First Advisor

Roy W. Koch

Term of Graduation

Summer 1997

Date of Publication


Document Type


Degree Name

Master of Science in Civil Engineering (MSCE)


Civil Engineering




Water-supply -- Columbia River Basin -- Forecasting -- Simulation methods, Runoff -- Columbia River Basin -- Forecasting -- Simulation methods



Physical Description

1 online resource (vii, 69 pages)


The purposes of this study were twofold. The first was to explore possible improvements that could be made over existing methods by applying alternative modeling approaches, including the one developed by Garen to forecast runoff volumes in the Columbia River at The Dalles, Oregon. The second was to use the Southern Oscillation as one of the variables to forecast spring and summer runoff and to evaluate whether this variable adds significant information early in the season before any snow has accumulated. The Columbia River basin's mountainous topography and northern latitude are ideally suited for water supply forecasting because much of the winter precipitation in the region falls as snow, a natural storage mechanism. It was discovered that precipitation and climate varied widely over the basin and that some regions contributed significantly more to the annual runoff total than others. In addition, a strong relationship was discovered between the basin's precipitation and streamflow and the global weather phenomenon known as El Nino-Southern Oscillation. These facts were used to develop an initial set of precipitation and snow water equivalent data to be used as the basis for both modeling approaches. In both approaches, the PSU and Garen methods, separate monthly forecast equations were developed in order to make use of only the data that were available at the time of the forecast. Both approaches used principal components to eliminate intercorrelation among the variables and both performed stepwise multiple linear regression on the resulting principal components. The difference between the two approaches was that the PSU model forced all data in the initial data set into the principal component and regression analyses while the Garen model first calculated which of the independent variables explained the greatest amount of variance then subjected only those data to the principal component and regression analyses. The Garen modeling approach, using the SOI variable, was superior to both the PSU and the historic models. The Garen forecast equations were approximately as accurate on the October 1 forecast as the historic models have been on January 1, based on error variance.


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