Scientific Investigations Report 2010–5066
Equations have been defined for estimating the depth of water for floods having a 67-, 50-, 20-, 10-, 4-, 2-, and 1-percent chance exceedance on rural streams in Alabama. Multiple regression analyses of streamgage data were used to define the equations. Eight basin and climatic characteristics that were computed by using a geographical information system were evaluated as independent variables to determine their statistical significance for the dependent variable, flood depth.
Drainage area was the most statistically significant independent variable tested. Addition of other significant variables did not decrease the standard error of prediction by more than 2 percent. Regression relations, for four different hydrologic regions, were developed to estimate flood depth for rural, ungaged streams as a function of the basin drainage area. These relations are based on computed depths that correspond to the flood magnitude and frequency for 164 streamgages in Alabama and 42 streamgages in adjacent States having at least 10 years of consecutive record. These relations utilize observed flood data collected through 2003. The geologic, physiographic, and climatic variability affecting flood depth is reflected in the constant (intercept) and exponent (slope) for each regional regression equation. Average standard errors of prediction for these regression equations range from 18 to 38 percent.
First posted July 14, 2010
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Lee, K.G., and Hedgecock, T.S., 2010, Flood-depth frequency relations for rural streams in Alabama, 2003: U.S. Geological Survey Scientific Investigations Report 2010–5066, 25 p. (Also available online at http://pubs.usgs.gov/sir/2010/5066/.)
Purpose and Scope
Description of the Study Area
Definition of Flood Depths
Flood Magnitude and Frequency at Streamgages
Determination of Flood-Depth Frequency Relations
Accuracy and Limitations
Application of Flood-Depth Frequency Equations
Appendix 1. Flood depths for selected exceedance probabilities at rural streamgages used in Alabama rural depth regression analyses