Multivariate Modelling of Water Temperature in the Okanagan Watershed

Abstract

Initiatives for the protection of river ecosystems must include the monitoring of key flow and water quality variables, as well as clear and quantifiable management goals. One variable which strongly influences water quality is water temperature, and its modification arising from human activity should be incorporated into ecosystem protection guidelines. This work, conducted as part of the Canadian National Agri-Environmental Standards Initiative (NAESI) program, presents a preliminary study investigating statistical regression methods and a geostatistical approach to model key water temperature characteristics that could assist in the development of standards. Water temperature time series recorded at 16 sites in the Okanagan watershed were used to develop the models. Monthly maxima were modelled for the period of April-September 2007 using four predictors: the site longitude, the drainage basin maximum altitude, the local slope at the station, and the log of the mean substrate diameter. Four types of multivariate regressions of monthly maxima were produced, and a leave-one-out resampling approach was used to validate the models. Relative Bias, Root Mean Square Errors (RMSE) and a corrected Akaike Information Criterion (AICc) were calculated for each month. Models gave RMSE values between 0.9°C and 2.1°C for the monthly maxima. All models generally performed best between May and July. Geostatistical interpolation of maxima was also performed in a multivariate physiographic space reduced to two orthogonal dimensions using canonical correlation analysis (CCA). Examples of interpolated maps show that the approach can be used to discriminate between warm and cool streams.