Predicting water temperatures using a deterministic model: Application on Miramichi River catchments (New Brunswick, Canada)

Water temperature influences many physical, chemical and biological properties of rivers. It also influences the distribution of fish and many aquatic organisms within the river environment. Hence, a good understanding of the thermal regime of rivers is essential for effective management of fisheries and aquatic resources. This study deals with the modelling of river water temperature using a deterministic model. This model calculates the net heat flux at the water surface using meteorological conditions within the study area. The water temperature model was applied on two watercourses of different size and thermal characteristics, but within a similar meteorological region, i.e. the Little Southwest Miramichi River and Catamaran Brook (New Brunswick, Canada). Data from 1992 to 1994 were used to calibrate the model, while data from 1995 to 1999 were used for the model validation. Results showed equally good agreement between observed and predicted water temperatures during the calibration period for both rivers with a root-mean-square error (RMSE) of 1.49 °C for the Little Southwest Miramichi River compared to 1.51 °C for Catamaran brook. During the validation period, RMSEs were calculated at 1.55 °C for the Little Southwest Miramichi River and 1.61 °C for Catamaran Brook. Poorer model performances were generally observed early in the season (e.g. spring), especially for the Little Southwest Miramichi River due to the influence of snowmelt conditions, while late summer to autumn performances showed among the best results for both rivers. Late autumn performances were more variable in Catamaran Brook and presumably influenced by the groundwater, geothermal conditions and potentially riparian shading. The geothermal aspect was further investigated at Catamaran Brook (using 1998 data) and results revealed that although geothermal fluxes are present, they explained very little of the unexplained variability (