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Dissertation/Thesis Abstract

Regionalization of hydrologic response in the Great Lakes basin: Considerations of temporal variability
by Kult, Jonathan, M.S., The University of Wisconsin - Milwaukee, 2013, 77; 1540448
Abstract (Summary)

Methods for predicting streamflow in areas with limited or nonexistent measures of hydrologic response commonly rely on regionalization techniques, where knowledge pertaining to gaged watersheds is transferred to ungaged watersheds. Hydrologic response indices have frequently been employed in contemporary regionalization research related to predictions in ungaged basins. In this study, regionalization models were developed using multiple linear regression and regression tree analysis to derive relationships between hydrologic response and watershed physical characteristics for 163 watersheds in the Great Lakes basin. These models provide a means for predicting runoff in ungaged basins at a monthly time step without implementation of any process-based rainfall-runoff model. Major findings from this research study include (1) Monthly runoff in ungaged watersheds was predicted with reasonable skill using regression-based relationships between runoff ratio and watershed physical characteristics; (2) Predictions in ungaged watersheds were highly influenced by the temporal characterization of runoff ratio used to condition the regression models; (3) Watershed classification using regression tree and multiple linear regression techniques resulted in comparable model predictive skill.

Indexing (document details)
Advisor: Choi, Woonsup
Commitee: Fredlund, Glen G., Schwartz, Mark D.
School: The University of Wisconsin - Milwaukee
Department: Geography
School Location: United States -- Wisconsin
Source: MAI 52/01M(E), Masters Abstracts International
Subjects: Geography, Geographic information science, Hydrologic sciences
Keywords: Great lakes basin, Hydrologic response, Prediction in ungaged basin, Regionalization, Regression model, Runoff
Publication Number: 1540448
ISBN: 978-1-303-18615-8
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