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Resource management technical reports Natural resources research

1-1-1987

Evaporation data for Western Australia Evaporation data for Western Australia

G J. Luke K L. Burke T M. O'Brien

Follow this and additional works at: https://researchlibrary.agric.wa.gov.au/rmtr Part of the Water Resource Management Commons

Recommended Citation Recommended Citation

Luke, G J, Burke, K L, and O'Brien, T M. (1987), Evaporation data for Western Australia. Department of Primary Industries and Regional Development, Western Australia, Perth. Report 65.

This report is brought to you for free and open access by the Natural resources research at Digital Library. It has been accepted for inclusion in Resource management technical reports by an authorized administrator of Digital Library. For more information, please contact [email protected].

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Evaporation Data for Western Australia

G.J. Luke, K.L. Burke and T.M. O’Brien

Resource Management Technical Report No. 65

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Disclaimer

The contents of this report were based on the best available information at the time of publication. It is based in part on various assumptions and predictions. Conditions may change over time and conclusions should be interpreted in the light of the latest information available.

 Chief Executive Officer, Department of Agriculture Western Australia 2003

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Contents

Evaporation Data for Western Australia ...1

Summary ...1

1. Evaporation Data for Western Australia ...2

2. Variability of Evaporation...19

3. Estimation of Evaporation Losses from Dams...25

4. Use of Data for Irrigation Scheduling ...31

5. References...32

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1

Summary

While parts of Western Australia have reliable, though seasonal rainfall, large areas are extremely arid. It is necessary, therefore, that water supplies be stored both for irrigation and for stock water. Estimating the quantities required relies heavily on accurately estimating the evaporation demand.

This report presents the latest evaporation data for Western Australia. The information is presented in four parts:

1. Evaporation data.

2. Variability of evaporation.

3. Estimates of losses from dams.

4. Use of data for irrigation scheduling.

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1. Evaporation Data for Western Australia

Until the late 1960s, most evaporation measurements in Western Australia came from standard metal sunken tanks. Some of these were galvanized iron and some copper. Other measurements came from concrete tanks and evaporating dishes.

The standard of care, presence or absence of bird guards, occurrence of leaks etc, made much of the data of questionable value.

Recently, the Bureau of Meteorology discarded all data except the Class A pan (with bird guard) records. It is the recently accepted data upon which maps 1 to 13 and tables 1 and 2 are based.

The evaporation maps are taken directly from Bureau of Meteorology maps which show evaporation isopleths (in millimetres) for Class A pan evaporimeters, fitted with bird guards. Next to each isopleth the figure in brackets is the estimated evaporation from a Class A pan with the bird guard removed. This figure is included because it is the common way in which evaporation data is used, and the internationally accepted expression of evaporation. A bird guard reduces the evaporation by 7% (Hoy and Stephens, 1979).

The data in table 1 (agricultural centres) and table 2 (pastoral centres) is the Class A pan data (without bird guards) from maps 1 to 13. These data were prepared by extrapolating between the isopleths for each centre. The same technique can be used to prepare data for any other centre.

Table 1. Monthly and annual Class A pan evaporation rates for various centres in the agricultural areas/ in mm

Station Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann.

Ajana 397 367 324 217 160 99 99 127 150 246 304 384 2874

Albany 220 171 150 91 63 47 49 67 84 106 150 199 1397

Armadale 297 257 224 123 87 59 60 69 106 154 203 259 1898 Berkshire

Valley

381 343 304 183 117 74 73 96 127 200 276 373 2547 Brookton 336 276 233 137 86 51 53 64 101 153 220 294 2004 Corrigin 381 301 260 153 91 54 54 73 110 177 243 339 2237 Cranbrook 267 230 184 110 67 44 47 61 89 119 176 257 1651 Dandaragan 353 293 283 166 113 77 74 97 119 187 264 350 2376 Donnybrook 221 187 150 84 63 47 50 63 73 109 149 203 1399 Esperance 266 211 193 133 93 64 79 90 114 151 191 254 1840 Geraldton 361 337 313 207 151 103 89 113 140 216 280 366 2676

