comparing groundwater recharge and base ﬂow in the … · 2008-11-12 · comparing groundwater...

Comparing groundwater recharge and base flow inthe Bukmoongol small-forested watershed, Korea

E A Combalicer1,2, S H Lee3, S Ahn1, D Y Kim1 and S Im3,∗

1Department of Forest Sciences, College of Agriculture and Life Sciences, Seoul National University,San 56-1, Sillim-dong, Gwanak-gu, Seoul, 151-921, Korea.

2College of Forestry, Nueva Vizcaya State University, 3700 Bayombong, Nueva Vizcaya, Philippines.3Department of Forest Sciences, Research Institute for Agriculture & Life Sciences, College of Agriculture and

Life Sciences, Seoul National University, San 56-1, Sillim-dong, Gwanak-gu, Seoul, 151-921, Korea.∗e-mail: [email protected]

Groundwater recharge and base flow using different investigated methods are simulated in the 15-haBukmoongol small-forested watershed located at the southern part of Korea. The WHAT system,PART, RORA, PULSE, BFI, and RAP software are used to estimate groundwater recharge or baseflow and base flow index from the measured streamflow. Results show that about 15–31 per cent ofannual rainfall might be contributed for base flow. The watershed groundwater recharge proportionsare computed to about 10–21 per cent during the wet period and 23–32 per cent for the remainderperiods. Mean annual base flow indices vary from 0.25 to 0.76 estimated using different methods.However, the study found out that all methods were significantly correlated with each other. Thesimilarity of various methods is expressed as a weighted relationship provided by the matrix productfrom the principal component analysis. Overall, the BFI and WHAT software appeared consistentin estimating recharge or base flow, and base flow index under Korea’s conditions. The case studyrecommends the application of different models to other watersheds as well as in low-lying areaswhere most observation groundwater wells are located with available streamflow data.

1. Introduction

In recent years, scarcity of water has been widelyrecognized due to a growing population and itsrelated purposes. Water in the watershed is basi-cally imminent through runoff and streamflow. Insome cases, the streamflow is seasonally availableonly on the basis of precipitation and is some-times dependent on the adequacy of groundwaterrecharge behaviour. Groundwater recharge refersto the replenishment of an aquifer with water fromthe land surface which is usually expressed as anaverage rate of water per year, similar to precipi-tation (Sophocleous and Schloss 2000).

There is a great interest in understandinggroundwater and surface water interactions asfundamental components in the water balance.

Forest hydrologists and watershed managers haveoften dealt with uncertainty in quantifying theamount of the base flow or recharge. A commonrecommendation in the literature is that rechargeshould be estimated from multiple methods andthe results compared, but in reality, comparing theresults may be difficult because of differences inher-ent in the methods (Risser et al 2005b). However,Delin and Risser (2007) perceived that the actualrecharge rate is never known with 100 per cent cer-tainty at a given location, use of multiple rechargeand base flow estimation methods is beneficial.

The groundwater recharge can be estimatedfrom the unsaturated-zone drainage (Wellings1984), water budget (Bauer and Vaccaro 1987;Jyrkama et al 2002), water table fluctua-tion (Sophocleous 1991), and recession-curve

Keywords. Base flow separation; base flow index; groundwater recharge; WHAT.

J. Earth Syst. Sci. 117, No. 5, October 2008, pp. 553–566© Printed in India. 553

554 E A Combalicer et al

displacement (Rorabaugh 1960; Daniel 1976). Inthe same manner, base flow separation meth-ods separate part of the streamflow hydrographattributed to groundwater runoff. According toRutledge (2005a), base flow is not a recharge. How-ever, base flow is sometimes used as an approx-imation of recharge when underflow (the flow ofground water beneath and by passing a stream),evapotranspiration from riparian vegetation, andother losses of groundwater from the watershed arethought to be minimal. The major assumptions inusing base flow for estimating recharge are thatbase flow equals groundwater discharge and thatground water discharge is approximately equal torecharge (Risser et al 2005a).

Groundwater estimation methods have beenintensively reviewed and discussed in the litera-ture (Lerner et al 1990; Rutledge 1998; Scanlonet al 2002; Moix and Galloway 2004; Bent 2005;Brodie and Hostetler 2005; Delin and Falteisek2007; Coes et al 2007). In Korea, however, Park(1996) and Lee and Yoon (1996) used the baseflow separation method while Moon et al (2004)utilized the water table fluctuation techniques toestimate the groundwater recharge ratio. Sinceearly 1990s, there have been computer programsconcerned with specific algorithms that had beendeveloped to simulate the base flow or rechargeof the watershed. Many studies utilized computercodes which provide acceptable results.

In this study, different software were used tocompare the estimated recharge or base flow basedon available streamflow records collected from thesmall-forested watershed in Korea. The methodsinvolved have inherent differences derived fromspecific algorithms or equations which provide alogical sense in comparing result differences.

This study aimed to

• utilize the capability of the RORA, PULSE,PART, BFI, RAP, and WHAT system softwarein relation to recharge and base flow separationanalyses,

• estimate the proportion of the base flow fromstreamflow and rainfall on the small-forestedwatershed,

• estimate the groundwater recharge and base flowfrom different methods, and

• determine the correlations and associations ofthe investigated methods.

