the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note: How physically based is hydrograph separation by recursive digital filtering?
Klaus Eckhardt
Recursive digital filtering of hydrographs is a widely used method to identify streamflow components, which react to precipitation with varying degrees of attenuation and delay. In this context, a distinction is often made between physically based and nonphysically based algorithms. A wellknown example of a physically based algorithm is that of Furey and Gupta (2001). In this paper, it is contrasted with the widely used algorithm of Eckhardt (2005). This algorithm is often considered merely a nonphysically based lowpass filter. However, the comparison shows that both algorithms largely agree. The algorithm of Eckhardt (2005) differs from the algorithm of Furey and Gupta (2001) only in the time delay assumed between precipitation and the exfiltration of baseflow into surface waters and in the fact that two parameters are combined into one, BFI_{max}. This parameter can thus be interpreted physically, and an approach for its calculation emerges.
A catchment can be understood as a signal converter. The precipitation is the input signal that is converted into the output signal, streamflow. In the course of this signal conversion, the water takes different paths through the catchment and is subject to different hydrological processes. This results in streamflow components that are attenuated and delayed to varying degrees compared to the input signal, the precipitation. Usually, two components are distinguished: on the one hand, the socalled baseflow as a lowfrequency signal component and, on the other hand, higherfrequency signal components that are generated more quickly and are less attenuated in response to precipitation events, the socalled direct runoff. From this idea, it is obvious that lowpass filtering of streamflow hydrographs can be used to identify these components.
This approach has been followed since Lyne and Hollick (1979) introduced the recursive digital lowpass filter to hydrology. The term “digital” refers to the fact that discrete, equidistantintime data of the streamflow are used, the processing of which can be easily automated by using a computer. The term “recursive” refers to the fact that the signals of the preceding time steps are included in the calculation of the output signal in the current time step.
Several such recursive digital lowpass filters were subsequently presented. In the following, the filter developed by Eckhardt (2005) is considered in particular. It is now one of the established methods of hydrograph separation – for example, as part of the US Geological Survey Hydrologic Toolbox (Barlow et al., 2022).
The “Eckhardt filter”, as it is oftentimes called, is usually counted among the nonphysical or “purely empirical” (Healy, 2010, p. 87) methods of hydrograph separation. The apparent lack of a physical basis repeatedly raises doubts about the justification of the recursive digital filtering: “Most hydrograph separations (apart from tracerbased separations) lack a physical basis. […] Therefore, choosing one method or the other introduces an undesirable element of uncertainty and randomness into the analysis and comparison of runoff coefficients” (Blume et al., 2007). “The digital filter methods have no physical meaning” (Kang et al. 2022). However, without a physically meaningful interpretation, it becomes impossible to objectively determine the parameters of the filter algorithms: “parameters used in the RDF [recursive digital filtering] method are often determined arbitrarily, resulting in high uncertainty of the estimated baseflow rate.” (Zhang et al., 2013), “quantitative results of the filtering change with the value of the parameters. Although the shape of the hydrograph separation can be visually consistent with the conceptualisation of a hydrograph separation, it is basically impossible to draw any conclusion from it.” (Pelletier and Andréassian, 2020), “To accurately separate the baseflow from streamflow with the digital filter methods, appropriate filter parameters must be estimated by trial and error, which act as a difficulty or limitation on their use.” (Kang et al., 2022).
Is this criticism justified? Does the widespread recursive digital filtering, especially that with Eckhardt's algorithm, really lack a physical, hydrologically plausible explanation, and does the choice of parameter values remain arbitrary?
In order to shed light on the answers to these questions, Eckhardt's filter is compared below with the algorithm of Furey and Gupta (2001). The latter has been developed explicitly from hydrological principles. Its developers therefore – rightly – describe it as physically based and emphasise the difference to the previously mentioned lowpass filters: “Unlike other filters, our filter is not founded on the assumption that base flow and overland flow are the low and highfrequency components of streamflow, respectively.” The analysis shows that there is nevertheless a close relationship between the Eckhardt (2005) and Furey and Gupta (2001) filters and thus provides a clue as to how the parameter BFI_{max} of the recursive digital filter of Eckhardt (2005) can be physically interpreted and determined.
2.1 The method of Eckhardt (2005)
The equation of this lowpass filter is
where b is the baseflow, y is the streamflow, k is the time step number, and a and BFI_{max} are parameters whose values must be set before applying the filter. Equation (1) is subject to the condition b_{k}≤y_{k}, that is, if, which is mathematically possible, b_{k}>y_{k} results, b_{k}=y_{k} is set.