Gingin 330 296 259 149 96 66 63 91 114 173 239 321 2197

Goodlands 439 391 337 214 133 81 86 109 144 239 311 413 2897 Holt Rock 380 284 247 160 97 67 79 89 121 191 253 331 2300

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Jerramun- gup

310 224 197 127 83 51 60 76 103 147 194 266 1839 Kalgoorlie 431 346 306 199 133 93 103 130 181 271 326 424 2943 Katanning 304 237 209 121 79 47 50 64 96 137 199 283 1826 Kondinin 379 296 256 156 94 59 60 77 113 180 247 339 2254 Lake Grace 357 269 231 147 90 57 57 76 110 163 227 313 2097 Lake King 354 261 227 151 94 67 77 89 117 179 234 304 2156

Manjimup 211 173 149 86 66 47 56 59 77 101 143 199 1366

Margaret River

159 157 69 57 53 47 49 49 53 77 129 171 1069

Medina 273 249 210 119 80 61 60 70 100 149 197 261 1829

Merredin 420 347 309 184 110 70 74 90 129 220 286 390 2629

Moora 366 333 291 173 113 73 70 91 124 193 269 360 2456

Mukinbudin 437 371 329 200 120 77 86 103 139 236 303 407 2807 Mullewa 437 393 339 231 160 97 96 130 159 259 316 416 3031 Munglinup 290 224 199 139 94 66 82 90 116 159 209 264 1932 Narembeen 397 319 279 167 100 67 69 83 120 200 201 361 2423 Narrogin 314 257 217 126 81 50 51 64 99 143 207 286 1896 Horseman 377 290 247 170 110 80 94 106 147 214 271 339 2446 Northam 366 311 263 150 91 56 57 69 110 167 236 329 2204

Pemberton 182 155 135 75 55 50 63 50 70 90 120 165 1210

Ravens- thorpe

314 234 204 143 93 67 79 89 113 163 216 273 1987 Rocky Gully 237 200 171 97 67 47 54 61 86 109 160 233 1523 Salmon

Gums

337 260 221 156 103 73 89 97 127 187 241 297 2189 Scaddan 300 234 219 142 97 68 83 93 121 169 220 272 2018 Southern

Cross

436 350 314 194 120 80 91 109 151 244 306 409 2804 Three

Springs

404 370 324 209 137 81 99 109 143 226 293 387 2781 Upper Swan 327 281 241 136 89 63 60 87 110 161 221 303 2079

Wagin 313 254 216 124 80 49 51 64 99 141 194 287 1873

Wialki 451 389 341 211 131 84 91 114 149 249 317 424 2953

Wokalup 233 211 163 87 70 57 56 69 80 114 160 220 1521

Wongan Hills

391 349 301 179 110 67 69 89 123 197 273 374 2521 Yanchep 293 279 240 143 96 76 66 91 117 161 220 300 2082

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Table 2. Monthly and annual Class A pan evaporation rates for various pastoral centres, in mm.

Station Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann.

Anna Plains 340 284 276 266 244 213 231 257 317 336 366 376 3506 Beverley

Springs

277 211 234 253 241 219 234 283 330 359 360 343 3344 Blina 311 241 254 274 256 224 259 299 363 384 394 390 3650 Bohemia

Downs

351 273 287 287 227 196 229 266 334 397 399 389 3634 Boolathana 369 317 309 210 167 127 127 150 174 253 300 300 2803 Broome 280 235 245 240 220 180 205 235 315 310 320 315 3100 Camballin 314 253 259 279 253 231 257 294 359 386 399 394 3677 Carnarvon 357 317 307 210 167 117 124 154 171 253 286 300 2764 Carnegie 596 457 420 299 193 139 157 206 281 310 419 493 4040 Christmas

Creek

340 261 279 283 240 206 243 277 347 394 399 390 3659 Coodardy 539 453 404 276 187 123 127 170 227 344 400 487 3737 Derby 291 236 244 269 254 231 253 291 350 364 391 373 3549 Earaheedy 596 457 416 304 195 143 154 208 282 387 437 511 4090 Exmouth 371 319 319 221 176 130 147 177 254 276 344 410 3144 Fairfield 309 236 253 270 249 219 244 283 347 380 390 381 3560 Fitzroy

Crossing

330 250 270 277 241 207 240 277 344 389 394 386 3606 Giralia 436 379 340 250 199 137 146 186 264 339 387 430 3424 Halls Creek 321 253 263 264 217 189 211 251 324 373 370 350 3387 Jiggalong 497 406 397 326 211 176 180 229 301 396 450 497 4066 Kalgoorlie 431 346 306 199 133 93 103 130 181 271 326 424 2943 Kalumburu 220 181 181 213 213 196 220 260 299 319 289 299 2889 Kimberley

Downs

300 236 244 270 250 237 254 293 350 381 390 386 3591 Kimberley

Res. St.