2. Materials and methods

2.1 Description of study area

The study area is the Bukmoongol small-forestedwatershed located within 35◦01′30′′–35◦03′00′′N

latitude and 127◦36′00–127◦37′30′′E longitude inthe southern part of Korea (figure 1). The totalarea of the watershed is about 15 hectares. The ele-vation ranges from 120–341 m above sea level witharound 850 m stream length lying within the area.The watershed is dentritic in shape with compact-ness and drainage density coefficients of 0.86 and56.8 m/ha, respectively.

The Bukmoongol watershed is fully vegetativeconsisting mostly of Pinus koraiensis and Pinusrigida tree species. The characteristics of the soilsurface up to 1m depth do not vary much in texturewhich is largely loam to clay loam. High soil organicmatter features is prevalent in most parts of thearea. The saturated hydraulic conductivity of thewatershed was estimated at 15.32 mm/h.

The climate is classified as monsoon. Thepeak monthly temperature recorded was −15.4◦Cin winter (January) and 34.9◦C during summer(August). The average annual precipitation is1374 mm, most of which occurs during the warmsummer season from June to August.

2.2 Data collection

Meteorological and hydrological parameters havebeen monitored in the area since 1991 using meteo-rological and water level gauging stations installedwithin the watershed. The meteorological station islocated about 30 m away from the monitoring sta-tion. At present, there are three water level mon-itoring stations in the watershed. In the presentcase study, however, the analysis was concentratedin the Bukmoongul area because it has longeravailable streamflow data. Likewise, the entirewatershed is homogenous in terms of vegetation,topography and soil characteristics.

The water level from rectangular sharp-crestedweir was monitored using a gauge recorder andlately OTT Thalimedes with integral data logger.In particular, water level data were converted to anequivalent streamflow over the surface flow moni-toring station from the period of 1992–1998. Fromstreamflow data, base flow separation and reces-sion analysis methods were conducted to charac-terize the base flow and recharge characteristics ofthe Bukmoongol small-forested watershed.

2.3 Base flow estimation methodsinvestigated

The different methods for estimating recharge orbase flow investigated in the study are summa-rized in table 1. Recharge was estimated on a daily,monthly and annual basis using Rorabaugh equa-tions (Rorabaugh 1964; Rutledge 1998). Base flowwas estimated using streamflow-hydrograph sepa-ration (Rutledge 2005a), digital filter algorithm

Comparing groundwater recharge and base flow 555

Figure 1. Location of the Bukmoongol watershed, Korea.

(Lyne and Hollick 1979; Marsh et al 2003), BritishInstitute of Hydrology standard method (Wahl andWahl 2001), modified method (Wahl and Wahl2001), low minimum method (Lim et al 2004),and recursive digital filter method (Eckhardt2005).

Six packages of software, namely: PART(Rutlegde 2007a), RORA (Rutlegde 2007b),PULSE (Rutlegde 2002), River Analysis Package(Marsh et al 2003), Base Flow Index (Wahl andWahl 2001), and WHAT (Lim et al 2004) were usedin the simulation of base flow or recharge estimates.Prior to the simulation of the recharge, RECESSprogram (Rutlegde 2005b) was used to deter-mine the recession index and to define the masterrecession curve from the analysis of streamflowrecords.

2.3.1 Recharge methods

Two packages of software were considered inestimating the recharge of the watershed. Applica-tion of both RORA and PULSE requires an esti-mate of the slope of the streamflow-recession curve(recession constant) representing periods when allstreamflow is from groundwater discharge. Therecession constant was computed by constructing amaster recession curve from the streamflow recordusing the RECESS program (Rutledge 1998). Inthis study, the computed median recession indexfor the forested watershed was 34.2 with 17 num-bers of segments.

2.3.1.1 RORA software

The RORA software estimates groundwaterrecharge based on streamflow records. The RORAmodel, also known as the Rorabaugh Method(Rorabaugh 1960; Daniel 1976; Rutledge 2007b),estimates groundwater recharges for each stream-flow peak using the recession-curve-displacementmethod. It is based on an analytical model thatdescribes groundwater discharge subsequent torecharge to the water table (Rorabaugh 1964).The analytical model represents a cross sectionfrom the hydrologic divide to the stream wheregroundwater discharge occurs. The aquifer is con-ceptualized according to assumptions of uniformaquifer thickness, hydraulic conductivity, and stor-age coefficient. A detailed update on the use ofthe RORA software for recharge estimation wasreported by Rutledge (2007b).

In the RORA model, the total recharge is calcu-lated using the following equation:

R =2(Q2 − Q1)K

2.3026, (1)

where R = total volume of recharge, Q1 = groundwater discharge at critical time as extrapolatedfrom the streamflow recession preceding the event,Q2 = ground water discharge at critical time as


Table 1. Summary of methods investigated for estimating recharge or base flow, and base flow index.

