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Hydrological Sciences—Journal—des Sciences Hydrologiques, 44(5) October 1999 743

Evaluation of the Glugla method for estimating évapotranspiration and groundwater recharge

JAMES V. BONTA USDA Agricultural Research Service, North Appalachian Experimental Watershed, PO Box 488, Coshocton, Ohio 43812, USA

e-mail: [email protected]

MIKE MÙLLER Dresden Ground Water Research Center, Meraner Strasse 10, D-01217 Dresden, Germany

Abstract Estimates of groundwater recharge are often needed for a variety of groundwater resource evaluation purposes. A method for estimating long-term groundwater recharge and actual évapotranspiration not known in the English literature is presented. The method uses long-term average annual precipitation, runoff, potential evaporation, and crop-yield information, and uses empirical parameter curves that depend on soil and crop types to determine long-term average annual groundwater recharge {GWR). The method is tested using historic lysimeter records from 10 lysimeters at Coshocton, Ohio, USA. Considering the coarse information required, the method provides good estimates of groundwater recharge and actual évapotranspiration, and is sensitive to a range of cropping and land-use conditions. Problems with practical application of the technique are mentioned, including the need for further testing using given parameter curves, and for incorporating parameters that describe current farming practices and other land uses. The method can be used for urban conditions, and can be incorporated into a GIS framework for rapid, large-area, spatially-distributed estimations of GWR. An example application of the method is given.

Evaluation de la méthode Glugla pour estimer l'évapotranspiration et la recharge de la nappe phréatique Résumé II est souvent nécessaire d'évaluer le renouvellement de la nappe phréatique pour apprécier les ressources en eau du sous-sol. Nous présentons ici une méthode d'estimation du renouvellement à long terme des eaux souterraines et de l'évapotranspiration réelle inédite dans la bibliographie anglophone. Cette méthode utilise des données concernant les précipitations moyennes annuelles à long terme, les écoulements de surface, 1'evaporation potentielle et le rendement des cultures, ainsi que sur des courbes paramétrées empiriques qui dépendent des types de sol et de culture afin de déterminer le renouvellement moyen annuel à long terme de la nappe phréatique {GWR). La méthode peut être utilisée pour apprécier la variabilité spatiale du renouvellement des eaux souterraines. Elle a été expérimentée à partir d'une base de données recueillie sur dix lysimètres de Coshocton dans l'Ohio. Compte tenu des données élémentaires requises, la méthode fournit une bonne estimation du renouvellement des eaux souterraines et de l'évapotranspiration réelle et rend compte de toute une gamme de conditions culturales et d'utilisation des sols. Les problèmes liés à l'application de cette méthode sont aussi abordés, y compris la nécessité d'expériences supplémentaires sur les courbes paramétrées, et la nécessité d'introduire de nouveaux paramètres décrivant les utilisations actuelles et potentielles des sols. La méthode peut-être utilisée en milieu urbain et peut être incorporée dans un SIG pour estimer la distribution spatiale des variations rapides à grande échelle de la nappe phréatique. Un exemple d'application de la méthode est présenté.

INTRODUCTION

Estimates of the rates of groundwater recharge (GWR) are often needed for groundwater modelling, watershed water balance studies, aquifer reclamation,

Open for discussion until 1 April 2000

mailto:[email protected]

744 James V. Bonta & Mike Millier

chemical transport studies, water supply engineering, and other water resource evaluation purposes. In order to estimate groundwater recharge in ungauged areas, a simple method using readily available data is needed.

Current techniques for estimating GWR can be broadly categorized into tracer and physical-computational techniques. Tracers can suggest GWR rates and the age and sources of water, and use naturally occurring and intentionally and unintentionally applied chemical constituents. Isotopes of carbon, hydrogen, oxygen, nitrogen, and chloride (G. Allison, 1988) occur either naturally, or in concentrations modified by nuclear bomb testing. An advantage of using isotopes is that they have entered the hydrological system in a spatially uniform manner through precipitation. However, their interpretation is affected by complex infiltration processes in the soil and vadose zones. Isotopes of hydrogen and oxygen provide the best tracers because they are part of the moving water molecule. Other tracers may move at a slower or faster rate compared with interstitial water because of adsorption and ion exclusion (G. Allison, 1988). Agricultural chemicals such as nitrogen have been applied in large quantities in agricultural areas and often large concentration differences in the soil can be used as a tracer of recharge. Tracer techniques are not often practical because data can be difficult or expensive to obtain.

Physical-computational techniques for estimating GWR include the following: zero-flux method (summing moisture changes below a plane of zero tension found with tensiometers (G. Allison, 1988); lysimeters (Harrold & Dreibelbis, 1959); use of soil physical characterization data such as hydraulic conductivity, moisture content, and tension relations and Darcy's law in the vadose zone (Stephens & Knowlton, 1986; Rice, 1975; Steenhuis et al, 1985); use of measured data to estimate values of the classical water budget equation from which GWR can be computed (Meinzer, 1959); groundwater level fluctuations (Meinzer, 1959; Johansson, 1988; Rushton, 1988); empirical equations where GWR is a power function of precipitation (Sinha, 1988); inverse numerical modelling (H. Allison, 1988); use of springflow measurements (Johansson, 1988); and use of measured subsurface temperature gradients (Boyle & Saleem, 1979). These methods have advantages and limitations due to spatial and temporal representativeness of parameters and associated errors relevant to particular problems facing a practitioner. However, they often provide reasonable estimates of GWR based on field-measured values.