247 210 204 231 223 201 226 266 314 337 297 299 3056 Leonora 523 406 369 244 163 108 116 157 221 324 378 464 3473 Leopold

Downs

321 241 261 273 240 211 241 279 343 383 390 379 3563 Liveringa 316 254 260 279 259 221 267 290 361 386 399 394 3686 Meekath-

arra

585 484 412 293 203 135 142 187 263 399 452 513 4068 Melrose 566 435 401 265 174 123 124 169 247 353 403 486 3746 Millajiddee 344 274 286 290 239 206 249 283 350 400 407 401 3729

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Minderoo 406 343 326 259 193 139 149 191 269 330 381 417 3401 Minnie

Creek

474 422 385 276 193 143 146 193 268 376 421 470 3764 Mt Clere 536 457 393 299 186 143 149 194 267 397 451 507 3979 Mt

Elizabeth

263 207 227 241 233 203 230 271 323 354 351 330 3234 Mt Magnet 519 443 391 264 179 116 119 157 204 323 383 477 3574 Mt Newman 461 369 343 290 174 173 199 193 264 377 424 466 3733 Mt Tom

Price

424 321 319 264 190 170 177 190 274 371 409 460 3570 Muggon 486 426 369 267 186 120 121 164 223 313 390 441 3513 Napier

Downs

300 229 243 267 243 223 246 287 347 379 387 376 3526 Noonkan-

bah

334 264 276 286 244 211 251 286 356 394 403 397 3703 Ord River 296 233 241 249 217 194 211 250 307 387 351 326 3210 Paynes

Find

477 420 367 239 157 99 103 133 171 281 347 449 3243 Port

Hedland

364 320 317 271 246 197 219 246 329 321 374 386 3590 Quanbun 334 259 274 283 243 213 249 283 349 393 399 391 3669 Sandstone 551 447 397 267 177 116 121 164 226 341 403 491 3749 Sturt Creek 347 294 287 279 209 183 199 246 303 409 380 371 3464 Tableland 294 227 249 254 230 194 216 266 311 369 366 339 3314 Three

Rivers

547 473 430 304 186 144 157 203 271 397 451 537 4101 Wiluna 611 473 400 291 216 131 141 187 263 397 451 511 5440 Wooramel 424 390 327 236 171 116 116 153 209 279 361 386 3167 Wyndham 234 201 200 226 223 200 224 264 309 333 314 321 3050 Yeeda 300 243 249 273 253 233 254 291 354 391 394 386 3621

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19

2. Variability of Evaporation

In order to make use of the data for water budgeting it is necessary to have an understanding of the probable range or variability of evaporation rates.

Variation can be expressed in terms of the coefficient of variation, or the standard deviation.

Coefficient of variation = Standard deviation mean

The larger the coefficient of variation, the greater the possible variation from the mean. In the case of evaporation, a large coefficient of variation would suggest that the mean data is an unreliable indicator of possible evaporation rates, for budgeting purposes. It would be necessary to know the probability of exceeding any particular level of evaporation, so that economic decisions can be made based on the cost of increased storage, compared to the cost of failing to supply the total water need.

Evaporation data for a number of Western Australian centres has been analysed to determine the variation around the mean rates, as quoted in the tables and maps.

On an annual basis the coefficient of variation arranged 7% and varied between 4%

and 13% for different locations (Table 3). A similar analysis by McMahon and Srikanthan (1983) showed a range of between 3% and 15% for different centres around Australia, with an average of 7%.

Using the mean data from Tables 1 and 2, and the coefficients of variation in Table 3, the standard deviation can be calculated. Based on the mean and standard

deviation for each centre, the frequency with which different levels of evaporation will be exceeded can be calculated.

