StreamStats in Oklahoma
Oklahoma StreamStats incorporates statewide regression equations for estimating instantaneous peak flows with annual exceedance probabilities of 50, 20, 10, 4, 2, 1, and 0.2 percent, including new peak-flow regression equations for the Panhandle that were published on Sept. 28, 2015. These peak flows have recurrence intervals of 2, 5, 10, 25, 50, 100, and 500 years, respectively. Oklahoma StreamStats also incorporates regional regression equations for estimating the mean annual flow and annual and monthly flow exceeded 20, 50, 80, 90, and 95 percent of the time. The reports below document the regression equations, the methods used to develop them and to measure the basin characteristics used in the equations, and the errors associated with the estimates obtained from the equations. Users should familiarize themselves with these reports before using StreamStats to obtain estimates of flows for ungaged sites in drainage basins.
- Lewis, J.M., 2010, Methods for Estimating the Magnitude and Frequency of Peak Streamflows for Unregulated Streams in Oklahoma: U.S. Geological Survey Scientific Investigations Report 2010-5137, 42 p.
- Esralew, R.A., Smith, S.J., 2009, Methods for estimating flow-duration and annual mean-flow statistics for ungaged streams in Oklahoma: U.S. Geological Survey Scientific Investigations Report 2009-5267, 131 p.
- Smith, S.J., Lewis, J.M., and Graves, G.M., 2015, Methods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle: U.S. Geological Survey Scientific Investigations Report 2015–5134, 35 p.
Oklahoma application, as well as specific sources and computation methods for basin characteristics.
Between approximately May 19, 2015 and November 1, 2015, Oklahoma’s StreamStats application was producing peakflow estimates based on an incorrect precipitation grid. Differences in the precipitation amounts varied with location and basin size, but the incorrect precipitation values averaged approximately 6 percent lower than the correct values. While these incorrect precipitation values would have resulted in peakflow estimates with a low bias, the error would be within the model error of the peakflow regression equations.