Agricultural water management

ELSEVIER Agricultural Water Management 28 ( 1995) 9-2 1

Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia

F.H.S. Chiew *, N.N. Kamaladasa, H.M. Malano, T.A. McMahon Department #Civil and Environmental Engineering, University of Melbourne Parkville, Victoria 3052,

Australia

Accepted 10 November 1994

Abstract

The Food and Agriculture Organisation (FAO) methods are recognisedas the international standard for estimating reference crop evapotranspiration (ET,). The Penman-Monteith method is currently favoured by the FAO over the FAO-24 methods. The FAO also recommends alternative methods which may be used where there are limited data. In this paper, ET, estimated using the Penman- Monteith and FAO-24 methods and class-A pan data for 16 Australian locations with a wide range of climate conditions are compared. The analyses indicate that the FAO-24 Penman ET, estimates are generally 20 to 40% higher than the Penman-Monteith estimates. However, the FAO-24 Radiation and Penman-Monteith methods give similar daily ET, estimates. Unlike Penman-Monteith, which also requires windspeed data, the FAO-24 Radiation method estimates ET, from temperature and sunshine hours, climate variables which are relatively conservative in space. The FAO-24 Radiation method can thus be used as a surrogate for Penman-Monteith to estimate daily ET, for areas where windspeed data are not available. The FAO-24 Blaney-Criddle method, which uses only temperature data, gives similar monthly ET, estimates as Penman-Monteith, and is therefore adequate for appli- cations where only long-term ET, estimates are required. The comparisons also show that there is a satisfactory correlation between class-A pan data and Penman-Monte&h ET, for evaporation totals over 3 or more days. However, the pan coefficient is very dependent on local climate and physical conditions, and it should be determined by comparing the pan data with either the Penman-Monteith or FAO-24 Radiation ET, estimates.

Keywords: Reference crop evapotranspiration; Penman-Monteith; Pan evaporation; FAO-24; Australia

1. Introduction

The estimation of crop water requirement is an important component in irrigation and agricultural water research, management and development. The Food and Agriculture

* Tel.: +61 3 344 6644. Fax: +61 3 344 6215. Email: fchs@engineering.unimelb.edu.au

0378.3774/95/$09.50 0 1995 Elsevier Science B.V. All rights reserved SSDIO378-3774(95)01172-2

10 F. Clziew et al. /Agricultural Water Management 28 (1995) 9-21

Organisation (FAO)-24 methodology (Doorenbos and Pruitt, 1977) is considered as the international standard for predicting crop water requirement and has been extensively used worldwide by irrigation engineers, agronomists and hydrologists. The first step in the FAO methodology considers the effect of climate by calculating the reference crop evapotran- spiration (ET,,) which is defined in FAO-24 as “the rate of evapotranspiration from an extensive surface of 8 to 15 cm tall, green grass cover of uniform height, actively growing, completely shading the ground and not short of water”. Crop coefficients and other factors which depend on the crop characteristics and local conditions are then used to convert ET, to the crop water requirement. This paper addresses only the estimation of ET,,.

The FAO-24 report (Doorenbos and Pruitt, 1977) recommends four methods for esti- mating ET, depending on data availability -Penman, radiation, Blaney-Criddle (tempera- ture) and pan evaporation. However, recognising that much research has been carried out since 1977, the FAO established an expert consultation in 1990 to revise the methods proposed in FAO-24. One of the main recommendations by the expert consultation (Smith et al., 1992) is to replace the FAO-24 Penman method with the Penman-Monteith method. According to FAO- 1992 (Smith et al., 1992), Penman-Monteith gives more consistent ET,, estimates and has been shown to perform better than other ET,, methods when compared with lysimeter data. The standardisation of certain parameters in the Penman-Monteith equation requires a revision of the FAO-24 ET,, definition. The FAO- 1992 Penman-Mon- tieth defines ET, as the “the rate of evapotranspiration from a hypothetic crop with an assumed crop height ( 12 cm) and a fixed canopy resistance (70 s m- ‘) and albedo (0.23) which would closely resemble evapotranspiration from an extensive surface of green grass cover of uniform height, actively growing, completely shading the ground and not short of water’ ’ .

