Date of Award:


Document Type:


Degree Name:

Master of Science (MS)


Civil and Environmental Engineering

Committee Chair(s)

Cleve H. Milligan


Cleve H. Milligan



Forecasting the annual water supply in an arid area by using the water content of snow on watersheds on some particular date, such as April 1, has become a very useful practice. Although these forecasts have given results of great practical value, they have sometimes been considerably in error. Seeking to minimize error, forecasters have incorporated various additional data such as temperature and antecedent rain to improve the relation between snow measurement and measured runoff.

Numerous methods have been suggested in the search for a reliable streamflow forecasting equation and various data have been used. Nearly all of the methods made some improvements, but in the attempt to minimize the number of variables, perhaps full use has not been made of all the available data.

A successful streamflow forecasting method for Logan River, Cache County, Utah was suggested by Professor Cleve H. Milligan (11) and Dr. Rex L. Hurst. They utilized Fourier Series and Multiple Linear Regression as a mathematical model. In their study, four primary factors were used which are antecedent streamflow, temperature, precipitation, and snow survey data. This method has also been used in the forecasting for the Blacksmith Fork River, south of the Logan River, by Fok (5) with a high degree of accuracy. In his study, temperature and precipitation data were both measured outside the watershed and showed a lower degree of significance in the complete forecasting equation. If these data had been measured in the watershed they might have yielded greater significance in the forecasting equation. Perhaps a better factor than temperature and precipitation would be soil moisture data obtained on the watershed.


The major objective of this thesis is to develop a method for use of soil moisture data in an equation for streamflow forecasting for the Logan River in Northern Utah. Several Investigators have recognized the need for soil moisture data and for a method of including it in the forecasting equations (see literature review).