Long-Range Forecasting of Colorado Streamflows Based on Hydrologic, Atmospheric, and Oceanic Data
Publication: Journal of Hydrologic Engineering
Volume 16, Issue 6
Abstract
Climatic fluctuations have profound effects on water resources variability in the western United States. The research reported herein centers on streamflow predictability at the medium- and long-range scales in rivers that originate in Colorado. Specifically, we want to improve forecasting seasonal and yearly streamflows based on atmospheric-oceanic forcing factors, such as geopotential height, wind, and sea surface temperature, as well as hydrologic factors, such as snow water equivalent. The approach followed in the study involves searching for potential predictors, applying principal component analysis (PCA) and multiple linear regression (MLR) for forecasting at individual sites, canonical correlation analysis (CCA) for forecasting at multiple sites, and testing the forecasts using various performance measures. The analysis includes comparisons of forecasts by using various combinations of possible predictors, such as hydrologic, atmospheric, and oceanic variables. The study brought into relevance the significant benefits of using atmospheric, oceanic, and hydrological predictors for long-range streamflow forecasting. It has been shown that forecasts based on PCA applied to individual sites give very good results for both seasonal and yearly timescales. We also found that although PCA has been applied on a site-by-site basis, the forecasts approximated well the historical cross correlations, although some underestimation was noted for two sites. Furthermore, the forecasts based on CCA were less efficient than those based on PCA.
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Acknowledgments
The authors would like to acknowledge the financial support of the project Predictability of the Upper Colorado River Streamflows funded by theUNSPECIFIED Colorado Water Resources Research Institute and the USGSU.S. Geological Survey. In addition, thank you to the reviewers and the associate editor for making important suggestions to improve the paper.
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© 2011 American Society of Civil Engineers.
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Received: Feb 26, 2010
Accepted: Oct 12, 2010
Published online: Oct 28, 2010
Published in print: Jun 1, 2011
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