Quantity Unit ofSoftware Method estimated Output estimates∗ Source

Recharge methods

RORA Recession-curve-displacement orRorabaugh approach

Recharge Monthly, annual in, cfs Rutledge(2007b)

PULSE Trial and errormatching usingRorabaugh approach

Recharge Daily, monthly,annual

in, cfs Rutledge (2002)

Base flow methods

PART Hydrograph separa-tion for base flow

Base flowBase flow index

Daily, monthly,annual

Per cent, in, cfs Rutledge(2007a)

RAP Lyn–Hollick digitalfiltering separation

Base flow index Annual Per cent Marsh et al(2003)

WHAT System Low minimummethod



Per cent, cfs Lim et al (2004)

One parametermethod (Lyn–Hollickfilter approach)




Recursive digitalfilter

(Eckhardt digitalfilter approach)




BFI Standard method Base flowBase flow index


Acre-foot,per cent

Wahl and Wahl(2001)

Modified method Base flowBase flow index


Acre-foot,per cent

Wahl and Wahl(2001)

∗Units are converted into metric for analysis.

extrapolated from the streamflow recession follow-ing the event, and K = recession index.

2.3.1.2 PULSE software

The PULSE software is intended for analyzingground-water-flow system that is characterized bydiffuse areal recharge to the water table andgroundwater discharge to a stream. The use of theprogram can be appropriate if all or most ground-water in the basin discharges to the stream and if astreamflow-gauging station at the downstream endof the basin measures all or most outflow (Rutledge1998, 2002). The assumptions required for applica-bility of the PULSE are similar to those of theRORA.

The model is applicable to a groundwaterflow system that is driven by a really uniformrecharge to the water table, and in which thegroundwater discharges to a gaining stream. Oneof the two formulations used by the model allowsfor an instantaneous recharge pulse and subsequentgroundwater discharge to the stream. The otherformulation, which allows for a gradual hydrologicgain or loss term in addition to the instantaneous

pulse, can be used to simulate the effects ofgradual recharge to the water table and ground-water evapotranspiration or downward leakage toa deeper aquifer (USGS 2007).

Rutledge (2002) provided the brief explana-tion of the mathematical model used in thePUSLE computer program. Depending on model-input information designated by the program user,the model uses one of two equations to calcu-late groundwater discharge over time. The firstequation describes groundwater discharge after aninstantaneous recharge amount:

Q =1.866ARi

K·

∞∑

m=1,3,5

e(−0.933m2π2r)/(4K), (2)

where Q is total basin groundwater discharge, A isbasin drainage area, Ri is instantaneous rechargedepth, K is recession index, and t is time elapsedafter the instantaneous recharge.

The second equation describes groundwaterdischarge caused by an instantaneous rechargeamount followed by a gradual recharge rate:


Q = RgA + 2RgA ·∞∑

m=1,3,5

[0.933Ri

RgK− 4

π2m2

]

× e(−0.933m2π2t)/(4K), (3)

where Rg is the gradual recharge rate. Equation (3)can be used to simulate the effect of gradualrecharge in the absence of instantaneous rechargeby specifying Ri to be zero. Gradual recharge willbegin at t = zero and will continue infinitely.

2.3.2 Base flow methods

Four different packages of software were used toprovide estimates of base flow in the Bukmoongolsmall-forested watershed. Base flow is the part ofstreamflow usually attributed to groundwater dis-charge. In the streamflow-hydrograph separationmethod, base flow is used as proxy for recharge(USGS 2007).

2.3.2.1 PART software

The PART software, which is supported by theUSGS, has been widely used to compute base flow(Rutledge and Mesko 1996; Holtschlag 1997; Nelmset al 1997; Bachman et al 1998). The PART wasderived from the principle of partitioning stream-flow to estimate a daily record of base flow (ground-water discharge) under the streamflow record. Itseparates base flow by equating streamflow to baseflow on those days after a storm, meeting a require-ment of antecedent-recession length greater than Nand rate of recession less than 0.1 log cycle per dayand uses linear interpolation to connect across peri-ods that do not meet those tests. N is the approx-imate duration of surface runoff from Linsley et al(1982) as expressed below.

N = (A)0.2, (4)

where N is the time after which surface runoffceases, in days; and A is the watershed area, insquare miles.

A detailed description of the algorithm used andoperation of PART is provided in the user manual(Rutledge 2005a).

2.3.2.2 BFI software

The Base Flow Index (BFI) software was devel-oped to make the base flow separation processless tedious and more consistent. The program

implements a deterministic procedure developedby the British Institute of Hydrology standardmethod (BFI-SM) and modified method (BFI-MM). The methods combine a local minimumsanalysis with a recession slope test. The programestimates the annual base flow volume of rivers andstreams and computes an annual base flow indexfor multiple years of data. The two methods differonly in the test they use to identify turning points(f) on the base flow hydrograph. Both separationmethods begin by partitioning the year into N -dayperiods and determining the minimum flow withineach period (Wahl and Wahl 2001).

A 5-day increment and a factor of 0.9 for the testto identify base flow turning points were used inthe studied forested watershed. Selections of incre-ments are influenced by the size of the drainagearea and were determined based upon methodsdescribed by Wahl and Wahl (2001). Minimumsin one location were compared to adjacent mini-mums to determine turning points on the base-flowhydrograph. The area beneath the hydrograph isthe estimate of the volume of base flow for theperiod. For the Bukmoongol watershed, the slopechange occurs at N = 5 days. This was consistentwith the observation that direct runoff generallyceases within 1–5 days following a storm. The pro-gram produces a table of base flow, total stream-flow, and base-flow index for each water year, aswell as summary statistics.