Assuming that evaporation data are normally distributed,

Mean + 1 x standard deviation will be exceeded 1 year in 3 (i.e. 66% reliability) Mean + 1.64 x standard deviation will be exceeded 1 year in 10 (i.e. 90%

reliability)

Mean + 2 x standard deviation will be exceeded 1 year in 20 (i.e. 95%

reliability)

Mean + 3 x standard deviation will be exceeded 1 year in 100 (i.e. 99%

reliability)

The frequency with which different levels of evaporation will be exceeded for a number of different centres in Western Australia are shown in Table 3.

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Table 3. Variation in annual evaporation for eleven locations in Western Australia

Evaporation which will be exceeded

Centre Mean Coefficient of

variation (%)

Standard deviation (mm)

1 year in 3 (mm)

1 year in 10 (mm)

1 year in 20 (mm)

Albany 1397 5 70 1467 1512 1537

Broome 3100 7 217 3317 3455 3534

Carnarvon 2764 6 166 2930 3036 3096

Esperance 1840 13 239 2079 2232 2318

Geraldton 2676 7 187 2863 2983 3050

Kalgoorlie 2943 8 235 3178 3328 3413

Kununurra 3056 5 153 3209 3307 3362

Merredin 2629 4 105 2734 2801 2839

Pemberton 1210 8 97 1307 1369 1404

Upper Swan

2079 6 125 2104 2284 2329

Wokalup 1521 4 61 1582 1621 1643

For any centre not shown in Table 3, the average coefficient of variation for annual evaporation of 7% may be used. The probabilities of experiencing different levels of evaporation are shown in Table 4.

Table 4. Evaporation rates likely to be exceeded with different frequencies for various mean evaporation zones

Evaporation which will be exceeded Mean evaporation

(50% probability)

1 year in 3 1 year in 10 1 year in 20

1000 1070 1114 1140

1500 1605 1672 1710

2000 2140 2230 2280

2500 2675 2787 2750

3000 3210 3344 3420

From table 4 it can be seen that in an area with an average annual evaporation of 2000 mm, in order to ensure a supply in 19 years out of 20, an allowance of 2280 mm must be made.

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21

Similar calculations can be made for monthly and daily evaporation rates. In Table 5 the coefficients of variation for monthly evaporation for Western Australian centres are shown.

Table 5. Variation in monthly evaporation for eleven centres in Western Australia (%)

Month

Centre Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Albany 9 9 10 15 14 12 8 10 27 12 12 10

Broome 15 13 10 13 15 13 12 9 12 11 8 12

Carnarvon 9 11 10 11 9 10 12 11 8 9 10 9

Esperance 13 15 17 19 19 22 19 31 15 29 14 12

Geraldton 12 29 13 16 18 11 11 14 14 11 10 12

Kalgoorlie 9 10 16 15 17 15 18 15 13 14 9 11

Kununurra 14 10 14 9 8 6 6 7 6 5 10 8

Merredin 7 8 5 14 16 16 24 14 16 12 11 7

Pemberton 11 12 8 15 17 14 23 10 16 12 14 8

Upper Swan

7 11 9 15 21 16 14 9 6 15 12 8

Wokalup 10 17 12 19 18 19 17 15 11 8 22 10

In general, the drier months, during which water budgeting is more critical, have a lower coefficient of variation of evaporation than the wetter months. The importance of the monthly variation will be realized in years of water shortage when a storage fails to fill. Under these circumstances an analysis of likely evaporation over the next few months will enable a management decision to be made on the area of annual crop to be planted, or the numbers of stock to be held. In table 6, calculations for a 95% reliable supply are given. Evaporation will only exceed these levels one year out of twenty.

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Table 6. Evaporation rate that must be allowed for to give 95% reliability of supply

Coefficient of variation of monthly evaporation (%) Mean

monthly Evaporation (mm)

8 10 12 14 16 18

200 232 240 248 256 264 272

250 290 300 310 320 330 340

300 348 360 372 384 396 408

350 406 420 434 448 462 476

400 464 480 496 412 428 444

450 522 540 558 576 594 612

500 580 600 620 640 660 680

In an area with a monthly evaporation rate of 300 mm and a coefficient of variation of 16% (e.g. Kalgoorlie in March) an allowance of 396 mm should be made for a 95%

reliable supply.