Of the methods listed above, the water balance equation is widely used to estimate GWR because of available data. One of the most difficult variables to estimate in the equation is actual évapotranspiration (Ea). Using the potential evaporation estimate alone to approximate Ea increases the error in computed GWR as climate becomes drier and warmer. The value of Ea is often obtained as a fraction of potential evaporation or precipitation, or by using a data-intensive Ea formula. This paper focuses on an easy-to-use method for estimating long-term average annual actual évapotranspiration in the water balance equation to determine long-term average annual GWR. It was developed during the 1960s and 1970s in former East Germany (Glugla, 1966, 1970; Glugla & Tiemer, 1971; Glugla & Enderlein, 1973; Glugla et al, 1976), and is termed the "Glugla method" in this paper. Due to restrictive governmental policies at the time, the method never became widely known worldwide. However, the method was taught routinely in former East German university hydrology classes, and is used in Germany today.

Evaluation of the Glugla method for estimating évapotranspiration and groundwater recharge 745

The objectives of this paper are to present the original Glugla method for estimating GWR, and to test the method using lysimeter data collected at the North Appalachian Experimental Watershed (NAEW) near Coshocton, Ohio, USA. The method was originally developed for German conditions, but because data from different climates, and soil and crop types around the world were used in its development, most aspects apply to other areas of the world. The method is compared with measured lysimeter data, followed by a discussion of the sensitivity of parameters and overall results, and areas for further research. An example application of the method is presented in the Appendix. While further development of the Glugla method has occurred in recent years, the original method has features that make it appealing for practical use and further research, so original sources were used. Recent developments (mentioned later) still use the original, basic concepts.

DEVELOPMENT OF THE GLUGLA METHOD FOR ESTIMATING GWR

The Glugla method approach

The Glugla method uses the expression of conservation of mass through the water balance equation:

GWR = P~Ea-R±AS (la)

R = R0 + Rt (lb)

where P = precipitation, Ea — actual évapotranspiration, R = total runoff not reaching ground water, GWR = groundwater recharge, R0 = surface runoff, Rt = interflow, and AS = the change in storage. All components in equation (1) represent long-term average annual values. Therefore, storage changes may be assumed to sum to zero. Investigation of the effects of errors in estimating each component in equation (1) on GWR is beyond the scope of this paper. However, corrections to P are easily made and were incorporated to describe better the water inputs to equation (1).

Two aspects of P were researched by investigators of the Glugla method: the accuracy of P and its spatial variability. Accuracy is important when working with equation (1) because it supplies the basic input to a hydro logical system. It is known that P is underestimated with standard raingauge installations with orifices above ground because of wind influences at the orifice. Glugla (1970) used a factor of 1.09 to correct average annual P, but the factor value may depend on local gauge exposure and physical characteristics. Grunske & Eschner (1975) developed a correction to account for spatial variation in P, both horizontally (Thiessen polygons) and vertically. Accounting for the spatial distribution of precipitation is important for estimating the spatial distribution of GWR using the Glugla method.

Runoff, R, was estimated using an empirical equation for Germany. Grunske & Eschner (1975) suggested basic hydro graph separation techniques to develop R0 and Rt

using semi-logarithmic plots of flow vs time. The estimate of R, its components, and associated errors will not be pursued further in this paper because this requires a separate investigation.

At the time of development of the method, easy-to-use methods for estimating long-term average Ea were not available. Glugla (1966) developed the method for


estimating Ea for a wide range of climatic and physical conditions by using worldwide data from about 40 lysimeters. Lysimeters were of various sizes and designs, and represented different soil and crop types, and climates. Glugla used, primarily, lysimeters that were deep enough (a minimum of 1-1.5 m) to make the assumption that percolation from the bottom of the lysimeters represented GWR, and that water would not move upwards by capillary forces, to be redistributed as Ea, if the soil profile was more shallow. Furthermore, lysimeters had to have a surface area of at least 1 m2. Glugla subdivided the lysimeter database into lysimeters with no vegetation, with grass type (meadow) vegetation, and those with different agricultural crop types. Crop types included those with agricultural crop rotations and trees. Furthermore, at least three years of data for a single land-use condition were required. Long-term average climatological, runoff, and percolation data were compiled for each lysimeter. The data sets exhibited wide scatter; however, trends in the data led to useful generalizations and, eventually, to the "Glugla method".

In an early formulation, Glugla (1966) used results from Kortiim (1961a,b) who correlated the combined use of the energy and water budget equations to obtain Ea, with soil and crop types. A useful finding of Glugla's (1966) research was the identification of a good relationship between a simple soil parameter, Go 02 and an evaporation parameter. The G0.02 parameter was defined as the average percentage of soil particles less than 0.02 mm (coarse silt to fine sand) in the surface soil profile (top -1-1.5 m) that would influence the évapotranspiration process. Glugla (1966) recommended using a weighted average G0.02 when soil texture changes rapidly with depth. He further simplified the determination of G0.02 by relating it to descriptive soil texture classifications.

Glugla method formulation

Two necessary boundary conditions for Ea in an area are (Bagrov, 1953; Glugla & Tiemer, 1971):

Ea -> Ep as P -» 8 (2)

Ea -» P as Ep^ 8 (3)

where Ep is average annual potential évapotranspiration. Glugla & Tiemer (1971) became aware of work by Bagrov (1953) that satisfied these boundary conditions and subsequently used his work in further development.

