Minnesota StreamStats was developed in cooperation with the Minnesota Department of Transportation, the Minnesota County Engineers Association, and the Minnesota Pollution Control Agency.
StreamStats version 3 for Minnesota incorporates regression equations for estimating instantaneous peak-flow frequencies, low-flow frequencies and flow durations. The equations for estimating low-flow frequencies and flow durations have not yet been added to version 4 due to current limitations with the software. Equations are available for estimating peak flows with annual exceedance probabilities of 66.7, 50, 20, 10, 4, 2, 1, and 0.002 percent, which correspond to recurrence intervals of 1.5, 2, 5, 10, 25, 50, 100, and 500 years, respectively. In addition, equations are available for estimating annual and seasonal 7-, 30-, and 112-day low flow that recur on average once in 10 years, and for estimating daily streamflows that exceeded between 99.99 and 0.01 percent of the time.
The reports below document the regression equations available in StreamStats version 3 for Minnesota, the methods used to develop the equations and to measure the basin characteristics used in the equations, and the errors associated with the estimates obtained from the equations. Only the peak-flow equations from Lorenz and others (2009) have been implemented in version 4. That report describes the development of a region-of-influence method for estimating peak flows as well as the development of regression equations. StreamStats has not implemented the region-of-influence method. Users should familiarize themselves with both reports before using StreamStats to obtain estimates of streamflow statistics for ungaged sites.
- Lorenz, D.L., Sanocki, C.A., and Kocian, M.J., 2009, Techniques for estimating the magnitude and frequency of peak flows on Small Streams in Minnesota based on data through water year 2005: U.S. Geological Survey Scientific Investigations Report 2009–5250, 54 p.
- Ziegeweid J.R. Lorenz D.L. Sanocki C.A. and Czuba C.R., 2015, Methods for estimating flow-duration curve and low-flow frequency statistics for ungaged locations on small streams in Minnesota: U.S. Geological Survey Scientific Investigations Report 2015–5170, 23 p.
Click on this link to obtain general information on the Minnesota application, as well as specific sources and computation methods for basin characteristics.
Important Note: The streamflow statistics for streamgages used to develop the regression equations from the above reports are not yet available from StreamStats. Users should refer to the tables from those reports to obtain the latest available statistics for the streamgages.
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Methods for estimating flow-duration curve and low-flow frequency statistics for ungaged locations on small streams in Minnesota
Techniques for Estimating the Magnitude and Frequency of Peak Flows on Small Streams in Minnesota Based on Data through Water Year 2005
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Methods for estimating flow-duration curve and low-flow frequency statistics for ungaged locations on small streams in Minnesota
Knowledge of the magnitude and frequency of low flows in streams, which are flows in a stream during prolonged dry weather, is fundamental for water-supply planning and design; waste-load allocation; reservoir storage design; and maintenance of water quality and quantity for irrigation, recreation, and wildlife conservation. This report presents the results of a statewide study for which regionalAuthorsJeffrey R. Ziegeweid, David L. Lorenz, Christopher A. Sanocki, Christiana R. CzubaTechniques for Estimating the Magnitude and Frequency of Peak Flows on Small Streams in Minnesota Based on Data through Water Year 2005
Knowledge of the peak flow of floods of a given recurrence interval is essential for regulation and planning of water resources and for design of bridges, culverts, and dams along Minnesota's rivers and streams. Statistical techniques are needed to estimate peak flow at ungaged sites because long-term streamflow records are available at relatively few places. Because of the need to have up-to-dateAuthorsDavid L. Lorenz, Christopher A. Sanocki, Matthew J. Kocian - Partners