Name
Fractional Snow Cover as a Predictor in Empirical Stream Temperature Modelling
Description
Stream temperature is an important water quality parameter, particularly as it influences thermal habitat suitability for a range of species. Despite stream temperature having such a vital role, stream temperature monitoring networks are sparse in many parts of the world, with few long-term records available. Empirical models are useful for estimating stream temperatures where no or limited data exist. Previous research shows that snow dynamics strongly influence stream thermal regimes in mountainous regions, yet no studies to date appear to have tried to incorporate catchment snow cover from MODIS or other snow-cover data products in empirical stream temperature models. The objective of this research was to explore the usefulness of fractional snow cover as a predictor of stream temperature in empirical modelling. The study focused on the southern Coast Mountains in British Columbia, where stream temperature data have been monitored for over 115 locations, with records spanning one to four years between 2016-2022. Daily air temperatures were estimated for the locations using ECMWF Reanalysis v5 (ERA5). Time series of fractional snow cover in drainage areas were extracted from MODIS imagery. Initially, the analysis explored site-specific models to determine the best model structure, after which spatio-temporal models with additional catchment characteristics were fit to the full dataset. Cross-validation was used to assess the predictive accuracy of the model based on the sites. This research supports advances in stream temperature modelling, which contributes to better understanding of changing thermal regimes and thus aquatic habitat suitability.