Similar calculations can be made for other levels of reliability, as discussed for annual rates.

e.g. How much evaporation should be allowed for to give a 90% reliability of supply at Esperance in November.

November evaporation (mean) = 191 mm Coefficient of variation for November = 14% (0.14)

Standard Deviation = 191 x 0.14 = 27 mm

90% reliability = Mean + (1.64 x Standard deviation)

= 191 + (1.64 x 27)

= 235 mm

During November in Esperance, evaporation in excess of 235 mm will only be exceeded one year in 10.

For general planning purposes, the annual and daily rates of evaporation are the most useful. The annual evaporation gives an estimate of total requirements, while daily evaporation indicates the daily demand. This daily demand is important when designing an irrigation system, which must replace daily evaporation/ or a reticulation system to supply stock water. Average daily evaporation rates can be calculated from tables 1 and 2. It is important to note however that the variation from these mean figures can be as high as 75% (Table 7).

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Table 7. Coefficient of variation for daily evaporation for various centres in Western Australia (%)

Month

Centre Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Albany 30 33 38 52 54 53 44 48 38 37 39 32

Broome 34 32 29 28 34 32 31 32 31 26 22 24

Carnarvon 23 27 26 30 35 38 38 34 26 22 23 21

Esperance 28 32 35 45 53 54 50 47 40 35 33 29

Geraldton 31 31 34 40 46 48 43 39 34 34 29 28

Kalgoorlie 26 31 35 41 49 52 50 45 40 36 31 27

Kununurra 37 35 32 21 22 21 20 18 19 18 24 28

Medina 26 29 32 37 50 56 53 40 39 30 32 24

Merredin 26 29 32 41 53 54 62 46 44 43 34 25

Pemberton 36 38 39 58 74 67 75 63 58 47 46 38

Upper Swan

26 39 39 58 59 76 67 56 41 53 37 33

Wokalup 31 36 38 51 62 67 70 54 42 44 39 34

As was the case with monthly data, the drier months (and those with highest evaporation) have a lower coefficient of variation.

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Calculating the range of daily evaporations for those months, using the same

technique as for annual rates, shows how much evaporation may be expected (Table 8).

Table 8. Evaporation rates that should be allowed for to cover 66%, 90%

and 95% of years

Percentage of years Centre Month with

peak daily evaporation

Average (mm)

66% 90% 95%

Albany January 7.1 9.3 10.6 11.3

Broome January 9.0 12.1 14.0 15.0

Carnarvon January 11.5 14.1 15.8 16.8

Esperance January 8.5 10.9 12.4 13.3

Geraldton January 11.6 15.2 17.5 18.8

Kalgoorlie January 13.9 17.5 19.8 21.1

Kununurra November 8.0 9.9 11.1 11.8

Medina February 8.8 11.4 13.0 13.9

Merredin January 13.5 17.0 19.2 20.5

Pemberton January 5.9 8.0 9.3 10.1

Upper Swan February 10.0 13.9 16.4 17.8

Wokalup January 7.5 9.8 11.3 12.2

The use of this information for budgeting purposes will be discussed in detail in sections 3 and 4 of this report.

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3. Estimation of Evaporation Losses from Dams

The evaporation data in tables 1 and 2 can be used for estimating evaporation losses from dams. There have been a number of attempts to relate Class A pan

evaporation to evaporation from a dam (AWRC, 1970; Hoy, 1977; Hoy and Garrett, 1978 and Hoy and Stephens 1979). The latter work has derived annual dam to pan coefficients based on regression analysis comparing evaporation to mean annual climatic values of rainfall, radiation, temperature and relative humidity. The results (map 14 and tables 9 and 10) are different to the more generally adopted coefficients of 0.70 or 0.78 found from earlier studies.

The regression equation used to produce map 14 and tables 9 and 10 is:

-ED/EP = 0.635 R.H. + 0.000076 P + 0.474 ED = Evaporation from dam

EP = Class A pan evaporation

R.H. = Mean annual relative humidity at 3.00 pm

P = Mean annual rainfall

The dam to pan coefficient relationship has proven successful for small storages (depth less than 4m). In larger dams, the effect of heat storage (particularly in higher latitudes) results in different dam/pan coefficients for different months.

