Abstract

The Curve Number (CN) method of estimating the direct runoff response to rainfall events was originally developed in the 1950's primarily for agricultural purposes in the mid-western United States. The accuracy of the CN method is greatly affected by variation of the soil type and land use of the region. Curve Numbers developed for a given region are not appropriate for application in other regions. In order to produce reliable, consistent results, Curve Numbers must be calibrated for the area in which the CN method is to be applied. Calibration is ideally accomplished by direct measurement using several rain and stream gauges within a watershed. Gauged data, however, is not always available or easily obtained. A more feasible method of calibration is therefore necessary for broad application of the CN method. The purpose of this study is to develop a method of CN calibration that can be easily applied to regions where no gauged data is available using the United States Geological Survey (USGS) regression equations. In this study, the peak flow values estimated using the regression equations were used in conjunction with a dimensionless hydrograph to compute runoff volume. The National Oceanic and Atmospheric Administration (NOAA) rainfall grids were used to estimate precipitation. Given the rainfall and runoff, a Curve Number can then calibrated through back-calculation. The method of CN calibration using the USGS regression equations was applied to nearly 60 watersheds in the state of Utah for this research. The calibration results obtained using the regression equations were compared to other CN calibrations developed using gauged data. Calibrations performed through the use of the regression equations were quite consistent with calibrations obtained using measured data. To ensure the validity of the application of this method in other regions, more comparisons to results obtained using measured data should be further pursued.

Degree

College and Department

Ira A. Fulton College of Engineering and Technology; Civil and Environmental Engineering

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