flood.

Les inondations de 2017 et 2019 au Québec ont affecté respectivement 293 et 240 municipalités. Ces inondations ont généré une cascade d’évènements stressants (stresseurs primaires et secondaires) qui ont eu des effets sur la santé mentale de la population et retardé le processus de rétablissement des individus. Cette période de rétablissement peut s’échelonner sur plusieurs mois voire plusieurs années. Cette étude s’inscrit dans la spécificité de la recherche mixte mise de l’avant à travers trois stratégies de recherche, réalisées de façon séquentielle : 1) sondage populationnelle réalisé auprès de 680 personnes, 2) analyse de documents produits par les organisations participant au processus de rétablissement social des sinistrés, ou sur des analyses externes portant sur ces interventions de rétablissement et 3) entrevues semi-dirigées auprès de 15 propriétaires occupants ayant complété une demande d’indemnisation à la suite des inondations de 2019 et auprès de 11 professionnels et gestionnaires participant au processus de rétablissement social. Les entrevues semi-dirigées et les questionnaires complétés par les personnes sinistrées lors des inondations de 2019 démontrent que les principales sources de stress ayant des impacts sur la santé et le bien-être des répondants sont : 1) l’absence d’avertissement et la vitesse de la montée des eaux; 2) l’obligation de se relocaliser et la peur d’être victime de pillage; 3) le manque de solidarité et d’empathie de la part de certains employés du MSP; 4) la gestion des conflits familiaux; 5) la gestion de problèmes de santé nouveaux ou préexistants; 6) la complexité des demandes d’indemnisation; 7) la lourdeur et les délais des travaux de nettoyage ou de restauration; 8) les indemnités inférieures aux coûts engendrés par l’inondation; 9) les pertes matérielles subies, particulièrement ceux d’une valeur de plus de 50 000 $; et 10) la diminution anticipée de la valeur de sa résidence. À cela s’ajoute l’insatisfaction à l’égard du programme d’indemnisation du gouvernement du Québec (PGIAF) qui fait plus que doubler la prévalence des symptômes de stress post-traumatique. Les inondations entraînent également une perte de satisfaction ou de bien-être statistiquement significative. La valeur monétaire de cette perte de jouissance peut être exprimée en équivalent salaires. En moyenne, cette diminution du bien-être équivaut à une baisse de salaire de 60 000$ pour les individus ayant vécu une première inondation et à 100 000$ pour les individus ayant vécu de multiples inondations. Ces résultats suggèrent que les coûts indirects et intangibles représentent une part importante des dommages découlant des inondations. Ce projet de recherche vise également à analyser l’application du PGIAF et son influence sur les stresseurs vécus par les sinistrés dans le contexte de la pandémie de COVID-19. La principale recommandation de cette étude repose sur une analyse de documents, un sondage populationnel et des entrevues semi-dirigées. Ainsi, s’attaquer à la réduction de principaux stresseurs nécessite 1) d’améliorer la gouvernance du risque d’inondation, 2) d’intensifier la communication et le support aux sinistrés, et 3) de revoir les mécanismes d’indemnisation existants.

In Canada, floods are the most common largely distributed hazard to life, property, the economy, water systems, and the environment costing the Canadian economy billions of dollars. Arising from this is FloodNet: a transdisciplinary strategic research network funded by Canadas Natural Sciences and Engineering Research Council, as a vehicle for a concerted nation-wide effort to improve flood forecasting and to better assess risk and manage the environmental and socio-economic consequences of floods. Four themes were explored in this network which include 1) Flood regimes in Canada; 2) Uncertainty of floods; 3) Development of a flood forecasting and early warning system and 4) Physical, socio-economic and environmental effects of floods. Over the years a range of statistical, hydrologic, modeling, and economic and psychometric analyses were used across the themes. FloodNet has made significant progress in: assessing spatial and temporal variation of extreme events; updating intensity-duration-frequency (IDF) curves; improving streamflow forecasting using novel techniques; development and testing of a Canadian adaptive flood forecasting and early warning system (CAFFEWS); a better understanding of flood impacts and risk. Despite these advancements FloodNet ends at a time when the World is still grappling with severe floods (e.g., Europe, China, Africa) and we report on several lessons learned. Mitigating the impact of flood hazards in Canada remains a challenging task due to the countrys varied geography, environment, and jurisdictional political boundaries. Canadian technical guide for developing IDF relations for infrastructure design in the climate change context has been recently updated. However, national guidelines for flood frequency analyses are needed since across the country there is not a unified approach to flood forecasting as each jurisdiction uses individual models and procedures. From the perspective of risk and vulnerability, there remains great need to better understand the direct and indirect impacts of floods on society, the economy and the environment.

