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A Simple Method to Classify Stream Thermal Stability with Single Observations of Daily Maximum Water and Air Temperatures
Author(s) -
Stoneman Christine L.,
Jones Michael L.
Publication year - 1996
Publication title -
north american journal of fisheries management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 72
eISSN - 1548-8675
pISSN - 0275-5947
DOI - 10.1577/1548-8675(1996)016<0728:asmtcs>2.3.co;2
Subject(s) - air temperature , homogeneous , environmental science , maximum temperature , regression analysis , linear regression , stability (learning theory) , nomogram , hydrology (agriculture) , confidence interval , covariance , atmospheric sciences , meteorology , mathematics , statistics , geography , geology , medicine , geotechnical engineering , combinatorics , machine learning , computer science
The relationship between instream water temperature and ambient air temperature at six stream sites in southern Ontario was examined. At two sites, maximum summer water temperatures never exceeded 17°C; at two others, temperatures remained below 23°C; and at the remaining two, temperatures reached 28°C. The relationship that best distinguished the three pairs of sites was the regression of water temperature measured at 1600 hours on maximum air temperature. Analysis of covariance indicated that the regression slopes for the first (cold) and second (cool) pairs of sites were nonhomogeneous; those for the second (cool) and third (warm) pairs were homogeneous, and the adjusted means were significantly different. Where data were available, analysis of covariance indicated that the relationship did not differ between years. Graphical analysis of the data indicated little overlap of 95% confidence intervals at air temperatures greater than 25°C. The regression results were used to develop a nomogram to determine the thermal stability of stream sites from a single observation of water temperature at 1600 hours on a warm summer day and a maximum air temperature estimate for the same day.