For on-farm storages in Western Australia, the data in map 14 and tables 9 and 10 can be used to estimate the evaporation losses from dams. For centres not listed in table 9 or 10, the dam/pan coefficient can be determined from map 14. This can be multiplied by the monthly evaporation data from maps 2-13, for an estimate of monthly evaporation losses from a dam.

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Table 9. Estimated monthly and annual evaporation from dams in various parts of the agricultural areas (in mm)

Station ED/EP Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual Ajana 0.740 293 271 239 160 118 73 73 93 111 182 224 284 2,121 Albany 0.970 213 165 145 88 61 45 47 64 81 102 145 193 1,349 Armadale 0.850 252 218 190 104 73 50 51 58 90 130 172 220 1,608 Berkshire

Valley

0.790 300 270 240 144 92 58 57 75 100 158 218 294 2,006 Brookton 0.790 265 218 184 108 67 40 41 50 79 120 173 232 1,577 Corrigin 0.760 289 228 197 116 69 41 41 55 83 134 184 257 1,694 Cranbrook 0.850 226 195 156 93 56 37 39 51 75 101 149 218 1,396 Dandaragan 0.810 285 237 229 134 91 62 59 78 96 151 213 283 1,918 Donnybrook 0.850 187 158 127 71 53 39 42 53 62 92 126 172 1,182 Esperance 0.930 247 196 179 123 86 59 73 83 106 140 177 236 1,705 Geraldton 0.820 296 276 256 169 123 84 72 92 114 177 229 300 2,188 Gingin 0.830 273 245 214 123 79 54 52 75 94 143 198 266 1,816 Goodlands 0.720 316 281 242 154 95 58 61 78 103 172 223 297 2,080 Holt Rock 0.760 288 215 187 121 73 50 60 67 91 145 192 251 1,740 Jerramungup 0.800 248 179 157 101 66 40 48 60 82 117 155 212 1,465 Kalgoorlie 0.710 306 245 217 141 94 66 73 92 128 192 231 301 2,086 Katanning 0.810 246 191 169 98 63 38 40 51 77 110 161 229 1,473 Kondinin 0.770 291 227 197 120 72 45 46 59 87 138 190 261 1,733 Lake Grace 0.770 274 207 177 113 69 43 43 58 84 125 174 241 1,608 Lake King 0.780 276 203 177 117 73 52 60 69 91 139 182 237 1,676 Manjimup 0.910 192 157 135 78 60 42 50 53 70 91 130 181 1,239 Margaret

River

0.960 152 150 66 54 50 45 47 47 50 73 123 164 1,021 Medina 0.870 237 216 182 103 69 53 52 60 87 129 171 227 1,586 Merredin 0.750 315 260 231 138 82 52 55 67 96 165 214 292 1,967 Moora 0.790 289 263 229 136 89 57 55 71 97 152 212 284 1,934 Mt. Magnet 0.680 352 301 265 179 121 78 80 106 138 219 260 324 2,423 Mukinbudin 0.730 319 270 240 146 87 56 62 75 101 172 221 297 2,046 Mullewa 0.720 314 282 244 166 115 69 69 93 114 186 227 299 2,178 Munglinup 0.850 246 190 169 118 79 56 69 76 98 135 177 224 1,637 Narembeen 0.750 297 239 209 125 75 50 51 62 90 150 195 270 1,813 Narrogin 0.830 260 213 180 104 67 41 42 53 82 118 171 237 1,568

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Norseman 0.740 278 214 182 125 81 59 69 78 108 158 200 250 1,802 Northam 0.780 285 242 205 117 70 43 44 53 85 130 184 256 1,714 Paynes Find 0.710 338 298 260 169 111 70 73 94 121 199 246 318 2,297 Ravensthorpe 0.830 260 194 169 118 77 55 65 73 93 135 179 226 1,644 Rocky Gully 0.900 213 180 153 87 60 42 48 54 77 98 144 209 1,365 Salmon