2.3.2.3 RAP software

The River Analysis Package (RAP) software wasused in estimating the average annual base flowindex. It contains the Time Series Analysis (TSA)module to help river managers, scientists and con-sultants to tackle a range of river managementprojects (Marsh et al 2003). TSA analyses usedaily time series data (e.g., flow data) to generatesummary statistics or to plot changes in statis-tics through time. It can be used for comparingalternate flow regimes, identifying changes in aflow regime through time and for generating flowstatistics that can be used to predict the likelybiological response to flow change. The programwas developed with a graphical user interface thatallows users to interactively investigate time seriesdata. However, the program seemed not capablefor measurement of daily and monthly base flowrate.

The Lyn–Hollick filter equation in calculatingthe base flow component of the hydrograph in RAPsoftware is shown below. It has an alpha value of0.975.

qf(i) = α · qf(i−1) + (q(i) − q(i−1))1 +α

2, (5)


where q(i) is the original streamflow for the ith sam-pling instant, qf(i) is the filtered quickflow for theith sampling instant, q(i−1) is the original stream-flow for the previous sampling instant to I, qf(i−1)

is the filtered quickflow for the previous samplinginstant to i, and α is the filter parameter.

2.3.2.4 WHAT system

The Web-based Hydrograph Analysis Tool(WHAT) system is an online based computationof the base flow from streamflow data. The latestversion of the WHAT system was developed withthree base flow separation modules, namely: thelocal minimum, one parameter digital filter (Lyneand Hollick 1979; Arnold and Allen 1999; Eckhardt2005; Lim et al 2005), and the recursive digital fil-ter methods (Eckhardt 2005). Each method in theWHAT system has a distinctive feature. Furtherdetails regarding formulations and applications ofthe program are described by Lim et al (2005).

Theoretically, the WHAT Local MinimumMethod (WHAT-LMM) searches the hydrographfor the minimum streamflow during an interval(2N × days). The Local Minimum Method simplyconnects the local minimum points by comparingthe slope of hydrograph. However, the Local Min-imum Method in the current WHAT system doesnot consider duration of direct runoff (Lim et al2005).

The WHAT One Parameter Method (WHAT-OPM) is analogous to Base flow (BFLOW) filterand used the Lyne and Hollick (1979) approach aspresented in equation (5). In the present study, afilter parameter of 0.925 was used.

The WHAT Recursive Digital Filter (WHAT-RDF), also known as Eckhardt filter method,requires a filter parameter and BFImax (maximumvalue of long term ratio of base flow to total stream-flow) values. The digital filter method has beenused in signal analysis and processing to separatehigh frequency signals from low frequency signals(Lyne and Hollick 1979). This method has beenused in base flow separation since high frequencywaves can be associated with direct runoff, andlow frequency waves can be associated with baseflow (Eckhardt 2005; Lim et al 2005). Thus, filter-ing direct runoff from base flow is similar to signalanalysis and processing (Eckhardt 2005; Lim et al2005).

Eckhardt (2005) digital filter is expressed asfollows:

bt =

(1 − BFImax) · α + bt−1

+1(1 − α) · BFImax · Qt

1 − α · BFImax

, (6)

where bt is the filtered base flow at the t time step,bt−1 is the filtered base flow at the t− 1 time step,BFImax is the maximum value of the long termratio of base flow to total streamflow, α is the filterparameter, and Qt is the total streamflow at the ttime step.

In the study area, the suggested values of 0.98 forfilter parameter and 0.80 for BFImax for a peren-nial stream with porous aquifer were used in thebase flow separation computation.

2.4 Data analysis

The recharge and base flow rates generated by thedifferent simulation models were analysed to deter-mine correlations between methods or programsusing bivariate correlations. In addition to theKendall’s tau correlation, the multivariate analy-sis, called principal component analysis (PCA) wasalso used on the dataset. The Kendall test was alsochosen for correlation analysis because it measuresthe association between the original pairs of datapoints which are insensitive to the effect of outliers.The statistical package SPSS 16.0 for Window�

was used for calculations.The data reduction factor analysis (FA) extrac-

tion method using principal component analysiswas applied to the datasets. PCA has been usedfor reduction of variables and clustering samples(Suk and Lee 1999; Thyne et al 2004; Lee et al2008). In this study, PCA was used for the eval-uation of the large ground water recharge or baseflow values generated from different models. It isa multivariate statistical procedure that is com-monly used to reveal patterns in a large dataset(Wold 1987; Winter et al 1998; Stetzenbach et al1999). The central idea of PCA was to reduce thedimensionality of a dataset that consists of a largenumber of interrelated variables, while retaining asmuch of the variation that was present in the dataset as possible. This was achieved by transformingthe variables into a new set of variables, the prin-cipal components (PCs), which are uncorrelated,and which are ordered so that the first few retainmost of the variation present in all of the originalvariables (Moon et al 2004).

