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Statistical tools for thermal regime characterization at segment river scale: Case study of the Ste‐Marguerite River
Author(s) -
Guillemette N.,
StHilaire A.,
Ouarda T. B. M. J.,
Bergeron N.
Publication year - 2011
Publication title -
river research and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.679
H-Index - 94
eISSN - 1535-1467
pISSN - 1535-1459
DOI - 10.1002/rra.1411
Subject(s) - kriging , interpolation (computer graphics) , multivariate statistics , environmental science , multivariate interpolation , scale (ratio) , metric (unit) , hydrology (agriculture) , mean squared error , statistics , mathematics , geology , computer science , geography , geotechnical engineering , cartography , engineering , animation , operations management , bilinear interpolation , computer graphics (images)
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. Copyright © 2010 John Wiley & Sons, Ltd.