Mountain glaciers as modifiers of streamflow in Western Canada : insights from data analysis and machine learning

Despite the social, ecological, and cultural importance of glaciers and glacier-fed rivers, a quantification of key glacier controls of streamflow remain elusive and outstanding questions persist. For example: which communities’ water supplies are most vulnerable to the loss of glacier ice? By how much do glaciers modify the streamflow response to heatwaves? First, I use principal component analysis, self-organizing maps, and multivariate linear regression to provide an assessment of community vulnerability to deglaciation in Alberta, Canada, by identifying and predicting signals of glacier runoff in historical streamflow datasets. I combine these models with a new dataset of community water supply sources to find that the most vulnerable locations are the communities of Hinton, Lake Louise, and Rocky Mountain House, as well as the Bighorn Dam, which forms the largest reservoir in the province and provides water for over a million people downstream. Next, I develop an accurate and interpretable convolutional long short-term memory neural network regional hydrological model for streamflow prediction across Alberta and British Columbia, Canada. This deep machine learning model is forced by gridded ERA5 temperature and precipitation data and predicts streamflow at 226 stream gauge stations. Finally, I use this model to systematically investigate the streamflow response to heatwaves. I determine how this streamflow response varies by basin glacier coverage, as well as by heatwave timing, duration, and intensity, under both normal and warmer climate scenarios. I quantify how increasing glacier coverage is associated with both increasing streamflow generation during summer heatwaves, as well as increasing compensation in summer to the loss of snow during spring heatwaves. My results advance understanding on multiple research fronts in glaciology and hydrology: I demonstrate the importance of local-scale water resource data for glacier runoff projections; I emphasize the interpretability of deep machine learning models as a means to apply machine learning to new frontiers in hydrology; and I offer new frameworks and metrics to understand and characterize the hydrological impacts of heatwaves. My findings motivate future inter- and trans-disciplinary research to develop better deep learning hydrological models, and make progress towards answering politically and socially relevant glacio-hydrological research questions.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.