Key Features

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Baseflow Separation

Baseflow separation techniques use the time-series record of stream flow to derive the baseflow signature. The common separation methods are either graphical which tend to focus on defining the points where baseflow intersects the rising and falling limbs of the quickflow response, or involve filtering where data processing of the entire stream hydrograph derives a baseflow hydrograph.

Graphical Separation Methods

Graphical methods are commonly used to plot the baseflow component of a flood hydrograph event, including the point where the baseflow intersects the falling limb (Figure 1). Stream flow subsequent to this point is assumed to be entirely baseflow, until the start of the hydrographic response to the next significant rainfall event. These graphical approaches to partitioning baseflow vary in complexity and include:

  1. An empirical relationship for estimating the point along the falling limb where quickflow has ceased and all of the stream flow is baseflow,

    (Equation 1)

    D = 0.827A0.2

    where D is the number of days between the storm crest and the end of quickflow, and A is the area of the catchment in square kilometres (Linsley et al, 1975). The value of the exponential constant (0.2) can vary depending on catchment characteristics such as slope, vegetation and geology;

  2. The constant discharge method assumes that baseflow is constant during the storm hydrograph (Linsley et al, 1958). The minimum streamflow immediately prior to the rising limb is used as the constant value;
  3. The constant slope method connects the start of the rising limb with the inflection point on the receeding limb. This assumes an instant response in baseflow to the rainfall event;
  4. The concave method attempts to represent the assumed initial decrease in baseflow during the climbing limb by projecting the declining hydrographic trend evident prior to the rainfall event to directly under the crest of the flood hydrograph (Linsley et al, 1958). This minima is then connected to the inflection point on the receeding limb of storm hydrograph to model the delayed increase in baseflow;
  5. Using the trends of the falling limbs before and after the storm hydrograph to set the bounding limits for the baseflow component (Frohlich et al, 1994);
  6. Use the Boussinesq equation as the basis for defining the point along the falling limb where all of the streamflow is baseflow (Szilagyi and Parlange, 1998);
Figure 1: Graphical baseflow separation techniques

Figure 1 : Graphical baseflow separation techniques including

  1. (1a) constant discharge method
  2. (1b) constant slope method and
  3. (1c) concave method (Linsley et al. 1958)

Filtering Separation Methods

The baseflow component of the streamflow time series can also be separated using data processing or filtering procedures. These methods tend not to have any hydrological basis but aim to generate an objective, repeatable and easily automated index that can be related to the baseflow response of a catchment (Nathan and McMahon, 1990). The baseflow index (BFI) or reliability index, which is the long-term ratio of baseflow to total streamflow, is commonly generated from this analysis. Other indices include the mean annual baseflow volume and the long-term average daily baseflow (Smakhtin, 2001). Examples of continuous hydrographic separation techniques based on processing or filtering the data record include:

  1. increasing the base flow at each time step, either at a constant rate or varied by a fraction of the runoff (Boughton, 1988);
  2. the smoothed minima technique which uses the minima of 5-day nonoverlapping periods derived from the hydrograph. (Institute of Hydrology, 1980; FREND, 1989). The baseflow hydrograph is generated by connecting a subset of points selected from this minima series. The HYSEP hydrograph separation program uses a variant of this called the local-minimum method (Sloto and Crouse, 1996);
  3. the fixed interval method discretises the hydrographic record into increments of fixed time (Pettyjohn and Henning, 1979). The magnitude of the time interval used is calculated by doubling (and rounding up) the duration of quickflow calculated empirically from Equation 1. The baseflow component of each time increment is assigned the minimum streamflow recorded within the increment;
  4. the sliding-interval method assigns a baseflow to each daily record in the hydrograph based on the lowest discharge found within a fixed time period before and after that particular day (Pettyjohn and Henning, 1979); and

  5. recursive digital filters, which are routine tools in signal analysis and processing, are used to remove the high-frequency quickflow signal to derive the low-frequency baseflow signal (Nathan and McMahon, 1990). Table 1 outlines some of the digital filters that have been applied to smooth hydrographic data. Eckhardt (2005) has developed a general formulation that can devolve into several of the commonly used one-parameter filters:

    (Equation 2)

    Mathematical equation

    Where qb(i) is the baseflow at time step i, qb(i-1) is the baseflow at the previous time step i-1, qi is the stream flow at time step i, a is the recession constant and BFImax is the maximum value of the baseflow index that can be measured; and

  6. the streamflow partitioning method uses both the daily record of streamflow and rainfall (Shirmohammadi et al, 1984). Baseflow equates to streamflow on a given day, if rainfall on that day and a set number of days previous, is less than a defined rainfall threshold value. Linear interpolation is used to separate the quickflow component during high rainfall events.
Table 1: Recursive digital filters used in base flow analysis (Grayson et al, 1996; Chapman, 1999; Furey and Gupta, 2001)
Filter Name Filter Equation Source Comments
One-parameter algorithm Mathematical equation Chapman and Maxwell (1996)

qb(i) £q(i)

Applied as a single pass through the data.

Boughton two-parameter algorithm Mathematical equation

Boughton (1993)

Chapman and Maxwell (1996)

qb(i) £q(i)

Applied as a single pass through the data

Allows calibration against other baseflow information such as tracers, by adjusting parameter C

IHACRES three-parameter algorithm Mathematical equation Jakeman and Hornberger (1993) Extension of Boughton two-parameter algorithm
Lyne and Hollick algorithm Mathematical equation

Lyne and Hollick (1979)

Nathan and McMahon, (1990)

qf(i) ³0

a value of 0.925 recommended for daily stream data

filter recommended to be applied in three passes

Baseflow is qb = q - qf

Chapman algorithm Mathematical equation

Chapman (1991)

Mau and Winter (1997)

 Baseflow is qb = q - qf
Furey and Gupta filter Mathematical equation Furey and Gupta (2001) Physically-based filter using mass balance equation for baseflow through a hillside

References