There are two parts to this paper. In the first part, ET, totals over different time periods estimated by the various methods for 16 Australian locations are compared. The aim here is to assess how the various methods compare with what is now considered to be the standard FAO Penman-Monteith ET,, method. In the second part, the relationship between Penman- Monteith estimates and class-A pan data is assessed. The climate data required to use Penman’s combination equation are not always available and, often, ET, is approximated as a factor (pan coefficient) times standard evaporation pan reading. The second part of this paper thus investigates whether pan data can be used successfully as a surrogate for Penman-Monteith ET,. Based on the analysis for these locations, appropriate methods for estimating ET, given the data availability, are discussed.

2. Data and method of computation

2.1. Climate data

Daily data recorded at 16 climate stations by the Australian Bureau of Meteorology are used for this study. The locations are given in Fig. 1 while Table 1 summarises the average climate characteristics. The spatial distribution provides a representation of the wide range of climate conditions throughout Australia. Fourteen years of data ( 1976-1989) are used for Mount Isa and Laverton, 16 years ( 1974-1989) for Sydney and 17 years of data ( 1973-

F. Chiew et al. /Agricultural Water Management 28 (199.5) 9-21 11

Fig. 1. Locations of climate stations.

1989) are used for Cobar and Tamworth while the other 11 stations have 20 years of data ( 1970-1989). The data used are: maximum and minimum temperatures; average, maximum and minimum relative humidities; wind speed and ratio of day time to night time wind; sunshine hours; class-A pan evaporation.

The daily totals of sunshine hours, pan evaporation, wind run (below 3 m) and the maximum and minimum temperatures are recorded directly by the Bureau. The humidity and ratio of daytime to night-time wind are compiled by analysing the Bureau’s 3-hourly instantaneous readings. They are computed only if there are more than four instantaneous readings over one day. Cloud cover data are used to infill the missing sunshine hours data using the regression equations given in Chiew and McMahon ( 1991). Apart from sunshine hours, other missing data are not infilled and days with missing data are not used for the analysis. There are three locations with more than 3% missing pan data. However, there is generally less than 1% missing data in the other climate variables.

2.2. Computation of ET,

The equations used by the various methods to estimate ET, are summarised in Table 2 and the data requirements are given in Table 3. The psychrometric terms (e,, ed, A, -y) are

12

Table 1

F. Chiew et al. /Agricultural Water Management 28 (1995) 9-21

Average climate characteristics of the 16 locations used for this study

LOCXiO" Altitude Annual Maximum Minimum Mea” Mea” Ratio of Ratio of Mean daily

(m) rainfall daily daily daily daily day time sunshine class-A pan

(mm) temperature temperature relative wind to night- hours to evaporation

(“C) (“C) humidity speed time maxi- (mm) (km wind mum

day-‘) possible

daylength

Alice

Springs

540 280

Brisbane 40 1200

Cairns 0

Canberra 570

Ceduna 20

2000

630

310

Cobar 220

Giles 580

Halls Creek 410

410

260

510

Lawton 20 570

Mount

Gambier 60 720

Mount Isa 340

Perth 20

Sydney 10

Tamworth 400

420

800

1100

670

Tennant

Creek

380

Woomem 170

430

200

33

24

28

23

31

27

25

15

27

20

31

19

34

24

36

31

24

16

23

15

35

28

28

20

25

19

21

I7

35

28 16 0.36 249 1.4 0.88 10.0

31 17 0.44 283 1.3 0.78 11.0

20 9 0.60 222 1.6 0.74 4.9

18

20

12

23

19

11

2

14

17

8

21

11

23

16

12

10

6

22

13

17

17

10

14

23

0.41

0.50 138 5.3 0.82 5.7 0.74 92 2.5 0.60 5.3 0.71 70 2.2 0.69 3.4

0.78 204 1.8 0.53 6.4

0.74 251 1.5 0.67 5.7

0.66 191 2.9 0.64 6.5

0.77 197 2.2 0.59 2.8

0.63 328 1.8 0.72 8.3

0.70 258 1.7 0.66 4.2

0.46 208 1.8 0.75 9.4

0.64 176 1.8 0.72 3.9

0.35 237 1.3 0.76 12.2

0.42 194 1.5 0.84 6.1

0.50 165 4.0 0.69 9.6

0.37 151 3.1 0.88 8.2

0.69 284 1.8 0.56 6.1

0.78 264 1.5 0.47 2.7

0.71 265 2.4 0.56 5.4

0.82 266 1.6 0.46 2.1

0.45 200 2.4 0.74 10.1

0.41 187 2.3 0.87 7.4

0.60 114 1.6 0.77 6.6

0.72 83 1.8 0.61 3.0

0.73 321 1.9 0.56 6.1

0.71 262 2.0 0.64 3.7

0.58 201 3.0 0.71 7.8

0.68 174 2.2 0.7 1 3.5

0.43 246 1.6 0.75 12.7

181 5.1 0.78 10.3

For each location, first row is November-April averages (summer-half) and second row is May-October averages (winter-half)