In this case study, factors were estimated fromprincipal component methods. The number offactors, called principal components (PCs), weredefined according to the criterion that only fac-tors that account for variances greater than 1(eigenvalue – one criterion) should be included.The rationale for this criterion is that any com-ponent should account for more variance thanany single variable in the standardized test scorespace.


Table

2.

Sum

mary

ofth

eannualst

ream

flow

and

rech

arg

eor

base

flow

usi

ng

diff

eren

tm

ethod

s.

Rec

harg

em

ethod

(m3/s)

Base

flow

met

hod

(m3/s)

Pre

cipit

ati

on

Str

eam

flow

WH

AT

-W

HA

T-

WH

AT

-Per

iod

(mm

)(m

3/s)

RO

RA

PU

LSE

PA

RT

BFI-

SM

BFI-

MM

LM

MO

PM

RD

F

1992

1244

0.8

48

0.6

46

0.8

07

0.6

66

0.4

23

0.4

42

0.5

04

0.5

14

0.4

67

1993

1471

0.8

86

0.5

09

0.6

70

0.5

32

0.3

95

0.3

95

0.4

27

0.4

38

0.3

99

1994

1135

0.5

79

0.3

90

0.3

21

0.3

81

0.3

06

0.3

07

0.3

42

0.3

33

0.2

93

1995

1167

0.8

92

0.5

44

0.6

80

0.5

55

0.3

32

0.3

32

0.4

66

0.4

35

0.3

98

1996

1112

0.7

39

0.3

77

0.5

11

0.3

82

0.2

14

0.2

15

0.3

08

0.2

74

0.2

53

1997

1493

1.2

30

0.5

83

0.8

65

0.5

74

0.4

13

0.4

10

0.3

39

0.4

45

0.4

13

1998

1993

2.2

99

1.1

43

1.5

83

1.1

69

0.6

63

0.6

72

0.7

26

0.8

71

0.8

10

Tota

l7.4

74

4.1

92

5.4

38

4.2

59

2.7

46

2.7

73

3.1

11

3.3

11

3.0

32

Aver

age

1374

1.0

68

0.5

99

0.7

77

0.6

08

0.3

92

0.3

96

0.4

44

0.4

73

0.4

33

Although the factor matrix obtained in theextraction phase indicates the relationship betweenthe factors and the individual variables, it is usu-ally difficult to identify meaningful factors basedon this matrix. Interpretation of the matrix maybe easier using the rotation procedure. Rotationdoes not affect goodness of fit of a factor solu-tion. That is, although the factor matrix changes,the commonalities and the percentage of total vari-ance explained do not change. The rotation processin FA allows flexibility by presenting a multi-plicity of views of the same dataset (Dillon andGoldstein 1984; Andrade et al 2008). Varimax withKaiser normalization, which is frequently appliedto increase the participation of the variables withhigher contribution and reduce those with lessercontributions, was adopted in this study. Thus,PCAs were subjected to raw varimax rotation.

3. Results and discussion

3.1 Base flow or recharge simulation

To facilitate the comparison of the different meth-ods, the annual recharge and/or base flow ratesassociated with the available annual historicalstreamflow records were predicted as shown intable 2. In the Bukmoongol small-forested water-shed, the results of the simulation using differentmethods varied annually depending on the avail-able streamflow, where the recharge methods gavereasonably higher estimates than the base flow sep-aration approaches. The average annual streamflowwas 1.068m3/s and the predicted recharge valuesranged from 0.608 to 0.777m3/s while the base flowestimates ranged from 0.392 to 0.559m3/s.

Estimates in PULSE and RORA softwareappeared favourably higher than remaining meth-ods. High values derived from PULSE and RORAsoftware conformed to the basic premise ofthe Rorabaugh approach that recharge eventsoccurred concurrently with peaks in streamflow(Rutledge 1998). In addition, the formulation ofthe Rorabaugh equation showed that the totalpotential groundwater discharge to the streamat critical time after a peak in streamflow wasequal to the approximately one-half of the totalvolume of water that recharged the groundwatersystem during the peak (Rutledge 1998). In suchcase, a large amount of recharge estimates weremostly accumulated during the rising limb of theentire streamflow hydrograph (figure 2). It shouldbe noted that the streamflow in the watershedoccurred largely from June to August. In con-trast, the BFI standard and modified methodswere continually producing lower estimated valuesbecause of the simplifying assumptions inherent in


Figure 2. Average monthly estimated recharge or baseflowin the Bukmoongol watershed.

Table 3. Ratio of the recharge or base flow from the precip-itation in different methods for the forested watershed.

Seasonal condition

DryMethods Wet (fall, winterinvestigated Annual (summer) and spring)

PUSLE 31 30 32

PART 24 19 31

BFI-SM 15 10 23

BFI-MM 16 10 24

RORA 31 34 28

WHAT-LMM 18 12 26

WHAT-OPM 18 13 24

WHAT-RDF 19 14 27

Average 21 18 27

the equation particularly the collection of 5-dayminimum flows, which did not really represent thedirect runoff conditions in the watershed. How-ever, the discrepancy of estimates in other methodsinvestigated was merely evident during high flows.The outcomes clearly demonstrated that rechargeor base flow rates varied annually relying on theprecipitation and streamflow availability. Overall,the results revealed that the higher the stream-flows, the higher the expected base flows.

