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

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

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

Impact of Assimilating Radar Solid Precipitation Data in the Canadian Precipitation Analysis System - Beaudry, Florence L.; Bélair, Stéphane; Thériault, Julie M.; Khedhaouiria, Dikra; Lespinas, Franck; Michelson, Daniel; Feng, Pei-Ning; Aubry, Catherine (Journal of Hydrometeorology, 2024)
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.
Keywords: Infolettre; Infolettre_Septembre_2024

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

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

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

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

A random forest machine learning model to detect fluvial hazards - Gava, Marco; Biron, Pascale M.; Buffin‐Bélanger, Thomas (River Research and Applications, 2024)
Abstract Fluvial hazards of river mobility and flooding are often problematic for road infrastructure and need to be considered in the planning process. The extent of river and road infrastructure networks and their tendency to be close to each other creates a need to be able to identify the most dangerous areas quickly and cost‐effectively. In this study, we propose a novel methodology using random forest (RF) machine learning methods to provide easily interpretable fine‐scale fluvial hazard predictions for large river systems. The tools developed provide predictions for three models: presence of flooding (PFM), presence of mobility (PMM) and type of erosion model (TEM, lateral migration, or incision) at reference points every 100 m along the fluvial network of three watersheds within the province of Quebec, Canada. The RF models use variables focused on river conditions and hydrogeomorphological processes such as confinement, sinuosity, and upstream slope. Training/validation data included field observations, results from hydraulic and erosion models, government infrastructure databases, and hydro‐ geomorphological assessments using 1‐m DEM and satellite/historical imagery. A total of 1807 reference points were classified for flooding, 1542 for mobility, and 847 for the type of erosion out of the 11,452 reference points for the 1145 km of rivers included in the study. These were divided into training (75%) and validation (25%) datasets, with the training dataset used to train supervised RF models. The validation dataset indicated the models were capable of accurately predicting the potential for fluvial hazards to occur, with precision results for the three models ranging from 83% to 94% of points accurately predicted. The results of this study suggest that RF models are a cost‐effective tool to quickly evaluate the potential for fluvial hazards to occur at the watershed scale.
Keywords: Infolettre; Infolettre_Septembre_2024