for the period of data used in the study

calculated from temperature and humidity records and the radiation terms are calculated using sunshine hours data. To be consistent, all the variables in Table 2 are computed using the equations and procedures recommended in the FAO- 1992 report.

F. Chiew et al. /Agricultural Water Management 28 (1995) 9-21 13

Table 2 Equations for computing reference crop evapotranspiration (in mm day-‘)

Penman-Monteith

0.408A(R,, - G) + $O;7$I(e, - ed)

A + $1 + 0.34U)

FAO-24 Penman

c[O.4O&R,, - G) + 2. 7L(1 + 0.864U)(e,, - ed)] A+Y A+r

FAO-24 Radiation

FAO-24 Blaney-Criddle

FAO-24 Pan

‘c’ depends on shortwave radiation, maximum relative humidity, daytime wind speed and ratio of daytime to night-time wind. c[O.408WR,] W depends on temperature and altitude ‘c’ depends on mean relative humidity and daytime wind speed. c[p(0.46T+ S)] p is daily percentage of total annual daytime hours and depends only on the latitude and time of year. ‘c’ depends on minimum relative humidity, sunshine hours, and daytime wind speed. c [pan reading] ‘c’ is a pan coefficient. FAO-24 provides recommended values depending on long-term average of mean relative humidity, wind speed and distance of windward side of green crop.

R, is net radiation at crop surface (MJ m-’ day-‘). R, is shortwave radiation (MJ m-’ day-‘). G is soil heat flux (MJ m-’ day-‘). 7’ is average daily temperature (“C). (I is wind speed at 2 m above ground surface (m s- ’ ). e, is saturation vapour pressure at air temperature (kPa). e,, is actual air vapour pressure (kPa) A is slope of saturation vapour pressure/temperature curve (kPa ‘C-‘). y is psychrometric constant (kPa “C- ‘).

Although daily ET, is calculated using daily data, the correction factor (‘6 in Table 2) is computed with the procedures outlined in FAO-24 using monthly averages (i.e. average of all the daily values in the month) of the climate conditions. The daily data are not used directly because in many cases there are insufficient daily data (e.g. there may only be one or two instantaneous night-time wind speed record for the day) to provide a meaningful representation of the terms required to calculate the correction factor. As monthly averages are used to calculate the correction factors, the FAO-24 radiation method requires only continuous daily temperature and shortwave radiation data and the Blaney-Criddle method estimates ET, using essentially only temperature data. The Penman-Monteith and FAO-24 Penman methods, however, require various types of temperature, relative humidity, wind- speed and radiation data.

2.3. Methods for assessing correlation

Three dimensionless coefficients based on the following statistical measure are used in this study to assess the correlation between the various ET, estimates

14 F. Chiew et al. /Agricultural Water Management 28 (1995) 9-2I

k(X;-X)‘- ~(x;-ki)2 ;= I ;=I

k (xi-x>’ i=l

(1)

where X is the mean of the X values. The linear regression is commonly used to describe the association between two variables,

X and Y (e.g. ET, and pan data). Here, Xi in Eq. ( 1) is given by ( Xi = a + bYi) where a and b are the intercept and gradient, respectively, of the line of best fit relating X and Y and the statistical measure in Eq. ( 1) is then called the coefficient of determination, R’.

In assessing the agreement (and not only the association) between the variables X and Y (e.g. ET, values computed using two different methods), Xi in Eq. ( 1) should be set to ( Xi = Yi) and the statistical measure in Eq. ( 1) is now called the coefficient of efficiency, E. Thus, while R2 provides an indication of the closeness of the data points to the line of best fit, E gives an indication of the closeness of the data points to the 1: 1 line in the X-Y plot.

One other measure is defined here where the line of best fit is forced through the origin (e.g. a single pan coefficient to relate ET, and pan data). Here, Xi is given by ( Xi = gYi) where g is the gradient of the straight line forced through the origin and, for the purpose of this study, the statistical measure for this relationship will be called the coefficient of determination for regression line forced through the origin, Rz.