The rate of precipitation greatly influenced thestreamflow and recharge estimates in the water-shed. Table 3 shows the recharge ratio computedfrom the precipitation at a given period. The meanrecharge or base flow ratio is the ratio of the meanrecharge or base flow to the mean precipitation asa percentage. In this study, mean annual rechargeand base flow rates ranged from 15–31 per cent ofprecipitation and averaged 21 per cent of precip-itation from different methods investigated. Lowrecharge proportions were computed ranging from10–21 per cent in all methods involved duringthe summer. Under this condition, the rechargeand precipitation rates were both higher and that

resulted in a low proportion. However, low rechargeand precipitation amounts occurred during thedry period when much of the rainfall is directlyabsorbed in soils.

Results for the RORA, PULSE and PART soft-ware were reasonably comparable to other stud-ies. Risser et al (2005a) found apparent rechargeratio for the RORA (33 per cent of the precipi-tation) and PULSE (24 per cent of the precipi-tation) in the small watershed in Spring Creekat Milesburg. In the eastern United States, Risseret al (2005b) applied the same approach whichthe RORA recharge rates estimated about 33–38 per cent of the precipitation, 25–29 per cent ofthe precipitation for PART, and 24 per cent of theprecipitation using PULSE. Likewise, Delin andFalteisek (2007) simulated RORA recharge ratesranged from 8 per cent to 44 per cent of precipita-tion from 38 basins in Minnesota, USA.

In South Korea’s river basins, Moon et al(2004) found a range from 6.6–10 per cent usingthe water table fluctuation method during theKorean monsoon season. Park (1996) establisheda range from 7.7–12 per cent ground water ratiousing base flow separation with data for both dryand rainy days. Lee and Yoon (1996) found arange from 5.1–7.9 per cent during dry season.Results of the estimated ground water rechargeratio were moderately higher with those from pre-vious studies. It is important to note that thisstudy was conducted in the small forested water-shed. From different methods, the BFI and WHATsystem simulated proportion appeared close to thereported range under this particular condition.However, variations of recharge can relate from fac-tors such as watershed size, vegetation influence,precipitation duration, evapotranspiration rates,and temperature which affect the groundwaterbehaviours.

The performance of different software variedseasonally in this watershed. Figure 2 showsthe average monthly recharge or base flow esti-mates ranged from 0.012m3/s to 0.219m3/s forall methods. As expected, recharge values weregreatest in the summer, which typically includedas much as 75 per cent of total annual rechargeof the watershed. A much smaller recharge andbase flow values usually occurred in winter and fallseasons. Highest values (0.219m3/s) were gener-ated by the RORA software. Other methods suchas PULSE, PART, WHAT-LMM, WHAT-RDF,WHAT-OPM, BFI-SM, and BFI-MM obtainedalso the peak monthly recharge or base flow valuesduring summer season with 0.214, 0.128, 0.095,0.094, 0.087, 0.071, and 0.072m3/s, respectively.

Results clearly showed that the simulatedrecharge rates were consistently higher than thebase flow methods. Similarly, the WHAT system


Figure 3. Average daily base flow or recharge of the Bukmoongol small-forested watershed.

and BFI roughly demonstrated the same patternin response to the seasonal variation. However, theresult of RORA simulation displayed that the esti-mated recharge rate was almost the same withthe mean streamflow in the month of June. Theresult is surprising because the RORA data weresimilar to other models input. This inconsistencycould be confused for groundwater discharge bythe RORA method. Halford and Mayer (2000) andRutledge (2000) referred to some factors such asslow drainage to streams from bank storage, wet-lands, surface-water bodies, soils, and snowmeltrunoff that can exceed groundwater discharge dur-ing recession periods. Rutledge (2007b) suggestedthat the minimum time scale for reporting pro-gram results might be the quarter year but notesthat there are applications in which results can bereported on a smaller time scale such as the month.Moreover, Risser et al (2005b) pointed out that theRORA and PULSE are not hydrograph separationtechniques although they are using streamflow datato estimate groundwater recharge.

On the basis of daily recharge or base flow, therecharge method using PULSE software obtainedhigh estimates than the base flow methods (fig-ure 4). The RORA model was excluded in theanalysis on a daily basis. The RORA does not esti-mate continuous or daily ground water dischargeunder the streamflow hydrograph (Risser et al2005b; Rutlegde 2007b). As a result, three mod-ules of WHAT and two methods of BFI softwareexhibited the same pattern of estimates respond-ing to changes in streamflow input. Daily estimatesfrom PART and PULSE programs dominate higherestimates than other base flow methods. Overall,mean daily recharge or base flow rates ranged from0.0003–0.0099m3/s in the watershed.

Different methods seem to have their own limita-tions, uncertainties, applications and advantages as

Figure 4. Average and standard deviation of the baseflow index using different methods at the Bukmoongolsmall-forested watershed.

manifested in results of this study. As far as appli-cation is concerned, the WHAT system appearedthe easiest method in separating the base flowfrom the streamflow. The software has a greatadvantage compared to other methods in whichlocal flow data are allowed to enter to the WHATserver and select appropriate filter parametervalues for a watershed. Direct runoff and base flowoutput values are provided in tabular format aswell as a graphical hydrograph. This capability pro-vides an efficient base flow estimation using localdatasets to a certain location.

