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Technical Announcement:
Streamflow Models Evaluated for Reliability and Application to Predicting Fish Health

Released: 4/19/2012 11:00:00 AM

Contact Information:
U.S. Department of the Interior, U.S. Geological Survey
Office of Communications and Publishing
12201 Sunrise Valley Dr, MS 119
Reston, VA 20192
Jennifer Murphy 1-click interview
Phone: 615-837-4712

Hannah Hamilton 1-click interview
Phone: 703-648-4356



A new USGS study found that streamflow characteristics critical to fish in the Tennessee River basin were estimated more accurately and precisely by a statistical model than a simulated hydrograph produced by a rainfall-runoff model.

Streamflow characteristics are summary statistics that describe timing, magnitude and duration aspects of streamflow.  Current conceptual models relating flow to ecology are strongly based on the use of streamflow characteristics. However until now, an evaluation of differing estimation methods, even on a small scale, had yet to be completed. 

In this USGS study, 19 ecologically relevant streamflow characteristics calculated from observed daily streamflows were compared to estimates from a statistical model and a rainfall-runoff model.  The statistical model was developed for the Tennessee and Cumberland River Valleys and is composed of 19 multivariate regression equations that each predicts a single streamflow characteristic.  The rainfall-runoff model was developed for the state of Kentucky and produces a simulated hydrograph from which the 19 streamflow characteristics are calculated.  Six sites with observed streamflow data occur within the geographic overlap of these two models – where the Tennessee and Cumberland River basins cross into Kentucky

When predictions from the models were compared to streamflow characteristics calculated from observed data, the statistical model produced more accurate and precise estimates than the rainfall-runoff model.  For the rainfall-runoff model, median departures (the median difference between predicted and observed values across all six sites) for 13 of 19 streamflow characteristics were greater than 30 percent.  Most of these median departures were between 30 percent and 50 percent and a few were greater than 100 percent.  In contrast, for the statistical model, median departures for only two characteristics were greater than 30 percent and 12 characteristics had median departures that were less than 10 percent.  Furthermore, the rainfall-runoff model either over or under predicted all six sites for eight streamflow characteristics.  Such biases are not as pronounced for the statistical model.

These results suggest that when compared to a rainfall-runoff model, a statistical model is a better predictor of a range of streamflow characteristics, meaning the overall flow regime.  The rainfall-runoff model was calibrated on a single streamflow characteristic, mean daily discharge, whereas the statistical model was calibrated individually on all 19 streamflow characteristics.  Poor model performance may misrepresent hydrologic conditions, potentially distorting the perceived risk of ecological degradation.  Without prior selection of streamflow characteristics, targeted calibration, and error quantification, the widespread application of general hydrologic models to ecological flow studies should be approached with caution.


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