A value of R2, E or Rg close to unity indicates a high degree of association or agreement

Table 3 Climate data requirements for the Penman-Monteith and FAO-24 methods

Average daily

Penman- Monteith

Y

FAO-24 FAO-24 Penman corrected (C’l) Penman

Y Y

FAO-24 radiation

Y

FAO-24 Blaney- Criddle

Y temperature Maximum temperature Minimum temperature Average relative humidity Maximum relative humidity Minimum relative humidity Total wind speed Ratio of day/night wind Daytime wind Sunshine hours

Y Y Y

Y Y Y

Y Y Y Y

Y Y Y

Y Y Y Y

Y Y Y

Y

Y Y Y Y Y Y Y Y

Y indicates that continuous recorded data are required. y indicates that only estimated data are required. In general, estimates of long-term conditions may be used.

F. Chiew et al. /Agricultural Water Management 28 (1995) 9-21

L

IJ Blaney-Criddle

2 4 6 8 10 -2 4 6 8 Penman-Monte&h ETo (mm/day) Penman-Monteith ETo (mm/day)

Fig. 2. Comparison of average monthly ET, (for the 16 locations) estimated using Penman-Monteith and FAO- 24 methods.

between the two variables. The value of R2 is always between 0 and 1, whereas E and Rz can take negative values. The value of E or R: is always smaller than R2 except when the line of best fit is Y=X, in which case all three coefficients are the same, or, when the line of best fit passes through the origin, the values of Rz and R* are the same.

2.4. Computation of ET, using climate averages over different time periods

The total ET, for more than 1 day can be calculated using the average climate condition over the period as recommended by FAO or as the sum of ET, computed for the individual days. However, the comparison of 5-day, lo-day and monthly ET,, values calculated using average climate conditions over the period and the total daily ET, values over the same period indicate that there is little difference between the two ET,, estimates. Analysis using all data for the 16 locations and ET, estimated using the Penman-Monteith and FAO-24 methods indicate that the coefficient of efficiency relating ET, calculated using the two different approaches always exceeds 0.95. Therefore, in the following comparisons, ET, is computed using only either daily data or monthly averages. The 3-day, 5-day and lo-day ET, values quoted in the following discussion are simply totals of daily ET, values over the period.

3. Comparison of Penman-Monteith and FAO-24 ET, estimates

The plots in Fig. 2 compare the average monthly ET, for the 16 locations estimated using Penman-Monteith and FAO-24 methods. Table 4 summarises the agreement between the ET, values estimated using the Penman-Monteith and FAO-24 methods given by the coefficient of efficiency, E.

Analyses of the results indicate that although there is a high correlation between Penman- Monteith and FAO-24 Penman (R* in excess of 0.9 for all locations), the actual values of the two estimates are very different. The values of E relating Penman-Monteith and FAO-

16 F. Chiew et al. /Agricultural Water Management 28 (1995) 9-21

Table 4 Coefficient of efficiency (E) which provides a measure of the agreement between ET, values estimated using Penman-Monteith and the FAO-24 methods (summary of values for the 16 locations)

Penman (c= I) Corrected Penman Radiation Blaney-Criddle Pan

E jtifbr correlation of’monthly values

Mean 0.25 0.06 Median 0.42 0.33 Lowest - 1.4 - 2.4

(Cairns) (Cairns) Highest 0.8 1 0.79

(Mt Isa) (Mt Isa) Ejor correlation oj’daily values

Mean 0.44 0.34 Median 0.48 0.46 Lowest -0.20 - 0.73 Highest 0.81 0.80

0.80 0.93 0.75 0.90 0.97 0.88 0.22 0.55 0.01 (Halls Ck) (Halls Ck) (Tennant Ck) 0.97 1.0 0.97 (Laverton) (Laverton) (Sydney)

0.71 0.58 0.80 0.71 0.22 - 0.33 0.9 I 0.90

E values in first column are for Penman-Monteith/FAO-24 Penman (c= 1) correlations, E values in second column are for Penman-Monteith/FAO-24 corrected Penman correlations, E values in the third column are for Penman-Monteith/FAO-24 radiation correlationsand so on.

24 Penman (both for c = 1 and for values corrected for wind and humidity conditions) are less than 0.5 at almost all locations. The FAO-24 Penman estimates are generally 2&40% higher than the Penman-Monteith estimates (see Fig. 2). This is consistent with the com- parison presented in the FAO-1992 report.

