Statistical tools for thermal regime characterization at segment river scale: Case study of the Ste‐Marguerite River

• Published in: River research and applications Vol. 27; no. 8; pp. 1058 - 1071
• Main Authors: Guillemette, N, St‐Hilaire, A, Ouarda, T. B. M. J, Bergeron, N
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.10.2011; Wiley
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Biological and medical sciences; case studies; char; Construction; Estimates; Fish; Fresh water ecosystems; Freshwater; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; geostatistical; geostatistics; habitats; hydrology; Interpolation; Kriging; managers; Methods and techniques (sampling, tagging, trapping, modelling...); Metric space; modelling; multivariate; risk; river; Rivers; salmon; summer; Synecology; temperature; thermal regime; Thresholds; trout; Salmonidae; Salmon; Trout; kriging; geostatistical; multivariate; Rivers; Modeling; modelling; Characterization; Case study; Vertebrata; Pisces; char; Stream; river; thermal regime
• ISSN: 1535-1459; 1535-1467; 1535-1467
• DOI: 10.1002/rra.1411

Suitable thermal fish habitats are constrained by both maximum and minimum temperature tolerances. A multivariate and geostatistical approach was developed to estimate stream thermal characteristics at the river segment scale. Data from 22 temperature‐monitoring stations during summer 2007 were used to estimate monthly maximum temperature as well as thermal characteristics such as the number of events, the cumulative degree–days and the associated duration over specific temperature thresholds of 19 and 21°C. The probability of exceeding these temperature thresholds has also been interpolated. The methodology relies on the construction of a multivariate space using physiographic and hydrological characteristics of gauging stations as inputs in a canonical correlation analysis (CCA). A geostatistical interpolation technique, ordinary kriging, was subsequently used to perform interpolation in the physiographical space constructed using CCA. Results from this study were obtained for thermal characteristics estimated into two different interpolation spaces: (1) a 7 metrics space, and (2) an 8 metrics space. Cross‐validation technique has been performed and satisfactory results were obtained. Kriging thermal characteristics (magnitude and duration) into the 7 metric space for a 19°C threshold exceedance leads to best results with Relative Root Mean Square Error (RRMSE) ranging between 9.66 and 15.08%. The study shows that kriging in a multivariate space is a promising tool for water resources managers, especially in cases where risk mapping for lethal or sub‐lethal temperature thresholds may be required for a specific fish species.