3.2 Base flow index estimates

Base flow index is described as the average rate ofbase flow relative to the average rate of thetotal flow and varies from zero to one whereincreasing values indicate an increasing contribu-tion of base flow to streamflow. The case studydetermined the base flow index for a seven-yearperiod in the Bukmoongol watershed using sevendifferent algorithms (figure 4). The computedmean annual indices ranged from 0.246 to 0.756,


which indicated as much as 24 to 75 per centof the total flow attributed to the base flow.Results exhibited that the WHAT-OPM methodobtained higher average estimates (0.756) thanWHAT-LMM (0.730), WHAT-RDF (0.691), PART(0.617), BFI-SM (0.404), BFI-MM (0.401), andRAP (0.246). The low index indicated a less per-centage of direct surface runoff in the watershed.The higher proportion of base flow may also beassociated to the present stable conditions of theforested watershed. In addition, the disparity ofan annual index to base flow for each programattributed from the total streamflow or runoff,filter method values, precipitation, and hydro-logical and hydrogeological characteristics of thewatershed.

Limited studies have been mentioned using a cer-tain program for the base flow index. For instance,Moix and Galloway (2004) found a range from0.28–0.33 base flow index at the Buffalo River innorthern Arkansas using BFI software. Likewise,Neff et al (2005) reported 0.70 average index usingthe PART model in the Greak Lakes basin inCanada. Lim et al (2005) for their part observedthat the base flow separated using a local minimummethod was typically overestimated than base flowvalues obtained using the digital filter methodswhen multiple high peaks occur in a short timeperiod.

Findings from the software mentioned aboveare really comparable to the simulated values inthis study. The BFI software demonstrated for alow base flow index simulation while the PARTcan generate a high index depending on the totalstreamflow discharge. Results also revealed thatthe RAP and WHAT-OPM models gave differentbase flow index values even though both methodsutilized the Lyn–Hollick filter approach. The dis-crepancy in the estimates can be attributed to thedifferent filter parameter values used in each par-ticular program. In essence, an increase in filterparameter value will result in a decrease in baseflow index and vice versa.

From the standpoint of prediction stability, theWHAT-RDF method gave the most stable resultsover the seven-year period as indicated by the leastvalue of standard deviation (s = 0.034), indicat-ing that the annual predicted values are more uni-form as compared to the other methods (figure 4).On the other extreme, the PART method gavethe least stable results as indicated by the com-puted s = 0.114 which is the highest among theseven methods evaluated. Ideally, since the differ-ent methods were used to predict base flow underidentical watershed conditions within each year,the yearly fluctuations of the predicted base flowsof each method, as represented by the standarddeviation, should be minimal. Methods with the

least base flow prediction fluctuations can thus beconsidered as more superior over those with largerfluctuations when prediction stability is the primeconsideration for selection of method.

3.3 Comparison of different methods

The scatter plot matrix describing the relationshipbetween the predicted recharge or base flows pre-dicted by the methods evaluated is presented infigure 5. Results show moderate to very high pos-itive association between the predicted base flowsof any pair of methods. The test of hypothesis alsorevealed significant (Pearson) correlation betweenany pair of prediction methods. Moderate correla-tions were noted in RORA paired with the othermethods except PULSE and PART, where highcorrelations were obtained. On the other hand,very high correlations were obtained in WHAT-RDF paired with any of the other methods, again,with the exception of RORA. The Kendall’s Tau(τb) coefficient was also considered because the testis relatively insensitive to the presence of individualoutliers. Under this test, all methods also demon-strated positive association to each other.

The correlation between all possible pairs ofmethods was statistically significant for a two-tailed test at the 0.01 level. The situation wasexpected because of the line of reasoning that thehigher streamflow, the higher base flow that canbe predicted. It must be pointed out that the baseflow or recharge predictions by the different modelswere based from a common set of streamflow data.The results implied that the significant relation-ships between the different methods investigatedsimply indicate that the trends or the fluctuationsof the base flow predicted by the different methodsare similar and conforms to the trend of the stream-flow. Consequently, the correlation coefficient can-not fully explain the accuracy of predictions of anyparticular method.

3.4 Principal component analysis

The validity of test applications was performed torecharge and base flow data prior to the factorreduction analysis. The test parameter used for theanalysis was the Kaiser–Meyer–Olkin (KMO) mea-sure of sampling adequacy and Bartlett’s test ofsphericity. The value was 1839.289 with a signifi-cance level of 0.0001, indicating that factor analysiscould be applied to the groundwater recharge andbase flow data. In particular, the KMO value wascloser to 1, it indicated that the factor analy-sis becomes more significant (Kaiser 1974). TheKMO > 0.5 is acceptable (Andradea et al 2008)while KMO values greater than 0.8 are generallyaccepted as significant (Moon et al 2004). In this


Figure 5. Scatter plot matrix examining the relationship among recharge or base flow methods.

study, the KMO value was 0.834, implying thatPCA can achieve a significant reduction of thedimensionality of the original dataset.

