Long Lead-Time Forecasting of U.S. Streamflow Using Partial Least Squares Regression
Publication: Journal of Hydrologic Engineering
Volume 12, Issue 5
Abstract
Pacific and Atlantic Ocean sea surface temperatures (SSTs) were used as predictors in a long lead-time streamflow forecast model in which the partial least squares regression (PLSR) technique was used with over 600 unimpaired streamflow stations in the continental United States. Initially, PLSR calibration (or test) models were developed for each station, using the previous spring-summer Pacific (or Atlantic) Ocean SSTs as predictors. Regions were identified in the Pacific Northwest, Upper Colorado River Basin, Midwest, and Atlantic states in which Pacific Ocean SSTs resulted in skillful forecasts. Atlantic Ocean SSTs resulted in significant regions being identified in the Pacific Northwest, Midwest, and Atlantic states. Next, streamflow stations were selected in the Columbia River Basin, Upper Colorado River Basin, and Mississippi River Basin and a PLSR cross-validation model (i.e., forecast) was developed. The results of the PLSR cross-validation model for each station varied with linear error in probability space scores of to where 10% is considered skillful forecasts using Pacific and Atlantic SSTs.
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Acknowledgments
This research is supported by the U.S. Geological Survey State Water Resources Research Programs of Nevada and Wyoming, National Science Foundation Award No. NSFCMS-0239334, the National Science Foundation State of Nevada EPSCOR Fellowship, the Wyoming NASA Space Grant Consortium, and the Wyoming Water Development Commission. The writers acknowledge and thank Ms. Sang Hee Lee for her assistance in SAS programming. The writers wish to thank the Editor, Section Editor, Associate Editor, and the two anonymous reviewers for their useful comments.
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Published In
Journal of Hydrologic Engineering
Volume 12 • Issue 5 • September 2007
Pages: 442 - 451
Copyright
© 2007 ASCE.
History
Received: Jun 29, 2005
Accepted: Jul 24, 2006
Published online: Sep 1, 2007
Published in print: Sep 2007
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