Even though the filter of Eckhardt (2005) is contrasted here with the filter of Furey and Gupta (2001), which is explicitly described as physically based, it is nevertheless also itself based on plausible assumptions:
 a.
The information about the baseflow b_{k} of the current time step k lies in the baseflow b_{k−1} of the preceding time step k−1 and in the total streamflow y_{k} of the current time step:
$$\begin{array}{}\text{(2)}& {b}_{k}=A{b}_{k\mathrm{1}}+B{y}_{k},\end{array}$$with parameters A and B that are functions of the filter parameter a and for which A>0 and B>0 are assumed (Eckhardt, 2005; Eq. 8).
 b.
Baseflow is runoff from a linear reservoir, i.e. it is proportional to the amount of water stored in this reservoir. The filter parameter a corresponds to the socalled recession constant of the reservoir, which can be derived from the streamflow data, as described in Eckhardt (2008).
 c.
The algorithm of Lyne and Hollick (1979) has been criticised as hydrologically implausible, since it shows a constant streamflow y or baseflow b, respectively, when direct runoff y−b has ceased (Chapman, 1991). Equation (1) does not have this disadvantage: from ${y}_{k}{b}_{k}=\mathrm{0}$ or y_{k}=b_{k} follows
$${y}_{k}={\displaystyle \frac{(\mathrm{1}{\mathrm{BFI}}_{\mathrm{max}})a{b}_{k\mathrm{1}}+(\mathrm{1}a){\mathrm{BFI}}_{\mathrm{max}}{y}_{k}}{\mathrm{1}a{\mathrm{BFI}}_{\mathrm{max}}}}.$$This equation can be simplified to ${y}_{k}=a{b}_{k\mathrm{1}}$ or, since in this situation the streamflow consists entirely of baseflow,
$$\begin{array}{}\text{(3)}& {b}_{k}=a{b}_{k\mathrm{1}}.\end{array}$$This is exactly the equation that describes the exponential decrease in runoff from a linear reservoir.
 d.
The second filter parameter BFI_{max} is the maximum value of the baseflow index (the longterm ratio of baseflow to total streamflow) that can be calculated with the filter algorithm. This maximum value is less than 1. This too is plausible. A catchment with a baseflow index of 1, i.e. a catchment without direct runoff, would have to have a soil with an extremely high infiltration and storage capacity and/or would have to be flat. In such an area, there would be no watercourse at all whose baseflow index could be determined.
The calculation with Eckhardt's algorithm requires streamflow data and the values of two parameters, with the streamflow data allowing one of the two parameters, the recession constant a, to be determined. How uncertainties in the two filter parameters affect the resulting baseflow index can be calculated as Eckhardt (2012) has shown.
2.2 The method of Furey and Gupta (2001)
Furey and Gupta formulated their filter algorithm as
(Furey and Gupta, 2001; Eq. 22). ${\stackrel{\mathrm{\u203e}}{Q}}_{\mathrm{B},j}$ is the baseflow at time step j, 1−γ is the recession constant, c_{1} and c_{3} are the proportions of precipitation that become overland flow and groundwater recharge, ${\stackrel{\mathrm{\u203e}}{Y}}_{\mathrm{B},jd\mathrm{1}}$ is the streamflow at time step $jd\mathrm{1}$, and d is the delay between precipitation and groundwater recharge. Using the same symbolic designation for time step number, recession constant, baseflow, and streamflow as in Eq. (1), Eq. (4) can also be written as
The calculation of the baseflow according to Furey and Gupta (2001) requires streamflow and precipitation data and the values of four parameters: a, c_{1}, c_{3}, and d. Precipitation is needed for the derivation of the values of c_{1} and c_{3}. How d can be estimated remains open.
2.3 The relation between the two algorithms
In deriving their filter equation, Furey and Gupta (2001) assume that the baseflow in the current time step is a function of baseflow and groundwater recharge one time step in the past (their Eq. 10). Further, they assume that the groundwater recharge is delayed by d time steps compared to precipitation (their Eq. 11). In their model of the emergence of baseflow, the number of time steps between precipitation and baseflow is d+1: d time steps between precipitation and groundwater recharge +1 time step between groundwater recharge and baseflow, hence the index $jd\mathrm{1}$ in Eq. (4) or $kd\mathrm{1}$ in Eq. (5).