<p>Spring floods have generated colossal damages to residential areas in the Province of Quebec, Canada, in 2017 and 2019. Government authorities need accurate modelling of the impact of theoretical floods in order to prioritize pre-disaster mitigation projects to reduce vulnerability. They also need accurate modelling of forecasted floods in order to direct emergency responses. </p><p>We present a governmental-academic collaboration that aims at modelling flood impact for both theoretical and forecasted flooding events over all populated river reaches of meridional Quebec. The project, funded by the ministère de la Sécurité publique du Québec (Quebec ministry in charge of public security), consists in developing a diagnostic tool and methods to assess the risk and impacts of flooding. Tools under development are intended to be used primarily by policy makers. </p><p>The project relies on water level data based on the hydrological regimes of nearly 25,000 km of rivers, on high-precision digital terrain models, and on a detailed database of building footprints and characterizations. It also relies on 24h and 48h forecasts of maximum flow for the subject rivers. The developed tools integrate large data sets and heterogeneous data sources and produce insightful metrics on the physical extent and costs of floods and on their impact on the population. The software also provides precise information about each building affected by rising water, including an estimated cost of the damages and impact on inhabitants.  </p>

<p>In snow-prone regions, snowmelt is one of the main drivers of runoff. For operational flood forecasting and mitigation, the spatial distribution of snow water equivalent (SWE) in near real time is necessary. In this context, in situ observations of SWE provide a valuable information. Nonetheless, the high spatial variability of snowpack characteristics makes it necessary to implement some kind of snow modelling to get a spatially continuous estimation. Data assimilation is thus a useful approach to combine information from both observation and modeling in near real-time. </p><p>For example, at the provincial government of Quebec (eastern Canada), the HYDROTEL Snowpack Model is applied on a daily basis over a 0.1 degree resolution mesh covering the whole province. The modelled SWE is corrected in real time by in situ manual snow survey which are assimilated using a spatial particles filter (Cantet et al., 2019). This assimilation method improves the reliability of SWE estimation at ungauged sites.</p><p>The availability of manual snow surveys is however limited both in space and time. These measurements are conducted on a bi-weekly basis in a limited number of sites. In order to further improve the temporal and spatial observation coverage, alternative sources of data should be considered.</p><p>In this research, it is hypothesized that data gathered by SR50 sonic sensors can be assimilated in the spatial particle filter to improve the SWE estimation. These automatic sensors provide hourly measurements of snow depth and have been deployed in Quebec since 2005. Beforehand, probabilistic SWE estimations were derived from the SR50 snow depth measurements using an ensemble of artificial neural networks (Odry et al. 2019). Considering the nature of the data and the conversion process, the uncertainty associated with this dataset is supposed larger than for the manual snow surveys. The objective of the research is to evaluate the potential interest of adding this lower-quality information in the assimilation framework.</p><p>The addition of frequent but uncertain data in the spatial particle filter required some adjustments in term of assimilation frequency and particle resampling. A reordering of the particles was implemented to maintain the spatial coherence between the different particles. With these changes, the consideration of both manual snow surveys and SR50 data in the spatial particle filter reached performances that are comparable to the initial particle filter that combines only the model and manual snow survey for estimating SWE in ungauged sites. However, the addition of SR50 data in the particle filter allows for continuous information in time, between manual snow surveys.</p><p> </p><p><strong>References:</strong></p><p>Cantet, P., Boucher, M.-A., Lachance-Coutier, S., Turcotte, R., Fortin, V. (2019). Using a particle filter to estimate the spatial distribution of the snowpack water equivalent. J. Hydrometeorol, 20.</p><p>Odry, J., Boucher, M.-A., Cantet,P., Lachance-Cloutier, S., Turcotte, R., St-Louis, P.-Y. (2019). Using artificial neural networks to estimate snow water equivalent from snow depth. Canadian water ressources journal (under review)</p>

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.

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.

Droughts are increasingly recognized as a significant global challenge, with severe impacts observed in Canada's Prairie provinces. While less frequent in Eastern Canada, prolonged precipitation deficits, particularly during summer, can lead to severe drought conditions. This study investigates the causes and consequences of droughts in New Brunswick (NB) by employing two drought indices: the Palmer Drought Severity Index (PDSI) and Standardized Evapotranspiration Deficit Index (SEDI)– at ten weather stations across NB from 1971 to 2020. Additionally, the Canadian Gridded Temperature and Precipitation Anomalies (CANGRD) dataset (1979–2014) was utilized to examine spatial and temporal drought variability and its alignment with station-based observations. Statistical analyses, including the Mann–Kendall test and Sen's slope estimator, were applied to assess trends in drought indices on annual and seasonal timescales using both station and gridded data. The results identified the most drought-vulnerable regions in NB and revealed significant spatial and temporal variability in drought severity over the 1971–2020 period. Trend analyses further highlighted the intensification of extreme drought events during specific years. Coastal areas in southern NB were found to be particularly susceptible to severe drought conditions compared to inland regions, consistent with observed declines in both the frequency of rainy days and daily precipitation amounts in these areas. These findings underscore the need for targeted drought mitigation strategies particularly in NB’s coastal zones, to address the region’s increasing vulnerability to extreme drought events.

