Research Collaboration to Model the Future of Freshwater Salinization

The increasing salinization of freshwater resources is a growing threat to ecosystems, particularly as urbanization rises and salts and deicers are widely applied on impervious surfaces such as roads, highways, parking lots, and driveways. Winter salting leads to higher chloride (Cl) level in streams, rivers, and lakes, which is detrimental to the health and reproduction of many freshwater species. However, there is a lack of predictive models to understand how urbanization, climate change, and land management practices influence Cl concentrations in streams. Previous research has relied on proprietary models or has not accounted for important factors like the long-term retention of salt in soil and groundwater, which can have delayed effects on streams, especially in areas with mixed land use. Moreover, many existing models are calibrated using short-term data (often less than a year) and lack proper validation. This study aims to fill these gaps by using an integrated modeling approach to predict Cl concentrations in urban streams, considering varying land use, climate conditions, groundwater Cl contributions, and salting practices. Researchers from Toronto Metropolitan University (Ontario) collaborated with the DataSquad team at Carleton University (Minnesota), combining their expertise in environmental modeling and data science through a summer internship. This led to ongoing collaboration and co-authorship opportunities for the students assisting in the project. This poster will highlight the research context, collaboration process, student contributions, and key learnings from the experience.