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The University of Massachusetts Amherst

Bringing People, Data, and Models Together: Understanding Stream Temperature in the Northeast

Authors:

Kyle O'Neill

Publication Type:
Miscellaneous
Year of Publication:
2013
Publisher:
University of Massachusetts
City:
Amherst
Volume:
Master of Science
Year:
2013
Date:
07/2013

Abstract

Water temperature is one of the important characteristics of a stream that can be impacted by anthropogenic change. Such change can have significant ecological implications for the health of riparian systems. It is important for decision-makers to understand the impact of various physical characteristics on the stream temperature regime in a watershed. This research applies a statistical stream temperature model (Mohseni et al, 1998) to 905 sites across the northeastern United States to determine if such models can be useful to resource managers. Statistical analysis on the calibrated model parameters across the best-fit sites is used to provide information on watershed characteristics which may be critical to stream temperature. In addition to air temperature, which is the obvious driver of stream temperature, groundwater influence, forest coverage, urban area, and drainage area, which is representative of travel time, are the most significant watershed characteristics that impact stream temperature. While the relationships between forested and urban landscapes and stream temperature generated in this research confirm past research, a definitive relationship between groundwater and stream temperature was not established. A predictive model of stream temperature is also developed, extending the work of Mohseni et al. (1998). This model uses watershed characteristics and meteorological data to generate stream temperatures at an ungaged location. Uses of this model include analyzing the impacts of anthropogenic changes on stream temperature regimes and the generation of realistic stream temperatures at a location for use in another model, such as the physical stream temperature model developed by Yearsley (2011). Results from the predictive model are comparable to those from the calibrated nonlinear stream temperature model analyzed.