If instead it is assumed that baseflow occurs in the same time step as groundwater recharge and that groundwater recharge is not delayed compared to precipitation (in other words, if it is assumed that the delay between precipitation and baseflow is smaller than one time step), then $d+\mathrm{1}=\mathrm{0}$ and thus $kd\mathrm{1}=k(d+\mathrm{1})=k$. Equation (5) is then
This equation can be transformed to
Equation (7) corresponds, in principle, to Eq. (2), which in turn is the basis of Eckhardt's algorithm. The comparison of Eqs. (7) and (1), or more precisely the comparison of the coefficients of b_{k−1} and y_{k} in both equations, yields
and
The solution of this system of equations results in
In other words, a single assumption, namely that baseflow still begins at the same time step as precipitation, is sufficient to transform the algorithm of Furey and Gupta (2001) into the algorithm of Eckhardt (2005), where Eq. (10) holds.
Eckhardt's algorithm represents a whole class of recursive digital filters that only differ by the value of BFI_{max}. These are the filters that are based on the assumption that baseflow is runoff from a linear reservoir and that are constructed according to Eq. (2). For example, setting BFI_{max} = 0.5 yields the filter of Chapman and Maxwell (1996). Do these filter algorithms lack a physical basis? Section 2 should have made it clear that this is not the case. The algorithm of Eckhardt (2005) differs from the algorithm of Furey and Gupta (2001) only in the time delay assumed between precipitation and the exfiltration of baseflow into surface waters and in the fact that two parameters, c_{1} and c_{3}, are combined into one, BFI_{max}.
3.1 Time delay
Furey and Gupta (2001) introduced the parameter d in Eq. (5) as the number of time steps between precipitation and groundwater recharge. A sensitivity analysis they conducted showed that the filter performance was “relatively insensitive to changes in d” so that d=0 seemed to be an acceptable choice. Furthermore, when using Eq. (1), it is assumed that not only the groundwater recharge but also the generation of baseflow still occurs in the same time step as precipitation. When assessing these prerequisites, two aspects should be considered:

The streamflow component calculated with Eq. (1) is usually likely to consist not only of groundwater but also of transient water sources, including interflow (Cartwright et al., 2014; Yang et al., 2021).

In this publication, the algorithm of Eckhardt (2005) is compared to the model ideas of Furey and Gupta (2001) on the formation of baseflow. It is not compared to the reality. If the baseflow calculated with Eq. (1) occurs in Furey and Gupta's model world at the same time step as precipitation, this does not necessarily mean that it also corresponds to a runoff component in the real world that occurs without a relevant time lag compared to precipitation.
3.2 Model parameters
c_{1} is the ratio of overland flow to precipitation, and c_{3} is the ratio of groundwater recharge to precipitation. Furey and Gupta (2001) propose a method to determine c_{1} and c_{3} using additional precipitation data. BFI_{max} could then be calculated with Eq. (10).
BFI_{max} could also be determined in another way. If the fraction on the righthand side of Eq. (10) is expanded with the precipitation, the result is
If one assumes that (a) there is no inflow or outflow of groundwater below the surface boundaries of the catchment and that (b) there is no evapotranspiration from groundwater or surface waters, then the sum of overland flow and groundwater recharge corresponds approximately to the streamflow:
Streamflow is given. Consequently, “only” a method for estimating mean groundwater recharge is needed to approximate BFI_{max}.
The recursive digital filter of Eckhardt (2005) largely coincides with the physically based algorithm of Furey and Gupta (2001). As Eckhardt (2005) has pointed out, his filter is identical to the filter of Boughton (1993) and passes for different values of the parameter BFI_{max} in oneparameter filters like the one of Chapman and Maxwell (1996). Thus, the question posed in the title of this paper can justifiably be answered for a whole family of recursive digital filters with the following: yes, they are physically based.
The preceding considerations also suggest a way in which the parameter BFI_{max} of Eckhardt's filter could be determined objectively, namely via groundwater recharge. Since the results of Eckhardt's filter are less sensitive to the parameter BFI_{max} than to the parameter a (Eckhardt, 2012), the estimate for BFI_{max} would not even have to be particularly accurate. The sensitivity of the baseflow index BFI to the parameter BFI_{max} can be described by the sensitivity index
(Eckhardt, 2012; Eq. 15). For sixty perennial streams with porous aquifers, Eckhardt (2012) has found a mean sensitivity index of 0.26. That is, a relative error of x percent in BFI_{max} would result in a relative error of 0.26 times x percent in BFI. Thus, even if BFI_{max} had an uncertainty of up to about 40 %, this would probably produce an uncertainty of, at most, 10 % in the calculated baseflow index.
