Date of Award:

5-2010

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Civil and Environmental Engineering

Advisor/Chair:

David G. Tarboton

Abstract

Maintaining the ecological health of streams is vital for sustainable water resources management. Streamflow is a primary factor influencing the structure and function of ecological communities. A quantitative understanding of how stream biota respond to variation in streamflow is required for stream bioassessment. This dissertation focuses on quantifying relationships between streamflow regime and stream macroinvertebrate richness and composition. The contribution comprises statistical models that predict stream macroinvertebrate class from streamflow regime and predict streamflow regime from watershed attributes, and a tool that helps derive watershed attribute variables used in these models. The dissertation is a collection of three papers. In the first paper 12 variables were used to represent streamflow regime at 543 sites in the western US. Principal component analysis (PCA) and K-means clustering were used to obtain statistically independent factors and streamflow regime classes. We examined the relationship between these characterizations of streamflow and macroinvertebrate richness and composition at 63 of the 543 sites where there was also biological data. This analysis identified specific aspects of the streamflow regime that were useful in predicting macroinvertebrate richness and composition and that have potential application in classification-based bioassessment and management. A regional-scale study such as this requires tools for efficiently delineating watersheds and deriving their attributes. Paper two presents a multiple watershed delineation tool that addresses issues such as a) incorrectly positioned outlets and b) large Digital Elevation Models. This tool has capabilities to delineate stream networks with the threshold that determines drainage density being objectively determined so that the resulting networks adhere to geomorphological stream network laws. It also derives a suite of geomorphological watershed attributes that were used in prediction models in paper three. In paper three, we developed statistical models to predict streamflow regime class from watershed attributes. Four popular statistical methods were used and the uncertainty associated with class predictions for each method was quantified. Paper three also identified the watershed attributes that were most important for discriminating streamflow regime classes.

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