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Oklahoma StreamStats

Oklahoma StreamStats was developed in cooperation with the Oklahoma Department of Transportation and the Oklahoma Water Resources Board.

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.

Click on this link to obtain general information on the Oklahoma application, as well as specific sources and computation methods for basin characteristics.

NOTE: In September of 2025, adjustments were made to hydrologic unit (HUC) 11120301, near the outlet, to correct Open Source global delineations. Adjustments included:
  • correction of flow direction values that pointed outside of 11120301,
  • modification of outlet catchment to include errant area, and
  • modification of the 11120301 HUC polygon. 
NOTE: 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.
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