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

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

Climatological context of the mid-November 2021 floods in the province of British Columbia, Canada - Richards-Thomas, Tamar S.; Déry, Stephen J.; Stewart, Ronald E.; Thériault, Julie M. (Weather and Climate Extremes, 2024)
Keywords: Infolettre; Infolettre_Septembre_2024

Drivers of rare earth elements (REEs) and radionuclides in changing subarctic (Nunavik, Canada) surface waters near a mining project - Marginson, H.; MacMillan, G.A.; Wauthy, M.; Sicaud, E.; Gérin-Lajoie, J.; Dedieu, J.-P.; Amyot, M. (Journal of Hazardous Materials, 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

Morphological and Habitat Quality of Salmonid Streams and their Relationship with Fish-Based Indices in Aotearoa New Zealand and Ontario (Canada) - Foote, Kyleisha J.; Biron, Pascale M.; Grant, James W. A. (Environmental Management, 2024)
Keywords: Infolettre; Infolettre_Janvier_2024

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

Large-scale flood modelling based on LiDAR data: a case study in the Southwest Miramichi watershed, New Brunswick, Canada - Choné, Guénolé; Mazgareanu, Iulia; Biron, Pascale M.; Buffin-Bélanger, Thomas; Larouche-Tremblay, François; Perry, Brian; Fortin, Maxim (Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2024)
Keywords: Infolettre; Infolettre_Decembre_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