USGS

Estimation of Peak Streamflows for Unregulated Rural Streams in Kansas

Water-Resources Investigations Report 00-4079

By Patrick P. Rasmussen and Charles A. Perry


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CONTENTS

Abstract

Introduction

Purpose and Scope

Previous Studies

Acknowledgments

Factors Affecting Occurrence of Floods

Physical Characteristics

Climatic Characteristics

Occurrence of Extreme Floods

Estimation of Peak-Streamflow Frequencies at Gaging Stations on Unregulated, Rural Streams

Log-Pearson Type III Techniques

Historical Peak Discharges and Outlier Thresholds

Skew Coefficient

Peak-Streamflow Frequencies at Gaging Stations

Regression Equations for Estimation of Peak-Streamflow Frequencies at Ungaged Sites on Unregulated, Rural Streams

Regression Analysis

Selected Physical and Climatic Characteristics

Regression Equation Results

Use of Regression Equations

Summary

Selected References

 

Abstract

Peak streamflows were estimated at selected recurrence intervals (frequencies) ranging from 2 to 200 years using log-Pearson Type III distributions for 253 streamflow-gaging stations in Kansas. The annual peak-streamflow data, through the 1997 water year, were from streamflow-gaging stations with unregulated flow in mostly rural basins. A weighted least-squares regression model was used to generalize the coefficients of station skewness. The resulting generalized skewness equation provides more reliable estimates than the previously developed equation for Kansas.

A generalized least-squares regression model then was used to develop equations for estimating peak streamflows for sites without stream gages for selected frequencies from selected physical and climatic basin characteristics for sites without stream gages. The equations can be used to estimate peak streamflows for selected frequencies using contributing-drainage area, mean annual precipitation, soil permeability, and slope of the main channel for ungaged sites in Kansas with a contributing-drainage area greater than 0.17 and less than 9,100 square miles. The errors of prediction for the generalized least-squares-generated equations range from 31 to 62 percent.



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POINTS OF CONTACT AND ADDITIONAL INFORMATION

District Chief
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049-3839
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Last modified: Thursday, September 01 2005, 02:00:46 PM
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