Bagrov (1953) started with general observations of regional climate, and energy and water balance considerations. He assumed the following relationship:

__ = Ly (4)

The variable, n, is a parameter called the "Bagrov factor". The structure of this equation conveniently constrains the derivative on the left-hand side to values between 0 and 1 ; an increase in Ea does not exceed an increase in P. Furthermore, Ea cannot exceed Ep on the right-hand side. Considering the region of large EJEP (humid regions), the equation states that there will be only small changes in Ea with an


increase in P. For regions of small EJEP (arid regions), the increase in Ea for an increase in P can be greater. The parameter, n, allows flexibility in how the inverse relationship can be described because an infinite number of curves is possible.

Equation (4) can be integrated to arrive at the "Bagrov equation" (Bagrov, 1953):

P-- IT. dEa

{EJEP)' (5)

This equation can be integrated analytically for only a few specific values of n. However, it can be integrated numerically for any n using Simpson's rule, resulting in Fig. 1. An infinite number of realizations on the graph are possible, and an n value must be determined in a practical application. The area to the left of the line of equivalence in Fig. 1 delineates an impossible region where long-term average Ea

cannot be greater than long-term average precipitation. Large values of PIEP describe humid climates, and small values describe arid climates. Dyck & Peschke (1989) note that the Bagrov method is not applicable in regions where PIEP is less than or equal to 0.5, and for large values of this ratio. However, no upper limit was given. The limitations of this ratio require further research for other applications.

Glugla & Enderlein (1973) modified the Bagrov equation (equation (5)) by redefining P to be an effective Petf.

P,tf = P-R (6) This removes the surface runoff that does not enter the soil and that would not be

subjected to losses by évapotranspiration processes in the soil. Relationships were needed to relate Bagrov n to soil and crop factors that influence

Ea. Interestingly, in a study of global climate change evaluations, Dooge (1992) notes

LU

TO

HI

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3,5 4.0

P/E p or Peff/Ep

Fig. 1 Plot of the Bagrov equation (equation (5)).


4.0

3.0

2 2.0 en ce

00

1.0

u0 20 40 60 Soil Parameter, G002

(% <0.02 mm) Fig. 2 Plot relating soil type, soil parameter, crop-yield class and Bagrov n factor.

that determining n values for Bagrov's (1953) equation would have been difficult. Yet Glugla & Tiemer (1971) developed n values related to soil and crop types, and crop yields (Fig. 2). Figure 2 shows that n also depends on a dimensionless variable called the "crop-yield class" (CYQ and soil parameter, Go.o2- Numeric CYC values correspond to crop condition descriptions "very poor" (20), "poor" (25), "average" (30), "good" (35), and "very good" (40).

Crop-yield class graphs were determined empirically by Glugla (1966) and Glugla & Tiemer (1971) to produce the graphs in Figs 3-5. Generally, the graphs show that CYC varies with soil parameter (G0.02) and crop yield (t ha"1 of non-dry biomass), with one exception. Meadow (grass area), pasture, etc. are functions of only crop yield and type, and not G0.02 (Fig. 3); no influence of soil type was found in the original development. For cereal crops (Fig. 4), an adjustment factor is required. For example, the crop yield of winter wheat must be multiplied by 0.81 (Fig. 4) to arrive at a corrected crop yield before entering this graph. Figure 5 shows the relationships between CYC, crop yield, and soil type (Glugla & Tiemer, 1971; Grunske & Eschner 1975) for potatoes and sugar beet. These graphs were developed from available statistical data on agricultural crops in Germany (Glugla, 1966), recognizing that the better the condition of the crop, the higher the Ea. Glugla related actual crop yields to qualitative descriptions such as "poor", "average", and "good", a desirable feature for practical application.

Glugla (1966) used data from lysimeters on which trees were also growing. He found that Ea for wooded conditions varied with age and type of trees, and that lysimeter data never reached an equilibrium E„ after as many as 13 years. However, recent research and data have led to the determination of the Bagrov factor as a


7000

6000

TO SZ

5000

33

> Q. O !_ O

•4000

3000

2000

1000

I i I Biomass (non-dry

Examp e ^ ^

# ^

c ^

—"̂ "us*°

-*• .

j i t /

^

^>

^ ^

10 50 20 30 40

Crop-Yield Class Fig. 3 Relationship between crop-yield class and crop yield for various crops.

500

100 0 10 20 30 40 50 60

Soil parameter G0.02 (% < 0 , 0 2 mm)

Fig. 4 Relationship between crop-yield class, crop yield, and soil parameter for cereal crops. (Soil texture legend in Fig. 1.)


5000

4000 ce

^3000

CL

o O

2000

1000 40 60

Soil parameter G0.02 (% < 0.02 mm) Fig. 5 Relationship between crop-yield class, crop yield, and soil type for potatoes and sugar beet. CYC descriptions: 40—very good, 35—good, 30—average, 25—poor, 20—very poor.

40<A<60

0.2

Clearcut and first year of new planting

•0<A<5 A = Tree age, yrs

25 10 15 20 Available Water Storage, % vol

Fig. 6 Relationship between soil type and Bagrov n for wooded areas (DVWK, 1996).

function of available water in the soil and age of tree stand (Fig. 6; DVWK, 1996). Available water is defined as the difference in moisture content between field capacity and wilting point.