Gums

0.780 262 202 172 121 80 56 69 75 99 145 187 231 1,699 Scaddan 0.820 246 191 179 116 79 55 68 76 99 138 180 223 1,650 Southern

Cross

0.720 313 252 226 139 86 57 65 78 108 175 220 294 2,013 Three

Springs

0.740 298 273 239 154 101 59 73 80 105 167 216 286 2,051 Upper Swan 0.840 274 236 202 114 74 52 50 73 92 135 185 254 1,741 Wagin 0.810 253 205 174 100 64 39 41 51 80 114 157 232 1,510 Wialki 0.720 324 280 245 151 94 60 65 82 107 179 228 305 2,120 Wokalup 0.900 209 189 146 80 63 51 50 62 72 102 144 198 1,366 Wongan Hills 0.740 289 258 222 132 81 49 51 65 91 145 202 276 1,861 Yanchep 0.850 249 237 204 121 81 64 56 77 99 136 187 255 1,766

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Table 10. Estimated monthly and annual evaporation from dams in various pastoral centres (in mm)

Station ED/EP Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual Anna Plains 0.740 251 210 204 196 180 157 170 190 234 248 270 278 2,88 Beverley

Springs

0.780 216 164 182 197 187 170 182 220 257 280 280 267 2,602 Blina 0.740 230 178 187 202 189 165 191 221 268 284 291 288 2,694 Bohemia

Downs

0.650 228 177 186 186 147 127 148 172 217 258 259 252 2,357 Boolathana 0.810 298 256 250 170 135 102 102 121 140 204 243 243 2,264 Camballin 0.730 229 184 189 203 184 168 187 214 262 281 291 287 2,679 Carnarvon 0.840 299 266 257 176 140 98 104 129 143 212 240 252 2,316 Carnegie 0.620 369 283 260 185 119 86 97 127 174 192 259 305 2,456 Christmas

Creek

0.670 227 174 186 189 160 138 162 185 232 263 267 261 2,444 Coodardy 0.670 361 303 270 184 125 82 85 113 152 230 268 326 2,499 Derby 0.730 212 172 178 196 185 168 184 212 255 265 285 272 2,584 Earaheedy 0.620 369 283 257 188 120 88 95 128 174 239 270 316 2,527 Exmouth 0.820 304 261 261 181 144 106 120 145 208 226 282 336 2,574 Fairfield 0.730 225 172 184 197 181 159 178 206 253 277 284 278 2,594 Fitzroy

Crossing

0.680 224 170 18 188 163 140 163 188 233 264 267 262 2,280 Giralia 0.800 348 303 272 200 159 109 116 148 211 271 309 344 2,790 Halls Creek 0.660 211 166 173 174 143 124 139 165 213 246 244 231 2,229 Jiggalong 0.620 308 251 246 202 130 109 111 141 186 245 279 308 2,516 Kalgoorlie 0.710 306 245 217 141 94 66 73 92 128 192 231 301 2,086 Kalumburu 0.900 198 162 162 191 191 176 198 234 269 287 260 269 2,597 Kimberley

Downs

0.760 228 179 185 205 190 180 193 222 266 289 296 293 2,726 Kimberley

Res. Stn.

0.760 187 159 155 175 169 152 171 202 238 256 225 227 2,316 Leonora 0.660 345 267 243 161 107 71 76 103 145 213 249 306 2,286 Leopold

Downs

0.710 227 171 185 193 170 149 171 198 243 271 276 269 2,523 Liveringa 0.730 230 185 189 203 189 161 194 211 263 281 291 287 2,684 Meekatharra 0.650 380 314 267 190 131 87 92 121 170 259 293 333 2,637 Melrose 0.650 367 282 260 172 113 79 80 109 160 229 261 315 2,427 Millajiddee 0.670 230 183 191 194 160 138 166 189 234 268 272 268 2,493 Minderoo 0.700 284 240 228 181 135 97 104 133 188 231 266 291 2,378

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Minnie Creek

0.660 312 278 254 182 127 94 96 127 176 248 277 310 2,481 Mt. Clere 0.630 337 287 247 188 117 90 93 122 168 250 284 319 2,502 Mt.

