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Stream Temperature Modeling Using Functional Regression Models
Author(s) -
Boudreault Jérémie,
Bergeron Normand E.,
StHilaire André,
Chebana Fateh
Publication year - 2019
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/1752-1688.12778
Subject(s) - river ecosystem , logistic regression , generalized additive model , regression , regression analysis , variable (mathematics) , econometrics , functional response , environmental science , statistics , mathematics , ecology , computer science , habitat , biology , mathematical analysis , predation , predator
Stream temperature is one of the most important environmental variables in lotic habitats as it has important and direct impacts on the ecosystem. Given the continuous nature of this variable, the aim of this paper was to introduce functional regression for the air‐stream temperature relation, being capable to model an entire seasonal or annual curve of temperatures as one entity, rather than multiple daily or weekly values in classical models. Three types of functional models were explored in the study and compared to two classical models (Generalized Additive Model and Logistic Model) for six rivers from the United States The results show the functional models have the best performance for all the considered rivers. When comparing functional models between them, one variant of the historical functional model performs better than the two other models and is the most parsimonious. Functional regression leads to encouraging results to model the complete annual stream temperature curve as one entity compared to other classical approaches.