Procedure and data used

The North Appalachian Experimental Watershed (NAEW) was established in the late 1930s to determine the effects of agricultural management practices on hydrology and water quality. The NAEW is located in East-Central Ohio, about 130 km east of


Columbus, and about 19 km northeast of Coshocton. It lies on the western edge of the Appalachian Plateau in the unglaciated portion of eastern Ohio. Sedimentary rocks of Pennsylvanian Age, comprising sandstone, shale, limestone, coal, and clay underlie the 425 ha NAEW facility. In about 1940, lysimeters were installed on three different soil types found on the NAEW. All lysimeters were installed on the side slopes of the upland areas on the NAEW. Each lysimeter comprises a monolith of original, undisturbed soil and bedrock. Several lysimeters were constructed at each site comprising three batteries. Individual lysimeters in a battery are spaced about 2 m apart. One lysimeter in each battery has been continuously weighed since the time the NAEW was established. The other lysimeters in each set are of the nonweighing type. Some lysimeters in battery Y101 received "improved" treatments (fertilizer) that increased their crop yields, compared with others that were in an "unimproved" condition. Lysimeters in battery Y101 have been in continuous meadow (grass condition) since their installation, and grazed occasionally. The other lysimeters have been in various crop rotations during their histories, including meadow. Mustonen & McGuinness (1968) and McGuinness & Bordne (1972) describe the lysimeters in more detail, and brief physical descriptions are presented in Table 1.

Glugla (1966) used data from the Coshocton, Ohio lysimeters in the original development of the method. Using only published reports, he assigned values to the parameters of the method. In the present evaluation, the parameters were independently assigned. The parameter values used in the Glugla method were always different from those used originally by Glugla (1966) because more detailed data and longer records were available in this study.

Lysimeter and climatological data at the NAEW are available from 1940 to the present. However, Glugla (1966) used Coshocton data in his developments for a period before 1956. Therefore, only the 39-year record of lysimeter hydrological data from 1956 to 1993 was used in this study for each of 10 Coshocton lysimeters. Lysimeter data were first grouped into years having similar crop types. Only the years with a crop type of "meadow" (grass) were considered ("meadow years"). This crop was chosen because a curve for this crop type was given by Glugla & Tiemer (1971, Fig. 3). Furthermore, a large body of lysimeter data with this crop type has been collected at the NAEW during the 39 year period, and this crop type has practical utility in urbanized, as well as agricultural areas. Daily values of runoff, lysimeter-derived precipitation, percolation, and Ea were summed for each year for the meadow years. Long-term average annual hydrological and crop-yield values were then computed for each lysimeter for use in testing the method. Changes in storage were ignored in this

Table 1 Description of the Coshocton lysimeters*.

Lysimeter ID

Y101-A,B,C,Dt Y102-A,B,Ct

Y103-At,B,C,D

Slope (%) 23 16

6

Aspect

ESE ESE

S

Profile drainage well drained well drained

moderately

Approx. depth to bedrock (m)

0.9+ 1.0+

1.8+

Fractured bedrock type sandstone shale

shale

Soil type

Dekalb silt loam Berks and Rayne silt loam Keene silt loam well drained

Each lysimeter is 1.87 m wide, 4.27 m long, and 2.4 m deep, with a surface area of 7.98 m2. 'Weighing lysimeter.


study because long-term average annual values are being used. This may lead to more scatter in the results. It was assumed that percolation leaving the bottom of the lysimeters is equivalent to groundwater recharge.

Glugla (1966) suggested that average annual precipitation measured at a raingauge be corrected for actual precipitation reaching the ground. The average annual precipitation used in this study was the precipitation measured by the lysimeter. The average lysimeter to gauge ratio was determined for the NAEW lysimeters. Raingauges were located within a few metres of the lysimeters.

Soil particle-size-distribution data were averaged from data provided by Kelley et al. (1975). The resulting G0.02 soil parameters for each lysimeter battery were as follows: 52% for Y101, 51% for Y102, and 76% for Y103. These values were different from those used by Glugla (1966): 35%, 45%, and 70%, respectively, because he apparently used soil profile descriptions from Harrold & Driebelbis (1959), rather than measured data to assign this parameter for each lysimeter.

One parameter in the Glugla method is the long-term average annual potential evaporation, Ep. The value of 1020 mm year4 used came from a study of long-term average measured Ep from the Coshocton lysimeters by McGuinness & Bordne (1972). This value was developed by using measured soil moisture and lysimeter weight data obtained from the NAEW lysimeters. It is higher than a value of about 813 mm year"1

for East-Central Ohio calculated by using pan-evaporation data. However, measured lysimeter Ea was often higher than the lower pan-derived Ep value. The higher value was used for this study because the lysimeter-derived value would be consistent with subsequent water budget calculations.

Conversions were made for meadow crop yields from t acre"1 of hay (dry weight) to t ha"1 (wet weight, corrected for moisture content) as required in Fig. 3. Examination of yield computations in NAEW records showed that on average, "wet" weight was about 65% of the total weight of hay harvested and this conversion was subsequently used.

The Glugla method was applied to each weighing and nonweighing lysimeter. All hydrological values were rounded to the nearest millimetre. Comparisons were made between measured Ea and percolation, and corresponding computed values of Ea and groundwater recharge for all lysimeters. Deviations from measured values and corresponding percentage differences from measured values were tabulated. The sensitivity of Glugla method outputs to model parameters is presented. An example of the application of the Glugla method is given in the Appendix.

RESULTS

Precipitation

Glugla (1970) used a factor of 1.09 to correct P for catch deficiency between gauge and actual ground precipitation. A comparison of gauge- and lysimeter-measured P (Table 2) shows, that for sloping uplands at the NAEW, this factor is about 1.11.