There is reasonably good agreement between the Penman-Monteith and FAO-24 radia- tion methods although, on average, the radiation method ET, estimates are lO-20% higher than the Penman-Monteith estimates (see Fig. 2). The values of E for correlation of daily and monthly ET, estimated using the two methods are greater than 0.6 at 13 of the 16 locations. Despite the high degree of empiricism in the Blaney-Criddlemethod, the monthly Blaney-Criddle ET, estimates are similar to Penman-Monteith, with the E values for monthly correlations between the two methods exceeding 0.8 at 15 of the 16 locations. The FAO does not recommend the Blaney-Criddle method for estimating daily ET,. The plots in Fig. 3 show comparisons of Penman-Monteith, FAO-24 radiation and Blaney-Criddle ET, estimates for two selected sites with E values of about 0.75 (about the average of E values at the 16 locations). In general, the correlations between the different methods are highest in autumn and lowest in winter when errors associated with the low evapotranspir- ation rates are high.

The values of E for correlation of monthly ET, estimated using Penman-Monteith and the FAO-24 methods are plotted against the mean annual rainfall and mean relative humidity in Fig. 4. Although the various methods (in particular, the radiation method) should show better agreement in wetter and more humid regions where the aerodynamic term in com- puting ET, is less important, this is not evident from the analysis using this data set. In fact, the plots in Fig. 4 suggest that both the radiation and BlaneyCriddle monthly ET, estimates are similar to the Penman-Monteith estimates even in the drier regions.

The radiation method estimates ET, from daily temperature and shortwave radiation (or sunshine hours) data, climate variables which are relatively conservative in space and can

F. Chiew et al. /Agricultural Water Management 28 (1995) 9-21 17

Monthly ETo for Cairns /

q Blaney-Criddle

0 5 10 15 20 2 4 6 8 Penman-Monteith ETo (mm/day) Penman-Monte&h ETo (mm/day)

Fig. 3. Typical comparisons of Penman-Monteith, FAO-24 Radiation and FAO-24 Blaney-Criddle ET, estimates.

be transferred some distance with minimal error. Although net radiation is not officially recorded by the Australian Bureau of Meteorology, shortwave radiation is recorded at a limited number of sites. Unlike Penman-Monteith, the radiation method does not require windspeed data, information which can vary considerably over short distances and is not

(negative E values are plotted as zero)

c

II

$3 g Y 0.2 - 8 + >: EX

X

5.2 0.0 -L I I

OG 0 500 1000 1500 2000 ‘3 Q) SO Mean annual rainfah (mm)

gt; 1.0 B 0

q Blaney-Griddle 0 ,x >

g$ 0.8 - fl +x X

n l rp= +a

+ + +B

A += ++

Fig. 4. Coefficient of efficiency (E) for correlation of monthly ET, estimated using Penman-Monteith and the FAO-24 methods plotted against mean annual rainfall and mean relative humidity.

I8 F. Chew et al. /Agricultural Water Munagernent 28 (1995) 9-21

readily available at all standard Australian climate stations. As such, in areas where wind data are limited, the FAO-24 radiation method can be used as a surrogate for Penman- Monteith to estimate daily ET,. The FAO-24 Blaney-Criddle method, which uses only monthly temperature data (and long-term average humidity, windspeed and radiation infor- mation), gives similar monthly ET, estimates as Penman-Monteith, and is therefore ade- quate for applications where only long-term ET, estimates are required.

The FAO-24 Pan ET, is calculated as the class-A pan data multiplied by the pan coefficient derived (from long-term wind and humidity information) using the procedure in the FAO- 24 report. Although the pan coefficient is very dependent on local climate and physical conditions, the FAO-24 Pan ET, estimates show reasonable agreement with the Penman- Monteith estimates, with the daily correlations (E) exceeding 0.6 at 12 of the 16 locations. However, at three locations, the E values for daily correlations are less than zero and the E values for monthly correlations are less than 0.2. The Penman-Monteith/FAO-24 pan correlations are also poorer than the Penman-Monteith/FAO-24 radiation correlations. Thus, while the pan coefficient should always be calibrated for local conditions against the Penman-Monteith (or FAO-24 Radiation) ET,, estimates, the analyses here suggest that where data are limited, the FAO-24 tables can be used to determine reasonably accurate pan coefficient values for some locations.

