Predicting ecological flow regime at ungaged sites: A comparison of methods
Nineteen ecologically relevant streamflow characteristics were estimated using published rainfall–runoff and regional regression models for six sites with observed daily streamflow records in Kentucky. The regional regression model produced median estimates closer to the observed median for all but two characteristics. The variability of predictions from both models was generally less than the observed variability. The variability of the predictions from the rainfall–runoff model was greater than that from the regional regression model for all but three characteristics. Eight characteristics predicted by the rainfall–runoff model display positive or negative bias across all six sites; biases are not as pronounced for the regional regression model. Results suggest that a rainfall–runoff model calibrated on a single characteristic is less likely to perform well as a predictor of a range of other characteristics (flow regime) when compared with a regional regression model calibrated individually on multiple characteristics used to represent the flow regime. 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 is problematic. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.
Citation Information
Publication Year | 2012 |
---|---|
Title | Predicting ecological flow regime at ungaged sites: A comparison of methods |
DOI | 10.1002/rra.2570 |
Authors | Jennifer C. Murphy, Rodney R. Knight, William J. Wolfe, W. Scott Gain |
Publication Type | Article |
Publication Subtype | Journal Article |
Series Title | River Research and Applications |
Index ID | 70037941 |
Record Source | USGS Publications Warehouse |
USGS Organization | Tennessee Water Science Center |