Actual évapotranspiration, Ea

The results from the application of the Glugla method shown in Table 2 and Fig. 7 for Ea are very close. Table 2 reveals that the actual errors in Ea for all lysimeters range


Table 2 Comparison of measured and computed long-term average actual évapotranspiration (£„) and groundwater recharge (GWR).

Lysimeter ID

Y101

A B C D ;

Y102

A B

c) Y103

AJ

B C

Avg. precip. (mm)

1066 1038 1019 1066

1062 1062 1062

1105 1105 1105

Ratio*

1.11

1.12

1.11

Years of data used

36 25 23 36

9 9 9

10 10 10

Average actual évapotranspiration (£„)

Computed Ea (mm)

734 721 716 762

864 864 836

927 924 884

Diff.f

(mm)

+76 -53 +42 -26

-46 -13 -28

+35 +32 +19 Avg. §

Diff. (%)

t-12 - 7 +9 ^3

- 5 - 2 —3

+4 +4 +2 -1

Average groundwater {GWR)

Computed GWR (mm)

316 297 291 297

185 178 213

165 163 201

Diff.+

(mm)

-76 +53 -42 + 12

+46 +13

0

-11 -32 -19 Avg.§

recharge

Diff. (%)

-19 +22 -13

+4

+34 +8

0

- 6 -16

-8 +0.6

*Lysimeter to raingauge catch ratio for average annual precipitation for all lysimeters in a battery. f Diff. = computed - measured. % Weighing lysimeter. § Without Y101A, Y101B, and Y101C where extrapolations were necessary.

only from - 7 % to +12%. Computed Ea values for lysimeters Y101A, B, and C are the result of estimations of Glugla's CYC curves beyond the limits of the graphs for meadow (grass) in Figs 2 and 3, and had the largest errors (Table 2). Crop-yield class was assigned a value of 10 in Fig. 2 because this was approximately the position of the curve for bare soil surfaces (Glugla & Tiemer, 1971). The points are balanced about the line of equivalence showing no apparent bias in E„ estimation (Fig. 7). Estimates of Ea for the weighing lysimeters were among the best (Fig. 7, Table 2). This is because actual storage changes are included in Ea estimates from weighing lysimeters and long-term storage changes are assumed to equal zero for the nonweighing lysimeters in the Glugla method.

Groundwater recharge, GWR

The results from the application of the Glugla method to compute GWR also are close (Fig. 8, Table 2). Percentage differences ranged from -19% to +34% for all lysimeters. Even with the extrapolations for the low crop yields of the Y101 battery (Table 3), the errors for Y101 were only between -19% and +22% (Table 2). Average GWR-estimate deviations for all lysimeters were only 0.6% (Table 2), suggesting that the Glugla method gives good estimates on average. Figure 8 shows no apparent bias for many applications, as is apparent from the balanced scatter about the line of equivalence. The weighing lysimeters gave the best estimates of GWR, ranging from - 8 % to +4% (Table 2).


950!

900

850 :

E E

; 800

Q) t ; 750 :

E o

O 700

6501

600 +

Y103A"

Y103B

Y103C

Line of Equivalence —

Y101A Y101C

~ ~\/

o Y101DW

Y101B

/ Y102B Y102A

Y102CW

1̂ = 0.88 "W" = weighing iysimeter

600 650 700 900 950 750 800 850

Measured Ea, mm

Fig. 7 Comparison of measured and computed long-term average annual, actual évapotranspiration, Ea.

Sensitivity

400

350 on C^ 300

CD

fe 250

o CD

a: 200 £ g_ 150

O 100

Y101E Y101D™

Y102CW

Y101C

Line of Equivalence

Y102A Y 1 0 2 B

s ° x Y103B

Y103AW

1^=0.86 "W" = weighing Iysimeter

100 150 200 250 300 350 400

Measured Recharge (GWR), mm Fig. 8 Comparison of measured and computed long-term average groundwater recharge (GWR).

Table 3 shows the Glugla method parameters used for computing E„ based on the given yields and the soil parameter Gom for each Iysimeter. The variation in Bagrov n with crop yield and soil type is apparent from the NAEW field data. Yields for individual lysimeters at Y101 reflect the different long-term management treatments. Lowest yields for Y101 ranged from 400 t ha"1 at Iysimeter A, where historically the Iysimeter was unmanaged, to 1620 t ha"1 for Iysimeter D, where the management included fertilizer and other management improvements. Table 3 shows that the CYC


Table 3 Glugla-method parameters used in this study.

Lysimeter ID*

Y101 A B C D+

Y102A B C+

Y103 A1" B C

Average annual measured crop yield (t ha4)

400 720 880 1620 3310 3390 2690 3410 3300 2260

Crop Yield Class (CYQ

2.8 5.1 6.2 14 42 43 33 43 42 25

Bagrov n

1.5 1.5 1.5 1.7 3.4 3.4 2.7 5.0 4.9 3.5

EJEP

0.72 0.71 0.70 0.75 0.85 0.85 0.82 0.91 0.91 0.87

*Soil parameters for Y101, Y102, and Y103 are 52%, 51%, and 76%>, respectively. Weighing lysimeter.

increased as crop yield increased at Y101. The Bagrov factor remained the same and then increased slightly. The trend is due to the assignment of CYC values for lysimeters A, B, and C. The ratio EJEP did not vary much with Bagrov n factor at Y101. The crop yields at the other two lysimeter batteries were generally much higher than at Y101, and corresponding values of CYC, n and EJEP ratios were also higher. Generally, decreases in percolation (GWR) occur with higher EJEP ratios as seen by comparing these two columns in Tables 2 and 3.

