How to use artificial intelligence to downscale climate change models’ data - Salimi, Amirhossein; Ghobrial, Tadros; Bonakdari, Hossein (Intelligence Systems for Earth, Environmental and Planetary Sciences, 2024)

A new interpolation method to resolve under-sampling of UAV-lidar snow depth observations in coniferous forests - Dharmadasa, Vasana; Kinnard, Christophe; Baraër, Michel (Cold Regions Science and Technology, 2024)
Keywords: Baraer_Michel; Kinnard_Christophe

A comprehensive review of AI-based methods used for forecasting ice jam floods occurrence, severity, timing, and location - Salimi, Amirhossein; Ghobrial, Tadros; Bonakdari, Hossein (Cold Regions Science and Technology, 2024)
Keywords: Infolettre; Infolettre_Octobre_2024

Prenatal maternal subjective distress predicts higher autistic-like traits in offspring: The Iowa Flood Study - Lapierre, Mylène; Elgbeili, Guillaume; Laplante, David P.; O’Hara, Michael W.; D’Antono, Bianca; King, Suzanne (Development and Psychopathology, 2024)
Abstract Autism spectrum disorder prevalence more than quadrupled in the United States between 2000 and 2020. Ice storm-related prenatal maternal stress (PNMS) predicts autistic-like trait severity in children exposed early in gestation. The objective was to determine the extent to which PNMS influences the severity and trajectory of autistic-like traits in prenatally flood-exposed children at ages 4–7 years and to test moderation by sex and gestational timing. Soon after the June 2008 floods in Iowa, USA, 268 women pregnant during the disaster were assessed for objective hardship, subjective distress, and cognitive appraisal of the experience. When their children were 4, 5½, and 7 years old, mothers completed the Social Communication Questionnaire (SCQ) to assess their children’s autistic-like traits; 137 mothers completed the SCQ for at least one age. The final longitudinal multilevel model showed that the greater the maternal subjective distress, the more severe the child’s autistic-like traits, controlling for objective hardship. The effect of PNMS on rate of change was not significant, and there were no significant main effects or interactions involving sex or timing. Prenatal maternal subjective distress, but not objective hardship or cognitive appraisal, predicted more severe autistic-like traits at age 4, and this effect remained stable through age 7.
Keywords: Infolettre; Infolettre_Novembre_2024

Urban flooding and the resultant pollution: What French-speaking scientists make of it? - Genouel, Maïlys; Comby, Emeline; Le Lay, Yves-François; Biron, Pascale (Anthropocene, 2024)
Keywords: Infolettre; Infolettre_Septembre_2024

Conséquences des inondations de 2019 chez les intervenants de première et de deuxième lignes : le point de vue des principaux intéressés - Maltais, Danielle; Hamel, Ariane; Lansard, Anne-Lise (Canadian Social Work Review, 2024)
Keywords: Infolettre; Infolettre_Octobre_2024

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

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