Principal component loadings can be visual-ized as a measure of the association between thegroundwater recharge or base flow models and eachPC. Factor analysis of the recharge and base flowdata produced two PCs with a 96.2 per cent totalvariance explained. In this case study, a 95 per centwas considered as cut-off. As a result, the similarityis expressed as a weighted relationship provided bythe matrix product as shown in figure 6. The load-ing of the recharge for the WHAT-OPM method(0.987) was the highest onto component 1, that ofthe RORA method (0.665) onto component 2. Inaddition, the RORA, PULSE, and WHAT-LMMmodels in component 1 found moderately low loa-ding and communalities, while remaining models

are high. The overlap between models obtainedwith negative and positive loadings in component2. Mostly, models with the highest loadings makethe largest contribution. In this forested water-shed, PC 1, which explains 87.4 per cent of thevariance, is highly and positively driven by allmethods under investigations. These high loadingscan be explained by the similarity of the fluctu-ation pattern under the condition in the forestedwatershed. PC 2 (8.8% of the variance) is mainlydriven by PULSE, PART and RORA, which maybe attributed to software that provide high esti-mates recharge during the wet season.

To reduce the overlap of original variables overeach principal component, a varimax rotation wasconducted. Usually, varimax rotation allows a bet-ter and more explicit assignment of experimen-tal variables to FA/PCAs, since the correlations


Figure 6. Component plot of factors 1 and 2 of differentrecharge and base flow methods for the forested watershed.

Table 4. First two principal rotated componentmatrix loadings for eight groundwater recharge andbase flow methods.

Component loadings

Methods 1 2

PULSE 0.622 0.755

PART 0.812 0.556

BFI-SM 0.922 0.329

BFI-MM 0.926 0.322

RORA 0.267 0.945

WHAT-LMM 0.906 0.314

WHAT-OPM 0.902 0.418

WHAT-RDF 0.896 0.420

Eigenvalues 5.26 2.43

% of Variance 65.8 30.4

Extraction method: Principal component analysis.Rotation method: Varimax with Kaiser normali-zation.

between some of the variables and componentsare high, others are low, and few correlations areintermediate (Helena et al 2000; Singh et al 2005;Andradea et al 2008). The intermediate loadingsindicate some relationship between the componentand variables (models input), but its real signifi-cance is difficult to assess (Andradea et al 2008).Models input loadings and variance explained inthe watershed after varimax rotation is presentedin table 4. For a study site, the total varianceis better spread out among the two rotated com-ponents (RCs). RC 1 explains 65.8 per cent oftotal variance and signifies strong positive loa-dings on the PART, BFI-SM, BFI-MM, WHAT-LMM, WHAT-RDF, and WHAT-OPM, moderateloading on the PULSE, and intermediate loadingon the RORA. RC 2 is strongly contributed bythe RORA, moderate loading on the PULSE,and intermediate loading on the PART, BFI-SM,

BFI-MM, WHAT-LMM, WHAT-RDF, and theWHAT-OPM methods. In consequence, no overlaphas been found and also conformed to the positivecorrelation of all models.

4. Conclusions

Groundwater recharge or base flow is a fundamen-tal component in the water balance in a watershed.In this study, multiple indirect methods were uti-lized to estimate the recharge or base flow from theavailable seven-year streamflow data at the Buk-moongol small-forested watershed in Korea. Sixtypes of software with a total of eight modules wereused to compare the recharge or base flow as well asbase flow index. Different methods expressed theirown capabilities, limitations, and uncertainties. Inparticular, the RORA program does not estimatecontinuous or daily groundwater discharge underthe streamflow hydrograph. The PULSE for itspart was not designed to analyze long periods ofrecord. Likewise, the RAP was just capable on thebasis of the mean base flow index estimation.

The correlation and principal component analy-sis demonstrated that the recharge or base flowestimates are comparable and associated to eachother. However, recharge methods (PULSE andRORA) provided moderate correlation and alwaysgreatest estimates especially during the highstreamflow fluctuation in the summer season whileother methods are consistently on the same pat-tern. Different observations were noticed in thebase flow index simulation where WHAT systemconstantly generated higher indices.

Based on the results of the simulation, the studygenerally came up to an observation that theBFI and WHAT appeared consistent in estimat-ing recharge or base flow, and base flow indexof the study site. These programs gave reason-able results which were closely comparable tothe previous groundwater recharge ratio studiesunder Korea’s conditions. As a result, ground-water recharge and base flow methods not onlyconsidered the streamflow data but also parame-ter filter values and identified days of negligiblesurface runoff which basically offered more reli-able estimates. Of the methods used in this study,the WHAT methods are the simplest and easiestto apply prediction stability. Overall, the WHAT-RDF method gave the most stable results overother models.

The use of multiple recharge and base flowestimation methods is beneficial particularly in theforest areas which seems to lack appropriate instru-ments to monitor the groundwater flow. The studyrecommends the application of different models toother watersheds in Korea and compared results


to the present case study. The same recommen-dation is addressed in the low-lying areas wheremost observation groundwater wells are locatedwith available streamflow data.

Acknowledgment

This study was conducted with the support of“Forest Science and Technology Projects (ProjectNo. S210607L0110304)” provided by Korea ForestService.

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MS received 18 March 2008; revised 3 June 2008; accepted 12 June 2008

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