The effect of the soil parameter G0.02 on Ea is shown by comparing Bagrov n values in Table 3 for Y102A and Y103A. Both measured crop yields averaged about 3350 t ha"1, however, the soil parameter G0.02 values for each were 51 and 76%, respectively. Yields gave nearly identical CYC (-42), but the soil type with the higher clay content (Y103A) yielded a higher Bagrov n factor, and consequently, a higher Ea

(Table 2). The results show that a higher Bagrov n value reflects plant characteristics, land management, and soil texture that support higher Ea.

DISCUSSION

Glugla method

The Glugla method uses simple inputs and basic assumptions about general regional climate conditions to determine Ea. Groundwater recharge is then calculated from the water balance equation provided R is known. The method appeared to work well at the NAEW site using the parameters determined in this study. The curve for meadow in Fig. 3 was tested over about three quarters of the range of crop yields given as provided by Glugla & Tiemer (1971). This curve can be used for grass cover in urbanized and agricultural areas. The meadow curve was sensitive to varying management levels and soil type at Coshocton, Ohio. In particular, the CYC appears to be a surrogate parameter for some types of agricultural management, including varying fertilizer inputs. This study only addresses meadow cover with data not used in the original development of the technique. Testing of other crop types given in Figs 3-6


and development of similar curves for other agricultural conditions, are needed before the method can be widely used.

Glugla (1966) and Glugla & Tiemer (1971) used lysimeter data from around the world to develop their method. The data sets included a variety of soil and crop types and climatological conditions. Glugla's method is empirical, yet was developed within a theoretical framework of the water balance equation, and Bagrov's (1953) regional climate formulation that constrains E„ estimates using known climatic boundary conditions. These facts, with the results presented in this paper, suggest that this method for estimating long-term average annual GWR shows promise to be applicable to many areas of the world.

The use of measured soil texture and crop-yield data is desirable. However, qualitative descriptions of these factors were provided in the form of descriptive soil texture (e.g. sandy loam) in the original and recent developments of the method, a practical feature.

The procedure for computing spatially-distributed values of GWR is outlined in the Appendix, and is illustrated in Fig. 9 where hydrologically "uniform" areas are delineated (Glugla et al, 1976). Spatial distribution of GWR estimates in urban and agricultural areas have also been computed using the computer programs RASTER (Glugla et ai, 1976) and ABIMO (Glugla & Furtig, 1997). These programs use grid points at which Glugla method parameters are determined, and GWR subsequently computed. While not an objective in this paper, the spatial variation of recharge over short distances was determined by computing GWR from individual lysimeters within

LEGEND

Land Use Soil Type

S Sand F Forest area SL Slightly loamy sand A Agricultural area LS Loamy sand W Surface-water body sL Sandy loam U Urban area L Loam

Notes: Depth to ground water noted in each area (eg-, >2 m).

0 500 1000 1500 2000 X Coordinate, m

Fig. 9 Map illustrating delineation of hydrologically uniform areas for application of Glugla method to determine spatial variability of groundwater recharge (Glugla et ai,


a battery, each lysimeter being a hydrologically "uniform" area (Tables 2 and 3). Superimposed maps of soil type, crop type, etc. delineate "uniform" areas such as in Fig. 9. In this paper, the only differences in inputs within a battery of lysimeters were crop yields. The method can be incorporated into a GIS framework for rapid, large area, spatially-distributed groundwater recharge estimations in modelling applications.

Recent developments of the Glugla method

The objective of this study was to present the development of the Glugla method. Therefore original sources were used. Since then the method has been developed and modified with new knowledge and data. However, the basic concepts remain the same (Glugla & Eyrich, 1993; Dyck & Peschke, 1995; DVWK, 1996).

Computer programs using the Glugla method have been written since the 1970s. The newest version, ABIMO (AbflupBIldungsMOdell—Runoff Formation Model, in German) (Glugla & Furtig, 1997) incorporates all the modifications made since the method was developed, including the recommendation for calculating Ep by the Deutscher Verband fur Wasserwirtschaft und Kulturbau (German Society for Water Resources and Landscape Engineering) (DVWK, 1996). Experiences from the use of older versions of the program were used to modify the method. The ABIMO model uses a grid to divide the watershed into area elements which are assigned properties such as soil type, land use, ground water depth, etc. The finer the grid, the more closely the model can represent the watershed. The primary modifications and developments can be summarized as follows: (a) The use of field capacity replaces the parameter G0.02. (b) Urban areas are incorporated in the model by assigning a factor for soil cover and

degree of drainage. Detailed data of land use and soil cover with different kinds of stones, asphalt, concrete, and field investigations in urban areas were used to develop relationships between this land use and the Bagrov factor n.

(c) The evaluation of long-term percolation measurements under trees lead to the modification of the relationship between n and soil type for wooded areas (DVWK, 1996).

(d) Capillary rise is calculated for shallow groundwater, dependent on the depth of groundwater and the average effective root depth. The effective root depth is a function of soil type and land use. The capillary groundwater is subjected to évapotranspiration.

(e) Crop irrigation can be included. The irrigation water is added to the corrected precipitation and capillary rise water. The sum is the input P to be used in the Glugla method.

