Abstracts

Estimating Streamflow-Recession Indices Using Automated Methods with Application to Groundwater-Surface Water Interaction

Author(s): Crowley-Ornelas, E.; Knight, R.; Asquith, W.

Statistical properties of streamflow recession provide evidence of hydrologic processes such as groundwater and surface-water interactions. Bingham (1982, 1986) sought regional definition of generalized connectivity between surface water and groundwater by calculating a persistent streamflow recession slope during winter low flows and then relating the recession slopes to surficial geology. For our study, the recession slope value was referred to as the Bingham "geologic factor" or G factor.

The recession slope determined by Bingham's process was somewhat subjective because it was hand drawn based on the visual inspection of the stream hydrograph. The G factor was derived through a hands-on graphical method for selected peak flows over a 20-year time period from U.S. Geological Survey (USGS) streamgages in Tennessee and Alabama. A streamflow recession curve, plotted on semi-log graph paper, was created by starting at peak streamflow after a precipitation event until the line neared asymptotic with the x-axis. The number of days (x-axis) required for streamflow to decrease one log cycle (y-axis) was the index of streamflow recession for each station, or the G factor expressed in days per log cycle decline in flow. Boundaries for G factor regionalization were determined using streamflow hydrographs, surficial geology, and lithologic contacts. Although G factor values have been useful in statistical regionalization studies (Bingham, 1982, 1986; Knight and others, 2012), the subjectivity and time-consuming manual method of the approach has made it problematic to calculate G factors for newer records and different regions.

The USGS has developed an automated process that calculates G factors and has applied this method to more than 300 streamgages and more than 4 million days of streamflow at streams in or bordering Tennessee. Results from the automated process will be compared to the original G factor estimates to assess whether this new method is capturing the same hydrologic process information. Using the one-way layout statistical method, the relative impact of factors such as soil type, aquifer outcrop, and lithology on G factors will be assessed to create a regionalization of G factors across Tennessee.

Developing an automated process using existing data to calculate the G factor will make it possible to estimate the factor for larger areas as well as for discrete time periods. This new approach, if successful, will provide a tool to evaluate the extent of connectivity between surface water and groundwater in a basin; the influence of groundwater withdrawals on baseflow; and could be an early indicator of potential drought effects.

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