No data were reevaluated for the present study.
The author has declared that there are no competing interests.
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This paper was edited by Thom Bogaard and reviewed by two anonymous referees.
Barlow, P. M., McHugh, A. R., Kiang, J. E., Zhai, T., Hummel, P., Duda, P., and Hinz, S.: U. S. Geological Survey Hydrologic Toolbox – A graphical and mapping interface for analysis of hydrologic data: U.S. Geological Survey Techniques and Methods, book 4, chap. D3, 23 pp., https://doi.org/10.3133/tm4D3, 2022.
Blume, T., Zehe, E., and Bronstert, A.: Rainfallrunoff response, eventbased runoff coefficients and hydrograph separation, Hydrolog. Sci. J., 52, 843–862, https://doi.org/10.1623/hysj.52.5.843, 2007.
Boughton, W. C.: A hydrographbased model for estimating the water yield of ungauged catchments, in: Hydrology and Water Resources Symposium, Institution of Engineers, Australia, Newcastle, 30 June–2 July 1993, 317–324, 1993.
Cartwright, I., Gilfedder, B., and Hofmann, H.: Contrasts between estimates of baseflow help discern multiple sources of water contributing to rivers, Hydrol. Earth Syst. Sci., 18, 15–30, https://doi.org/10.5194/hess18152014, 2014.
Chapman, T. G..: Comment on 'Evaluation of automated techniques for base flow and recession analyses' by R. J. Nathan and T. A. McMahon, Water Resour. Res., 27, 1783–1784, https://doi.org/10.1029/91WR01007, 1991.
Chapman, T. G., and Maxwell, A. I.: Baseflow separation – Comparison of numerical methods with tracer experiments, Hydrology and Water Resources Symposium, Institution of Engineers, Australia, Hobart, 21–24 May 1996, 539–545, 1996.
Eckhardt, K.: How to construct recursive digital filters for baseflow separation, Hydrol. Process., 19, 507–515, https://doi.org/10.1002/hyp.5675, 2005.
Eckhardt, K.: A comparison of baseflow indices, which were calculated with seven different baseflow separation methods, J. Hydrol., 352, 168–173, https://doi.org/10.1016/j.jhydrol.2008.01.005, 2008.
Eckhardt, K.: Technical Note: Analytical sensitivity analysis of a two parameter recursive digital baseflow separation filter, Hydrol. Earth Syst. Sci., 16, 451–455, https://doi.org/10.5194/hess164512012, 2012.
Furey, P. and Gupta, V. K.: A physically based filter for separating base flow from streamflow time series, Water Resour. Res., 37, 2709–2722, https://doi.org/10.1029/2001WR000243, 2001.
Healy, R. W.: Estimating groundwater recharge, Cambridge University Press, ISBN 9780521863964, 2010.
Kang, T., Lee, S., Lee, N., and Jin, Y.: Baseflow separation using the digital filter method: Review and sensitivity analysis, Water, 14, 485, https://doi.org/10.3390/w14030485, 2022.
Lyne, V. D. and Hollick, M.: Stochastic timevariable rainfall runoff modelling, Hydrology and Water Resources Symposium, Institution of Engineers, Australia, Perth, 10–12 September 1979, 89–92, 1979.
Pelletier, A. and Andréassian, V.: Hydrograph separation: an impartial parametrisation for an imperfect method, Hydrol. Earth Syst. Sci., 24, 1171–1187, https://doi.org/10.5194/hess2411712020, 2020.
Yang, W., Xiao, C., Zhang, Z., and Liang, X.: Can the twoparameter recursive digital filter baseflow separation method really be calibrated by the conductivity mass balance method?, Hydrol. Earth Syst. Sci., 25, 1747–1760, https://doi.org/10.5194/hess2517472021, 2021.
Zhang, R., Li, Q., Chow, T. L., Li, S., and Danielescu, S.: Baseflow separation in a small watershed in New Brunswick, Canada, using a recursive digital filter calibrated with the conductivity mass balance method, Hydrol. Process., 27, 2659–2665, https://doi.org/10.1002/hyp.9417, 2013.