(f) The potential évapotranspiration (Ep) is calculated using the approach from Turc (1961) and is adjusted by using a factor of 1.1 for German conditions. Currently Glugla, the program developer, is researching a method to determine Ep dependent on land use to account for different évapotranspiration rates for different land cover (Glugla, personal communication, 1998).

(g) For the case where the recharge-receiving aquifer is covered by a low-permeable layer, a reduction factor accounts for lateral interflow that does not reach groundwater.

758 James V. Bonta & Mike Miiller

Areas of future research

Methods need to be explored to subtract interflow abstractions (i?,-) in equation (lb) from potential groundwater recharge. This component can be significant in upland watersheds such as those at the NAEW.

Potential evaporation estimation methods need to be investigated to make the technique practical. The Ep value used in this study was derived from measured Coshocton lysimeter data, using computed and measured soil moisture deficits and other available data (McGuirrness & Bordne, 1972). The Glugla method should be evaluated with other lysimeters where measurements of precipitation, runoff, and percolation exist. From such a study, an examination of possible Ep calculation methods can be made and an estimation technique specified for use with the method.

Preferential flow in soil occurs to varying degrees in nature. The Glugla method accounted for any effect of preferential flow paths that occurred in the lysimeters Glugla (1966) used. However, the Glugla method does not directly account for preferential flow paths in the vadose zone. Direct incorporation of preferential flow in the method would include effects due to different soil structure, soil macrofauna, tillage practice, and land use. This would be especially important if long-term average monthly estimates of GWR would be attempted. Current trends for upland agriculture in the Northern Appalachians in the USA are toward increased use of conservation-tillage farming, which often causes decreases in runoff.

The Glugla method provides estimates of long-term average annual Ea and groundwater recharge. However, often estimates are needed for antecedent, "current," and proposed conditions for modelling or other purposes at a particular time of year. Preliminary efforts in this study to determine the temporal distribution of Ea and GWR at Coshocton, Ohio throughout the year did not yield good results. However, DVWK (1996) present a method for estimating a monthly distribution ofEa. More research is needed to develop such a monthly distribution of long-term annual recharge for other areas.

Crop types given in Figs 5-8 do not include many agricultural crops. For example, com for grain and soybeans are not included in Glugla's graphs. Crop-yield increases due to improved hybrids may also not be accounted for, and CYC curves may need adjusting.

Dyck & Peschke (1989) mention that the Glugla method may not be applicable in very dry or very humid regions, but do not elaborate on this. The applicability of the method should be tested under those conditions to quantify possible limitations. Investigation of the soil depth at which water contributes to GWR is needed under dry conditions.

The results from this study can lead to further evaluation and development of the method for other conditions. While further improvements could be made to the Glugla method, one must be careful not to overly refine this coarse technique because the Bagrov equation considers long-term average climate conditions.

CONCLUSIONS

The Glugla method for estimating actual évapotranspiration, and subsequently groundwater recharge, was presented as developed by Glugla (1966) and others.


Independent variables for the Glugla method include values of long-term average annual potential evaporation, precipitation, runoff, and crop yield, and crop and soil types.

Parameters for the method were derived empirically, but the method was developed in a theoretical framework (equations (4) and (5)) with close attention to boundary conditions for a variety of climates. The method was tested using the parameter graphs as presented by Glugla, and data from Coshocton, Ohio lysimeters in a meadow (grass) land-use practice. The following conclusions can be made based on this study: - The method works well for the given meadow (grass) crop curve, considering the

simple inputs needed and the necessary assumptions made in the input data. - The method provides spatially variable estimates of GWR over small and large

areas. - The method is sensitive to varying crop conditions through the Bagrov n

parameter. - The method is sensitive to varying soil conditions through the G0.02 soil parameter.

More research is needed to test the method and develop parameters and procedures for specific areas before it can be widely used. Parameter curves provided by Glugla & Tiemer (1971), and incorporation of other factors that better describe current agriculture should be investigated (e.g. other crops, conservation tillage, soil conditions with differing degrees of preferential flow, improved hybrids, etc.). More recent developments should be examined for utility beyond German conditions. Methods for estimating potential évapotranspiration with the model need to be tested. The simple soil parameter (G0.02) has potential utility for further évapotranspiration and groundwater recharge research. Generally, the method appears to work well, is easy to use, requires readily available inputs, and can use qualitative inputs that can be translated into numeric outputs. However, the method requires further investigation.

Acknowledgements The authors would like to thank Dr Gerhard Glugla for reviewing a draft of the manuscript and providing helpful suggestions. L. Amiotte, H. Lefebvre, and P. Lippert are thanked for translation of the abstract into French.

REFERENCES

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Allison, H. (1988) The principles of inverse modeling for estimation of recharge from hydraulic head. In: Estimation of Natural Groundwater Recharge (ed. by [. Simmers), 271-282. Reidel, Boston, Massachusetts, USA.

Bagrov, N. A. (1953) O srednem mnogoletnem isparenii s poverchnosti susi (On the mean long-term evaporation rates from the continental surface, in Russian). Met. Gidrol. 10,10.

Boyle, J. M. & Saleem, Z. A. (1979) Determination of recharge rates using temperature-depth profiles in wells. Wat. Resour. Res. 15(6), 1616-1622.

Dooge, J. (1992) Sensitivity of runoff to climate change: a Hortonian approach. Bull. Am. Met. Soc. 73(12), 2013-2024. Dyck, S. & Peschke, G. (1989) Grundlagen der Hydrologie (Basics of Hydrology, in German), second edn. Verlag fur

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from land and water surfaces. DVWK Leaflet Series for Water Management no. 238. DVWK, Bonn, Germany. Glugla, G. (1966) Zur Problematik der Bestimmung der Grundwasserneubildung. Erniittlung von Werten der

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values of groundwater recharge under consideration of the relationships between heat and water budgets and from evaluation of lysimeter observations, in German). Unpublished research report, Institut fur Wasserwirtschaft, Berlin, Germany.

