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Quantifying and modelling urban stream temperature: a central US watershed study
Author(s) -
Zeiger Sean,
Hubbart Jason A.,
Anderson Stephen H.,
Stambaugh Michael C.
Publication year - 2015
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.10617
Subject(s) - linear regression , watershed , hydrology (agriculture) , environmental science , regression analysis , hydrological modelling , linear model , statistics , mathematics , climatology , geology , computer science , geotechnical engineering , machine learning
Hydrologic models that rely on site specific linear and non‐linear regression water temperature ( T w ) subroutines forced solely with observed air temperature ( T a ) may not accurately estimate T w in mixed‐use urbanizing watersheds where hydrogeological and land use complexity may confound common T w regime assumptions. A nested‐scale experimental watershed study design was used to test T w model predictions in a representative mixed‐use urbanizing watershed of the central USA. The linear regression T w model used in the Soil and Water Assessment Tool (SWAT), a non‐linear regression T w model, and a process‐based T w model that accounts for watershed hydrology were evaluated. The non‐linear regression T w model tested at a daily time step performed significantly ( P < 0.01) better than the linear T w model currently used in SWAT. Both regression T w models overestimated T w in lower temperature ranges ( T w < 10.0 °C) with percent bias (PBIAS) values ranging from −28.2% (non‐linear T w model) to −66.1% (linear regression T w model) and underestimated T w in the higher temperature range ( T w > 25.0 °C) by 3.2%, and 7.2%, respectively. Conversely, the process‐based T w model closely estimated T w in lower temperature ranges (PBIAS = 4.5%) and only slightly underestimated T w in the higher temperature range (PBIAS = 1.7%). Findings illustrate the benefit of integrating process‐based T w models with hydrologic models to improve model transferability and T w predictive confidence in urban mixed‐land use watersheds. The findings in this work are distinct geographically and in terms of mixed‐land use complexity and are therefore of immediate value to land‐use managers in similarly urbanizing watersheds globally. Copyright © 2015 John Wiley & Sons, Ltd.