4. Class-A pan evaporation and Penman-Monteith ET,,

Two methods are used to assess the association between class-A pan data and Penman- Monteith ET,, estimates; line of best fit (ET, = a + b PAN) and regression line forced through the origin (ET, =g PAN). Data over the four seasons are also considered separately. The comparisons are carried out for daily, 3-day, 5-day and IO-day totals. The analyses indicate that R* for the line of best fit and Rz for the regression line forced through the origin are practically the same, except in winter and for daily totals, where R* is generally lO--15% greater than R$ As the use of an additional parameter (intercept and gradient as opposed to the gradient alone) does not improve the correlation significantly, only the latter method is considered in the following discussion. It is also physically more meaningful to use the regression line forced through the origin compared with the line of best fit. This is because the use of an intercept in the line of best fit may imply that there is a finite (or even negative) value of ET, even when the pan reading is zero. In addition, the magnitude of the intercept would vary considerably for ET, totals over different time periods. The regression line forced through the origin also follows the concept of pan coefficient.

The plot in Fig. 5 illustrates the correlation, Rz, of the relationship, Penman-Monteith ET, = g PAN, for evapotranspiration totals over different time periods for different seasons at the 16 locations. Fig. 6 shows some typical comparisons of Penman-Monteith ET, and pan evaporation for daily and 3-day totals for summer and winter months. The correlations are significantly lower during winter when evapotranspiration is low compared with the other seasons. In general, higher correlations are obtained for the higher values of evapo- transpiration owing to a smaller percentage error associated with higher pan evaporation readings.

The daily correlations (Rz) are generally less than 0.7 at most locations and, in winter,

F. Chiew et al. /Agricultural Water Management 28 (1995) 9-21

(squares represent average of the Ro2 values at the 16 locations)

1 -day mot xx LXXX 3-day X#< XX_

S-day x m mQooocm lo-day X w xDcxxm

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ., xx SC >43(xx XII

xx WX q Xanu x x X a.

X X x x0= . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

X X x xx=xxKKx xx X x x=l3<nBoc x

X xx XX13<#om x X z xx q XXocrr>o<x

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

)I( x xrox##x xx xaxrrx

xx x xC3>o#cx x x X Wa_#KX

I I I

19

0.0 0.2 0.4 0.6 0.8 1.0

Coefficient of determination for regression line forced through the origin (Ro2)

Fig. 5. Correlations for the regression Penman-Monteith ET,= coefficient times class-A pan for the 16 locations for the four seasons.

2o 1 Ceduna (Summer) I

MI Gambier (Winter)

-0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7

ETo = 0.61 PAN

0 , &%I.78 N&O0

0 5 10 15 20 25

Class-A pan evaporation (mm/day)

Fig. 6. Typical regressions between Penman-Monteith ET, and class-A pan for daily and 3-day totals in summer and winter.

20 F. Chiew et al. /Agricultural Water Management 28 (1995) 9-21

Table 5 Gradient (pan coefficient) relating class-A pan data to Penman-Monteith ET,

Summer ( Dee-Feb) Autumn (Mar-May) Winter (Jun-Aug) Spring ( Sep-Nov)

Alice Springs 0.62 0.61 0.6 1 0.64 Brisbane 0.78 0.75 0.66 0.74 Cairns 0.69 0.74 0.69 0.69 Canberra 0.67 0.65 0.61 0.68 Ceduna 0.68 0.68 0.69 0.70 Cobar 0.65 0.64 0.68 0.66 Giles 0.61 0.57 0.56 0.63 Halls Creek 0.67 0.63 0.60 0.62 Laverton 0.72 0.69 0.69 0.73 Mount Gambier 0.75 0.76 0.81 0.80 Mount Isa 0.67 0.65 0.65 0.65 Perth 0.73 0.67 0.64 0.77 Sydney 0.74 0.72 0.67 0.74 Tamworth 0.69 0.68 0.7 1 0.69 Tennant Creek 0.58 0.56 0.56 0.57 Woomera 0.63 0.63 0.64 0.66

the average of the daily correlations at the 16 locations is only 0.5. However, the correlations improve considerably when the 3-day, 5-day and IO-day totals are considered (see Fig. 5). Apart from winter, Rz for the 3-day totals exceed 0.7 at 13 of the 16 locations. Except for winter at one location, the Ri values for the 3-day totals are always greater than 0.5. The main reason for the higher correlations is the cancellation of positive and negative differ- ences between daily estimates of Penman-Monteith ET, and pan readings. Fig. 5 also shows that the correlations of the 5-day and lo-day totals are similar to the correlations for the 3- day totals.