Glugla, G. (1970) Zur Ermittlung der Grundwasserneubildung unter Beriirksichtigung der Beziehungen zwischen Wàrme-und Wasserhaushalt (On the determination of groundwater recharge under consideration of the relationships between energy and water budgets, in German). Wasserwirtsch. u. Wassertech. 20(12), 397—403.

Glugla, G. & Enderlein, R. (1973) Richtlinien zur Berechnung langjâhriger Mittelwerte der Grundwasserneubildung im Lockergesteinsbereich (Guidelines for the calculation of long-term average values of groundwater recharge in the unconsolidated rock area, in German). Unpublished report, Institut ftir Wasserwirtschaft, Berlin, Germany.

Glugla, G., R. Enderlein, & Eyrich, A. (1976) Das Programm RASTER—ein effektives Verfahren zur Berechnung der Grundwasserneubildung im Lockergestein (The computer program RASTER—an effective method for calculating groundwater recharge in unconsolidated rock). Wasserwirtschaft u. Wassertechnik 11, 377-382.

Glugla G. & Eyrich, A. (1993) Ergebnisse und Erfahrungen bei der Anwendung des Bagrov-Glugla-Verfahrens zur Berechnung von Gundwasserhaushalt und Grundwasserneubildung im Lockergestein Norddeutschlands (Results and experiences with the application of the Bagrov-Glugla method to calculate water budget and groundwater recharge in unconsolidated rocks in northern Germany, in German). In: Hydrogeologie Nordhausen (HGN), Colloquium Hydrogeology. Nordhausen, Germany.

Glugla, G. & Fiirtig, G. (1997) Dokumentation zur Anwendung des Rechenprogrammes ABIMO. Berechnung langjaehriger Mittelwerte des Wasserhaushalts fur den Lockergesteinsbereich. (Documentation for application of the calculation program ABIMO. Calculation of long-term annual average values of the water budget for unconsolidated rocks, in German). Bundesanstalt fur Gewâsserkunde, Aussenstelle Berlin, Germany.

Glugla, G. & Tiemer, K. (1971) Ein verbessertes Verfahren zur Berechnung der Grundwasserneubildung (An improved method for the calculation of groundwater recharge, in German). Wasserwirtsch. u. Wassertech. 20(10), 349-353.

Grunske, K. & Eschner, M. (1975) Zur Methodik der Berechnung der Grundwasserneubildung bzw. des Grundwasserdargebotes {On the Methodology of the Calculation of Groundwater Recharge and Available Groundwater, in German). Special Publ. no. 5, Wissenschaftlich-Technischer Informationsdienst des Zentral Geologischen Instituts, Nordhausen, Germany.

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Kortiim, F. (1961b) Ober den Zusammenhang zwischen dem Energie- und Niederschlagsdargebot und der Gesamtverdunstung (On the connection between available energy and precipitation, and total évapotranspiration, in German). Z.f Meteorol 15(1), 189-192.

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APPENDIX

Application of the Glugla method

The procedure for using the Glugla method is illustrated with data from lysimeter Y102C at Coshocton, Ohio (NAEW). The given inputs are as follows: the crop is


meadow, the soil parameter G0.02 is 51%, the "wet biomass" crop yield is 2690 t ha" , long-term average potential evaporation is assumed to be 1020 mm year"1

(McGuinness & Bordne, 1972), long-term average annual measured runoff is 14 mm year"1, and long-term average annual precipitation reaching the ground is 1062 mm year"1. Using Fig. 3, the CYC is 33. The CYC for meadow depends on crop yield only, and not soil type, unlike for other crops shown in Figs 4 and 5. If other crops were used, the graphs would be entered from each axis, and a CYC value would be interpolated. Furthermore, if Fig. 4 is required, then a crop-yield adjustment factor is needed as explained earlier. Once the CYC is found from Fig. 3, 4, or 5, then soil type and CYC values are used in Fig. 2 to obtain a value of the Bagrov factor, n (here n = 2.7). Next, the ratio ofPcff/Ep is computed as 1.03, and Fig. 1 is entered on the abscissa at this value. The intersection of the vertical line at this value and the interpolated curve representing n = 2.7 yields the value of EJEP of 0.82. The value of Ea is then found to be 836 mm. Groundwater recharge, GWR is subsequently computed using equation (1) to be 212 mm.

Glugla et al. (1976) provide an example of use of the Glugla method in which GWR estimates are spatially determined (Fig. 9). Here, the area of interest is divided into hydrologically similar ("uniform") areas based first on depth to groundwater, and then on crop type, crop yield, land use, and soil type. A further area subdivision could include spatially varying P (Thiessen polygons or isohyets), and possibly Ep. One would determine the GWR for each "uniform" area to determine spatial variations in GWR, or total recharge for a region.

In the Glugla method, areas with groundwater less than 1 m from the surface, are assumed to evaporate at the potential rate (Ea = Ep to account for capillary rise of water in the soil). In wooded areas, Ea = Ep for areas with groundwater less than 2 m deep. Free-water body areas are assumed to evaporate at a higher rate than land areas.

Received 13 January 1998; accepted 11 January 1999

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