Elizabeth

0.820 215 169 186 197 191 166 188 222 264 290 287 270 2,645 Mt. Magnet 0.680 352 301 265 179 121 78 80 106 138 219 260 324 2,423 Mt. Newman 0.630 290 232 216 182 109 108 125 121 166 237 267 293 2,346 Mt. Tom

Price

0.650 275 208 207 171 123 110 115 123 178 241 265 299 2,315 Muggon 0.670 325 285 247 178 124 80 81 109 149 209 261 295 2,343 Napier

Downs

0.760 228 174 184 202 184 169 186 218 263 288 294 285 2,675 Nookanbah 0.690 230 182 190 197 168 145 173 197 245 271 278 273 2,549 Ord River 0.680 201 158 163 169 147 131 143 170 208 263 238 221 2,212 Paynes Find 0.710 338 298 260 169 111 70 73 94 121 199 246 318 2,297 Port

Hedland

0.730 265 233 231 197 179 143 159 179 240 234 273 281 2,614 Quanbun 0.670 223 173 183 189 162 142 166 189 233 263 267 261 2,451 Sandstone 0.670 369 299 265 178 118 77 81 109 151 228 270 328 2,473 Sturt Creek 0.630 218 185 180 175 131 115 125 154 190 257 239 233 2,202 Tableland 0.710 208 161 176 180 163 137 153 188 220 261 259 240 2,346 Three

Rivers

0.620 339 293 266 188 115 89 97 125 168 246 279 332 2,537 Wiluna 0.640 391 302 256 186 138 83 90 119 168 254 288 327 2,602 Wooramel 0.710 301 276 232 167 121 82 82 108 148 198 256 274 2,245 Wyndham 0.770 180 154 154 174 171 154 172 203 237 256 241 247 2,343 Yeeda 0.770 231 187 191 210 194 179 195 224 272 301 303 297 2,784

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4. Use of Data for Irrigation Scheduling

Evaporation data is often used as a guide to irrigation scheduling. The data is multiplied by a crop factor to indicate the amount of irrigation required. The crop factor is affected by many different crop and environmental characteristics (Doorenboos and Pruitt, 1977 and George and Cripps, 1985).

The evaporation data produced and discussed here can be used to aid in predicting crop water requirements for designing storages or irrigation systems.

When designing storages, the mean annual (or monthly for short term crops) Class A pan data, plus an allowance to account for the variability/ is used to predict total water requirements. This of course is in combination with the correct crop factor, or the factor for the highest water use crop that may be grown. Whether the storage is designed using the mean evaporation data, or a higher amount to allow for variations from season to season, depends upon the grower's crops (annual versus perennial) and the consequence of failure. These data may also be used to predict the area that may be watered from a partially full dam. Knowing the volume in storage and a predicted requirement per hectare per month, a calculation of the maximum area irrigable can be made.

When it comes to day by day scheduling, however, it is preferable to use actual evaporation data. Due to the variability discussed, it is unwise to rely on "average"

data for day by day decision making on watering needs.

The other aspect of irrigation design, which can be assisted using the data

presented, is the design of the equipment. As discussed in Section 2 (see table 8), there is a considerable variation in daily evaporation, around the mean figures quoted. For Medina, although the average daily evaporation in February is 8.8 mm, the rate may reach 13.9 mm in 5% of years. If a grower's system is unable to supply 14 mm per day then in 5% of years the system will fail to deliver the crops water requirements. In circumstances such as for avocados, lettuce or other drought

sensitive crops, a water shortage may have a detrimental effect on yield. The grower must decide whether the cost of larger pumps or mainlines is economically justified in terms of the crop losses resulting from a failure of the system. These data can be used to illustrate the range of evaporation rates likely, to act as a guide for decision- making.

Figure

Table 1. Monthly and annual Class A pan evaporation rates for various centres in the agricultural areas/ in mm
Table 2. Monthly and annual Class A pan evaporation rates for various pastoral centres, in mm.
Table 3. Variation in annual evaporation for eleven locations in Western Australia
Table 4. Evaporation rates likely to be exceeded with different frequencies for various mean evaporation zones
+6

References

Related documents

1980 "The sandgroper : a sometimes not-so-friendly Western Australian," Journal of the Department of Agriculture, Western Australia, Series 4: Vol.. Available at: