Feasibility study of a geostatistical modelling of monthly maximum stream temperatures in a multivariate space

Healthy river conditions through optimal thermal regime controls water quality as well as the availability and distribution of fish habitat. A multivariate and geostatistical approach was developed to estimate maximum stream temperatures at a large basin scale. The methodology relies on the construction of a physiographical space using characteristics of gauging stations by testing two multivariate methods: principal components analysis (PCA) and canonical correlation analysis (CCA). Within the physiographical space, a geostatistical technique called ordinary kriging was then used to interpolate stream temperatures. Data from 12 temperature monitoring stations during July 1996 and July 1997 were used to estimate monthly maximum temperature. Results from the proposed approach were evaluated by comparing kriging performance obtained using both multivariate methods. Cross-validation technique has been performed on both approaches and satisfactory results were obtained. Kriging in the CCA physiographical space leads to better results because this approach seems more adapted to link physiographical information with specific water temperature. In addition, CCA requires less physiographical information than PCA (i.e. 10 metrics for PCA vs 8 metrics for CCA) to provide more satisfactory results (up to 15% decrease in RMSEr). In physiographical space, the gauging stations were found to cluster, potentially providing information to improve the accuracy of interpolation in that space. An example is provided to illustrate how to estimate one of the stream temperature properties at ungauged stations using the PCA algorithm. The relevance of the results regarding the quality of fish habitats of the Moisie river is discussed.