<p>The applicability of the Canadian Precipitation Analysis products known as the Regional Deterministic Precipitation Analysis (CaPA-RDPA) for hydrological modelling in boreal watersheds in Canada, which are constrained with shortage of precipitation information, has been the subject of a number of recent studies. The northern and mid-cordilleran alpine, sub-alpine, and boreal watersheds in Yukon, Canada, are prime examples of such Nordic regions where any hydrological modelling application is greatly scrambled due to lack of accurate precipitation information. In the course of the past few years, proper advancements were tailored to resolve these challenges and a forecasting system was designed at the operational level for short- to medium-range flow and inflow forecasting in major watersheds of interest to Yukon Energy. This forecasting system merges the precipitation products from the North American Ensemble forecasting System (NAEFS) and recorded flows or reconstructed reservoir inflows into the HYDROTEL distributed hydrological model, using the Ensemble Kalman Filtering (EnKF) data assimilation technique. In order to alleviate the adverse effects of scarce precipitation information, the forecasting system also enjoys a snow data assimilation routine in which simulated snowpack water content is updated through a distributed snow correction scheme. Together, both data assimilation schemes offer the system with a framework to accurately estimate flow magnitudes. This robust system not only mitigates the adverse effects of meteorological data constrains in Yukon, but also offers an opportunity to investigate the hydrological footprint of CaPA-RDPA products in Yukon, which is exactly the motivation behind this presentation. However, our overall goal is much more comprehensive as we are trying to elucidate whether assimilating snow monitoring information in a distributed hydrological model could meet the flow estimation accuracy in sparsely gauged basins to the same extent that would be achieved through either (i) the application of precipitation analysis products, or (ii) expanding the meteorological network. A proper answer to this question would provide us with valuable information with respect to the robustness of the snow data assimilation routine in HYDROTEL and the intrinsic added-value of using CaPA-RDPA products in sparsely gauged basins of Yukon.</p>

The water content of wetlands represents a key driver of their hydrological services and it is highly dependent on short- and long-term weather conditions, which will change, to some extent, under evolving climate conditions. The impact on stream flows of this critical dynamic component of wetlands remains poorly studied. While hydrodynamic modelling provide a framework to describe the functioning of individual wetland, hydrological modelling offers the opportunity to assess their services at the watershed scale with respect to their type (i.e., isolated or riparian). This study uses a novel approach combining hydrological modelling and limited field monitoring, to explore the effectiveness of wetlands under changing climate conditions. To achieve this, two isolated wetlands and two riparian wetlands, located in the Becancour River watershed within the St Lawrence Lowlands (Quebec, Canada), were monitored using piezometers and stable water isotopes (δD – δ18O) between October 2013 and October 2014. For the watershed hydrology component of this study, reference (1986–2015) and future meteorological data (2041–2070) were used as inputs to the PHYSITEL/HYDROTEL modelling platform. Results obtained from in-situ data illustrate singular hydrological dynamics for each typology of wetlands (i.e., isolated and riparian) and support the hydrological modelling approach used in this study. Meanwhile, simulation results indicate that climate change could affect differently the hydrological dynamics of wetlands and associated services (e.g., storage and slow release of water), including their seasonal contribution (i.e., flood mitigation and low flow support) according to each wetland typology. The methodological framework proposed in this paper meets the requirements of a functional tool capable of anticipating hydrological changes in wetlands at both the land management scale and the watershed management scale. Accordingly, this framework represents a starting point towards the design of effective wetland conservation and/or restoration programs.

Fluvial flooding in Canada is often snowmelt-driven, thus occurs mostly in spring, and has caused billions of dollars in damage in the past decade alone. In a warmer climate, increasing rainfall and changing snowmelt rates could lead to significant shifts in flood-generating mechanisms. Here, projected changes to flood-generating mechanisms in terms of the relative contribution of snowmelt and rainfall are assessed across Canada, based on an ensemble of transient climate change simulations performed using a state-of-the-art regional climate model. Changes to flood-generating mechanisms are assessed for both a late 21st century, high warming (i.e., Representative Concentration Pathway 8.5) scenario, and in a 2 °C global warming context. Under 2 °C of global warming, the relative contribution of snowmelt and rainfall to streamflow peaks is projected to remain close to that of the current climate, despite slightly increased rainfall contribution. In contrast, a high warming scenario leads to widespread increases in rainfall contribution and the emergence of hotspots of change in currently snowmelt-dominated regions across Canada. In addition, several regions in southern Canada would be projected to become rainfall dominated. These contrasting projections highlight the importance of climate change mitigation, as remaining below the 2 °C global warming threshold can avoid large changes over most regions, implying a low likelihood that expensive flood adaptation measures would be necessary.

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.