The ‘pan coefficients’ for the different seasons derived for the 16 locations are given in Table 5. The coefficients are for 3-day totals, but there is little difference between the coefficients for ET, totals over the different time periods. The pan coefficients also do not vary considerably over the different seasons. However, the values for the different locations can be quite different, highlighting the dependence of the pan coefficients on local condi- tions. The pan coefficients in Table 5 are generally between 0.6 and 0.75 with a mean value of 0.65.

The ET, values calculated as ET, = g PAN, using the coefficients (g) in Table 5, are also compared with the Penman-Monteith ET, estimates to assess directly the agreement between the two estimates. The mean value of E averaged over the 16 locations (presented in a format similar to Table 4) for the daily correlations is E = 0.80 (lowest E = 0.59, median E = 0.84, highest E= 0.89) and for the monthly correlation is E = 0.94 (lowest E = 0.85, median E = 0.96, highest E = 0.98). The ET, values estimated using the above relationship are therefore similar with the daily ET, values estimated using the FAO-24 radiation method and the monthly ET,, values estimated using the FAO-24 radiation and Blaney-Criddle methods (see Table 4).

The results suggest that class-A pan data can give satisfactory estimates of Penman- Monteith ET, totals over 3 or more days if a reliable pan coefficient is used. In deriving the

F. Chiew et al. /Agricultural Water Management 28 (1995) 9-21 21

values in Table 5, some of the climate conditions affecting pan data have been inevitably accounted for. As such, like the coefficients recommended by FAO-24, the values in Table 5 can also be used to guide selection of pan coefficients in these regions.

5. Conclusions

There are three main conclusions from this study where ET,, values estimated using various methods and class-A pan evaporation for 16 Australian locations with a wide range of climate conditions are compared. First, the Penman-Monteith and FAO-24 Penman methods give different ET,, estimates, with the FAO-24 Penman estimates being consistently 2&40% higher than the Penman-Monteith estimates. Given that Penman-Monteith is the current standard method recommended by FAO, ET, values calculated using FAO-24 Penman should therefore be used with caution.

Second, the FAO-24 radiation, FAO-24 Blaney-Criddle and Penman-Monteith give similar monthly ET, estimates. The Blaney-Criddle method, which uses only temperature data (and some long-term average climate information), is therefore adequate for applica- tions where only monthly estimates of ET, are required. The radiation method also gives similar daily ET,, estimates as Penman-Monteith. Unlike Penman-Monte&, which also requires wind data, the FAO-24 Radiation method estimates ET, from temperature and sunshine hours, climate variables which are relatively conservative in space. The FAO-24 radiation method can thus be used as a surrogate for Penman-Monteith to estimate daily ET, for areas where wind data are not available.

Third, there is a satisfactory correlation between class-A pan data and Penman-Monteith ET,, for evaporation totals over three or more days. However, pan data is useful only if an accurate pan coefficient is used to relate the pan data to Penman-Monteith ET,. The pan coefficient is very dependent on local conditions and should be determined by comparing the pan data with the Penman-Monteith (or FAO-24 radiation) ET, estimates. As most climate variables are relatively conservative in space, suitable pan coefficients may be derived by comparing pan data and ET,, estimated using climate data from a nearby station. In areas where climate data are not available, the FAO-24 tables can be used to guide the selection of appropriate pan coefficient values.

References

Chiew, F.H.S. and McMahon, T.A., 1991. The applicability of Morton’s and Penman’s evapotranspiration esti- mates in rainfall-runoff modelling. Wat. Resour. Bull., 27(4): 61 l-620.

Doorenbos, J. and Pruitt, W.O., 1977. Guidelines for Predicting Crop Water Requirements. Food and Agriculture Organisation of the United Nations, FAO Irrigation and Drainage Paper 24, Rome, 143 pp.

Smith, M., Allen, R., Monteith, J.L., Perrier, A., Santos Pereira, L. and Segeren, A., 1992. Expert Consultation on Revision of FAO Methodologies for Crop Water Requirements. Food and Agriculture Organisation of the United Nations (Land and Water Development Division), Rome, 60 pp.